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Research Article

Assessing the impact of healthcare research: A systematic review of methodological frameworks

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliation Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

Roles Data curation, Formal analysis, Methodology, Validation, Writing – review & editing

Roles Formal analysis, Methodology, Supervision, Validation, Writing – review & editing

• Samantha Cruz Rivera, 
• Derek G. Kyte, 
• Olalekan Lee Aiyegbusi, 
• Thomas J. Keeley, 
• Melanie J. Calvert

• Published: August 9, 2017
• https://doi.org/10.1371/journal.pmed.1002370
• Reader Comments

Increasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix.

Methods and findings

Two independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched.

Conclusions

The measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Author summary

Why was this study done.

• There is a growing interest in demonstrating the impact of research in order to minimise research waste, allocate resources efficiently, and maximise the benefit of research. However, there is no consensus on which is the most appropriate tool to measure the impact of research.
• To our knowledge, this review is the first to synthesise existing methodological frameworks for healthcare research impact, and the associated impact metrics by which various authors have proposed impact should be measured, into a unified matrix.

What did the researchers do and find?

• We conducted a systematic review identifying 24 existing methodological research impact frameworks.
• We scrutinised the sample, identifying and summarising 5 proposed impact categories, 16 impact subcategories, and over 80 metrics into an impact matrix and methodological framework.

What do these findings mean?

• This simplified consolidated methodological framework will help researchers to understand how a research study may give rise to differing forms of impact, as well as in what ways and at which time points these potential impacts might be measured.
• Incorporating these insights into the design of a study could enhance impact, optimizing the use of research resources.

Citation: Cruz Rivera S, Kyte DG, Aiyegbusi OL, Keeley TJ, Calvert MJ (2017) Assessing the impact of healthcare research: A systematic review of methodological frameworks. PLoS Med 14(8): e1002370. https://doi.org/10.1371/journal.pmed.1002370

Academic Editor: Mike Clarke, Queens University Belfast, UNITED KINGDOM

Received: February 28, 2017; Accepted: July 7, 2017; Published: August 9, 2017

Copyright: © 2017 Cruz Rivera et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and supporting files.

Funding: Funding was received from Consejo Nacional de Ciencia y Tecnología (CONACYT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript ( http://www.conacyt.mx/ ).

Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: MJC has received consultancy fees from Astellas and Ferring pharma and travel fees from the European Society of Cardiology outside the submitted work. TJK is in full-time paid employment for PAREXEL International.

Abbreviations: AIHS, Alberta Innovates—Health Solutions; CAHS, Canadian Academy of Health Sciences; CIHR, Canadian Institutes of Health Research; CINAHL+, Cumulative Index to Nursing and Allied Health Literature; EMBASE, Excerpta Medica Database; ERA, Excellence in Research for Australia; HEFCE, Higher Education Funding Council for England; HMIC, Health Management Information Consortium; HTA, Health Technology Assessment; IOM, Impact Oriented Monitoring; MDG, Millennium Development Goal; NHS, National Health Service; MEDLINE, Medical Literature Analysis and Retrieval System Online; PHC RIS, Primary Health Care Research & Information Service; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROM, patient-reported outcome measures; QALY, quality-adjusted life year; R&D, research and development; RAE, Research Assessment Exercise; REF, Research Excellence Framework; RIF, Research Impact Framework; RQF, Research Quality Framework; SDG, Sustainable Development Goal; SIAMPI, Social Impact Assessment Methods for research and funding instruments through the study of Productive Interactions between science and society

Introduction

In 2010, approximately US$240 billion was invested in healthcare research worldwide [ 1 ]. Such research is utilised by policy makers, healthcare providers, and clinicians to make important evidence-based decisions aimed at maximising patient benefit, whilst ensuring that limited healthcare resources are used as efficiently as possible to facilitate effective and sustainable service delivery. It is therefore essential that this research is of high quality and that it is impactful—i.e., it delivers demonstrable benefits to society and the wider economy whilst minimising research waste [ 1 , 2 ]. Research impact can be defined as ‘any identifiable ‘benefit to, or positive influence on the economy, society, public policy or services, health, the environment, quality of life or academia’ (p. 26) [ 3 ].

There are many purported benefits associated with the measurement of research impact, including the ability to (1) assess the quality of the research and its subsequent benefits to society; (2) inform and influence optimal policy and funding allocation; (3) demonstrate accountability, the value of research in terms of efficiency and effectiveness to the government, stakeholders, and society; and (4) maximise impact through better understanding the concept and pathways to impact [ 4 – 7 ].

Measuring and monitoring the impact of healthcare research has become increasingly common in the United Kingdom [ 5 ], Australia [ 5 ], and Canada [ 8 ], as governments, organisations, and higher education institutions seek a framework to allocate funds to projects that are more likely to bring the most benefit to society and the economy [ 5 ]. For example, in the UK, the 2014 Research Excellence Framework (REF) has recently been used to assess the quality and impact of research in higher education institutions, through the assessment of impact cases studies and selected qualitative impact metrics [ 9 ]. This is the first initiative to allocate research funding based on the economic, societal, and cultural impact of research, although it should be noted that research impact only drives a proportion of this allocation (approximately 20%) [ 9 ].

In the UK REF, the measurement of research impact is seen as increasingly important. However, the impact element of the REF has been criticised in some quarters [ 10 , 11 ]. Critics deride the fact that REF impact is determined in a relatively simplistic way, utilising researcher-generated case studies, which commonly attempt to link a particular research outcome to an associated policy or health improvement despite the fact that the wider literature highlights great diversity in the way research impact may be demonstrated [ 12 , 13 ]. This led to the current debate about the optimal method of measuring impact in the future REF [ 10 , 14 ]. The Stern review suggested that research impact should not only focus on socioeconomic impact but should also include impact on government policy, public engagement, academic impacts outside the field, and teaching to showcase interdisciplinary collaborative impact [ 10 , 11 ]. The Higher Education Funding Council for England (HEFCE) has recently set out the proposals for the REF 2021 exercise, confirming that the measurement of such impact will continue to form an important part of the process [ 15 ].

With increasing pressure for healthcare research to lead to demonstrable health, economic, and societal impact, there is a need for researchers to understand existing methodological impact frameworks and the means by which impact may be quantified (i.e., impact metrics; see Box 1 , 'Definitions’) to better inform research activities and funding decisions. From a researcher’s perspective, understanding the optimal pathways to impact can help inform study design aimed at maximising the impact of the project. At the same time, funders need to understand which aspects of impact they should focus on when allocating awards so they can make the most of their investment and bring the greatest benefit to patients and society [ 2 , 4 , 5 , 16 , 17 ].

Box 1. Definitions

• Research impact: ‘any identifiable benefit to, or positive influence on, the economy, society, public policy or services, health, the environment, quality of life, or academia’ (p. 26) [ 3 ].
• Methodological framework: ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ].
• Pathway: ‘a way of achieving a specified result; a course of action’ [ 19 ].
• Quantitative metrics: ‘a system or standard of [quantitative] measurement’ [ 20 ].
• Narrative metrics: ‘a spoken or written account of connected events; a story’ [ 21 ].

Whilst previous researchers have summarised existing methodological frameworks and impact case studies [ 4 , 22 – 27 ], they have not summarised the metrics for use by researchers, funders, and policy makers. The aim of this review was therefore to (1) identify the methodological frameworks used to measure healthcare research impact using systematic methods, (2) summarise common impact themes and metrics in an impact matrix, and (3) provide a simplified consolidated resource for use by funders, researchers, and policy makers.

Search strategy and selection criteria

Initially, a search strategy was developed to identify the available literature regarding the different methods to measure research impact. The following keywords: ‘Impact’, ‘Framework’, and ‘Research’, and their synonyms, were used during the search of the Medical Literature Analysis and Retrieval System Online (MEDLINE; Ovid) database, the Excerpta Medica Database (EMBASE), the Health Management Information Consortium (HMIC) database, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL+) database (inception to May 2017; see S1 Appendix for the full search strategy). Additionally, the nonindexed Journal of Research Evaluation was hand searched during the same timeframe using the keyword ‘Impact’. Other relevant articles were identified through 3 Internet search engines (Google, Google Scholar, and Google Images) using the keywords ‘Impact’, ‘Framework’, and ‘Research’, with the first 50 results screened. Google Images was searched because different methodological frameworks are summarised in a single image and can easily be identified through this search engine. Finally, additional publications were sought through communication with experts.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (see S1 PRISMA Checklist ), 2 independent investigators systematically screened for publications describing, evaluating, or utilising a methodological research impact framework within the context of healthcare research [ 28 ]. Papers were eligible if they included full or partial methodological frameworks or pathways to research impact; both primary research and systematic reviews fitting these criteria were included. We included any methodological framework identified (original or modified versions) at the point of first occurrence. In addition, methodological frameworks were included if they were applicable to the healthcare discipline with no need of modification within their structure. We defined ‘methodological framework’ as ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ], whereas we defined ‘pathway’ as ‘a way of achieving a specified result; a course of action’ [ 19 ]. Studies were excluded if they presented an existing (unmodified) methodological framework previously available elsewhere, did not explicitly describe a methodological framework but rather focused on a single metric (e.g., bibliometric analysis), focused on the impact or effectiveness of interventions rather than that of the research, or presented case study data only. There were no language restrictions.

Data screening

Records were downloaded into Endnote (version X7.3.1), and duplicates were removed. Two independent investigators (SCR and OLA) conducted all screening following a pilot aimed at refining the process. The records were screened by title and abstract before full-text articles of potentially eligible publications were retrieved for evaluation. A full-text screening identified the publications included for data extraction. Discrepancies were resolved through discussion, with the involvement of a third reviewer (MJC, DGK, and TJK) when necessary.

Data extraction and analysis

Data extraction occurred after the final selection of included articles. SCR and OLA independently extracted details of impact methodological frameworks, the country of origin, and the year of publication, as well as the source, the framework description, and the methodology used to develop the framework. Information regarding the methodology used to develop each methodological framework was also extracted from framework webpages where available. Investigators also extracted details regarding each framework’s impact categories and subgroups, along with their proposed time to impact (‘short-term’, ‘mid-term’, or ‘long-term’) and the details of any metrics that had been proposed to measure impact, which are depicted in an impact matrix. The structure of the matrix was informed by the work of M. Buxton and S. Hanney [ 2 ], P. Buykx et al. [ 5 ], S. Kuruvila et al. [ 29 ], and A. Weiss [ 30 ], with the intention of mapping metrics presented in previous methodological frameworks in a concise way. A consensus meeting with MJC, DGK, and TJK was held to solve disagreements and finalise the data extraction process.

Included studies

Our original search strategy identified 359 citations from MEDLINE (Ovid), EMBASE, CINAHL+, HMIC, and the Journal of Research Evaluation, and 101 citations were returned using other sources (Google, Google Images, Google Scholar, and expert communication) (see Fig 1 ) [ 28 ]. In total, we retrieved 54 full-text articles for review. At this stage, 39 articles were excluded, as they did not propose new or modified methodological frameworks. An additional 15 articles were included following the backward and forward citation method. A total of 31 relevant articles were included in the final analysis, of which 24 were articles presenting unique frameworks and the remaining 7 were systematic reviews [ 4 , 22 – 27 ]. The search strategy was rerun on 15 May 2017. A further 19 publications were screened, and 2 were taken forward to full-text screening but were ineligible for inclusion.

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https://doi.org/10.1371/journal.pmed.1002370.g001

Methodological framework characteristics

The characteristics of the 24 included methodological frameworks are summarised in Table 1 , 'Methodological framework characteristics’. Fourteen publications proposed academic-orientated frameworks, which focused on measuring academic, societal, economic, and cultural impact using narrative and quantitative metrics [ 2 , 3 , 5 , 8 , 29 , 31 – 39 ]. Five publications focused on assessing the impact of research by focusing on the interaction process between stakeholders and researchers (‘productive interactions’), which is a requirement to achieve research impact. This approach tries to address the issue of attributing research impact to metrics [ 7 , 40 – 43 ]. Two frameworks focused on the importance of partnerships between researchers and policy makers, as a core element to accomplish research impact [ 44 , 45 ]. An additional 2 frameworks focused on evaluating the pathways to impact, i.e., linking processes between research and impact [ 30 , 46 ]. One framework assessed the ability of health technology to influence efficiency of healthcare systems [ 47 ]. Eight frameworks were developed in the UK [ 2 , 3 , 29 , 37 , 39 , 42 , 43 , 45 ], 6 in Canada [ 8 , 33 , 34 , 44 , 46 , 47 ], 4 in Australia [ 5 , 31 , 35 , 38 ], 3 in the Netherlands [ 7 , 40 , 41 ], and 2 in the United States [ 30 , 36 ], with 1 model developed with input from various countries [ 32 ].

https://doi.org/10.1371/journal.pmed.1002370.t001

Methodological framework development

The included methodological frameworks varied in their development process, but there were some common approaches employed. Most included a literature review [ 2 , 5 , 7 , 8 , 31 , 33 , 36 , 37 , 40 – 46 ], although none of them used a recognised systematic method. Most also consulted with various stakeholders [ 3 , 8 , 29 , 31 , 33 , 35 – 38 , 43 , 44 , 46 , 47 ] but used differing methods to incorporate their views, including quantitative surveys [ 32 , 35 , 43 , 46 ], face-to-face interviews [ 7 , 29 , 33 , 35 , 37 , 42 , 43 ], telephone interviews [ 31 , 46 ], consultation [ 3 , 7 , 36 ], and focus groups [ 39 , 43 ]. A range of stakeholder groups were approached across the sample, including principal investigators [ 7 , 29 , 43 ], research end users [ 7 , 42 , 43 ], academics [ 3 , 8 , 39 , 40 , 43 , 46 ], award holders [ 43 ], experts [ 33 , 38 , 39 ], sponsors [ 33 , 39 ], project coordinators [ 32 , 42 ], and chief investigators [ 31 , 35 ]. However, some authors failed to identify the stakeholders involved in the development of their frameworks [ 2 , 5 , 34 , 41 , 45 ], making it difficult to assess their appropriateness. In addition, only 4 of the included papers reported using formal analytic methods to interpret stakeholder responses. These included the Canadian Academy of Health Sciences framework, which used conceptual cluster analysis [ 33 ]. The Research Contribution [ 42 ], Research Impact [ 29 ], and Primary Health Care & Information Service [ 31 ] used a thematic analysis approach. Finally, some authors went on to pilot their framework, which shaped refinements on the methodological frameworks until approval. Methods used to pilot the frameworks included a case study approach [ 2 , 3 , 30 , 32 , 33 , 36 , 40 , 42 , 44 , 45 ], contrasting results against available literature [ 29 ], the use of stakeholders’ feedback [ 7 ], and assessment tools [ 35 , 46 ].

Major impact categories

1. primary research-related impact..

A number of methodological frameworks advocated the evaluation of ‘research-related impact’. This encompassed content related to the generation of new knowledge, knowledge dissemination, capacity building, training, leadership, and the development of research networks. These outcomes were considered the direct or primary impacts of a research project, as these are often the first evidenced returns [ 30 , 62 ].

A number of subgroups were identified within this category, with frameworks supporting the collection of impact data across the following constructs: ‘research and innovation outcomes’; ‘dissemination and knowledge transfer’; ‘capacity building, training, and leadership’; and ‘academic collaborations, research networks, and data sharing’.

1 . 1 . Research and innovation outcomes . Twenty of the 24 frameworks advocated the evaluation of ‘research and innovation outcomes’ [ 2 , 3 , 5 , 7 , 8 , 29 – 39 , 41 , 43 , 44 , 46 ]. This subgroup included the following metrics: number of publications; number of peer-reviewed articles (including journal impact factor); citation rates; requests for reprints, number of reviews, and meta-analysis; and new or changes in existing products (interventions or technology), patents, and research. Additionally, some frameworks also sought to gather information regarding ‘methods/methodological contributions’. These advocated the collection of systematic reviews and appraisals in order to identify gaps in knowledge and determine whether the knowledge generated had been assessed before being put into practice [ 29 ].

1 . 2 . Dissemination and knowledge transfer . Nineteen of the 24 frameworks advocated the assessment of ‘dissemination and knowledge transfer’ [ 2 , 3 , 5 , 7 , 29 – 32 , 34 – 43 , 46 ]. This comprised collection of the following information: number of conferences, seminars, workshops, and presentations; teaching output (i.e., number of lectures given to disseminate the research findings); number of reads for published articles; article download rate and number of journal webpage visits; and citations rates in nonjournal media such as newspapers and mass and social media (i.e., Twitter and blogs). Furthermore, this impact subgroup considered the measurement of research uptake and translatability and the adoption of research findings in technological and clinical applications and by different fields. These can be measured through patents, clinical trials, and partnerships between industry and business, government and nongovernmental organisations, and university research units and researchers [ 29 ].

1 . 3 . Capacity building , training , and leadership . Fourteen of 24 frameworks suggested the evaluation of ‘capacity building, training, and leadership’ [ 2 , 3 , 5 , 8 , 29 , 31 – 35 , 39 – 41 , 43 ]. This involved collecting information regarding the number of doctoral and postdoctoral studentships (including those generated as a result of the research findings and those appointed to conduct the research), as well as the number of researchers and research-related staff involved in the research projects. In addition, authors advocated the collection of ‘leadership’ metrics, including the number of research projects managed and coordinated and the membership of boards and funding bodies, journal editorial boards, and advisory committees [ 29 ]. Additional metrics in this category included public recognition (number of fellowships and awards for significant research achievements), academic career advancement, and subsequent grants received. Lastly, the impact metric ‘research system management’ comprised the collection of information that can lead to preserving the health of the population, such as modifying research priorities, resource allocation strategies, and linking health research to other disciplines to maximise benefits [ 29 ].

1 . 4 . Academic collaborations , research networks , and data sharing . Lastly, 10 of the 24 frameworks advocated the collection of impact data regarding ‘academic collaborations (internal and external collaborations to complete a research project), research networks, and data sharing’ [ 2 , 3 , 5 , 7 , 29 , 34 , 37 , 39 , 41 , 43 ].

2. Influence on policy making.

Methodological frameworks addressing this major impact category focused on measurable improvements within a given knowledge base and on interactions between academics and policy makers, which may influence policy-making development and implementation. The returns generated in this impact category are generally considered as intermediate or midterm (1 to 3 years). These represent an important interim stage in the process towards the final expected impacts, such as quantifiable health improvements and economic benefits, without which policy change may not occur [ 30 , 62 ]. The following impact subgroups were identified within this category: ‘type and nature of policy impact’, ‘level of policy making’, and ‘policy networks’.

2 . 1 . Type and nature of policy impact . The most common impact subgroup, mentioned in 18 of the 24 frameworks, was ‘type and nature of policy impact’ [ 2 , 7 , 29 – 38 , 41 – 43 , 45 – 47 ]. Methodological frameworks addressing this subgroup stressed the importance of collecting information regarding the influence of research on policy (i.e., changes in practice or terminology). For instance, a project looking at trafficked adolescents and women (2003) influenced the WHO guidelines (2003) on ethics regarding this particular group [ 17 , 21 , 63 ].

2 . 2 . Level of policy impact . Thirteen of 24 frameworks addressed aspects surrounding the need to record the ‘level of policy impact’ (international, national, or local) and the organisations within a level that were influenced (local policy makers, clinical commissioning groups, and health and wellbeing trusts) [ 2 , 5 , 8 , 29 , 31 , 34 , 38 , 41 , 43 – 47 ]. Authors considered it important to measure the ‘level of policy impact’ to provide evidence of collaboration, coordination, and efficiency within health organisations and between researchers and health organisations [ 29 , 31 ].

2 . 3 . Policy networks . Five methodological frameworks highlighted the need to collect information regarding collaborative research with industry and staff movement between academia and industry [ 5 , 7 , 29 , 41 , 43 ]. A policy network emphasises the relationship between policy communities, researchers, and policy makers. This relationship can influence and lead to incremental changes in policy processes [ 62 ].

3. Health and health systems impact.

A number of methodological frameworks advocated the measurement of impacts on health and healthcare systems across the following impact subgroups: ‘quality of care and service delivering’, ‘evidence-based practice’, ‘improved information and health information management’, ‘cost containment and effectiveness’, ‘resource allocation’, and ‘health workforce’.

3 . 1 . Quality of care and service delivery . Twelve of the 24 frameworks highlighted the importance of evaluating ‘quality of care and service delivery’ [ 2 , 5 , 8 , 29 – 31 , 33 – 36 , 41 , 47 ]. There were a number of suggested metrics that could be potentially used for this purpose, including health outcomes such as quality-adjusted life years (QALYs), patient-reported outcome measures (PROMs), patient satisfaction and experience surveys, and qualitative data on waiting times and service accessibility.

3 . 2 . Evidence-based practice . ‘Evidence-based practice’, mentioned in 5 of the 24 frameworks, refers to making changes in clinical diagnosis, clinical practice, treatment decisions, or decision making based on research evidence [ 5 , 8 , 29 , 31 , 33 ]. The suggested metrics to demonstrate evidence-based practice were adoption of health technologies and research outcomes to improve the healthcare systems and inform policies and guidelines [ 29 ].

3 . 3 . Improved information and health information management . This impact subcategory, mentioned in 5 of the 24 frameworks, refers to the influence of research on the provision of health services and management of the health system to prevent additional costs [ 5 , 29 , 33 , 34 , 38 ]. Methodological frameworks advocated the collection of health system financial, nonfinancial (i.e., transport and sociopolitical implications), and insurance information in order to determine constraints within a health system.

3 . 4 . Cost containment and cost-effectiveness . Six of the 24 frameworks advocated the subcategory ‘cost containment and cost-effectiveness’ [ 2 , 5 , 8 , 17 , 33 , 36 ]. ‘Cost containment’ comprised the collection of information regarding how research has influenced the provision and management of health services and its implication in healthcare resource allocation and use [ 29 ]. ‘Cost-effectiveness’ refers to information concerning economic evaluations to assess improvements in effectiveness and health outcomes—for instance, the cost-effectiveness (cost and health outcome benefits) assessment of introducing a new health technology to replace an older one [ 29 , 31 , 64 ].

3 . 5 . Resource allocation . ‘Resource allocation’, mentioned in 6frameworks, can be measured through 2 impact metrics: new funding attributed to the intervention in question and equity while allocating resources, such as improved allocation of resources at an area level; better targeting, accessibility, and utilisation; and coverage of health services [ 2 , 5 , 29 , 31 , 45 , 47 ]. The allocation of resources and targeting can be measured through health services research reports, with the utilisation of health services measured by the probability of providing an intervention when needed, the probability of requiring it again in the future, and the probability of receiving an intervention based on previous experience [ 29 , 31 ].

3 . 6 . Health workforce . Lastly, ‘health workforce’, present in 3 methodological frameworks, refers to the reduction in the days of work lost because of a particular illness [ 2 , 5 , 31 ].

4. Health-related and societal impact.

Three subgroups were included in this category: ‘health literacy’; ‘health knowledge, attitudes, and behaviours’; and ‘improved social equity, inclusion, or cohesion’.

4 . 1 . Health knowledge , attitudes , and behaviours . Eight of the 24 frameworks suggested the assessment of ‘health knowledge, attitudes, behaviours, and outcomes’, which could be measured through the evaluation of levels of public engagement with science and research (e.g., National Health Service (NHS) Choices end-user visit rate) or by using focus groups to analyse changes in knowledge, attitudes, and behaviour among society [ 2 , 5 , 29 , 33 – 35 , 38 , 43 ].

4 . 2 . Improved equity , inclusion , or cohesion and human rights . Other methodological frameworks, 4 of the 24, suggested capturing improvements in equity, inclusion, or cohesion and human rights. Authors suggested these could be using a resource like the United Nations Millennium Development Goals (MDGs) (superseded by Sustainable Development Goals [SDGs] in 2015) and human rights [ 29 , 33 , 34 , 38 ]. For instance, a cluster-randomised controlled trial in Nepal, which had female participants, has demonstrated the reduction of neonatal mortality through the introduction of maternity health care, distribution of delivery kits, and home visits. This illustrates how research can target vulnerable and disadvantaged groups. Additionally, this research has been introduced by the World Health Organisation to achieve the MDG ‘improve maternal health’ [ 16 , 29 , 65 ].

4 . 3 . Health literacy . Some methodological frameworks, 3 of the 24, focused on tracking changes in the ability of patients to make informed healthcare decisions, reduce health risks, and improve quality of life, which were demonstrably linked to a particular programme of research [ 5 , 29 , 43 ]. For example, a systematic review showed that when HIV health literacy/knowledge is spread among people living with the condition, antiretroviral adherence and quality of life improve [ 66 ].

5. Broader economic impacts.

Some methodological frameworks, 9 of 24, included aspects related to the broader economic impacts of health research—for example, the economic benefits emerging from the commercialisation of research outputs [ 2 , 5 , 29 , 31 , 33 , 35 , 36 , 38 , 67 ]. Suggested metrics included the amount of funding for research and development (R&D) that was competitively awarded by the NHS, medical charities, and overseas companies. Additional metrics were income from intellectual property, spillover effects (any secondary benefit gained as a repercussion of investing directly in a primary activity, i.e., the social and economic returns of investing on R&D) [ 33 ], patents granted, licences awarded and brought to the market, the development and sales of spinout companies, research contracts, and income from industry.

The benefits contained within the categories ‘health and health systems impact’, ‘health-related and societal impact’, and ‘broader economic impacts’ are considered the expected and final returns of the resources allocated in healthcare research [ 30 , 62 ]. These benefits commonly arise in the long term, beyond 5 years according to some authors, but there was a recognition that this could differ depending on the project and its associated research area [ 4 ].

Data synthesis

Five major impact categories were identified across the 24 included methodological frameworks: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These major impact categories were further subdivided into 16 impact subgroups. The included publications proposed 80 different metrics to measure research impact. This impact typology synthesis is depicted in ‘the impact matrix’ ( Fig 2 and Fig 3 ).

CIHR, Canadian Institutes of Health Research; HTA, Health Technology Assessment; PHC RIS, Primary Health Care Research & Information Service; RAE, Research Assessment Exercise; RQF, Research Quality Framework.

https://doi.org/10.1371/journal.pmed.1002370.g002

AIHS, Alberta Innovates—Health Solutions; CAHS, Canadian Institutes of Health Research; IOM, Impact Oriented Monitoring; REF, Research Excellence Framework; SIAMPI, Social Impact Assessment Methods for research and funding instruments through the study of Productive Interactions between science and society.

https://doi.org/10.1371/journal.pmed.1002370.g003

Commonality and differences across frameworks

The ‘Research Impact Framework’ and the ‘Health Services Research Impact Framework’ were the models that encompassed the largest number of the metrics extracted. The most dominant methodological framework was the Payback Framework; 7 other methodological framework models used the Payback Framework as a starting point for development [ 8 , 29 , 31 – 35 ]. Additional methodological frameworks that were commonly incorporated into other tools included the CIHR framework, the CAHS model, the AIHS framework, and the Exchange model [ 8 , 33 , 34 , 44 ]. The capture of ‘research-related impact’ was the most widely advocated concept across methodological frameworks, illustrating the importance with which primary short-term impact outcomes were viewed by the included papers. Thus, measurement of impact via number of publications, citations, and peer-reviewed articles was the most common. ‘Influence on policy making’ was the predominant midterm impact category, specifically the subgroup ‘type and nature of policy impact’, in which frameworks advocated the measurement of (i) changes to legislation, regulations, and government policy; (ii) influence and involvement in decision-making processes; and (iii) changes to clinical or healthcare training, practice, or guidelines. Within more long-term impact measurement, the evaluations of changes in the ‘quality of care and service delivery’ were commonly advocated.

In light of the commonalities and differences among the methodological frameworks, the ‘pathways to research impact’ diagram ( Fig 4 ) was developed to provide researchers, funders, and policy makers a more comprehensive and exhaustive way to measure healthcare research impact. The diagram has the advantage of assorting all the impact metrics proposed by previous frameworks and grouping them into different impact subgroups and categories. Prospectively, this global picture will help researchers, funders, and policy makers plan strategies to achieve multiple pathways to impact before carrying the research out. The analysis of the data extraction and construction of the impact matrix led to the development of the ‘pathways to research impact’ diagram ( Fig 4 ). The diagram aims to provide an exhaustive and comprehensive way of tracing research impact by combining all the impact metrics presented by the different 24 frameworks, grouping those metrics into different impact subgroups, and grouping these into broader impact categories.

NHS, National Health Service; PROM, patient-reported outcome measure; QALY, quality-adjusted life year; R&D, research and development.

https://doi.org/10.1371/journal.pmed.1002370.g004

This review has summarised existing methodological impact frameworks together for the first time using systematic methods ( Fig 4 ). It allows researchers and funders to consider pathways to impact at the design stage of a study and to understand the elements and metrics that need to be considered to facilitate prospective assessment of impact. Users do not necessarily need to cover all the aspects of the methodological framework, as every research project can impact on different categories and subgroups. This review provides information that can assist researchers to better demonstrate impact, potentially increasing the likelihood of conducting impactful research and reducing research waste. Existing reviews have not presented a methodological framework that includes different pathways to impact, health impact categories, subgroups, and metrics in a single methodological framework.

Academic-orientated frameworks included in this review advocated the measurement of impact predominantly using so-called ‘quantitative’ metrics—for example, the number of peer-reviewed articles, journal impact factor, and citation rates. This may be because they are well-established measures, relatively easy to capture and objective, and are supported by research funding systems. However, these metrics primarily measure the dissemination of research finding rather than its impact [ 30 , 68 ]. Whilst it is true that wider dissemination, especially when delivered via world-leading international journals, may well lead eventually to changes in healthcare, this is by no means certain. For instance, case studies evaluated by Flinders University of Australia demonstrated that some research projects with non-peer-reviewed publications led to significant changes in health policy, whilst the studies with peer-reviewed publications did not result in any type of impact [ 68 ]. As a result, contemporary literature has tended to advocate the collection of information regarding a variety of different potential forms of impact alongside publication/citations metrics [ 2 , 3 , 5 , 7 , 8 , 29 – 47 ], as outlined in this review.

The 2014 REF exercise adjusted UK university research funding allocation based on evidence of the wider impact of research (through case narrative studies and quantitative metrics), rather than simply according to the quality of research [ 12 ]. The intention was to ensure funds were directed to high-quality research that could demonstrate actual realised benefit. The inclusion of a mixed-method approach to the measurement of impact in the REF (narrative and quantitative metrics) reflects a widespread belief—expressed by the majority of authors of the included methodological frameworks in the review—that individual quantitative impact metrics (e.g., number of citations and publications) do not necessary capture the complexity of the relationships involved in a research project and may exclude measurement of specific aspects of the research pathway [ 10 , 12 ].

Many of the frameworks included in this review advocated the collection of a range of academic, societal, economic, and cultural impact metrics; this is consistent with recent recommendations from the Stern review [ 10 ]. However, a number of these metrics encounter research ‘lag’: i.e., the time between the point at which the research is conducted and when the actual benefits arise [ 69 ]. For instance, some cardiovascular research has taken up to 25 years to generate impact [ 70 ]. Likewise, the impact may not arise exclusively from a single piece of research. Different processes (such as networking interactions and knowledge and research translation) and multiple individuals and organisations are often involved [ 4 , 71 ]. Therefore, attributing the contribution made by each of the different actors involved in the process can be a challenge [ 4 ]. An additional problem associated to attribution is the lack of evidence to link research and impact. The outcomes of research may emerge slowly and be absorbed gradually. Consequently, it is difficult to determine the influence of research in the development of a new policy, practice, or guidelines [ 4 , 23 ].

A further problem is that impact evaluation is conducted ‘ex post’, after the research has concluded. Collecting information retrospectively can be an issue, as the data required might not be available. ‘ex ante’ assessment is vital for funding allocation, as it is necessary to determine the potential forthcoming impact before research is carried out [ 69 ]. Additionally, ex ante evaluation of potential benefit can overcome the issues regarding identifying and capturing evidence, which can be used in the future [ 4 ]. In order to conduct ex ante evaluation of potential benefit, some authors suggest the early involvement of policy makers in a research project coupled with a well-designed strategy of dissemination [ 40 , 69 ].

Providing an alternate view, the authors of methodological frameworks such as the SIAMPI, Contribution Mapping, Research Contribution, and the Exchange model suggest that the problems of attribution are a consequence of assigning the impact of research to a particular impact metric [ 7 , 40 , 42 , 44 ]. To address these issues, these authors propose focusing on the contribution of research through assessing the processes and interactions between stakeholders and researchers, which arguably take into consideration all the processes and actors involved in a research project [ 7 , 40 , 42 , 43 ]. Additionally, contributions highlight the importance of the interactions between stakeholders and researchers from an early stage in the research process, leading to a successful ex ante and ex post evaluation by setting expected impacts and determining how the research outcomes have been utilised, respectively [ 7 , 40 , 42 , 43 ]. However, contribution metrics are generally harder to measure in comparison to academic-orientated indicators [ 72 ].

Currently, there is a debate surrounding the optimal methodological impact framework, and no tool has proven superior to another. The most appropriate methodological framework for a given study will likely depend on stakeholder needs, as each employs different methodologies to assess research impact [ 4 , 37 , 41 ]. This review allows researchers to select individual existing methodological framework components to create a bespoke tool with which to facilitate optimal study design and maximise the potential for impact depending on the characteristic of their study ( Fig 2 and Fig 3 ). For instance, if researchers are interested in assessing how influential their research is on policy making, perhaps considering a suite of the appropriate metrics drawn from multiple methodological frameworks may provide a more comprehensive method than adopting a single methodological framework. In addition, research teams may wish to use a multidimensional approach to methodological framework development, adopting existing narratives and quantitative metrics, as well as elements from contribution frameworks. This approach would arguably present a more comprehensive method of impact assessment; however, further research is warranted to determine its effectiveness [ 4 , 69 , 72 , 73 ].

Finally, it became clear during this review that the included methodological frameworks had been constructed using varied methodological processes. At present, there are no guidelines or consensus around the optimal pathway that should be followed to develop a robust methodological framework. The authors believe this is an area that should be addressed by the research community, to ensure future frameworks are developed using best-practice methodology.

For instance, the Payback Framework drew upon a literature review and was refined through a case study approach. Arguably, this approach could be considered inferior to other methods that involved extensive stakeholder involvement, such as the CIHR framework [ 8 ]. Nonetheless, 7 methodological frameworks were developed based upon the Payback Framework [ 8 , 29 , 31 – 35 ].

Limitations

The present review is the first to summarise systematically existing impact methodological frameworks and metrics. The main limitation is that 50% of the included publications were found through methods other than bibliographic databases searching, indicating poor indexing. Therefore, some relevant articles may not have been included in this review if they failed to indicate the inclusion of a methodological impact framework in their title/abstract. We did, however, make every effort to try to find these potentially hard-to-reach publications, e.g., through forwards/backwards citation searching, hand searching reference lists, and expert communication. Additionally, this review only extracted information regarding the methodology followed to develop each framework from the main publication source or framework webpage. Therefore, further evaluations may not have been included, as they are beyond the scope of the current paper. A further limitation was that although our search strategy did not include language restrictions, we did not specifically search non-English language databases. Thus, we may have failed to identify potentially relevant methodological frameworks that were developed in a non-English language setting.

In conclusion, the measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise benefit, and to help minimise research waste. This review provides a collective summary of existing methodological impact frameworks and metrics, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Supporting information

S1 appendix. search strategy..

https://doi.org/10.1371/journal.pmed.1002370.s001

S1 PRISMA Checklist. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist.

https://doi.org/10.1371/journal.pmed.1002370.s002

Acknowledgments

We would also like to thank Mrs Susan Bayliss, Information Specialist, University of Birmingham, and Mrs Karen Biddle, Research Secretary, University of Birmingham.

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• Research article
• Open access
• Published: 10 April 2021

The role of artificial intelligence in healthcare: a structured literature review

• Silvana Secinaro 1 ,
• Davide Calandra 1 ,
• Aurelio Secinaro 2 ,
• Vivek Muthurangu 3 &
• Paolo Biancone 1  

BMC Medical Informatics and Decision Making volume  21 , Article number:  125 ( 2021 ) Cite this article

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Background/Introduction

Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions.

The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package.

The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths.

Conclusions

The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Peer Review reports

Artificial intelligence (AI) generally applies to computational technologies that emulate mechanisms assisted by human intelligence, such as thought, deep learning, adaptation, engagement, and sensory understanding [ 1 , 2 ]. Some devices can execute a role that typically involves human interpretation and decision-making [ 3 , 4 ]. These techniques have an interdisciplinary approach and can be applied to different fields, such as medicine and health. AI has been involved in medicine since as early as the 1950s, when physicians made the first attempts to improve their diagnoses using computer-aided programs [ 5 , 6 ]. Interest and advances in medical AI applications have surged in recent years due to the substantially enhanced computing power of modern computers and the vast amount of digital data available for collection and utilisation [ 7 ]. AI is gradually changing medical practice. There are several AI applications in medicine that can be used in a variety of medical fields, such as clinical, diagnostic, rehabilitative, surgical, and predictive practices. Another critical area of medicine where AI is making an impact is clinical decision-making and disease diagnosis. AI technologies can ingest, analyse, and report large volumes of data across different modalities to detect disease and guide clinical decisions [ 3 , 8 ]. AI applications can deal with the vast amount of data produced in medicine and find new information that would otherwise remain hidden in the mass of medical big data [ 9 , 10 , 11 ]. These technologies can also identify new drugs for health services management and patient care treatments [ 5 , 6 ].

Courage in the application of AI is visible through a search in the primary research databases. However, as Meskò et al. [ 7 ] find, the technology will potentially reduce care costs and repetitive operations by focusing the medical profession on critical thinking and clinical creativity. As Cho et al. and Doyle et al. [ 8 , 9 ] add, the AI perspective is exciting; however, new studies will be needed to establish the efficacy and applications of AI in the medical field [ 10 ].

Our paper will also concentrate on AI strategies for healthcare from the accounting, business, and management perspectives. The authors used the structured literature review (SLR) method for its reliable and replicable research protocol [ 11 ] and selected bibliometric variables as sources of investigation. Bibliometric usage enables the recognition of the main quantitative variables of the study stream [ 12 ]. This method facilitates the detection of the required details of a particular research subject, including field authors, number of publications, keywords for interaction between variables (policies, properties and governance) and country data [ 13 ]. It also allows the application of the science mapping technique [ 14 ]. Our paper adopted the Bibliometrix R package and the biblioshiny web interface as tools of analysis [ 14 ].

The investigation offers the following insights for future researchers and practitioners:

bibliometric information on 288 peer-reviewed English papers from the Scopus collection.

Identification of leading journals in this field, such as Journal of Medical Systems, Studies in Health Technology and Informatics, IEEE Journal of Biomedical and Health Informatics, and Decision Support Systems.

Qualitative and quantitative information on authors’ Lotka’s law, h-index, g-index, m-index, keyword, and citation data.

Research on specific countries to assess AI in the delivery and effectiveness of healthcare, quotes, and networks within each region.

A topic dendrogram study that identifies five research clusters: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making.

An in-depth discussion that develops theoretical and practical implications for future studies.

The paper is organised as follows. Section  2 lists the main bibliometric articles in this field. Section  3 elaborates on the methodology. Section  4 presents the findings of the bibliometric analysis. Section  5 discusses the main elements of AI in healthcare based on the study results. Section  6 concludes the article with future implications for research.

Related works and originality

As suggested by Zupic and Čater [ 15 ], a research stream can be evaluated with bibliometric methods that can introduce objectivity and mitigate researcher bias. For this reason, bibliometric methods are attracting increasing interest among researchers as a reliable and impersonal research analytical approach [ 16 , 17 ]. Recently, bibliometrics has been an essential method for analysing and predicting research trends [ 18 ]. Table  1 lists other research that has used a similar approach in the research stream investigated.

The scientific articles reported show substantial differences in keywords and research topics that have been previously studied. The bibliometric analysis of Huang et al. [ 19 ] describes rehabilitative medicine using virtual reality technology. According to the authors, the primary goal of rehabilitation is to enhance and restore functional ability and quality of life for patients with physical impairments or disabilities. In recent years, many healthcare disciplines have been privileged to access various technologies that provide tools for both research and clinical intervention.

Hao et al. [ 20 ] focus on text mining in medical research. As reported, text mining reveals new, previously unknown information by using a computer to automatically extract information from different text resources. Text mining methods can be regarded as an extension of data mining to text data. Text mining is playing an increasingly significant role in processing medical information. Similarly, the studies by dos Santos et al. [ 21 ] focus on applying data mining and machine learning (ML) techniques to public health problems. As stated in this research, public health may be defined as the art and science of preventing diseases, promoting health, and prolonging life. Using data mining and ML techniques, it is possible to discover new information that otherwise would be hidden. These two studies are related to another topic: medical big data. According to Liao et al. [ 22 ], big data is a typical “buzzword” in the business and research community, referring to a great mass of digital data collected from various sources. In the medical field, we can obtain a vast amount of data (i.e., medical big data). Data mining and ML techniques can help deal with this information and provide helpful insights for physicians and patients. More recently, Choudhury et al. [ 23 ] provide a systematic review on the use of ML to improve the care of elderly patients, demonstrating eligible studies primarily in psychological disorders and eye diseases.

Tran et al. [ 2 ] focus on the global evolution of AI research in medicine. Their bibliometric analysis highlights trends and topics related to AI applications and techniques. As stated in Connelly et al.’s [ 24 ] study, robot-assisted surgeries have rapidly increased in recent years. Their bibliometric analysis demonstrates how robotic-assisted surgery has gained acceptance in different medical fields, such as urological, colorectal, cardiothoracic, orthopaedic, maxillofacial and neurosurgery applications. Additionally, the bibliometric analysis of Guo et al. [ 25 ] provides an in-depth study of AI publications through December 2019. The paper focuses on tangible AI health applications, giving researchers an idea of how algorithms can help doctors and nurses. A new stream of research related to AI is also emerging. In this sense, Choudhury and Asan’s [ 26 ] scientific contribution provides a systematic review of the AI literature to identify health risks for patients. They report on 53 studies involving technology for clinical alerts, clinical reports, and drug safety. Considering the considerable interest within this research stream, this analysis differs from the current literature for several reasons. It aims to provide in-depth discussion, considering mainly the business, management, and accounting fields and not dealing only with medical and health profession publications.

Additionally, our analysis aims to provide a bibliometric analysis of variables such as authors, countries, citations and keywords to guide future research perspectives for researchers and practitioners, as similar analyses have done for several publications in other research streams [ 15 , 16 , 27 ]. In doing so, we use a different database, Scopus, that is typically adopted in social sciences fields. Finally, our analysis will propose and discuss a dominant framework of variables in this field, and our analysis will not be limited to AI application descriptions.

Methodology

This paper evaluated AI in healthcare research streams using the SLR method [ 11 ]. As suggested by Massaro et al. [ 11 ], an SLR enables the study of the scientific corpus of a research field, including the scientific rigour, reliability and replicability of operations carried out by researchers. As suggested by many scholars, the methodology allows qualitative and quantitative variables to highlight the best authors, journals and keywords and combine a systematic literature review and bibliometric analysis [ 27 , 28 , 29 , 30 ]. Despite its widespread use in business and management [ 16 , 31 ], the SLR is also used in the health sector based on the same philosophy through which it was originally conceived [ 32 , 33 ]. A methodological analysis of previously published articles reveals that the most frequently used steps are as follows [ 28 , 31 , 34 ]:

defining research questions;

writing the research protocol;

defining the research sample to be analysed;

developing codes for analysis; and

critically analysing, discussing, and identifying a future research agenda.

Considering the above premises, the authors believe that an SLR is the best method because it combines scientific validity, replicability of the research protocol and connection between multiple inputs.

As stated by the methodological paper, the first step is research question identification. For this purpose, we benefit from the analysis of Zupic and Čater [ 15 ], who provide several research questions for future researchers to link the study of authors, journals, keywords and citations. Therefore, RQ1 is “What are the most prominent authors, journal keywords and citations in the field of the research study?” Additionally, as suggested by Haleem et al. [ 35 ], new technologies, including AI, are changing the medical field in unexpected timeframes, requiring studies in multiple areas. Therefore, RQ2 is “How does artificial intelligence relate to healthcare, and what is the focus of the literature?” Then, as discussed by Massaro et al. [ 36 ], RQ3 is “What are the research applications of artificial intelligence for healthcare?”.

The first research question aims to define the qualitative and quantitative variables of the knowledge flow under investigation. The second research question seeks to determine the state of the art and applications of AI in healthcare. Finally, the third research question aims to help researchers identify practical and theoretical implications and future research ideas in this field.

The second fundamental step of the SLR is writing the research protocol [ 11 ]. Table  2 indicates the currently known literature elements, uniquely identifying the research focus, motivations and research strategy adopted and the results providing a link with the following points. Additionally, to strengthen the analysis, our investigation benefits from the PRISMA statement methodological article [ 37 ]. Although the SLR is a validated method for systematic reviews and meta-analyses, we believe that the workflow provided may benefit the replicability of the results [ 37 , 38 , 39 , 40 ]. Figure  1 summarises the researchers’ research steps, indicating that there are no results that can be referred to as a meta-analysis.

Source : Authors’ elaboration on Liberati et al. [ 37 ]

PRISMA workflow.

The third step is to specify the search strategy and search database. Our analysis is based on the search string “Artificial Intelligence” OR “AI” AND “Healthcare” with a focus on “Business, Management, and Accounting”, “Decision Sciences”, and “Health professions”. As suggested by [ 11 , 41 ] and motivated by [ 42 ], keywords can be selected through a top-down approach by identifying a large search field and then focusing on particular sub-topics. The paper uses data retrieved from the Scopus database, a multi-disciplinary database, which allowed the researchers to identify critical articles for scientific analysis [ 43 ]. Additionally, Scopus was selected based on Guo et al.’s [ 25 ] limitations, which suggest that “future studies will apply other databases, such as Scopus, to explore more potential papers” . The research focuses on articles and reviews published in peer-reviewed journals for their scientific relevance [ 11 , 16 , 17 , 29 ] and does not include the grey literature, conference proceedings or books/book chapters. Articles written in any language other than English were excluded [ 2 ]. For transparency and replicability, the analysis was conducted on 11 January 2021. Using this research strategy, the authors retrieved 288 articles. To strengthen the study's reliability, we publicly provide the full bibliometric extract on the Zenodo repository [ 44 , 45 ].

The fourth research phase is defining the code framework that initiates the analysis of the variables. The study will identify the following:

descriptive information of the research area;

source analysis [ 16 ];

author and citation analysis [ 28 ];

keywords and network analysis [ 14 ]; and

geographic distribution of the papers [ 14 ].

The final research phase is the article’s discussion and conclusion, where implications and future research trends will be identified.

At the research team level, the information is analysed with the statistical software R-Studio and the Bibliometrix package [ 15 ], which allows scientific analysis of the results obtained through the multi-disciplinary database.

The analysis of bibliometric results starts with a description of the main bibliometric statistics with the aim of answering RQ1, What are the most prominent authors, journal keywords and citations in the field of the research study?, and RQ2, How does artificial intelligence relate to healthcare, and what is the focus of the literature? Therefore, the following elements were thoroughly analysed: (1) type of document; (2) annual scientific production; (3) scientific sources; (4) source growth; (5) number of articles per author; (6) author’s dominance ranking; (7) author’s h-index, g-index, and m-index; (8) author’s productivity; (9) author’s keywords; (10) topic dendrogram; (11) a factorial map of the document with the highest contributions; (12) article citations; (13) country production; (14) country citations; (15) country collaboration map; and (16) country collaboration network.

Main information

Table  3 shows the information on 288 peer-reviewed articles published between 1992 and January 2021 extracted from the Scopus database. The number of keywords is 946 from 136 sources, and the number of keywords plus, referring to the number of keywords that frequently appear in an article’s title, was 2329. The analysis period covered 28 years and 1 month of scientific production and included an annual growth rate of 5.12%. However, the most significant increase in published articles occurred in the past three years (please see Fig.  2 ). On average, each article was written by three authors (3.56). Finally, the collaboration index (CI), which was calculated as the total number of authors of multi-authored articles/total number of multi-authored articles, was 3.97 [ 46 ].

Source : Authors’ elaboration

Annual scientific production.

Table  4 shows the top 20 sources related to the topic. The Journal of Medical Systems is the most relevant source, with twenty-one of the published articles. This journal's main issues are the foundations, functionality, interfaces, implementation, impacts, and evaluation of medical technologies. Another relevant source is Studies in Health Technology and Informatics, with eleven articles. This journal aims to extend scientific knowledge related to biomedical technologies and medical informatics research. Both journals deal with cloud computing, machine learning, and AI as a disruptive healthcare paradigm based on recent publications. The IEEE Journal of Biomedical and Health Informatics investigates technologies in health care, life sciences, and biomedicine applications from a broad perspective. The next journal, Decision Support Systems, aims to analyse how these technologies support decision-making from a multi-disciplinary view, considering business and management. Therefore, the analysis of the journals revealed that we are dealing with an interdisciplinary research field. This conclusion is confirmed, for example, by the presence of purely medical journals, journals dedicated to the technological growth of healthcare, and journals with a long-term perspective such as futures.

The distribution frequency of the articles (Fig.  3 ) indicates the journals dealing with the topic and related issues. Between 2008 and 2012, a significant growth in the number of publications on the subject is noticeable. However, the graph shows the results of the Loess regression, which includes the quantity and publication time of the journal under analysis as variables. This method allows the function to assume an unlimited distribution; that is, feature can consider values below zero if the data are close to zero. It contributes to a better visual result and highlights the discontinuity in the publication periods [ 47 ].

Source growth. Source : Authors’ elaboration

Finally, Fig.  4 provides an analytical perspective on factor analysis for the most cited papers. As indicated in the literature [ 48 , 49 ], using factor analysis to discover the most cited papers allows for a better understanding of the scientific world’s intellectual structure. For example, our research makes it possible to consider certain publications that effectively analyse subject specialisation. For instance, Santosh’s [ 50 ] article addresses the new paradigm of AI with ML algorithms for data analysis and decision support in the COVID-19 period, setting a benchmark in terms of citations by researchers. Moving on to the application, an article by Shickel et al. [ 51 ] begins with the belief that the healthcare world currently has much health and administrative data. In this context, AI and deep learning will support medical and administrative staff in extracting data, predicting outcomes, and learning medical representations. Finally, in the same line of research, Baig et al. [ 52 ], with a focus on wearable patient monitoring systems (WPMs), conclude that AI and deep learning may be landmarks for continuous patient monitoring and support for healthcare delivery.

Factorial map of the most cited documents.

This section identifies the most cited authors of articles on AI in healthcare. It also identifies the authors’ keywords, dominance factor (DF) ranking, h-index, productivity, and total number of citations. Table  5 identifies the authors and their publications in the top 20 rankings. As the table shows, Bushko R.G. has the highest number of publications: four papers. He is the editor-in-chief of Future of Health Technology, a scientific journal that aims to develop a clear vision of the future of health technology. Then, several authors each wrote three papers. For instance, Liu C. is a researcher active in the topic of ML and computer vision, and Sharma A. from Emory University Atlanta in the USA is a researcher with a clear focus on imaging and translational informatics. Some other authors have two publications each. While some authors have published as primary authors, most have published as co-authors. Hence, in the next section, we measure the contributory power of each author by investigating the DF ranking through the number of elements.

Authors’ dominance ranking

The dominance factor (DF) is a ratio measuring the fraction of multi-authored articles in which an author acts as the first author [ 53 ]. Several bibliometric studies use the DF in their analyses [ 46 , 54 ]. The DF ranking calculates an author’s dominance in producing articles. The DF is calculated by dividing the number of an author’s multi-authored papers as the first author (Nmf) by the author's total number of multi-authored papers (Nmt). This is omitted in the single-author case due to the constant value of 1 for single-authored articles. This formulation could lead to some distortions in the results, especially in fields where the first author is entered by surname alphabetical order [ 55 ].

The mathematical equation for the DF is shown as:

Table  6 lists the top 20 DF rankings. The data in the table show a low level of articles per author, either for first-authored or multi-authored articles. The results demonstrate that we are dealing with an emerging topic in the literature. Additionally, as shown in the table, Fox J. and Longoni C. are the most dominant authors in the field.

Authors’ impact

Table  7 shows the impact of authors in terms of the h-index [ 56 ] (i.e., the productivity and impact of citations of a researcher), g-index [ 57 ] (i.e., the distribution of citations received by a researcher's publications), m-index [ 58 ] (i.e., the h-index value per year), total citations, total paper and years of scientific publication. The H-index was introduced in the literature as a metric for the objective comparison of scientific results and depended on the number of publications and their impact [ 59 ]. The results show that the 20 most relevant authors have an h-index between 2 and 1. For the practical interpretation of the data, the authors considered data published by the London School of Economics [ 60 ]. In the social sciences, the analysis shows values of 7.6 for economic publications by professors and researchers who had been active for several years. Therefore, the youthfulness of the research area has attracted young researchers and professors. At the same time, new indicators have emerged over the years to diversify the logic of the h-index. For example, the g-index indicates an author's impact on citations, considering that a single article can generate these. The m-index, on the other hand, shows the cumulative value over the years.

The analysis, also considering the total number of citations, the number of papers published and the year of starting to publish, thus confirms that we are facing an expanding research flow.

Authors’ productivity

Figure  5 shows Lotka’s law. This mathematical formulation originated in 1926 to describe the publication frequency by authors in a specific research field [ 61 ]. In practice, the law states that the number of authors contributing to research in a given period is a fraction of the number who make up a single contribution [ 14 , 61 ].

Lotka’s law.

The mathematical relationship is expressed in reverse in the following way:

where y x is equal to the number of authors producing x articles in each research field. Therefore, C and n are constants that can be estimated in the calculation.

The figure's results are in line with Lotka's results, with an average of two publications per author in a given research field. In addition, the figure shows the percentage of authors. Our results lead us to state that we are dealing with a young and growing research field, even with this analysis. Approximately 70% of the authors had published only their first research article. Only approximately 20% had published two scientific papers.

Authors’ keywords

This section provides information on the relationship between the keywords artificial intelligence and healthcare . This analysis is essential to determine the research trend, identify gaps in the discussion on AI in healthcare, and identify the fields that can be interesting as research areas [ 42 , 62 ].

Table  8 highlights the total number of keywords per author in the top 20 positions. The ranking is based on the following elements: healthcare, artificial intelligence, and clinical decision support system . Keyword analysis confirms the scientific area of reference. In particular, we deduce the definition as “Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” [ 2 , 63 ]. Panch et al. [ 4 ] find that these technologies can be used in different business and management areas. After the first keyword, the analysis reveals AI applications and related research such as machine learning and deep learning.

Additionally, data mining and big data are a step forward in implementing exciting AI applications. According to our specific interest, if we applied AI in healthcare, we would achieve technological applications to help and support doctors and medical researchers in decision-making. The link between AI and decision-making is the reason why we find, in the seventh position, the keyword clinical decision support system . AI techniques can unlock clinically relevant information hidden in the massive amount of data that can assist clinical decision-making [ 64 ]. If we analyse the following keywords, we find other elements related to decision-making and support systems.

The TreeMap below (Fig.  6 ) highlights the combination of possible keywords representing AI and healthcare.

Keywords treemap.

The topic dendrogram in Fig.  7 represents the hierarchical order and the relationship between the keywords generated by hierarchical clustering [ 42 ]. The cut in the figure and the vertical lines facilitate an investigation and interpretation of the different clusters. As stated by Andrews [ 48 ], the figure is not intended to find the perfect level of associations between clusters. However, it aims to estimate the approximate number of clusters to facilitate further discussion.

Topic dendrogram.

The research stream of AI in healthcare is divided into two main strands. The blue strand focuses on medical information systems and the internet. Some papers are related to healthcare organisations, such as the Internet of Things, meaning that healthcare organisations use AI to support health services management and data analysis. AI applications are also used to improve diagnostic and therapeutic accuracy and the overall clinical treatment process [ 2 ]. If we consider the second block, the red one, three different clusters highlight separate aspects of the topic. The first could be explained as AI and ML predictive algorithms. Through AI applications, it is possible to obtain a predictive approach that can ensure that patients are better monitored. This also allows a better understanding of risk perception for doctors and medical researchers. In the second cluster, the most frequent words are decisions , information system , and support system . This means that AI applications can support doctors and medical researchers in decision-making. Information coming from AI technologies can be used to consider difficult problems and support a more straightforward and rapid decision-making process. In the third cluster, it is vital to highlight that the ML model can deal with vast amounts of data. From those inputs, it can return outcomes that can optimise the work of healthcare organisations and scheduling of medical activities.

Furthermore, the word cloud in Fig.  8 highlights aspects of AI in healthcare, such as decision support systems, decision-making, health services management, learning systems, ML techniques and diseases. The figure depicts how AI is linked to healthcare and how it is used in medicine.

Word cloud.

Figure  9 represents the search trends based on the keywords analysed. The research started in 2012. First, it identified research topics related to clinical decision support systems. This topic was recurrent during the following years. Interestingly, in 2018, studies investigated AI and natural language processes as possible tools to manage patients and administrative elements. Finally, a new research stream considers AI's role in fighting COVID-19 [ 65 , 66 ].

Keywords frequency.

Table  9 represents the number of citations from other articles within the top 20 rankings. The analysis allows the benchmark studies in the field to be identified [ 48 ]. For instance, Burke et al. [ 67 ] writes the most cited paper and analyses efficient nurse rostering methodologies. The paper critically evaluates tangible interdisciplinary solutions that also include AI. Immediately thereafter, Ahmed M.A.'s article proposes a data-driven optimisation methodology to determine the optimal number of healthcare staff to optimise patients' productivity [ 68 ]. Finally, the third most cited article lays the groundwork for developing deep learning by considering diverse health and administrative information [ 51 ].

This section analyses the diffusion of AI in healthcare around the world. It highlights countries to show the geographies of this research. It includes all published articles, the total number of citations, and the collaboration network. The following sub-sections start with an analysis of the total number of published articles.

Country total articles

Figure  9 and Table  10 display the countries where AI in healthcare has been considered. The USA tops the list of countries with the maximum number of articles on the topic (215). It is followed by China (83), the UK (54), India (51), Australia (54), and Canada (32). It is immediately evident that the theme has developed on different continents, highlighting a growing interest in AI in healthcare. The figure shows that many areas, such as Russia, Eastern Europe and Africa except for Algeria, Egypt, and Morocco, have still not engaged in this scientific debate.

Country publications and collaboration map

This section discusses articles on AI in healthcare in terms of single or multiple publications in each country. It also aims to observe collaboration and networking between countries. Table  11 and Fig.  10 highlight the average citations by state and show that the UK, the USA, and Kuwait have a higher average number of citations than other countries. Italy, Spain and New Zealand have the most significant number of citations.

Articles per country.

Figure  11 depicts global collaborations. The blue colour on the map represents research cooperation among nations. Additionally, the pink border linking states indicates the extent of collaboration between authors. The primary cooperation between nations is between the USA and China, with two collaborative articles. Other collaborations among nations are limited to a few papers.

Collaboration map.

Artificial intelligence for healthcare: applications

This section aims to strengthen the research scope by answering RQ3: What are the research applications of artificial intelligence for healthcare?

Benefiting from the topical dendrogram, researchers will provide a development model based on four relevant variables [ 69 , 70 ]. AI has been a disruptive innovation in healthcare [ 4 ]. With its sophisticated algorithms and several applications, AI has assisted doctors and medical professionals in the domains of health information systems, geocoding health data, epidemic and syndromic surveillance, predictive modelling and decision support, and medical imaging [ 2 , 9 , 10 , 64 ]. Furthermore, the researchers considered the bibliometric analysis to identify four macro-variables dominant in the field and used them as authors' keywords. Therefore, the following sub-sections aim to explain the debate on applications in healthcare for AI techniques. These elements are shown in Fig.  12 .

Dominant variables for AI in healthcare.

Health services management

One of the notable aspects of AI techniques is potential support for comprehensive health services management. These applications can support doctors, nurses and administrators in their work. For instance, an AI system can provide health professionals with constant, possibly real-time medical information updates from various sources, including journals, textbooks, and clinical practices [ 2 , 10 ]. These applications' strength is becoming even more critical in the COVID-19 period, during which information exchange is continually needed to properly manage the pandemic worldwide [ 71 ]. Other applications involve coordinating information tools for patients and enabling appropriate inferences for health risk alerts and health outcome prediction [ 72 ]. AI applications allow, for example, hospitals and all health services to work more efficiently for the following reasons:

Clinicians can access data immediately when they need it.

Nurses can ensure better patient safety while administering medication.

Patients can stay informed and engaged in their care by communicating with their medical teams during hospital stays.

Additionally, AI can contribute to optimising logistics processes, for instance, realising drugs and equipment in a just-in-time supply system based totally on predictive algorithms [ 73 , 74 ]. Interesting applications can also support the training of personnel working in health services. This evidence could be helpful in bridging the gap between urban and rural health services [ 75 ]. Finally, health services management could benefit from AI to leverage the multiplicity of data in electronic health records by predicting data heterogeneity across hospitals and outpatient clinics, checking for outliers, performing clinical tests on the data, unifying patient representation, improving future models that can predict diagnostic tests and analyses, and creating transparency with benchmark data for analysing services delivered [ 51 , 76 ].

Predictive medicine

Another relevant topic is AI applications for disease prediction and diagnosis treatment, outcome prediction and prognosis evaluation [ 72 , 77 ]. Because AI can identify meaningful relationships in raw data, it can support diagnostic, treatment and prediction outcomes in many medical situations [ 64 ]. It allows medical professionals to embrace the proactive management of disease onset. Additionally, predictions are possible for identifying risk factors and drivers for each patient to help target healthcare interventions for better outcomes [ 3 ]. AI techniques can also help design and develop new drugs, monitor patients and personalise patient treatment plans [ 78 ]. Doctors benefit from having more time and concise data to make better patient decisions. Automatic learning through AI could disrupt medicine, allowing prediction models to be created for drugs and exams that monitor patients over their whole lives [ 79 ].

• Clinical decision-making

One of the keyword analysis main topics is that AI applications could support doctors and medical researchers in the clinical decision-making process. According to Jiang et al. [ 64 ], AI can help physicians make better clinical decisions or even replace human judgement in healthcare-specific functional areas. According to Bennett and Hauser [ 80 ], algorithms can benefit clinical decisions by accelerating the process and the amount of care provided, positively impacting the cost of health services. Therefore, AI technologies can support medical professionals in their activities and simplify their jobs [ 4 ]. Finally, as Redondo and Sandoval [ 81 ] find, algorithmic platforms can provide virtual assistance to help doctors understand the semantics of language and learning to solve business process queries as a human being would.

Patient data and diagnostics

Another challenging topic related to AI applications is patient data and diagnostics. AI techniques can help medical researchers deal with the vast amount of data from patients (i.e., medical big data ). AI systems can manage data generated from clinical activities, such as screening, diagnosis, and treatment assignment. In this way, health personnel can learn similar subjects and associations between subject features and outcomes of interest [ 64 ].

These technologies can analyse raw data and provide helpful insights that can be used in patient treatments. They can help doctors in the diagnostic process; for example, to realise a high-speed body scan, it will be simpler to have an overall patient condition image. Then, AI technology can recreate a 3D mapping solution of a patient’s body.

In terms of data, interesting research perspectives are emerging. For instance, we observed the emergence of a stream of research on patient data management and protection related to AI applications [ 82 ].

For diagnostics, AI techniques can make a difference in rehabilitation therapy and surgery. Numerous robots have been designed to support and manage such tasks. Rehabilitation robots physically support and guide, for example, a patient’s limb during motor therapy [ 83 ]. For surgery, AI has a vast opportunity to transform surgical robotics through devices that can perform semi-automated surgical tasks with increasing efficiency. The final aim of this technology is to automate procedures to negate human error while maintaining a high level of accuracy and precision [ 84 ]. Finally, the -19 period has led to increased remote patient diagnostics through telemedicine that enables remote observation of patients and provides physicians and nurses with support tools [ 66 , 85 , 86 ].

This study aims to provide a bibliometric analysis of publications on AI in healthcare, focusing on accounting, business and management, decision sciences and health profession studies. Using the SLR method of Massaro et al. [ 11 ], we provide a reliable and replicable research protocol for future studies in this field. Additionally, we investigate the trend of scientific publications on the subject, unexplored information, future directions, and implications using the science mapping workflow. Our analysis provides interesting insights.

In terms of bibliometric variables, the four leading journals, Journal of Medical Systems , Studies in Health Technology and Informatics , IEEE Journal of Biomedical and Health Informatics , and Decision Support Systems , are optimal locations for the publication of scientific articles on this topic. These journals deal mainly with healthcare, medical information systems, and applications such as cloud computing, machine learning, and AI. Additionally, in terms of h-index, Bushko R.G. and Liu C. are the most productive and impactful authors in this research stream. Burke et al.’s [ 67 ] contribution is the most cited with an analysis of nurse rostering using new technologies such as AI. Finally, in terms of keywords, co-occurrence reveals some interesting insights. For instance, researchers have found that AI has a role in diagnostic accuracy and helps in the analysis of health data by comparing thousands of medical records, experiencing automatic learning with clinical alerts, efficient management of health services and places of care, and the possibility of reconstructing patient history using these data.

Second, this paper finds five cluster analyses in healthcare applications: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making. These technologies can also contribute to optimising logistics processes in health services and allowing a better allocation of resources.

Third, the authors analysing the research findings and the issues under discussion strongly support AI's role in decision support. These applications, however, are demonstrated by creating a direct link to data quality management and the technology awareness of health personnel [ 87 ].

The importance of data quality for the decision-making process

Several authors have analysed AI in the healthcare research stream, but in this case, the authors focus on other literature that includes business and decision-making processes. In this regard, the analysis of the search flow reveals a double view of the literature. On the one hand, some contributions belong to the positivist literature and embrace future applications and implications of technology for health service management, data analysis and diagnostics [ 6 , 80 , 88 ]. On the other hand, some investigations also aim to understand the darker sides of technology and its impact. For example, as Carter [ 89 ] states, the impact of AI is multi-sectoral; its development, however, calls for action to protect personal data. Similarly, Davenport and Kalakota [ 77 ] focus on the ethical implications of using AI in healthcare. According to the authors, intelligent machines raise issues of accountability, transparency, and permission, especially in automated communication with patients. Our analysis does not indicate a marked strand of the literature; therefore, we argue that the discussion of elements such as the transparency of technology for patients is essential for the development of AI applications.

A large part of our results shows that, at the application level, AI can be used to improve medical support for patients (Fig.  11 ) [ 64 , 82 ]. However, we believe that, as indicated by Kalis et al. [ 90 ] on the pages of Harvard Business Review, the management of costly back-office problems should also be addressed.

The potential of algorithms includes data analysis. There is an immense quantity of data accessible now, which carries the possibility of providing information about a wide variety of medical and healthcare activities [ 91 ]. With the advent of modern computational methods, computer learning and AI techniques, there are numerous possibilities [ 79 , 83 , 84 ]. For example, AI makes it easier to turn data into concrete and actionable observations to improve decision-making, deliver high-quality patient treatment, adapt to real-time emergencies, and save more lives on the clinical front. In addition, AI makes it easier to leverage capital to develop systems and facilities and reduce expenses at the organisational level [ 78 ]. Studying contributions to the topic, we noticed that data accuracy was included in the debate, indicating that a high standard of data will benefit decision-making practitioners [ 38 , 77 ]. AI techniques are an essential instrument for studying data and the extraction of medical insight, and they may assist medical researchers in their practices. Using computational tools, healthcare stakeholders may leverage the power of data not only to evaluate past data ( descriptive analytics ) but also to forecast potential outcomes ( predictive analytics ) and to define the best actions for the present scenario ( prescriptive analytics ) [ 78 ]. The current abundance of evidence makes it easier to provide a broad view of patient health; doctors should have access to the correct details at the right time and location to provide the proper treatment [ 92 ].

Will medical technology de-skill doctors?

Further reflection concerns the skills of doctors. Studies have shown that healthcare personnel are progressively being exposed to technology for different purposes, such as collecting patient records or diagnosis [ 71 ]. This is demonstrated by the keywords (Fig.  6 ) that focus on technology and the role of decision-making with new innovative tools. In addition, the discussion expands with Lu [ 93 ], which indicates that the excessive use of technology could hinder doctors’ skills and clinical procedures' expansion. Among the main issues arising from the literature is the possible de-skilling of healthcare staff due to reduced autonomy in decision-making concerning patients [ 94 ]. Therefore, the challenges and discussion we uncovered in Fig.  11 are expanded by also considering the ethical implications of technology and the role of skills.

Implications

Our analysis also has multiple theoretical and practical implications.

In terms of theoretical contribution, this paper extends the previous results of Connelly et al., dos Santos et al, Hao et al., Huang et al., Liao et al. and Tran et al. [ 2 , 19 , 20 , 21 , 22 , 24 ] in considering AI in terms of clinical decision-making and data management quality.

In terms of practical implications, this paper aims to create a fruitful discussion with healthcare professionals and administrative staff on how AI can be at their service to increase work quality. Furthermore, this investigation offers a broad comprehension of bibliometric variables of AI techniques in healthcare. It can contribute to advancing scientific research in this field.

Limitations

Like any other, our study has some limitations that could be addressed by more in-depth future studies. For example, using only one research database, such as Scopus, could be limiting. Further analysis could also investigate the PubMed, IEEE, and Web of Science databases individually and holistically, especially the health parts. Then, the use of search terms such as "Artificial Intelligence" OR "AI" AND "Healthcare" could be too general and exclude interesting studies. Moreover, although we analysed 288 peer-reviewed scientific papers, because the new research topic is new, the analysis of conference papers could return interesting results for future researchers. Additionally, as this is a young research area, the analysis will be subject to recurrent obsolescence as multiple new research investigations are published. Finally, although bibliometric analysis has limited the subjectivity of the analysis [ 15 ], the verification of recurring themes could lead to different results by indicating areas of significant interest not listed here.

Future research avenues

Concerning future research perspectives, researchers believe that an analysis of the overall amount that a healthcare organisation should pay for AI technologies could be helpful. If these technologies are essential for health services management and patient treatment, governments should invest and contribute to healthcare organisations' modernisation. New investment funds could be made available in the healthcare world, as in the European case with the Next Generation EU programme or national investment programmes [ 95 ]. Additionally, this should happen especially in the poorest countries around the world, where there is a lack of infrastructure and services related to health and medicine [ 96 ]. On the other hand, it might be interesting to evaluate additional profits generated by healthcare organisations with AI technologies compared to those that do not use such technologies.

Further analysis could also identify why some parts of the world have not conducted studies in this area. It would be helpful to carry out a comparative analysis between countries active in this research field and countries that are not currently involved. It would make it possible to identify variables affecting AI technologies' presence or absence in healthcare organisations. The results of collaboration between countries also present future researchers with the challenge of greater exchanges between researchers and professionals. Therefore, further research could investigate the difference in vision between professionals and academics.

In the accounting, business, and management research area, there is currently a lack of quantitative analysis of the costs and profits generated by healthcare organisations that use AI technologies. Therefore, research in this direction could further increase our understanding of the topic and the number of healthcare organisations that can access technologies based on AI. Finally, as suggested in the discussion section, more interdisciplinary studies are needed to strengthen AI links with data quality management and AI and ethics considerations in healthcare.

In pursuing the philosophy of Massaro et al.’s [ 11 ] methodological article, we have climbed on the shoulders of giants, hoping to provide a bird's-eye view of the AI literature in healthcare. We performed this study with a bibliometric analysis aimed at discovering authors, countries of publication and collaboration, and keywords and themes. We found a fast-growing, multi-disciplinary stream of research that is attracting an increasing number of authors.

The research, therefore, adopts a quantitative approach to the analysis of bibliometric variables and a qualitative approach to the study of recurring keywords, which has allowed us to demonstrate strands of literature that are not purely positive. There are currently some limitations that will affect future research potential, especially in ethics, data governance and the competencies of the health workforce.

Availability of data and materials

All the data are retrieved from public scientific platforms.

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Secinaro, S., Calandra, D., Secinaro, A. et al. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 21 , 125 (2021). https://doi.org/10.1186/s12911-021-01488-9

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The use of Big Data Analytics in healthcare

• Kornelia Batko   ORCID: orcid.org/0000-0001-6561-3826 1 &
• Andrzej Ślęzak 2  

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The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

Introduction

The main contribution of this paper is to present an analytical overview of using structured and unstructured data (Big Data) analytics in medical facilities in Poland. Medical facilities use both structured and unstructured data in their practice. Structured data has a predetermined schema, it is extensive, freeform, and comes in variety of forms [ 27 ]. In contrast, unstructured data, referred to as Big Data (BD), does not fit into the typical data processing format. Big Data is a massive amount of data sets that cannot be stored, processed, or analyzed using traditional tools. It remains stored but not analyzed. Due to the lack of a well-defined schema, it is difficult to search and analyze such data and, therefore, it requires a specific technology and method to transform it into value [ 20 , 68 ]. Integrating data stored in both structured and unstructured formats can add significant value to an organization [ 27 ]. Organizations must approach unstructured data in a different way. Therefore, the potential is seen in Big Data Analytics (BDA). Big Data Analytics are techniques and tools used to analyze and extract information from Big Data. The results of Big Data analysis can be used to predict the future. They also help in creating trends about the past. When it comes to healthcare, it allows to analyze large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 60 ].

This paper is the first study to consolidate and characterize the use of Big Data from different perspectives. The first part consists of a brief literature review of studies on Big Data (BD) and Big Data Analytics (BDA), while the second part presents results of direct research aimed at diagnosing the use of big data analyses in medical facilities in Poland.

Healthcare is a complex system with varied stakeholders: patients, doctors, hospitals, pharmaceutical companies and healthcare decision-makers. This sector is also limited by strict rules and regulations. However, worldwide one may observe a departure from the traditional doctor-patient approach. The doctor becomes a partner and the patient is involved in the therapeutic process [ 14 ]. Healthcare is no longer focused solely on the treatment of patients. The priority for decision-makers should be to promote proper health attitudes and prevent diseases that can be avoided [ 81 ]. This became visible and important especially during the Covid-19 pandemic [ 44 ].

The next challenges that healthcare will have to face is the growing number of elderly people and a decline in fertility. Fertility rates in the country are found below the reproductive minimum necessary to keep the population stable [ 10 ]. The reflection of both effects, namely the increase in age and lower fertility rates, are demographic load indicators, which is constantly growing. Forecasts show that providing healthcare in the form it is provided today will become impossible in the next 20 years [ 70 ]. It is especially visible now during the Covid-19 pandemic when healthcare faced quite a challenge related to the analysis of huge data amounts and the need to identify trends and predict the spread of the coronavirus. The pandemic showed it even more that patients should have access to information about their health condition, the possibility of digital analysis of this data and access to reliable medical support online. Health monitoring and cooperation with doctors in order to prevent diseases can actually revolutionize the healthcare system. One of the most important aspects of the change necessary in healthcare is putting the patient in the center of the system.

Technology is not enough to achieve these goals. Therefore, changes should be made not only at the technological level but also in the management and design of complete healthcare processes and what is more, they should affect the business models of service providers. The use of Big Data Analytics is becoming more and more common in enterprises [ 17 , 54 ]. However, medical enterprises still cannot keep up with the information needs of patients, clinicians, administrators and the creator’s policy. The adoption of a Big Data approach would allow the implementation of personalized and precise medicine based on personalized information, delivered in real time and tailored to individual patients.

To achieve this goal, it is necessary to implement systems that will be able to learn quickly about the data generated by people within clinical care and everyday life. This will enable data-driven decision making, receiving better personalized predictions about prognosis and responses to treatments; a deeper understanding of the complex factors and their interactions that influence health at the patient level, the health system and society, enhanced approaches to detecting safety problems with drugs and devices, as well as more effective methods of comparing prevention, diagnostic, and treatment options [ 40 ].

In the literature, there is a lot of research showing what opportunities can be offered to companies by big data analysis and what data can be analyzed. However, there are few studies showing how data analysis in the area of healthcare is performed, what data is used by medical facilities and what analyses and in which areas they carry out. This paper aims to fill this gap by presenting the results of research carried out in medical facilities in Poland. The goal is to analyze the possibilities of using Big Data Analytics in healthcare, especially in Polish conditions. In particular, the paper is aimed at determining what data is processed by medical facilities in Poland, what analyses they perform and in what areas, and how they assess their analytical maturity. In order to achieve this goal, a critical analysis of the literature was performed, and the direct research was based on a research questionnaire conducted on a sample of 217 medical facilities in Poland. It was hypothesized that medical facilities in Poland are working on both structured and unstructured data and moving towards data-based healthcare and its benefits. Examining the maturity of healthcare facilities in the use of Big Data and Big Data Analytics is crucial in determining the potential future benefits that the healthcare sector can gain from Big Data Analytics. There is also a pressing need to predicate whether, in the coming years, healthcare will be able to cope with the threats and challenges it faces.

This paper is divided into eight parts. The first is the introduction which provides background and the general problem statement of this research. In the second part, this paper discusses considerations on use of Big Data and Big Data Analytics in Healthcare, and then, in the third part, it moves on to challenges and potential benefits of using Big Data Analytics in healthcare. The next part involves the explanation of the proposed method. The result of direct research and discussion are presented in the fifth part, while the following part of the paper is the conclusion. The seventh part of the paper presents practical implications. The final section of the paper provides limitations and directions for future research.

Considerations on use Big Data and Big Data Analytics in the healthcare

In recent years one can observe a constantly increasing demand for solutions offering effective analytical tools. This trend is also noticeable in the analysis of large volumes of data (Big Data, BD). Organizations are looking for ways to use the power of Big Data to improve their decision making, competitive advantage or business performance [ 7 , 54 ]. Big Data is considered to offer potential solutions to public and private organizations, however, still not much is known about the outcome of the practical use of Big Data in different types of organizations [ 24 ].

As already mentioned, in recent years, healthcare management worldwide has been changed from a disease-centered model to a patient-centered model, even in value-based healthcare delivery model [ 68 ]. In order to meet the requirements of this model and provide effective patient-centered care, it is necessary to manage and analyze healthcare Big Data.

The issue often raised when it comes to the use of data in healthcare is the appropriate use of Big Data. Healthcare has always generated huge amounts of data and nowadays, the introduction of electronic medical records, as well as the huge amount of data sent by various types of sensors or generated by patients in social media causes data streams to constantly grow. Also, the medical industry generates significant amounts of data, including clinical records, medical images, genomic data and health behaviors. Proper use of the data will allow healthcare organizations to support clinical decision-making, disease surveillance, and public health management. The challenge posed by clinical data processing involves not only the quantity of data but also the difficulty in processing it.

In the literature one can find many different definitions of Big Data. This concept has evolved in recent years, however, it is still not clearly understood. Nevertheless, despite the range and differences in definitions, Big Data can be treated as a: large amount of digital data, large data sets, tool, technology or phenomenon (cultural or technological.

Big Data can be considered as massive and continually generated digital datasets that are produced via interactions with online technologies [ 53 ]. Big Data can be defined as datasets that are of such large sizes that they pose challenges in traditional storage and analysis techniques [ 28 ]. A similar opinion about Big Data was presented by Ohlhorst who sees Big Data as extremely large data sets, possible neither to manage nor to analyze with traditional data processing tools [ 57 ]. In his opinion, the bigger the data set, the more difficult it is to gain any value from it.

In turn, Knapp perceived Big Data as tools, processes and procedures that allow an organization to create, manipulate and manage very large data sets and storage facilities [ 38 ]. From this point of view, Big Data is identified as a tool to gather information from different databases and processes, allowing users to manage large amounts of data.

Similar perception of the term ‘Big Data’ is shown by Carter. According to him, Big Data technologies refer to a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data by enabling high velocity capture, discovery and/or analysis [ 13 ].

Jordan combines these two approaches by identifying Big Data as a complex system, as it needs data bases for data to be stored in, programs and tools to be managed, as well as expertise and personnel able to retrieve useful information and visualization to be understood [ 37 ].

Following the definition of Laney for Big Data, it can be state that: it is large amount of data generated in very fast motion and it contains a lot of content [ 43 ]. Such data comes from unstructured sources, such as stream of clicks on the web, social networks (Twitter, blogs, Facebook), video recordings from the shops, recording of calls in a call center, real time information from various kinds of sensors, RFID, GPS devices, mobile phones and other devices that identify and monitor something [ 8 ]. Big Data is a powerful digital data silo, raw, collected with all sorts of sources, unstructured and difficult, or even impossible, to analyze using conventional techniques used so far to relational databases.

While describing Big Data, it cannot be overlooked that the term refers more to a phenomenon than to specific technology. Therefore, instead of defining this phenomenon, trying to describe them, more authors are describing Big Data by giving them characteristics included a collection of V’s related to its nature [ 2 , 3 , 23 , 25 , 58 ]:

Volume (refers to the amount of data and is one of the biggest challenges in Big Data Analytics),

Velocity (speed with which new data is generated, the challenge is to be able to manage data effectively and in real time),

Variety (heterogeneity of data, many different types of healthcare data, the challenge is to derive insights by looking at all available heterogenous data in a holistic manner),

Variability (inconsistency of data, the challenge is to correct the interpretation of data that can vary significantly depending on the context),

Veracity (how trustworthy the data is, quality of the data),

Visualization (ability to interpret data and resulting insights, challenging for Big Data due to its other features as described above).

Value (the goal of Big Data Analytics is to discover the hidden knowledge from huge amounts of data).

Big Data is defined as an information asset with high volume, velocity, and variety, which requires specific technology and method for its transformation into value [ 21 , 77 ]. Big Data is also a collection of information about high-volume, high volatility or high diversity, requiring new forms of processing in order to support decision-making, discovering new phenomena and process optimization [ 5 , 7 ]. Big Data is too large for traditional data-processing systems and software tools to capture, store, manage and analyze, therefore it requires new technologies [ 28 , 50 , 61 ] to manage (capture, aggregate, process) its volume, velocity and variety [ 9 ].

Undoubtedly, Big Data differs from the data sources used so far by organizations. Therefore, organizations must approach this type of unstructured data in a different way. First of all, organizations must start to see data as flows and not stocks—this entails the need to implement the so-called streaming analytics [ 48 ]. The mentioned features make it necessary to use new IT tools that allow the fullest use of new data [ 58 ]. The Big Data idea, inseparable from the huge increase in data available to various organizations or individuals, creates opportunities for access to valuable analyses, conclusions and enables making more accurate decisions [ 6 , 11 , 59 ].

The Big Data concept is constantly evolving and currently it does not focus on huge amounts of data, but rather on the process of creating value from this data [ 52 ]. Big Data is collected from various sources that have different data properties and are processed by different organizational units, resulting in creation of a Big Data chain [ 36 ]. The aim of the organizations is to manage, process and analyze Big Data. In the healthcare sector, Big Data streams consist of various types of data, namely [ 8 , 51 ]:

clinical data, i.e. data obtained from electronic medical records, data from hospital information systems, image centers, laboratories, pharmacies and other organizations providing health services, patient generated health data, physician’s free-text notes, genomic data, physiological monitoring data [ 4 ],

biometric data provided from various types of devices that monitor weight, pressure, glucose level, etc.,

financial data, constituting a full record of economic operations reflecting the conducted activity,

data from scientific research activities, i.e. results of research, including drug research, design of medical devices and new methods of treatment,

data provided by patients, including description of preferences, level of satisfaction, information from systems for self-monitoring of their activity: exercises, sleep, meals consumed, etc.

data from social media.

These data are provided not only by patients but also by organizations and institutions, as well as by various types of monitoring devices, sensors or instruments [ 16 ]. Data that has been generated so far in the healthcare sector is stored in both paper and digital form. Thus, the essence and the specificity of the process of Big Data analyses means that organizations need to face new technological and organizational challenges [ 67 ]. The healthcare sector has always generated huge amounts of data and this is connected, among others, with the need to store medical records of patients. However, the problem with Big Data in healthcare is not limited to an overwhelming volume but also an unprecedented diversity in terms of types, data formats and speed with which it should be analyzed in order to provide the necessary information on an ongoing basis [ 3 ]. It is also difficult to apply traditional tools and methods for management of unstructured data [ 67 ]. Due to the diversity and quantity of data sources that are growing all the time, advanced analytical tools and technologies, as well as Big Data analysis methods which can meet and exceed the possibilities of managing healthcare data, are needed [ 3 , 68 ].

Therefore, the potential is seen in Big Data analyses, especially in the aspect of improving the quality of medical care, saving lives or reducing costs [ 30 ]. Extracting from this tangle of given association rules, patterns and trends will allow health service providers and other stakeholders in the healthcare sector to offer more accurate and more insightful diagnoses of patients, personalized treatment, monitoring of the patients, preventive medicine, support of medical research and health population, as well as better quality of medical services and patient care while, at the same time, the ability to reduce costs (Fig.  1 ).

(Source: Own elaboration)

Healthcare Big Data Analytics applications

The main challenge with Big Data is how to handle such a large amount of information and use it to make data-driven decisions in plenty of areas [ 64 ]. In the context of healthcare data, another major challenge is to adjust big data storage, analysis, presentation of analysis results and inference basing on them in a clinical setting. Data analytics systems implemented in healthcare are designed to describe, integrate and present complex data in an appropriate way so that it can be understood better (Fig.  2 ). This would improve the efficiency of acquiring, storing, analyzing and visualizing big data from healthcare [ 71 ].

Process of Big Data Analytics

The result of data processing with the use of Big Data Analytics is appropriate data storytelling which may contribute to making decisions with both lower risk and data support. This, in turn, can benefit healthcare stakeholders. To take advantage of the potential massive amounts of data in healthcare and to ensure that the right intervention to the right patient is properly timed, personalized, and potentially beneficial to all components of the healthcare system such as the payer, patient, and management, analytics of large datasets must connect communities involved in data analytics and healthcare informatics [ 49 ]. Big Data Analytics can provide insight into clinical data and thus facilitate informed decision-making about the diagnosis and treatment of patients, prevention of diseases or others. Big Data Analytics can also improve the efficiency of healthcare organizations by realizing the data potential [ 3 , 62 ].

Big Data Analytics in medicine and healthcare refers to the integration and analysis of a large amount of complex heterogeneous data, such as various omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, interactomics, pharmacogenetics, deasomics), biomedical data, talemedicine data (sensors, medical equipment data) and electronic health records data [ 46 , 65 ].

When analyzing the phenomenon of Big Data in the healthcare sector, it should be noted that it can be considered from the point of view of three areas: epidemiological, clinical and business.

From a clinical point of view, the Big Data analysis aims to improve the health and condition of patients, enable long-term predictions about their health status and implementation of appropriate therapeutic procedures. Ultimately, the use of data analysis in medicine is to allow the adaptation of therapy to a specific patient, that is personalized medicine (precision, personalized medicine).

From an epidemiological point of view, it is desirable to obtain an accurate prognosis of morbidity in order to implement preventive programs in advance.

In the business context, Big Data analysis may enable offering personalized packages of commercial services or determining the probability of individual disease and infection occurrence. It is worth noting that Big Data means not only the collection and processing of data but, most of all, the inference and visualization of data necessary to obtain specific business benefits.

In order to introduce new management methods and new solutions in terms of effectiveness and transparency, it becomes necessary to make data more accessible, digital, searchable, as well as analyzed and visualized.

Erickson and Rothberg state that the information and data do not reveal their full value until insights are drawn from them. Data becomes useful when it enhances decision making and decision making is enhanced only when analytical techniques are used and an element of human interaction is applied [ 22 ].

Thus, healthcare has experienced much progress in usage and analysis of data. A large-scale digitalization and transparency in this sector is a key statement of almost all countries governments policies. For centuries, the treatment of patients was based on the judgment of doctors who made treatment decisions. In recent years, however, Evidence-Based Medicine has become more and more important as a result of it being related to the systematic analysis of clinical data and decision-making treatment based on the best available information [ 42 ]. In the healthcare sector, Big Data Analytics is expected to improve the quality of life and reduce operational costs [ 72 , 82 ]. Big Data Analytics enables organizations to improve and increase their understanding of the information contained in data. It also helps identify data that provides insightful insights for current as well as future decisions [ 28 ].

Big Data Analytics refers to technologies that are grounded mostly in data mining: text mining, web mining, process mining, audio and video analytics, statistical analysis, network analytics, social media analytics and web analytics [ 16 , 25 , 31 ]. Different data mining techniques can be applied on heterogeneous healthcare data sets, such as: anomaly detection, clustering, classification, association rules as well as summarization and visualization of those Big Data sets [ 65 ]. Modern data analytics techniques explore and leverage unique data characteristics even from high-speed data streams and sensor data [ 15 , 16 , 31 , 55 ]. Big Data can be used, for example, for better diagnosis in the context of comprehensive patient data, disease prevention and telemedicine (in particular when using real-time alerts for immediate care), monitoring patients at home, preventing unnecessary hospital visits, integrating medical imaging for a wider diagnosis, creating predictive analytics, reducing fraud and improving data security, better strategic planning and increasing patients’ involvement in their own health.

Big Data Analytics in healthcare can be divided into [ 33 , 73 , 74 ]:

descriptive analytics in healthcare is used to understand past and current healthcare decisions, converting data into useful information for understanding and analyzing healthcare decisions, outcomes and quality, as well as making informed decisions [ 33 ]. It can be used to create reports (i.e. about patients’ hospitalizations, physicians’ performance, utilization management), visualization, customized reports, drill down tables, or running queries on the basis of historical data.

predictive analytics operates on past performance in an effort to predict the future by examining historical or summarized health data, detecting patterns of relationships in these data, and then extrapolating these relationships to forecast. It can be used to i.e. predict the response of different patient groups to different drugs (dosages) or reactions (clinical trials), anticipate risk and find relationships in health data and detect hidden patterns [ 62 ]. In this way, it is possible to predict the epidemic spread, anticipate service contracts and plan healthcare resources. Predictive analytics is used in proper diagnosis and for appropriate treatments to be given to patients suffering from certain diseases [ 39 ].

prescriptive analytics—occurs when health problems involve too many choices or alternatives. It uses health and medical knowledge in addition to data or information. Prescriptive analytics is used in many areas of healthcare, including drug prescriptions and treatment alternatives. Personalized medicine and evidence-based medicine are both supported by prescriptive analytics.

discovery analytics—utilizes knowledge about knowledge to discover new “inventions” like drugs (drug discovery), previously unknown diseases and medical conditions, alternative treatments, etc.

Although the models and tools used in descriptive, predictive, prescriptive, and discovery analytics are different, many applications involve all four of them [ 62 ]. Big Data Analytics in healthcare can help enable personalized medicine by identifying optimal patient-specific treatments. This can influence the improvement of life standards, reduce waste of healthcare resources and save costs of healthcare [ 56 , 63 , 71 ]. The introduction of large data analysis gives new analytical possibilities in terms of scope, flexibility and visualization. Techniques such as data mining (computational pattern discovery process in large data sets) facilitate inductive reasoning and analysis of exploratory data, enabling scientists to identify data patterns that are independent of specific hypotheses. As a result, predictive analysis and real-time analysis becomes possible, making it easier for medical staff to start early treatments and reduce potential morbidity and mortality. In addition, document analysis, statistical modeling, discovering patterns and topics in document collections and data in the EHR, as well as an inductive approach can help identify and discover relationships between health phenomena.

Advanced analytical techniques can be used for a large amount of existing (but not yet analytical) data on patient health and related medical data to achieve a better understanding of the information and results obtained, as well as to design optimal clinical pathways [ 62 ]. Big Data Analytics in healthcare integrates analysis of several scientific areas such as bioinformatics, medical imaging, sensor informatics, medical informatics and health informatics [ 65 ]. Big Data Analytics in healthcare allows to analyze large datasets from thousands of patients, identifying clusters and correlation between datasets, as well as developing predictive models using data mining techniques [ 65 ]. Discussing all the techniques used for Big Data Analytics goes beyond the scope of a single article [ 25 ].

The success of Big Data analysis and its accuracy depend heavily on the tools and techniques used to analyze the ability to provide reliable, up-to-date and meaningful information to various stakeholders [ 12 ]. It is believed that the implementation of big data analytics by healthcare organizations could bring many benefits in the upcoming years, including lowering health care costs, better diagnosis and prediction of diseases and their spread, improving patient care and developing protocols to prevent re-hospitalization, optimizing staff, optimizing equipment, forecasting the need for hospital beds, operating rooms, treatments, and improving the drug supply chain [ 71 ].

Challenges and potential benefits of using Big Data Analytics in healthcare

Modern analytics gives possibilities not only to have insight in historical data, but also to have information necessary to generate insight into what may happen in the future. Even when it comes to prediction of evidence-based actions. The emphasis on reform has prompted payers and suppliers to pursue data analysis to reduce risk, detect fraud, improve efficiency and save lives. Everyone—payers, providers, even patients—are focusing on doing more with fewer resources. Thus, some areas in which enhanced data and analytics can yield the greatest results include various healthcare stakeholders (Table 1 ).

Healthcare organizations see the opportunity to grow through investments in Big Data Analytics. In recent years, by collecting medical data of patients, converting them into Big Data and applying appropriate algorithms, reliable information has been generated that helps patients, physicians and stakeholders in the health sector to identify values and opportunities [ 31 ]. It is worth noting that there are many changes and challenges in the structure of the healthcare sector. Digitization and effective use of Big Data in healthcare can bring benefits to every stakeholder in this sector. A single doctor would benefit the same as the entire healthcare system. Potential opportunities to achieve benefits and effects from Big Data in healthcare can be divided into four groups [ 8 ]:

Improving the quality of healthcare services:

assessment of diagnoses made by doctors and the manner of treatment of diseases indicated by them based on the decision support system working on Big Data collections,

detection of more effective, from a medical point of view, and more cost-effective ways to diagnose and treat patients,

analysis of large volumes of data to reach practical information useful for identifying needs, introducing new health services, preventing and overcoming crises,

prediction of the incidence of diseases,

detecting trends that lead to an improvement in health and lifestyle of the society,

analysis of the human genome for the introduction of personalized treatment.

Supporting the work of medical personnel

doctors’ comparison of current medical cases to cases from the past for better diagnosis and treatment adjustment,

detection of diseases at earlier stages when they can be more easily and quickly cured,

detecting epidemiological risks and improving control of pathogenic spots and reaction rates,

identification of patients who are predicted to have the highest risk of specific, life-threatening diseases by collating data on the history of the most common diseases, in healing people with reports entering insurance companies,

health management of each patient individually (personalized medicine) and health management of the whole society,

capturing and analyzing large amounts of data from hospitals and homes in real time, life monitoring devices to monitor safety and predict adverse events,

analysis of patient profiles to identify people for whom prevention should be applied, lifestyle change or preventive care approach,

the ability to predict the occurrence of specific diseases or worsening of patients’ results,

predicting disease progression and its determinants, estimating the risk of complications,

detecting drug interactions and their side effects.

Supporting scientific and research activity

supporting work on new drugs and clinical trials thanks to the possibility of analyzing “all data” instead of selecting a test sample,

the ability to identify patients with specific, biological features that will take part in specialized clinical trials,

selecting a group of patients for which the tested drug is likely to have the desired effect and no side effects,

using modeling and predictive analysis to design better drugs and devices.

Business and management

reduction of costs and counteracting abuse and counseling practices,

faster and more effective identification of incorrect or unauthorized financial operations in order to prevent abuse and eliminate errors,

increase in profitability by detecting patients generating high costs or identifying doctors whose work, procedures and treatment methods cost the most and offering them solutions that reduce the amount of money spent,

identification of unnecessary medical activities and procedures, e.g. duplicate tests.

According to research conducted by Wang, Kung and Byrd, Big Data Analytics benefits can be classified into five categories: IT infrastructure benefits (reducing system redundancy, avoiding unnecessary IT costs, transferring data quickly among healthcare IT systems, better use of healthcare systems, processing standardization among various healthcare IT systems, reducing IT maintenance costs regarding data storage), operational benefits (improving the quality and accuracy of clinical decisions, processing a large number of health records in seconds, reducing the time of patient travel, immediate access to clinical data to analyze, shortening the time of diagnostic test, reductions in surgery-related hospitalizations, exploring inconceivable new research avenues), organizational benefits (detecting interoperability problems much more quickly than traditional manual methods, improving cross-functional communication and collaboration among administrative staffs, researchers, clinicians and IT staffs, enabling data sharing with other institutions and adding new services, content sources and research partners), managerial benefits (gaining quick insights about changing healthcare trends in the market, providing members of the board and heads of department with sound decision-support information on the daily clinical setting, optimizing business growth-related decisions) and strategic benefits (providing a big picture view of treatment delivery for meeting future need, creating high competitive healthcare services) [ 73 ].

The above specification does not constitute a full list of potential areas of use of Big Data Analysis in healthcare because the possibilities of using analysis are practically unlimited. In addition, advanced analytical tools allow to analyze data from all possible sources and conduct cross-analyses to provide better data insights [ 26 ]. For example, a cross-analysis can refer to a combination of patient characteristics, as well as costs and care results that can help identify the best, in medical terms, and the most cost-effective treatment or treatments and this may allow a better adjustment of the service provider’s offer [ 62 ].

In turn, the analysis of patient profiles (e.g. segmentation and predictive modeling) allows identification of people who should be subject to prophylaxis, prevention or should change their lifestyle [ 8 ]. Shortened list of benefits for Big Data Analytics in healthcare is presented in paper [ 3 ] and consists of: better performance, day-to-day guides, detection of diseases in early stages, making predictive analytics, cost effectiveness, Evidence Based Medicine and effectiveness in patient treatment.

Summarizing, healthcare big data represents a huge potential for the transformation of healthcare: improvement of patients’ results, prediction of outbreaks of epidemics, valuable insights, avoidance of preventable diseases, reduction of the cost of healthcare delivery and improvement of the quality of life in general [ 1 ]. Big Data also generates many challenges such as difficulties in data capture, data storage, data analysis and data visualization [ 15 ]. The main challenges are connected with the issues of: data structure (Big Data should be user-friendly, transparent, and menu-driven but it is fragmented, dispersed, rarely standardized and difficult to aggregate and analyze), security (data security, privacy and sensitivity of healthcare data, there are significant concerns related to confidentiality), data standardization (data is stored in formats that are not compatible with all applications and technologies), storage and transfers (especially costs associated with securing, storing, and transferring unstructured data), managerial skills, such as data governance, lack of appropriate analytical skills and problems with Real-Time Analytics (health care is to be able to utilize Big Data in real time) [ 4 , 34 , 41 ].

The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities in Poland.

Presented research results are part of a larger questionnaire form on Big Data Analytics. The direct research was based on an interview questionnaire which contained 100 questions with 5-point Likert scale (1—strongly disagree, 2—I rather disagree, 3—I do not agree, nor disagree, 4—I rather agree, 5—I definitely agree) and 4 metrics questions. The study was conducted in December 2018 on a sample of 217 medical facilities (110 private, 107 public). The research was conducted by a specialized market research agency: Center for Research and Expertise of the University of Economics in Katowice.

When it comes to direct research, the selected entities included entities financed from public sources—the National Health Fund (23.5%), and entities operating commercially (11.5%). In the surveyed group of entities, more than a half (64.9%) are hybrid financed, both from public and commercial sources. The diversity of the research sample also applies to the size of the entities, defined by the number of employees. Taking into account proportions of the surveyed entities, it should be noted that in the sector structure, medium-sized (10–50 employees—34% of the sample) and large (51–250 employees—27%) entities dominate. The research was of all-Poland nature, and the entities included in the research sample come from all of the voivodships. The largest group were entities from Łódzkie (32%), Śląskie (18%) and Mazowieckie (18%) voivodships, as these voivodships have the largest number of medical institutions. Other regions of the country were represented by single units. The selection of the research sample was random—layered. As part of medical facilities database, groups of private and public medical facilities have been identified and the ones to which the questionnaire was targeted were drawn from each of these groups. The analyses were performed using the GNU PSPP 0.10.2 software.

The aim of the study was to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Characteristics of the research sample is presented in Table 2 .

The research is non-exhaustive due to the incomplete and uneven regional distribution of the samples, overrepresented in three voivodeships (Łódzkie, Mazowieckie and Śląskie). The size of the research sample (217 entities) allows the authors of the paper to formulate specific conclusions on the use of Big Data in the process of its management.

For the purpose of this paper, the following research hypotheses were formulated: (1) medical facilities in Poland are working on both structured and unstructured data (2) medical facilities in Poland are moving towards data-based healthcare and its benefits.

The paper poses the following research questions and statements that coincide with the selected questions from the research questionnaire:

From what sources do medical facilities obtain data? What types of data are used by the particular organization, whether structured or unstructured, and to what extent?

From what sources do medical facilities obtain data?

In which area organizations are using data and analytical systems (clinical or business)?

Is data analytics performed based on historical data or are predictive analyses also performed?

Determining whether administrative and medical staff receive complete, accurate and reliable data in a timely manner?

Determining whether real-time analyses are performed to support the particular organization’s activities.

Results and discussion

On the basis of the literature analysis and research study, a set of questions and statements related to the researched area was formulated. The results from the surveys show that medical facilities use a variety of data sources in their operations. These sources are both structured and unstructured data (Table 3 ).

According to the data provided by the respondents, considering the first statement made in the questionnaire, almost half of the medical institutions (47.58%) agreed that they rather collect and use structured data (e.g. databases and data warehouses, reports to external entities) and 10.57% entirely agree with this statement. As much as 23.35% of representatives of medical institutions stated “I agree or disagree”. Other medical facilities do not collect and use structured data (7.93%) and 6.17% strongly disagree with the first statement. Also, the median calculated based on the obtained results (median: 4), proves that medical facilities in Poland collect and use structured data (Table 4 ).

In turn, 28.19% of the medical institutions agreed that they rather collect and use unstructured data and as much as 9.25% entirely agree with this statement. The number of representatives of medical institutions that stated “I agree or disagree” was 27.31%. Other medical facilities do not collect and use structured data (17.18%) and 13.66% strongly disagree with the first statement. In the case of unstructured data the median is 3, which means that the collection and use of this type of data by medical facilities in Poland is lower.

In the further part of the analysis, it was checked whether the size of the medical facility and form of ownership have an impact on whether it analyzes unstructured data (Tables 4 and 5 ). In order to find this out, correlation coefficients were calculated.

Based on the calculations, it can be concluded that there is a small statistically monotonic correlation between the size of the medical facility and its collection and use of structured data (p < 0.001; τ = 0.16). This means that the use of structured data is slightly increasing in larger medical facilities. The size of the medical facility is more important according to use of unstructured data (p < 0.001; τ = 0.23) (Table 4 .).

To determine whether the form of medical facility ownership affects data collection, the Mann–Whitney U test was used. The calculations show that the form of ownership does not affect what data the organization collects and uses (Table 5 ).

Detailed information on the sources of from which medical facilities collect and use data is presented in the Table 6 .

The questionnaire results show that medical facilities are especially using information published in databases, reports to external units and transaction data, but they also use unstructured data from e-mails, medical devices, sensors, phone calls, audio and video data (Table 6 ). Data from social media, RFID and geolocation data are used to a small extent. Similar findings are concluded in the literature studies.

From the analysis of the answers given by the respondents, more than half of the medical facilities have integrated hospital system (HIS) implemented. As much as 43.61% use integrated hospital system and 16.30% use it extensively (Table 7 ). 19.38% of exanimated medical facilities do not use it at all. Moreover, most of the examined medical facilities (34.80% use it, 32.16% use extensively) conduct medical documentation in an electronic form, which gives an opportunity to use data analytics. Only 4.85% of medical facilities don’t use it at all.

Other problems that needed to be investigated were: whether medical facilities in Poland use data analytics? If so, in what form and in what areas? (Table 8 ). The analysis of answers given by the respondents about the potential of data analytics in medical facilities shows that a similar number of medical facilities use data analytics in administration and business (31.72% agreed with the statement no. 5 and 12.33% strongly agreed) as in the clinical area (33.04% agreed with the statement no. 6 and 12.33% strongly agreed). When considering decision-making issues, 35.24% agree with the statement "the organization uses data and analytical systems to support business decisions” and 8.37% of respondents strongly agree. Almost 40.09% agree with the statement that “the organization uses data and analytical systems to support clinical decisions (in the field of diagnostics and therapy)” and 15.42% of respondents strongly agree. Exanimated medical facilities use in their activity analytics based both on historical data (33.48% agree with statement 7 and 12.78% strongly agree) and predictive analytics (33.04% agrees with the statement number 8 and 15.86% strongly agree). Detailed results are presented in Table 8 .

Medical facilities focus on development in the field of data processing, as they confirm that they conduct analytical planning processes systematically and analyze new opportunities for strategic use of analytics in business and clinical activities (38.33% rather agree and 10.57% strongly agree with this statement). The situation is different with real-time data analysis, here, the situation is not so optimistic. Only 28.19% rather agree and 14.10% strongly agree with the statement that real-time analyses are performed to support an organization’s activities.

When considering whether a facility’s performance in the clinical area depends on the form of ownership, it can be concluded that taking the average and the Mann–Whitney U test depends. A higher degree of use of analyses in the clinical area can be observed in public institutions.

Whether a medical facility performs a descriptive or predictive analysis do not depend on the form of ownership (p > 0.05). It can be concluded that when analyzing the mean and median, they are higher in public facilities, than in private ones. What is more, the Mann–Whitney U test shows that these variables are dependent from each other (p < 0.05) (Table 9 ).

When considering whether a facility’s performance in the clinical area depends on its size, it can be concluded that taking the Kendall’s Tau (τ) it depends (p < 0.001; τ = 0.22), and the correlation is weak but statistically important. This means that the use of data and analytical systems to support clinical decisions (in the field of diagnostics and therapy) increases with the increase of size of the medical facility. A similar relationship, but even less powerful, can be found in the use of descriptive and predictive analyses (Table 10 ).

Considering the results of research in the area of analytical maturity of medical facilities, 8.81% of medical facilities stated that they are at the first level of maturity, i.e. an organization has developed analytical skills and does not perform analyses. As much as 13.66% of medical facilities confirmed that they have poor analytical skills, while 38.33% of the medical facility has located itself at level 3, meaning that “there is a lot to do in analytics”. On the other hand, 28.19% believe that analytical capabilities are well developed and 6.61% stated that analytics are at the highest level and the analytical capabilities are very well developed. Detailed data is presented in Table 11 . Average amounts to 3.11 and Median to 3.

The results of the research have enabled the formulation of following conclusions. Medical facilities in Poland are working on both structured and unstructured data. This data comes from databases, transactions, unstructured content of emails and documents, devices and sensors. However, the use of data from social media is smaller. In their activity, they reach for analytics in the administrative and business, as well as in the clinical area. Also, the decisions made are largely data-driven.

In summary, analysis of the literature that the benefits that medical facilities can get using Big Data Analytics in their activities relate primarily to patients, physicians and medical facilities. It can be confirmed that: patients will be better informed, will receive treatments that will work for them, will have prescribed medications that work for them and not be given unnecessary medications [ 78 ]. Physician roles will likely change to more of a consultant than decision maker. They will advise, warn, and help individual patients and have more time to form positive and lasting relationships with their patients in order to help people. Medical facilities will see changes as well, for example in fewer unnecessary hospitalizations, resulting initially in less revenue, but after the market adjusts, also the accomplishment [ 78 ]. The use of Big Data Analytics can literally revolutionize the way healthcare is practiced for better health and disease reduction.

The analysis of the latest data reveals that data analytics increase the accuracy of diagnoses. Physicians can use predictive algorithms to help them make more accurate diagnoses [ 45 ]. Moreover, it could be helpful in preventive medicine and public health because with early intervention, many diseases can be prevented or ameliorated [ 29 ]. Predictive analytics also allows to identify risk factors for a given patient, and with this knowledge patients will be able to change their lives what, in turn, may contribute to the fact that population disease patterns may dramatically change, resulting in savings in medical costs. Moreover, personalized medicine is the best solution for an individual patient seeking treatment. It can help doctors decide the exact treatments for those individuals. Better diagnoses and more targeted treatments will naturally lead to increases in good outcomes and fewer resources used, including doctors’ time.

The quantitative analysis of the research carried out and presented in this article made it possible to determine whether medical facilities in Poland use Big Data Analytics and if so, in which areas. Thanks to the results obtained it was possible to formulate the following conclusions. Medical facilities are working on both structured and unstructured data, which comes from databases, transactions, unstructured content of emails and documents, devices and sensors. According to analytics, they reach for analytics in the administrative and business, as well as in the clinical area. It clearly showed that the decisions made are largely data-driven. The results of the study confirm what has been analyzed in the literature. Medical facilities are moving towards data-based healthcare and its benefits.

In conclusion, Big Data Analytics has the potential for positive impact and global implications in healthcare. Future research on the use of Big Data in medical facilities will concern the definition of strategies adopted by medical facilities to promote and implement such solutions, as well as the benefits they gain from the use of Big Data analysis and how the perspectives in this area are seen.

Practical implications

This work sought to narrow the gap that exists in analyzing the possibility of using Big Data Analytics in healthcare. Showing how medical facilities in Poland are doing in this respect is an element that is part of global research carried out in this area, including [ 29 , 32 , 60 ].

Limitations and future directions

The research described in this article does not fully exhaust the questions related to the use of Big Data Analytics in Polish healthcare facilities. Only some of the dimensions characterizing the use of data by medical facilities in Poland have been examined. In order to get the full picture, it would be necessary to examine the results of using structured and unstructured data analytics in healthcare. Future research may examine the benefits that medical institutions achieve as a result of the analysis of structured and unstructured data in the clinical and management areas and what limitations they encounter in these areas. For this purpose, it is planned to conduct in-depth interviews with chosen medical facilities in Poland. These facilities could give additional data for empirical analyses based more on their suggestions. Further research should also include medical institutions from beyond the borders of Poland, enabling international comparative analyses.

Future research in the healthcare field has virtually endless possibilities. These regard the use of Big Data Analytics to diagnose specific conditions [ 47 , 66 , 69 , 76 ], propose an approach that can be used in other healthcare applications and create mechanisms to identify “patients like me” [ 75 , 80 ]. Big Data Analytics could also be used for studies related to the spread of pandemics, the efficacy of covid treatment [ 18 , 79 ], or psychology and psychiatry studies, e.g. emotion recognition [ 35 ].

Availability of data and materials

The datasets for this study are available on request to the corresponding author.

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This research was fully funded as statutory activity—subsidy of Ministry of Science and Higher Education granted for Technical University of Czestochowa on maintaining research potential in 2018. Research Number: BS/PB–622/3020/2014/P. Publication fee for the paper was financed by the University of Economics in Katowice.

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What are the strengths and limitations to utilising creative methods in public and patient involvement in health and social care research? A qualitative systematic review

• Olivia R. Phillips 1 , 2   na1 ,
• Cerian Harries 2 , 3   na1 ,
• Jo Leonardi-Bee 1 , 2 , 4   na1 ,
• Holly Knight 1 , 2 ,
• Lauren B. Sherar 2 , 3 ,
• Veronica Varela-Mato 2 , 3 &
• Joanne R. Morling 1 , 2 , 5  

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There is increasing interest in using patient and public involvement (PPI) in research to improve the quality of healthcare. Ordinarily, traditional methods have been used such as interviews or focus groups. However, these methods tend to engage a similar demographic of people. Thus, creative methods are being developed to involve patients for whom traditional methods are inaccessible or non-engaging.

To determine the strengths and limitations to using creative PPI methods in health and social care research.

Electronic searches were conducted over five databases on 14th April 2023 (Web of Science, PubMed, ASSIA, CINAHL, Cochrane Library). Studies that involved traditional, non-creative PPI methods were excluded. Creative PPI methods were used to engage with people as research advisors, rather than study participants. Only primary data published in English from 2009 were accepted. Title, abstract and full text screening was undertaken by two independent reviewers before inductive thematic analysis was used to generate themes.

Twelve papers met the inclusion criteria. The creative methods used included songs, poems, drawings, photograph elicitation, drama performance, visualisations, social media, photography, prototype development, cultural animation, card sorting and persona development. Analysis identified four limitations and five strengths to the creative approaches. Limitations included the time and resource intensive nature of creative PPI, the lack of generalisation to wider populations and ethical issues. External factors, such as the lack of infrastructure to support creative PPI, also affected their implementation. Strengths included the disruption of power hierarchies and the creation of a safe space for people to express mundane or “taboo” topics. Creative methods are also engaging, inclusive of people who struggle to participate in traditional PPI and can also be cost and time efficient.

‘Creative PPI’ is an umbrella term encapsulating many different methods of engagement and there are strengths and limitations to each. The choice of which should be determined by the aims and requirements of the research, as well as the characteristics of the PPI group and practical limitations. Creative PPI can be advantageous over more traditional methods, however a hybrid approach could be considered to reap the benefits of both. Creative PPI methods are not widely used; however, this could change over time as PPI becomes embedded even more into research.

Plain English Summary

It is important that patients and public are included in the research process from initial brainstorming, through design to delivery. This is known as public and patient involvement (PPI). Their input means that research closely aligns with their wants and needs. Traditionally to get this input, interviews and group discussions are held, but this can exclude people who find these activities non-engaging or inaccessible, for example those with language challenges, learning disabilities or memory issues. Creative methods of PPI can overcome this. This is a broad term describing different (non-traditional) ways of engaging patients and public in research, such as through the use or art, animation or performance. This review investigated the reasons why creative approaches to PPI could be difficult (limitations) or helpful (strengths) in health and social care research. After searching 5 online databases, 12 studies were included in the review. PPI groups included adults, children and people with language and memory impairments. Creative methods included songs, poems, drawings, the use of photos and drama, visualisations, Facebook, creating prototypes, personas and card sorting. Limitations included the time, cost and effort associated with creative methods, the lack of application to other populations, ethical issues and buy-in from the wider research community. Strengths included the feeling of equality between academics and the public, creation of a safe space for people to express themselves, inclusivity, and that creative PPI can be cost and time efficient. Overall, this review suggests that creative PPI is worthwhile, however each method has its own strengths and limitations and the choice of which will depend on the research project, PPI group characteristics and other practical limitations, such as time and financial constraints.

Peer Review reports

Introduction

Patient and public involvement (PPI) is the term used to describe the partnership between patients (including caregivers, potential patients, healthcare users etc.) or the public (a community member with no known interest in the topic) with researchers. It describes research that is done “‘with’ or ‘by’ the public, rather than ‘to,’ ‘about’ or ‘for’ them” [ 1 ]. In 2009, it became a legislative requirement for certain health and social care organisations to include patients, families, carers and communities in not only the planning of health and social care services, but the commissioning, delivery and evaluation of them too [ 2 ]. For example, funding applications for the National Institute of Health and Care Research (NIHR), a UK funding body, mandates a demonstration of how researchers plan to include patients/service users, the public and carers at each stage of the project [ 3 ]. However, this should not simply be a tokenistic, tick-box exercise. PPI should help formulate initial ideas and should be an instrumental, continuous part of the research process. Input from PPI can provide unique insights not yet considered and can ensure that research and health services are closely aligned to the needs and requirements of service users PPI also generally makes research more relevant with clearer outcomes and impacts [ 4 ]. Although this review refers to both patients and the public using the umbrella term ‘PPI’, it is important to acknowledge that these are two different groups with different motivations, needs and interests when it comes to health research and service delivery [ 5 ].

Despite continuing recognition of the need of PPI to improve quality of healthcare, researchers have also recognised that there is no ‘one size fits all’ method for involving patients [ 4 ]. Traditionally, PPI methods invite people to take part in interviews or focus groups to facilitate discussion, or surveys and questionnaires. However, these can sometimes be inaccessible or non-engaging for certain populations. For example, someone with communication difficulties may find it difficult to engage in focus groups or interviews. If individuals lack the appropriate skills to interact in these types of scenarios, they cannot take advantage of the participation opportunities it can provide [ 6 ]. Creative methods, however, aim to resolve these issues. These are a relatively new concept whereby researchers use creative methods (e.g., artwork, animations, Lego), to make PPI more accessible and engaging for those whose voices would otherwise go unheard. They ensure that all populations can engage in research, regardless of their background or skills. Seminal work has previously been conducted in this area, which brought to light the use of creative methodologies in research. Leavy (2008) [ 7 ] discussed how traditional interviews had limits on what could be expressed due to their sterile, jargon-filled and formulaic structure, read by only a few specialised academics. It was this that called for more creative approaches, which included narrative enquiry, fiction-based research, poetry, music, dance, art, theatre, film and visual art. These practices, which can be used in any stage of the research cycle, supported greater empathy, self-reflection and longer-lasting learning experiences compared to interviews [ 7 ]. They also pushed traditional academic boundaries, which made the research accessible not only to researchers, but the public too. Leavy explains that there are similarities between arts-based approaches and scientific approaches: both attempts to investigate what it means to be human through exploration, and used together, these complimentary approaches can progress our understanding of the human experience [ 7 ]. Further, it is important to acknowledge the parallels and nuances between creative and inclusive methods of PPI. Although creative methods aim to be inclusive (this should underlie any PPI activity, whether creative or not), they do not incorporate all types of accessible, inclusive methodologies e.g., using sign language for people with hearing impairments or audio recordings for people who cannot read. Given that there was not enough scope to include an evaluation of all possible inclusive methodologies, this review will focus on creative methods of PPI only.

We aimed to conduct a qualitative systematic review to highlight the strengths of creative PPI in health and social care research, as well as the limitations, which might act as a barrier to their implementation. A qualitative systematic review “brings together research on a topic, systematically searching for research evidence from primary qualitative studies and drawing the findings together” [ 8 ]. This review can then advise researchers of the best practices when designing PPI.

Public involvement

The PHIRST-LIGHT Public Advisory Group (PAG) consists of a team of experienced public contributors with a diverse range of characteristics from across the UK. The PAG was involved in the initial question setting and study design for this review.

Search strategy

For the purpose of this review, the JBI approach for conducting qualitative systematic reviews was followed [ 9 ]. The search terms were (“creativ*” OR “innovat*” OR “authentic” OR “original” OR “inclu*”) AND (“public and patient involvement” OR “patient and public involvement” OR “public and patient involvement and engagement” OR “patient and public involvement and engagement” OR “PPI” OR “PPIE” OR “co-produc*” OR “co-creat*” OR “co-design*” OR “cooperat*” OR “co-operat*”). This search string was modified according to the requirements of each database. Papers were filtered by title, abstract and keywords (see Additional file 1 for search strings). The databases searched included Web of Science (WoS), PubMed, ASSIA and CINAHL. The Cochrane Library was also searched to identify relevant reviews which could lead to the identification of primary research. The search was conducted on 14/04/23. As our aim was to report on the use of creative PPI in research, rather than more generic public engagement, we used electronic databases of scholarly peer-reviewed literature, which represent a wide range of recognised databases. These identified studies published in general international journals (WoS, PubMed), those in social sciences journals (ASSIA), those in nursing and allied health journals (CINAHL), and trials of interventions (Cochrane Library).

Inclusion criteria

Only full-text, English language, primary research papers from 2009 to 2023 were included. This was the chosen timeframe as in 2009 the Health and Social Reform Act made it mandatory for certain Health and Social Care organisations to involve the public and patients in planning, delivering, and evaluating services [ 2 ]. Only creative methods of PPI were accepted, rather than traditional methods, such as interviews or focus groups. For the purposes of this paper, creative PPI included creative art or arts-based approaches (e.g., e.g. stories, songs, drama, drawing, painting, poetry, photography) to enhance engagement. Titles were related to health and social care and the creative PPI was used to engage with people as research advisors, not as study participants. Meta-analyses, conference abstracts, book chapters, commentaries and reviews were excluded. There were no limits concerning study location or the demographic characteristics of the PPI groups. Only qualitative data were accepted.

Quality appraisal

Quality appraisal using the Critical Appraisal Skills Programme (CASP) checklist [ 10 ] was conducted by the primary authors (ORP and CH). This was done independently, and discrepancies were discussed and resolved. If a consensus could not be reached, a third independent reviewer was consulted (JRM). The full list of quality appraisal questions can be found in Additional file 2 .

Data extraction

ORP extracted the study characteristics and a subset of these were checked by CH. Discrepancies were discussed and amendments made. Extracted data included author, title, location, year of publication, year study was carried out, research question/aim, creative methods used, number of participants, mean age, gender, ethnicity of participants, setting, limitations and strengths of creative PPI and main findings.

Data analysis

The included studies were analysed using inductive thematic analysis [ 11 ], where themes were determined by the data. The familiarisation stage took place during full-text reading of the included articles. Anything identified as a strength or limitation to creative PPI methods was extracted verbatim as an initial code and inputted into the data extraction Excel sheet. Similar codes were sorted into broader themes, either under ‘strengths’ or ‘limitations’ and reviewed. Themes were then assigned a name according to the codes.

The search yielded 9978 titles across the 5 databases: Web of Science (1480 results), PubMed (94 results), ASSIA (2454 results), CINAHL (5948 results) and Cochrane Library (2 results), resulting in 8553 different studies after deduplication. ORP and CH independently screened their titles and abstracts, excluding those that did not meet the criteria. After assessment, 12 studies were included (see Fig.  1 ).

PRISMA flowchart of the study selection process

Study characteristics

The included studies were published between 2018 and 2022. Seven were conducted in the UK [ 12 , 14 , 15 , 17 , 18 , 19 , 23 ], two in Canada [ 21 , 22 ], one in Australia [ 13 ], one in Norway [ 16 ] and one in Ireland [ 20 ]. The PPI activities occurred across various settings, including a school [ 12 ], social club [ 12 ], hospital [ 17 ], university [ 22 ], theatre [ 19 ], hotel [ 20 ], or online [ 15 , 21 ], however this information was omitted in 5 studies [ 13 , 14 , 16 , 18 , 23 ]. The number of people attending the PPI sessions varied, ranging from 6 to 289, however the majority (ten studies) had less than 70 participants [ 13 , 14 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Seven studies did not provide information on the age or gender of the PPI groups. Of those that did, ages ranged from 8 to 76 and were mostly female. The ethnicities of the PPI group members were also rarely recorded (see Additional file 3 for data extraction table).

Types of creative methods

The type of creative methods used to engage the PPI groups were varied. These included songs, poems, drawings, photograph elicitation, drama performance, visualisations, Facebook, photography, prototype development, cultural animation, card sorting and creating personas (see Table  1 ). These were sometimes accompanied by traditional methods of PPI such as interviews and focus group discussions.

The 12 included studies were all deemed to be of good methodological quality, with scores ranging from 6/10 to 10/10 with the CASP critical appraisal tool [ 10 ] (Table  2 ).

Thematic analysis

Analysis identified four limitations and five strengths to creative PPI (see Fig.  2 ). Limitations included the time and resource intensity of creative PPI methods, its lack of generalisation, ethical issues and external factors. Strengths included the disruption of power hierarchies, the engaging and inclusive nature of the methods and their long-term cost and time efficiency. Creative PPI methods also allowed mundane and “taboo” topics to be discussed within a safe space.

Theme map of strengths and limitations

Limitations of creative PPI

Creative ppi methods are time and resource intensive.

The time and resource intensive nature of creative PPI methods is a limitation, most notably for the persona-scenario methodology. Valaitis et al. [ 22 ] used 14 persona-scenario workshops with 70 participants to co-design a healthcare intervention, which aimed to promote optimal aging in Canada. Using the persona method, pairs composed of patients, healthcare providers, community service providers and volunteers developed a fictional character which they believed represented an ‘end-user’ of the healthcare intervention. Due to the depth and richness of the data produced the authors reported that it was time consuming to analyse. Further, they commented that the amount of information was difficult to disseminate to scientific leads and present at team meetings. Additionally, to ensure the production of high-quality data, to probe for details and lead group discussion there was a need for highly skilled facilitators. The resource intensive nature of the creative co-production was also noted in a study using the persona scenario and creative worksheets to develop a prototype decision support tool for individuals with malignant pleural effusion [ 17 ]. With approximately 50 people, this was also likely to yield a high volume of data to consider.

To prepare materials for populations who cannot engage in traditional methods of PPI was also timely. Kearns et al. [ 18 ] developed a feedback questionnaire for people with aphasia to evaluate ICT-delivered rehabilitation. To ensure people could participate effectively, the resources used during the workshops, such as PowerPoints, online images and photographs, had to be aphasia-accessible, which was labour and time intensive. The author warned that this time commitment should not be underestimated.

There are further practical limitations to implementing creative PPI, such as the costs of materials for activities as well as hiring a space for workshops. For example, the included studies in this review utilised pens, paper, worksheets, laptops, arts and craft supplies and magazines and took place in venues such as universities, a social club, and a hotel. Further, although not limited to creative PPI methods exclusively but rather most studies involving the public, a financial incentive was often offered for participation, as well as food, parking, transport and accommodation [ 21 , 22 ].

Creative PPI lacks generalisation

Another barrier to the use of creative PPI methods in health and social care research was the individual nature of its output. Those who participate, usually small in number, produce unique creative outputs specific to their own experiences, opinions and location. Craven et al. [ 13 ], used arts-based visualisations to develop a toolbox for adults with mental health difficulties. They commented, “such an approach might still not be worthwhile”, as the visualisations were individualised and highly personal. This indicates that the output may fail to meet the needs of its end-users. Further, these creative PPI groups were based in certain geographical regions such as Stoke-on-Trent [ 19 ] Sheffield [ 23 ], South Wales [ 12 ] or Ireland [ 20 ], which limits the extent the findings can be applied to wider populations, even within the same area due to individual nuances. Further, the study by Galler et al. [ 16 ], is specific to the Norwegian context and even then, maybe only a sub-group of the Norwegian population as the sample used was of higher socioeconomic status.

However, Grindell et al. [ 17 ], who used persona scenarios, creative worksheets and prototype development, pointed out that the purpose of this type of research is to improve a certain place, rather than apply findings across other populations and locations. Individualised output may, therefore, only be a limitation to research wanting to conduct PPI on a large scale.

If, however, greater generalisation within PPI is deemed necessary, then social media may offer a resolution. Fedorowicz et al. [ 15 ], used Facebook to gain feedback from the public on the use of video-recording methodology for an upcoming project. This had the benefit of including a more diverse range of people (289 people joined the closed group), who were spread geographically around the UK, as well as seven people from overseas.

Creative PPI has ethical issues

As with other research, ethical issues must be taken into consideration. Due to the nature of creative approaches, as well as the personal effort put into them, people often want to be recognised for their work. However, this compromises principles so heavily instilled in research such as anonymity and confidentiality. With the aim of exploring issues related to health and well-being in a town in South Wales, Byrne et al. [ 12 ], asked year 4/5 and year 10 pupils to create poems, songs, drawings and photographs. Community members also created a performance, mainly of monologues, to explore how poverty and inequalities are dealt with. Byrne noted the risks of these arts-based approaches, that being the possibility of over-disclosure and consequent emotional distress, as well as people’s desire to be named for their work. On one hand, the anonymity reduces the sense of ownership of the output as it does not portray a particular individual’s lived experience anymore. On the other hand, however, it could promote a more honest account of lived experience. Supporting this, Webber et al. [ 23 ], who used the persona method to co-design a back pain educational resource prototype, claimed that the anonymity provided by this creative technique allowed individuals to externalise and anonymise their own personal experience, thus creating a more authentic and genuine resource for future users. This implies that anonymity can be both a limitation and strength here.

The use of creative PPI methods is impeded by external factors

Despite the above limitations influencing the implementation of creative PPI techniques, perhaps the most influential is that creative methodologies are simply not mainstream [ 19 ]. This could be linked to the issues above, like time and resource intensity, generalisation and ethical issues but it is also likely to involve more systemic factors within the research community. Micsinszki et al. [ 21 ], who co-designed a hub for the health and well-being of vulnerable populations, commented that there is insufficient infrastructure to conduct meaningful co-design as well as a dominant medical model. Through a more holistic lens, there are “sociopolitical environments that privilege individualism over collectivism, self-sufficiency over collaboration, and scientific expertise over other ways of knowing based on lived experience” [ 21 ]. This, it could be suggested, renders creative co-design methodologies, which are based on the foundations of collectivism, collaboration and imagination an invalid technique in the research field, which is heavily dominated by more scientific methods offering reproducibility, objectivity and reliability.

Although we acknowledge that creative PPI techniques are not always appropriate, it may be that their main limitation is the lack of awareness of these methods or lack of willingness to use them. Further, there is always the risk that PPI, despite being a mandatory part of research, is used in a tokenistic or tick-box fashion [ 20 ], without considering the contribution that meaningful PPI could make to enhancing the research. It may be that PPI, let alone creative PPI, is not at the forefront of researchers’ minds when planning research.

Strengths of creative PPI

Creative ppi disrupts power hierarchies.

One of the main strengths of creative PPI techniques, cited most frequently in the included literature, was that they disrupt traditional power hierarchies [ 12 , 13 , 17 , 19 , 23 ]. For example, the use of theatre performance blurred the lines between professional and lay roles between the community and policy makers [ 12 ]. Individuals created a monologue to portray how poverty and inequality impact daily life and presented this to representatives of the National Assembly of Wales, Welsh Government, the Local Authority, Arts Council and Westminster. Byrne et al. [ 12 ], states how this medium allowed the community to engage with the people who make decisions about their lives in an environment of respect and understanding, where the hierarchies are not as visible as in other settings, e.g., political surgeries. Creative PPI methods have also removed traditional power hierarchies between researchers and adolescents. Cook et al. [ 13 ], used arts-based approaches to explore adolescents’ ideas about the “perfect” condom. They utilised the “Life Happens” resource, where adolescents drew and then decorated a person with their thoughts about sexual relationships, not too dissimilar from the persona-scenario method. This was then combined with hypothetical scenarios about sexuality. A condom-mapping exercise was then implemented, where groups shared the characteristics that make a condom “perfect” on large pieces of paper. Cook et al. [ 13 ], noted that usually power imbalances make it difficult to elicit information from adolescents, however these power imbalances were reduced due to the use of creative co-design techniques.

The same reduction in power hierarchies was noted by Grindell et al. [ 17 ], who used the person-scenario method and creative worksheets with individuals with malignant pleural effusion. This was with the aim of developing a prototype of a decision support tool for patients to help with treatment options. Although this process involved a variety of stakeholders, such as patients, carers and healthcare professionals, creative co-design was cited as a mechanism that worked to reduce power imbalances – a limitation of more traditional methods of research. Creative co-design blurred boundaries between end-users and clinical staff and enabled the sharing of ideas from multiple, valuable perspectives, meaning the prototype was able to suit user needs whilst addressing clinical problems.

Similarly, a specific creative method named cultural animation was also cited to dissolve hierarchies and encourage equal contributions from participants. Within this arts-based approach, Keleman et al. [ 19 ], explored the concept of “good health” with individuals from Stoke-on Trent. Members of the group created art installations using ribbons, buttons, cardboard and straws to depict their idea of a “healthy community”, which was accompanied by a poem. They also created a 3D Facebook page and produced another poem or song addressing the government to communicate their version of a “picture of health”. Public participants said that they found the process empowering, honest, democratic, valuable and practical.

This dissolving of hierarchies and levelling of power is beneficial as it increases the sense of ownership experienced by the creators/producers of the output [ 12 , 17 , 23 ]. This is advantageous as it has been suggested to improve its quality [ 23 ].

Creative PPI allows the unsayable to be said

Creative PPI fosters a safe space for mundane or taboo topics to be shared, which may be difficult to communicate using traditional methods of PPI. For example, the hypothetical nature of condom mapping and persona-scenarios meant that adolescents could discuss a personal topic without fear of discrimination, judgement or personal disclosure [ 13 ]. The safe space allowed a greater volume of ideas to be generated amongst peers where they might not have otherwise. Similarly, Webber et al. [ 23 ], , who used the persona method to co-design the prototype back pain educational resource, also noted how this method creates anonymity whilst allowing people the opportunity to externalise personal experiences, thoughts and feelings. Other creative methods were also used, such as drawing, collaging, role play and creating mood boards. A cardboard cube (labelled a “magic box”) was used to symbolise a physical representation of their final prototype. These creative methods levelled the playing field and made personal experiences accessible in a safe, open environment that fostered trust, as well as understanding from the researchers.

It is not only sensitive subjects that were made easier to articulate through creative PPI. The communication of mundane everyday experiences were also facilitated, which were deemed typically ‘unsayable’. This was specifically given in the context of describing intangible aspects of everyday health and wellbeing [ 11 ]. Graphic designers can also be used to visually represent the outputs of creative PPI. These captured the movement and fluidity of people and well as the relationships between them - things that cannot be spoken but can be depicted [ 21 ].

Creative PPI methods are inclusive

Another strength of creative PPI was that it is inclusive and accessible [ 17 , 19 , 21 ]. The safe space it fosters, as well as the dismantling of hierarchies, welcomed people from a diverse range of backgrounds and provided equal opportunities [ 21 ], especially for those with communication and memory difficulties who might be otherwise excluded from PPI. Kelemen et al. [ 19 ], who used creative methods to explore health and well-being in Stoke-on-Trent, discussed how people from different backgrounds came together and connected, discussed and reached a consensus over a topic which evoked strong emotions, that they all have in common. Individuals said that the techniques used “sets people to open up as they are not overwhelmed by words”. Similarly, creative activities, such as the persona method, have been stated to allow people to express themselves in an inclusive environment using a common language. Kearns et al. [ 18 ], who used aphasia-accessible material to develop a questionnaire with aphasic individuals, described how they felt comfortable in contributing to workshops (although this material was time-consuming to make, see ‘Limitations of creative PPI’ ).

Despite the general inclusivity of creative PPI, it can also be exclusive, particularly if online mediums are used. Fedorowicz et al. [ 15 ], used Facebook to create a PPI group, and although this may rectify previous drawbacks about lack of generalisation of creative methods (as Facebook can reach a greater number of people, globally), it excluded those who are not digitally active or have limited internet access or knowledge of technology. Online methods have other issues too. Maintaining the online group was cited as challenging and the volume of responses required researchers to interact outside of their working hours. Despite this, online methods like Facebook are very accessible for people who are physically disabled.

Creative PPI methods are engaging

The process of creative PPI is typically more engaging and produces more colourful data than traditional methods [ 13 ]. Individuals are permitted and encouraged to explore a creative self [ 19 ], which can lead to the exploration of new ideas and an overall increased enjoyment of the process. This increased engagement is particularly beneficial for younger PPI groups. For example, to involve children in the development of health food products, Galler et al. [ 16 ] asked 9-12-year-olds to take photos of their food and present it to other children in a “show and tell” fashion. They then created a newspaper article describing a new healthy snack. In this creative focus group, children were given lab coats to further their identity as inventors. Galler et al. [ 16 ], notes that the methods were highly engaging and facilitated teamwork and group learning. This collaborative nature of problem-solving was also observed in adults who used personas and creative worksheets to develop the resource for lower back pain [ 23 ]. Dementia patients too have been reported to enjoy the creative and informal approach to idea generation [ 20 ].

The use of cultural animation allowed people to connect with each other in a way that traditional methods do not [ 19 , 21 ]. These connections were held in place by boundary objects, such as ribbons, buttons, fabric and picture frames, which symbolised a shared meaning between people and an exchange of knowledge and emotion. Asking groups to create an art installation using these objects further fostered teamwork and collaboration, both at an individual and collective level. The exploration of a creative self increased energy levels and encouraged productive discussions and problem-solving [ 19 ]. Objects also encouraged a solution-focused approach and permitted people to think beyond their usual everyday scope [ 17 ]. They also allowed facilitators to probe deeper about the greater meanings carried by the object, which acted as a metaphor [ 21 ].

From the researcher’s point of view, co-creative methods gave rise to ideas they might not have initially considered. Valaitis et al. [ 22 ], found that over 40% of the creative outputs were novel ideas brought to light by patients, healthcare providers/community care providers, community service providers and volunteers. One researcher commented, “It [the creative methods] took me on a journey, in a way that when we do other pieces of research it can feel disconnected” [ 23 ]. Another researcher also stated they could not return to the way they used to do research, as they have learnt so much about their own health and community and how they are perceived [ 19 ]. This demonstrates that creative processes not only benefit the project outcomes and the PPI group, but also facilitators and researchers. However, although engaging, creative methods have been criticised for not demonstrating academic rigour [ 17 ]. Moreover, creative PPI may also be exclusive to people who do not like or enjoy creative activities.

Creative PPI methods are cost and time efficient

Creative PPI workshops can often produce output that is visible and tangible. This can save time and money in the long run as the output is either ready to be implemented in a healthcare setting or a first iteration has already been developed. This may also offset the time and costs it takes to implement creative PPI. For example, the prototype of the decision support tool for people with malignant pleural effusion was developed using personas and creative worksheets. The end result was two tangible prototypes to drive the initial idea forward as something to be used in practice [ 17 ]. The use of creative co-design in this case saved clinician time as well as the time it would take to develop this product without the help of its end-users. In the development of this particular prototype, analysis was iterative and informed the next stage of development, which again saved time. The same applies for the feedback questionnaire for the assessment of ICT delivered aphasia rehabilitation. The co-created questionnaire, designed with people with aphasia, was ready to be used in practice [ 18 ]. This suggests that to overcome time and resource barriers to creative PPI, researchers should aim for it to be engaging whilst also producing output.

That useable products are generated during creative workshops signals to participating patients and public members that they have been listened to and their thoughts and opinions acted upon [ 23 ]. For example, the development of the back pain resource based on patient experiences implies that their suggestions were valid and valuable. Further, those who participated in the cultural animation workshop reported that the process visualises change, and that it already feels as though the process of change has started [ 19 ].

The most cost and time efficient method of creative PPI in this review is most likely the use of Facebook to gather feedback on project methodology [ 15 ]. Although there were drawbacks to this, researchers could involve more people from a range of geographical areas at little to no cost. Feedback was instantaneous and no training was required. From the perspective of the PPI group, they could interact however much or little they wish with no time commitment.

This systematic review identified four limitations and five strengths to the use of creative PPI in health and social care research. Creative PPI is time and resource intensive, can raise ethical issues and lacks generalisability. It is also not accepted by the mainstream. These factors may act as barriers to the implementation of creative PPI. However, creative PPI disrupts traditional power hierarchies and creates a safe space for taboo or mundane topics. It is also engaging, inclusive and can be time and cost efficient in the long term.

Something that became apparent during data analysis was that these are not blanket strengths and limitations of creative PPI as a whole. The umbrella term ‘creative PPI’ is broad and encapsulates a wide range of activities, ranging from music and poems to prototype development and persona-scenarios, to more simplistic things like the use of sticky notes and ordering cards. Many different activities can be deemed ‘creative’ and the strengths and limitations of one does not necessarily apply to another. For example, cultural animation takes greater effort to prepare than the use of sticky notes and sorting cards, and the use of Facebook is cheaper and wider reaching than persona development. Researchers should use their discretion and weigh up the benefits and drawbacks of each method to decide on a technique which suits the project. What might be a limitation to creative PPI in one project may not be in another. In some cases, creative PPI may not be suitable at all.

Furthermore, the choice of creative PPI method also depends on the needs and characteristics of the PPI group. Children, adults and people living with dementia or language difficulties all have different engagement needs and capabilities. This indicates that creative PPI is not one size fits all and that the most appropriate method will change depending on the composition of the group. The choice of method will also be determined by the constraints of the research project, namely time, money and the research aim. For example, if there are time constraints, then a method which yields a lot of data and requires a lot of preparation may not be appropriate. If generalisation is important, then an online method is more suitable. Together this indicates that the choice of creative PPI method is highly individualised and dependent on multiple factors.

Although the limitations discussed in this review apply to creative PPI, they are not exclusive to creative PPI. Ethical issues are a consideration within general PPI research, especially when working with more vulnerable populations, such as children or adults living with a disability. It can also be the case that traditional PPI methods lack generalisability, as people who volunteer to be part of such a group are more likely be older, middle class and retired [ 24 ]. Most research is vulnerable to this type of bias, however, it is worth noting that generalisation is not always a goal and research remains valid and meaningful in its absence. Although online methods may somewhat combat issues related to generalisability, these methods still exclude people who do not have access to the internet/technology or who choose not to use it, implying that online PPI methods may not be wholly representative of the general population. Saying this, however, the accessibility of creative PPI techniques differs from person to person, and for some, online mediums may be more accessible (for example for those with a physical disability), and for others, this might be face-to-face. To combat this, a range of methods should be implemented. Planning multiple focus group and interviews for traditional PPI is also time and resource intensive, however the extra resources required to make this creative may be even greater. Although, the rich data provided may be worth the preparation and analysis time, which is also likely to depend on the number of participants and workshop sessions required. PPI, not just creative PPI, often requires the provision of a financial incentive, refreshments, parking and accommodation, which increase costs. These, however, are imperative and non-negotiable, as they increase the accessibility of research, especially to minority and lower-income groups less likely to participate. Adequate funding is also important for co-design studies where repeated engagement is required. One barrier to implementation, which appears to be exclusive to creative methods, however, is that creative methods are not mainstream. This cannot be said for traditional PPI as this is often a mandatory part of research applications.

Regarding the strengths of creative PPI, it could be argued that most appear to be exclusive to creative methodologies. These are inclusive by nature as multiple approaches can be taken to evoke ideas from different populations - approaches that do not necessarily rely on verbal or written communication like interviews and focus groups do. Given the anonymity provided by some creative methods, such as personas, people may be more likely to discuss their personal experiences under the guise of a general end-user, which might be more difficult to maintain when an interviewer is asking an individual questions directly. Additionally, creative methods are by nature more engaging and interactive than traditional methods, although this is a blanket statement and there may be people who find the question-and-answer/group discussion format more engaging. Creative methods have also been cited to eliminate power imbalances which exist in traditional research [ 12 , 13 , 17 , 19 , 23 ]. These imbalances exist between researchers and policy makers and adolescents, adults and the community. Lastly, although this may occur to a greater extent in creative methods like prototype development, it could be suggested that PPI in general – regardless of whether it is creative - is more time and cost efficient in the long-term than not using any PPI to guide or refine the research process. It must be noted that these are observations based on the literature. To be certain these differences exist between creative and traditional methods of PPI, direct empirical evaluation of both should be conducted.

To the best of our knowledge, this is the first review to identify the strengths and limitations to creative PPI, however, similar literature has identified barriers and facilitators to PPI in general. In the context of clinical trials, recruitment difficulties were cited as a barrier, as well as finding public contributors who were free during work/school hours. Trial managers reported finding group dynamics difficult to manage and the academic environment also made some public contributors feel nervous and lacking confidence to speak. Facilitators, however, included the shared ownership of the research – something that has been identified in the current review too. In addition, planning and the provision of knowledge, information and communication were also identified as facilitators [ 25 ]. Other research on the barriers to meaningful PPI in trial oversight committees included trialist confusion or scepticism over the PPI role and the difficulties in finding PPI members who had a basic understanding of research [ 26 ]. However, it could be argued that this is not representative of the average patient or public member. The formality of oversight meetings and the technical language used also acted as a barrier, which may imply that the informal nature of creative methods and its lack of dependency on literacy skills could overcome this. Further, a review of 42 reviews on PPI in health and social care identified financial compensation, resources, training and general support as necessary to conduct PPI, much like in the current review where the resource intensiveness of creative PPI was identified as a limitation. However, others were identified too, such as recruitment and representativeness of public contributors [ 27 ]. Like in the current review, power imbalances were also noted, however this was included as both a barrier and facilitator. Collaboration seemed to diminish hierarchies but not always, as sometimes these imbalances remained between public contributors and healthcare staff, described as a ‘them and us’ culture [ 27 ]. Although these studies compliment the findings of the current review, a direct comparison cannot be made as they do not concern creative methods. However, it does suggest that some strengths and weaknesses are shared between creative and traditional methods of PPI.

Strengths and limitations of this review

Although a general definition of creative PPI exists, it was up to our discretion to decide exactly which activities were deemed as such for this review. For example, we included sorting cards, the use of interactive whiteboards and sticky notes. Other researchers may have a more or less stringent criteria. However, two reviewers were involved in this decision which aids the reliability of the included articles. Further, it may be that some of the strengths and limitations cannot fully be attributed to the creative nature of the PPI process, but rather their co-created nature, however this is hard to disentangle as the included papers involved both these aspects.

During screening, it was difficult to decide whether the article was utilising creative qualitative methodology or creative PPI , as it was often not explicitly labelled as such. Regardless, both approaches involved the public/patients refining a healthcare product/service. This implies that if this review were to be replicated, others may do it differently. This may call for greater standardisation in the reporting of the public’s involvement in research. For example, the NIHR outlines different approaches to PPI, namely “consultation”, “collaboration”, “co-production” and “user-controlled”, which each signify an increased level of public power and influence [ 28 ]. Papers with elements of PPI could use these labels to clarify the extent of public involvement, or even explicitly state that there was no PPI. Further, given our decision to include only scholarly peer-reviewed literature, it is possible that data were missed within the grey literature. Similarly, the literature search will not have identified all papers relating to different types of accessible inclusion. However, the intent of the review was to focus solely on those within the definition of creative.

This review fills a gap in the literature and helps circulate and promote the concept of creative PPI. Each stage of this review, namely screening and quality appraisal, was conducted by two independent reviewers. However, four full texts could not be accessed during the full text reading stage, meaning there are missing data that could have altered or contributed to the findings of this review.

Research recommendations

Given that creative PPI can require effort to prepare, perform and analyse, sufficient time and funding should be allocated in the research protocol to enable meaningful and continuous PPI. This is worthwhile as PPI can significantly change the research output so that it aligns closely with the needs of the group it is to benefit. Researchers should also consider prototype development as a creative PPI activity as this might reduce future time/resource constraints. Shifting from a top-down approach within research to a bottom-up can be advantageous to all stakeholders and can help move creative PPI towards the mainstream. This, however, is the collective responsibility of funding bodies, universities and researchers, as well as committees who approve research bids.

A few of the included studies used creative techniques alongside traditional methods, such as interviews, which could also be used as a hybrid method of PPI, perhaps by researchers who are unfamiliar with creative techniques or to those who wish to reap the benefits of both. Often the characteristics of the PPI group were not included, including age, gender and ethnicity. It would be useful to include such information to assess how representative the PPI group is of the population of interest.

Creative PPI is a relatively novel approach of engaging the public and patients in research and it has both advantages and disadvantages compared to more traditional methods. There are many approaches to implementing creative PPI and the choice of technique will be unique to each piece of research and is reliant on several factors. These include the age and ability of the PPI group as well as the resource limitations of the project. Each method has benefits and drawbacks, which should be considered at the protocol-writing stage. However, given adequate funding, time and planning, creative PPI is a worthwhile and engaging method of generating ideas with end-users of research – ideas which may not be otherwise generated using traditional methods.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Critical Appraisal Skills Programme

The Joanna Briggs Institute

National Institute of Health and Care Research

Public Advisory Group

Public and Patient Involvement

Web of Science

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Acknowledgements

With thanks to the PHIRST-LIGHT public advisory group and consortium for their thoughts and contributions to the design of this work.

The research team is supported by a National Institute for Health and Care Research grant (PHIRST-LIGHT Reference NIHR 135190).

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Olivia R. Phillips and Cerian Harries share joint first authorship.

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Nottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Clinical Sciences Building, City Hospital Campus, Hucknall Road, Nottingham, NG5 1PB, UK

Olivia R. Phillips, Jo Leonardi-Bee, Holly Knight & Joanne R. Morling

National Institute for Health and Care Research (NIHR) PHIRST-LIGHT, Nottingham, UK

Olivia R. Phillips, Cerian Harries, Jo Leonardi-Bee, Holly Knight, Lauren B. Sherar, Veronica Varela-Mato & Joanne R. Morling

School of Sport, Exercise and Health Sciences, Loughborough University, Epinal Way, Loughborough, Leicestershire, LE11 3TU, UK

Cerian Harries, Lauren B. Sherar & Veronica Varela-Mato

Nottingham Centre for Evidence Based Healthcare, School of Medicine, University of Nottingham, Nottingham, UK

Jo Leonardi-Bee

NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust, University of Nottingham, Nottingham, NG7 2UH, UK

Joanne R. Morling

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Author contributions: study design: ORP, CH, JRM, JLB, HK, LBS, VVM, literature searching and screening: ORP, CH, JRM, data curation: ORP, CH, analysis: ORP, CH, JRM, manuscript draft: ORP, CH, JRM, Plain English Summary: ORP, manuscript critical review and editing: ORP, CH, JRM, JLB, HK, LBS, VVM.

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Additional file 3: Table 1: Description of data: elements of the data extraction table that are not in the main manuscript

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Phillips, O.R., Harries, C., Leonardi-Bee, J. et al. What are the strengths and limitations to utilising creative methods in public and patient involvement in health and social care research? A qualitative systematic review. Res Involv Engagem 10 , 48 (2024). https://doi.org/10.1186/s40900-024-00580-4

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Research Topics & Ideas: Healthcare

100+ Healthcare Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a healthcare-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of healthcare-related research ideas and topic thought-starters across a range of healthcare fields, including allopathic and alternative medicine, dentistry, physical therapy, optometry, pharmacology and public health.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the healthcare domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: Healthcare Research Topics

• Allopathic medicine
• Alternative /complementary medicine
• Veterinary medicine
• Physical therapy/ rehab
• Optometry and ophthalmology
• Pharmacy and pharmacology
• Public health
• Examples of healthcare-related dissertations

Allopathic (Conventional) Medicine

• The effectiveness of telemedicine in remote elderly patient care
• The impact of stress on the immune system of cancer patients
• The effects of a plant-based diet on chronic diseases such as diabetes
• The use of AI in early cancer diagnosis and treatment
• The role of the gut microbiome in mental health conditions such as depression and anxiety
• The efficacy of mindfulness meditation in reducing chronic pain: A systematic review
• The benefits and drawbacks of electronic health records in a developing country
• The effects of environmental pollution on breast milk quality
• The use of personalized medicine in treating genetic disorders
• The impact of social determinants of health on chronic diseases in Asia
• The role of high-intensity interval training in improving cardiovascular health
• The efficacy of using probiotics for gut health in pregnant women
• The impact of poor sleep on the treatment of chronic illnesses
• The role of inflammation in the development of chronic diseases such as lupus
• The effectiveness of physiotherapy in pain control post-surgery

Topics & Ideas: Alternative Medicine

• The benefits of herbal medicine in treating young asthma patients
• The use of acupuncture in treating infertility in women over 40 years of age
• The effectiveness of homoeopathy in treating mental health disorders: A systematic review
• The role of aromatherapy in reducing stress and anxiety post-surgery
• The impact of mindfulness meditation on reducing high blood pressure
• The use of chiropractic therapy in treating back pain of pregnant women
• The efficacy of traditional Chinese medicine such as Shun-Qi-Tong-Xie (SQTX) in treating digestive disorders in China
• The impact of yoga on physical and mental health in adolescents
• The benefits of hydrotherapy in treating musculoskeletal disorders such as tendinitis
• The role of Reiki in promoting healing and relaxation post birth
• The effectiveness of naturopathy in treating skin conditions such as eczema
• The use of deep tissue massage therapy in reducing chronic pain in amputees
• The impact of tai chi on the treatment of anxiety and depression
• The benefits of reflexology in treating stress, anxiety and chronic fatigue
• The role of acupuncture in the prophylactic management of headaches and migraines

Topics & Ideas: Dentistry

• The impact of sugar consumption on the oral health of infants
• The use of digital dentistry in improving patient care: A systematic review
• The efficacy of orthodontic treatments in correcting bite problems in adults
• The role of dental hygiene in preventing gum disease in patients with dental bridges
• The impact of smoking on oral health and tobacco cessation support from UK dentists
• The benefits of dental implants in restoring missing teeth in adolescents
• The use of lasers in dental procedures such as root canals
• The efficacy of root canal treatment using high-frequency electric pulses in saving infected teeth
• The role of fluoride in promoting remineralization and slowing down demineralization
• The impact of stress-induced reflux on oral health
• The benefits of dental crowns in restoring damaged teeth in elderly patients
• The use of sedation dentistry in managing dental anxiety in children
• The efficacy of teeth whitening treatments in improving dental aesthetics in patients with braces
• The role of orthodontic appliances in improving well-being
• The impact of periodontal disease on overall health and chronic illnesses

Tops & Ideas: Veterinary Medicine

• The impact of nutrition on broiler chicken production
• The role of vaccines in disease prevention in horses
• The importance of parasite control in animal health in piggeries
• The impact of animal behaviour on welfare in the dairy industry
• The effects of environmental pollution on the health of cattle
• The role of veterinary technology such as MRI in animal care
• The importance of pain management in post-surgery health outcomes
• The impact of genetics on animal health and disease in layer chickens
• The effectiveness of alternative therapies in veterinary medicine: A systematic review
• The role of veterinary medicine in public health: A case study of the COVID-19 pandemic
• The impact of climate change on animal health and infectious diseases in animals
• The importance of animal welfare in veterinary medicine and sustainable agriculture
• The effects of the human-animal bond on canine health
• The role of veterinary medicine in conservation efforts: A case study of Rhinoceros poaching in Africa
• The impact of veterinary research of new vaccines on animal health

Topics & Ideas: Physical Therapy/Rehab

• The efficacy of aquatic therapy in improving joint mobility and strength in polio patients
• The impact of telerehabilitation on patient outcomes in Germany
• The effect of kinesiotaping on reducing knee pain and improving function in individuals with chronic pain
• A comparison of manual therapy and yoga exercise therapy in the management of low back pain
• The use of wearable technology in physical rehabilitation and the impact on patient adherence to a rehabilitation plan
• The impact of mindfulness-based interventions in physical therapy in adolescents
• The effects of resistance training on individuals with Parkinson’s disease
• The role of hydrotherapy in the management of fibromyalgia
• The impact of cognitive-behavioural therapy in physical rehabilitation for individuals with chronic pain
• The use of virtual reality in physical rehabilitation of sports injuries
• The effects of electrical stimulation on muscle function and strength in athletes
• The role of physical therapy in the management of stroke recovery: A systematic review
• The impact of pilates on mental health in individuals with depression
• The use of thermal modalities in physical therapy and its effectiveness in reducing pain and inflammation
• The effect of strength training on balance and gait in elderly patients

Topics & Ideas: Optometry & Opthalmology

• The impact of screen time on the vision and ocular health of children under the age of 5
• The effects of blue light exposure from digital devices on ocular health
• The role of dietary interventions, such as the intake of whole grains, in the management of age-related macular degeneration
• The use of telemedicine in optometry and ophthalmology in the UK
• The impact of myopia control interventions on African American children’s vision
• The use of contact lenses in the management of dry eye syndrome: different treatment options
• The effects of visual rehabilitation in individuals with traumatic brain injury
• The role of low vision rehabilitation in individuals with age-related vision loss: challenges and solutions
• The impact of environmental air pollution on ocular health
• The effectiveness of orthokeratology in myopia control compared to contact lenses
• The role of dietary supplements, such as omega-3 fatty acids, in ocular health
• The effects of ultraviolet radiation exposure from tanning beds on ocular health
• The impact of computer vision syndrome on long-term visual function
• The use of novel diagnostic tools in optometry and ophthalmology in developing countries
• The effects of virtual reality on visual perception and ocular health: an examination of dry eye syndrome and neurologic symptoms

Topics & Ideas: Pharmacy & Pharmacology

• The impact of medication adherence on patient outcomes in cystic fibrosis
• The use of personalized medicine in the management of chronic diseases such as Alzheimer’s disease
• The effects of pharmacogenomics on drug response and toxicity in cancer patients
• The role of pharmacists in the management of chronic pain in primary care
• The impact of drug-drug interactions on patient mental health outcomes
• The use of telepharmacy in healthcare: Present status and future potential
• The effects of herbal and dietary supplements on drug efficacy and toxicity
• The role of pharmacists in the management of type 1 diabetes
• The impact of medication errors on patient outcomes and satisfaction
• The use of technology in medication management in the USA
• The effects of smoking on drug metabolism and pharmacokinetics: A case study of clozapine
• Leveraging the role of pharmacists in preventing and managing opioid use disorder
• The impact of the opioid epidemic on public health in a developing country
• The use of biosimilars in the management of the skin condition psoriasis
• The effects of the Affordable Care Act on medication utilization and patient outcomes in African Americans

Topics & Ideas: Public Health

• The impact of the built environment and urbanisation on physical activity and obesity
• The effects of food insecurity on health outcomes in Zimbabwe
• The role of community-based participatory research in addressing health disparities
• The impact of social determinants of health, such as racism, on population health
• The effects of heat waves on public health
• The role of telehealth in addressing healthcare access and equity in South America
• The impact of gun violence on public health in South Africa
• The effects of chlorofluorocarbons air pollution on respiratory health
• The role of public health interventions in reducing health disparities in the USA
• The impact of the United States Affordable Care Act on access to healthcare and health outcomes
• The effects of water insecurity on health outcomes in the Middle East
• The role of community health workers in addressing healthcare access and equity in low-income countries
• The impact of mass incarceration on public health and behavioural health of a community
• The effects of floods on public health and healthcare systems
• The role of social media in public health communication and behaviour change in adolescents

Examples: Healthcare Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a healthcare-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various healthcare-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Improving Follow-Up Care for Homeless Populations in North County San Diego (Sanchez, 2021)
• On the Incentives of Medicare’s Hospital Reimbursement and an Examination of Exchangeability (Elzinga, 2016)
• Managing the healthcare crisis: the career narratives of nurses (Krueger, 2021)
• Methods for preventing central line-associated bloodstream infection in pediatric haematology-oncology patients: A systematic literature review (Balkan, 2020)
• Farms in Healthcare: Enhancing Knowledge, Sharing, and Collaboration (Garramone, 2019)
• When machine learning meets healthcare: towards knowledge incorporation in multimodal healthcare analytics (Yuan, 2020)
• Integrated behavioural healthcare: The future of rural mental health (Fox, 2019)
• Healthcare service use patterns among autistic adults: A systematic review with narrative synthesis (Gilmore, 2021)
• Mindfulness-Based Interventions: Combatting Burnout and Compassionate Fatigue among Mental Health Caregivers (Lundquist, 2022)
• Transgender and gender-diverse people’s perceptions of gender-inclusive healthcare access and associated hope for the future (Wille, 2021)
• Efficient Neural Network Synthesis and Its Application in Smart Healthcare (Hassantabar, 2022)
• The Experience of Female Veterans and Health-Seeking Behaviors (Switzer, 2022)
• Machine learning applications towards risk prediction and cost forecasting in healthcare (Singh, 2022)
• Does Variation in the Nursing Home Inspection Process Explain Disparity in Regulatory Outcomes? (Fox, 2020)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Need more help?

If you’re still feeling a bit unsure about how to find a research topic for your healthcare dissertation or thesis, check out Topic Kickstarter service below.

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15 Comments

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• The Regulatory Review In Depth

The Future of Technology in Health Care

Alyson diaz , julia englebert , and carson turner.

Scholars discuss the need for federal regulations to combat risks associated with technology in health care.

Most U.S. adults use technology to improve their health—nearly 60 percent browse the Internet for medical information, and over 40 percent obtain care through telemedicine .  Despite technology’s health care potential, however, six out of ten Americans are uncomfortable with their health care provider relying on AI to diagnose diseases and recommend treatments.

AI can enhance quality of care by helping physicians verify their diagnoses and detect diseases earlier. For example, researchers have found that AI technology can help predict a patient’s risk of breast cancer. Similarly, a combination of physician expertise and AI algorithms can increase the accuracy of diagnoses.

Yet, AI systems can fail, and if humans rely too much on software, an underlying problem in one algorithm can injure more patients than a single physician’s error. In addition, AI algorithms incorporate biases from available data. For example, Black patients receive , on average, less pain medication than white patients. An algorithm trained to recommend pain treatment from these health records could suggest lower doses of painkillers for Black patients, irrespective of biological needs.

At the same time, technology can help underserved communities gain access to health care. These communities often experience shortages of trained practitioners and standard health care facilities, resulting in higher risk of disease and misdiagnoses. Telehealth, as one example, increases access to quality care by allowing patients to meet with doctors online or have their vitals monitored remotely.

Currently, no federal law regulates the use of AI in health care. Although the U.S. Food and Drug Administration (FDA) reviews most products using technology or AI software on patients, it does not currently make determinations as to whether uses of AI in health care are safe for patients. Instead, FDA approves AI-enabled devices through a process known as 510(k) review . During a 510(k) review, a manufacturer must show that its technology is “substantially equivalent” to a product already available in the market. The process allows AI-enabled devices to be approved without clinical trials proving their safety or accuracy.

Last year, the Biden Administration pledged to oversee the responsible development of AI, including in health-related fields. President Joseph R. Biden’s executive order on the subject includes requirements for health care providers to inform users when the content they provide is AI-generated and not reviewed by a physician. In addition, providers are responsible for mitigating potential risks posed by the technology and ensuring that it expands access to care.

Health professionals have also expressed concern about adolescents self-diagnosing medical conditions discussed by influencers who promote telemedicine on social media. Currently, FDA does not require telemedicine companies to disclose information about potential risks of services, and companies receive free speech protections as “advertisers.”

Advocates for stricter regulation of technology in health care point out that telehealth providers escape regulation by classifying themselves as communication platforms that connect patients with doctors, and not as providers of medical services. Telehealth companies maintain their independence from medical providers, allowing them to avoid legal liability for those providers’ actions.

In this week’s Saturday Seminar, scholars offer varying suggestions on regulating the use of technology in health care.

• AI algorithms are inherently biased, yet no federal regulation addresses the risk of biased diagnostics when AI is used in health care, recent Seattle University School of Law graduate Natalie Shen argues in an article in the Seattle Journal of Technology, Environmental & Innovation Law . Shen explains that in the absence of federal action, states have taken the lead in passing laws to address automated decision systems such as AI in health care. By analyzing New Jersey’s and California’s approaches, Shen recommends improvements to future state legislation, including extending any future law’s coverage to the private health insurance sector, and imposing continuous assessment requirements as AI technology evolves.
• In an article for the Virginia Law Review , Berkeley Law Schools Khiara M. Bridges argues that educating patients about the risk of race-based algorithmic bias should be a prerequisite before using AI in health care. Bridges explains that people of color are more likely to distrust physicians and health care institutions and thus, are likely to be skeptical of medical AI. Furthermore, medical algorithms are developed based on a primarily white “general population,” reducing their predictive accuracy for communities of color, Bridges notes . She argues that disclosure of AI-related risks would foster patient-physician dialogue in communities of color, encouraging more patients of color to use the technology and ultimately remedying existing algorithmic biases.
• Regulation of AI-enabled health tools must include pre-market authorization and continued performance monitoring processes, urge Joana Gonçalves-Sá of Complexity Science Hub and Flávio Pinheiro of NOVA Information Management School in an chapter in Multidisciplinary Perspectives on Artificial Intelligence and the Law . Gonçalves-Sá and Pinheiro propose improvements to FDA’s pilot program, Total Product Lifecycle , which tracks the safety risks of AI. Under the program, an AI company can achieve “precertified status” if it can demonstrate that it develops high quality algorithms and continues to monitor their effectiveness after market entry, Gonçalves-Sá and Pinheiro explain . FDA should also investigate the reliability of datasets and engineers that train AI tools, Gonçalves-Sá and Pinheiro recommend .
• Regulators should lower legal barriers that prevent community organizations such as Black churches from helping poor and marginalized people to gain access to telehealth services, argues Meighan Parker of the University of Chicago Law School in a recent article in the Columbia Science and Technology Law Review . Parker notes that although community organizations such as Black churches could help some people to overcome mistrust of health care providers, involving them could cause conflicts between the churches’ beliefs and patients’ medical needs, or open the churches to malpractice liability. In response, Parker proposes softening or adjusting regulatory barriers to ensure that churches will not face ethical conflict or legal liability for connecting people with needed telehealth services.
• In a note in the Washington Journal of Law, Technology & Arts , Kaitlin Campanini , a student at Pace University Elisabeth Haub School of Law , argues that the U.S. Drug Enforcement Administration’s lax regulation of telehealth providers has worsened inadequate mental health treatment and increased excessive drug prescriptions. Although telehealth providers’ business models can render treatment more convenient and affordable, the expedited treatment model they offer “blurs the line between offering health care to patients and selling controlled substances to customers.” This is because such companies fall into a regulatory gray area. They disclaim providing medical services by maintaining that they are independent from providers. Yet they aggressively market stimulants to consumers and facilitate questionable prescriptions after short, virtual evaluations.
• In a recent note in the Belmont Law Review , J.D. candidate Nora Klein argues that regulators should close legal loopholes that allow direct-to-consumer (DTC) pharmaceutical companies to unfairly influence social media users. Klein notes that DTC pharmaceutical companies have avoided FDA advertising regulations in part by labeling themselves as entities over which FDA has no regulatory authority. Accordingly, these entities are only subject to FTC advertising regulations, which are difficult to enforce, Klein observes . She argues that the DTC model is harmful because it leads to misdiagnoses and patient complications more often than traditional health care services. To address the problem, Klein proposes that FDA require DTC pharmaceutical companies to disclose important drug information to consumers.

The Saturday Seminar is a weekly feature that aims to put into written form the kind of content that would be conveyed in a live seminar involving regulatory experts. Each week,  The Regulatory Review  publishes a brief overview of a selected regulatory topic and then distills recent research and scholarly writing on that topic.

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• Open access
• Published: 13 May 2024

Sexual and reproductive health implementation research in humanitarian contexts: a scoping review

• Alexandra Norton 1 &
• Hannah Tappis 2  

Reproductive Health volume  21 , Article number:  64 ( 2024 ) Cite this article

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Meeting the health needs of crisis-affected populations is a growing challenge, with 339 million people globally in need of humanitarian assistance in 2023. Given one in four people living in humanitarian contexts are women and girls of reproductive age, sexual and reproductive health care is considered as essential health service and minimum standard for humanitarian response. Despite growing calls for increased investment in implementation research in humanitarian settings, guidance on appropriate methods and analytical frameworks is limited.

A scoping review was conducted to examine the extent to which implementation research frameworks have been used to evaluate sexual and reproductive health interventions in humanitarian settings. Peer-reviewed papers published from 2013 to 2022 were identified through relevant systematic reviews and a literature search of Pubmed, Embase, PsycInfo, CINAHL and Global Health databases. Papers that presented primary quantitative or qualitative data pertaining to a sexual and reproductive health intervention in a humanitarian setting were included.

Seven thousand thirty-six unique records were screened for inclusion, and 69 papers met inclusion criteria. Of these, six papers explicitly described the use of an implementation research framework, three citing use of the Consolidated Framework for Implementation Research. Three additional papers referenced other types of frameworks used in their evaluation. Factors cited across all included studies as helping the intervention in their presence or hindering in their absence were synthesized into the following Consolidated Framework for Implementation Research domains: Characteristics of Systems, Outer Setting, Inner Setting, Characteristics of Individuals, Intervention Characteristics, and Process.

This review found a wide range of methodologies and only six of 69 studies using an implementation research framework, highlighting an opportunity for standardization to better inform the evidence for and delivery of sexual and reproductive health interventions in humanitarian settings. Increased use of implementation research frameworks such as a modified Consolidated Framework for Implementation Research could work toward both expanding the evidence base and increasing standardization.

Plain English summary

Three hundred thirty-nine million people globally were in need of humanitarian assistance in 2023, and meeting the health needs of crisis-affected populations is a growing challenge. One in four people living in humanitarian contexts are women and girls of reproductive age, and provision of sexual and reproductive health care is considered to be essential within a humanitarian response. Implementation research can help to better understand how real-world contexts affect health improvement efforts. Despite growing calls for increased investment in implementation research in humanitarian settings, guidance on how best to do so is limited. This scoping review was conducted to examine the extent to which implementation research frameworks have been used to evaluate sexual and reproductive health interventions in humanitarian settings. Of 69 papers that met inclusion criteria for the review, six of them explicitly described the use of an implementation research framework. Three used the Consolidated Framework for Implementation Research, a theory-based framework that can guide implementation research. Three additional papers referenced other types of frameworks used in their evaluation. This review summarizes how factors relevant to different aspects of implementation within the included papers could have been organized using the Consolidated Framework for Implementation Research. The findings from this review highlight an opportunity for standardization to better inform the evidence for and delivery of sexual and reproductive health interventions in humanitarian settings. Increased use of implementation research frameworks such as a modified Consolidated Framework for Implementation Research could work toward both expanding the evidence base and increasing standardization.

Peer Review reports

Over the past few decades, the field of public health implementation research (IR) has grown as a means by which the real-world conditions affecting health improvement efforts can be better understood. Peters et al. put forward the following broad definition of IR for health: “IR is the scientific inquiry into questions concerning implementation – the act of carrying an intention into effect, which in health research can be policies, programmes, or individual practices (collectively called interventions)” [ 1 ].

As IR emphasizes real-world circumstances, the context within which a health intervention is delivered is a core consideration. However, much IR implemented to date has focused on higher-resource settings, with many proposed frameworks developed with particular utility for a higher-income setting [ 2 ]. In recognition of IR’s potential to increase evidence across a range of settings, there have been numerous reviews of the use of IR in lower-resource settings as well as calls for broader use [ 3 , 4 ]. There have also been more focused efforts to modify various approaches and frameworks to strengthen the relevance of IR to low- and middle-income country settings (LMICs), such as the work by Means et al. to adapt a specific IR framework for increased utility in LMICs [ 2 ].

Within LMIC settings, the centrality of context to a health intervention’s impact is of particular relevance in humanitarian settings, which present a set of distinct implementation challenges [ 5 ]. Humanitarian responses to crisis situations operate with limited resources, under potential security concerns, and often under pressure to relieve acute suffering and need [ 6 ]. Given these factors, successful implementation of a particular health intervention may require different qualities than those that optimize intervention impact under more stable circumstances [ 7 ]. Despite increasing recognition of the need for expanded evidence of health interventions in humanitarian settings, the evidence base remains limited [ 8 ]. Furthermore, despite its potential utility, there is not standardized guidance on IR in humanitarian settings, nor are there widely endorsed recommendations for the frameworks best suited to analyze implementation in these settings.

Sexual and reproductive health (SRH) is a core aspect of the health sector response in humanitarian settings [ 9 ]. Yet, progress in addressing SRH needs has lagged far behind other services because of challenges related to culture and ideology, financing constraints, lack of data and competing priorities [ 10 ]. The Minimum Initial Service Package (MISP) for SRH in Crisis Situations is the international standard for the minimum set of SRH services that should be implemented in all crisis situations [ 11 ]. However, as in other areas of health, there is need for expanded evidence for planning and implementation of SRH interventions in humanitarian settings. Recent systematic reviews of SRH in humanitarian settings have focused on the effectiveness of interventions and service delivery strategies, as well as factors affecting utilization, but have not detailed whether IR frameworks were used [ 12 , 13 , 14 , 15 ]. There have also been recent reviews examining IR frameworks used in various settings and research areas, but none have explicitly focused on humanitarian settings [ 2 , 16 ].

Given the need for an expanded evidence base for SRH interventions in humanitarian settings and the potential for IR to be used to expand the available evidence, a scoping review was undertaken. This scoping review sought to identify IR approaches that have been used in the last ten years to evaluate SRH interventions in humanitarian settings.

This review also sought to shed light on whether there is a need for a common framework to guide research design, analysis, and reporting for SRH interventions in humanitarian settings and if so, if there are any established frameworks already in use that would be fit-for-purpose or could be tailored to meet this need.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews was utilized to guide the elements of this review [ 17 ]. The review protocol was retrospectively registered with the Open Science Framework ( https://osf.io/b5qtz ).

Search strategy

A two-fold search strategy was undertaken for this review, which covered the last 10 years (2013–2022). First, recent systematic reviews pertaining to research or evaluation of SRH interventions in humanitarian settings were identified through keyword searches on PubMed and Google Scholar. Four relevant systematic reviews were identified [ 12 , 13 , 14 , 15 ] Table 1 .

Second, a literature search mirroring these reviews was conducted to identify relevant papers published since the completion of searches for the most recent review (April 2017). Additional file 1 includes the search terms that were used in the literature search [see Additional file 1 ].

The literature search was conducted for papers published from April 2017 to December 2022 in the databases that were searched in one or more of the systematic reviews: PubMed, Embase, PsycInfo, CINAHL and Global Health. Searches were completed in January 2023 Table 2 .

Two reviewers screened each identified study for alignment with inclusion criteria. Studies in the four systematic reviews identified were considered potentially eligible if published during the last 10 years. These papers then underwent full-text review to confirm satisfaction of all inclusion criteria, as inclusion criteria were similar but not fully aligned across the four reviews.

Literature search results were exported into a citation manager (Covidence), duplicates were removed, and a step-wise screening process for inclusion was applied. First, all papers underwent title and abstract screening. The remaining papers after abstract screening then underwent full-text review to confirm satisfaction of all inclusion criteria. Title and abstract screening as well as full-text review was conducted independently by both authors; disagreements after full-text review were resolved by consensus.

Data extraction and synthesis

The following content areas were summarized in Microsoft Excel for each paper that met inclusion criteria: publication details including author, year, country, setting [rural, urban, camp, settlement], population [refugees, internally displaced persons, general crisis-affected], crisis type [armed conflict, natural disaster], crisis stage [acute, chronic], study design, research methods, SRH intervention, and intervention target population [specific beneficiaries of the intervention within the broader population]; the use of an IR framework; details regarding the IR framework, how it was used, and any rationale given for the framework used; factors cited as impacting SRH interventions, either positively or negatively; and other key findings deemed relevant to this review.

As the focus of this review was on the approach taken for SRH intervention research and evaluation, the quality of the studies themselves was not assessed.

Twenty papers underwent full-text review due to their inclusion in one or more of the four systematic reviews and meeting publication date inclusion criteria. The literature search identified 7,016 unique papers. After full-text screening, 69 met all inclusion criteria and were included in the review. Figure  1 illustrates the search strategy and screening process.

Flow chart of paper identification

Papers published in each of the 10 years of the review timeframe (2013–2022) were included. 29% of the papers originated from the first five years of the time frame considered for this review, with the remaining 71% papers coming from the second half. Characteristics of included publications, including geographic location, type of humanitarian crisis, and type of SRH intervention, are presented in Table  3 .

A wide range of study designs and methods were used across the papers, with both qualitative and quantitative studies well represented. Twenty-six papers were quantitative evaluations [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ], 17 were qualitative [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ], and 26 used mixed methods [ 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ]. Within the quantitative evaluations, 15 were observational, while five were quasi-experimental, five were randomized controlled trials, and one was an economic evaluation. Study designs as classified by the authors of this review are summarized in Table  4 .

Six papers (9%) explicitly cited use of an IR framework. Three of these papers utilized the Consolidated Framework for Implementation Research (CFIR) [ 51 , 65 , 70 ]. The CFIR is a commonly used determinant framework that—in its originally proposed form in 2009—is comprised of five domains, each of which has constructs to further categorize factors that impact implementation. The CFIR domains were identified as core content areas influencing the effectiveness of implementation, and the constructs within each domain are intended to provide a range of options for researchers to select from to “guide diagnostic assessments of implementation context, evaluate implementation progress, and help explain findings.” [ 87 ] To allow for consistent terminology throughout this review, the original 2009 CFIR domains and constructs are used.

Guan et al. conducted a mixed methods study to assess the feasibility and effectiveness of a neonatal hepatitis B immunization program in a conflict-affected rural region of Myanmar. Guan et al. report mapping data onto the CFIR as a secondary analysis step. They describe that “CFIR was used as a comprehensive meta-theoretical framework to examine the implementation of the Hepatitis B Virus vaccination program,” and implementation themes from multiple study data sources (interviews, observations, examination of monitoring materials) were mapped onto CFIR constructs. They report their results in two phases – Pre-implementation training and community education, and Implementation – with both anchored in themes that they had mapped onto CFIR domains and constructs. All but six constructs were included in their analysis, with a majority summarized in a table and key themes explored further in the narrative text. They specify that most concerns were identified within the Outer Setting and Process domains, while elements identified within the Inner Setting domain provided strength to the intervention and helped mitigate against barriers [ 70 ].

Sarker et al. conducted a qualitative study to assess provision of maternal, newborn and child health services to Rohingya refugees residing in camps in Cox’s Bazar, Bangladesh. They cite using CFIR as a guide for thematic analysis, applying it after a process of inductive and deductive coding to index these codes into the CFIR domains. They utilized three of the five CFIR domains (Outer Setting, Inner Setting, and Process), stating that the remaining two domains (Intervention Characteristics and Characteristics of Individuals) were not relevant to their analysis. They then proposed two additional CFIR domains, Context and Security, for use in humanitarian contexts. In contrast to Guan et al., CFIR constructs are not used nor mentioned by Sarker et al., with content under each domain instead synthesized as challenges and potential solutions. Regarding the CFIR, Sarker et al. write, “The CFIR guided us for interpretative coding and creating the challenges and possible solutions into groups for further clarification of the issues related to program delivery in a humanitarian crisis setting.” [ 51 ]

Sami et al. conducted a mixed methods case study to assess the implementation of a package of neonatal interventions at health facilities within refugee and internally displaced persons camps in South Sudan. They reference use of the CFIR earlier in the study than Sarker et al., basing their guides for semi-structured focus group discussions on the CFIR framework. They similarly reference a general use of the CFIR framework as they conducted thematic analysis. Constructs are referenced once, but they do not specify whether their application of the CFIR framework included use of domains, constructs, or both. This may be in part because they then applied an additional framework, the World Health Organization (WHO) Health System Framework, to present their findings. They describe a nested approach to their use of these frameworks: “Exploring these [CFIR] constructs within the WHO Health Systems Framework can identify specific entry points to improve the implementation of newborn interventions at critical health system building blocks.” [ 65 ]

Three papers cite use of different IR frameworks. Bolan et al. utilized the Theoretical Domains Framework in their mixed methods feasibility study and pilot cluster randomized trial evaluating pilot use of the Safe Delivery App by maternal and newborn health workers providing basic emergency obstetric and newborn care in facilities in the conflict-affected Maniema province of the Democratic Republic of the Congo (DRC). They used the Theroetical Domains Framework in designing interview questions, and further used it as the coding framework for their analysis. Similar to the CFIR, the Theoretical Domains Framework is a determinant framework that consists of domains, each of which then includes constructs. Bolan et al. utilized the Theoretical Domains Framework at the construct level in interview question development and at the domain level in their analysis, mapping interview responses to eight of the 14 domains [ 83 ]. Berg et al. report using an “exploratory design guided by the principles of an evaluation framework” developed by the Medical Research Council to analyze the implementation process, mechanisms of impact, and outcomes of a three-pillar training intervention to improve maternal and neonatal healthcare in the conflict-affected South Kivu province of the DRC [ 67 , 88 ]. Select components of this evaluation framework were used to guide deductive analysis of focus group discussions and in-depth interviews [ 67 ]. In their study of health workers’ knowledge and attitudes toward newborn health interventions in South Sudan, before and after training and supply provision, Sami et al. report use of the Conceptual Framework of the Role of Attitudes in Evidence-Based Practice Implementation in their analysis process. The framework was used to group codes following initial inductive coding analysis of in-depth interviews [ 72 ].

Three other papers cite use of specific frameworks in their intervention evaluation [ 19 , 44 , 76 ]. As a characteristic of IR is the use of an explicit framework to guide the research, the use of the frameworks in these three papers meets the intention of IR and serves the purpose that an IR framework would have in strengthening the analytical rigor. Castle et al. cite use of their program’s theory of change as a framework for a mixed methods evaluation of the provision of family planning services and more specifically uptake of long-acting reversible contraception use in the DRC. They describe use of the theory of change to “enhance effectiveness of [long-acting reversible contraception] access and uptake.” [ 76 ] Thommesen et al. cite use of the AAAQ (Availability, Accessibility, Acceptability and Quality) framework in their qualitative study assessing midwifery services provided to pregnant women in Afghanistan. This framework is focused on the “underlying elements needed for attainment of optimum standard of health care,” but the authors used it in this paper to evaluate facilitators and barriers to women accessing midwifery services [ 44 ]. Jarrett et al. cite use of the Centers for Disease Control and Prevention’s (CDC) Guidelines for Evaluating Public Health Surveillance Systems to explore the characteristics of a population mobility, mortality and birth surveillance system in South Kivu, DRC. Use of these CDC guidelines is cited as one of four study objectives, and commentary is included in the Results section pertaining to each criteria within these guidelines, although more detail regarding use of these guidelines or the authors’ experience with their use in the study is not provided [ 19 ].

Overall, 22 of the 69 papers either explicitly or implicitly identified IR as relevant to their work. Nineteen papers include a focus on feasibility (seven of which did not otherwise identify the importance of exploring questions concerning implementation), touching on a common outcome of interest in implementation research [ 89 ].

While a majority of papers did not explicitly or implicitly use an IR framework to evaluate their SRH intervention of focus, most identified factors that facilitated implementation when they were present or served as a barrier when absent. Sixty cite factors that served as facilitators and 49 cite factors that served as barriers, with just three not citing either. Fifty-nine distinct factors were identified across the papers.

Three of the six studies that explicitly used an IR framework used the CFIR, and the CFIR is the only IR framework that was used by multiple studies. As previously mentioned, Means et al. put forth an adaptation of the CFIR to increase its relevance in LMIC settings, proposing a sixth domain (Characteristics of Systems) and 11 additional constructs [ 2 ]. Using the expanded domains and constructs as proposed by Means et al., the 59 factors cited by papers in this review were thematically grouped into the six domains: Characteristics of Systems, Outer Setting, Inner Setting, Characteristics of Individuals, Intervention Characteristics, and Process. Within each domain, alignment with CFIR constructs was assessed for, and alignment was found with 29 constructs: eight of Means et al.’s 11 constructs, and 21 of the 39 standard CFIR constructs. Three factors did not align with any construct (all fitting within the Outer Setting domain), and 14 aligned with a construct label but not the associated definition. Table 5 synthesizes the mapping of factors affecting SRH intervention implementation to CFIR domains and constructs, with the construct appearing in italics if it is considered to align with that factor by label but not by definition.

Table 6 lists the CFIR constructs that were not found to have alignment with any factor cited by the papers in this review.

This scoping review sought to assess how IR frameworks have been used to bolster the evidence base for SRH interventions in humanitarian settings, and it revealed that IR frameworks, or an explicit IR approach, are rarely used. All four of the systematic reviews identified with a focus on SRH in humanitarian settings articulate the need for more research examining the effectiveness of SRH interventions in humanitarian settings, with two specifically citing a need for implementation research/science [ 12 , 13 ]. The distribution of papers across the timeframe included in this review does suggest that more research on SRH interventions for crisis-affected populations is taking place, as a majority of relevant papers were published in the second half of the review period. The papers included a wide range of methodologies, which reflect the differing research questions and contexts being evaluated. However, it also invites the question of whether there should be more standardization of outcomes measured or frameworks used to guide analysis and to facilitate increased comparison, synthesis and application across settings.

Three of the six papers that used an IR framework utilized the CFIR. Guan et al. used the CFIR at both a domain and construct level, Sarker et al. used the CFIR at the domain level, and Sami et al. did not specify which CFIR elements were used in informing the focus group discussion guide [ 51 , 65 , 70 ]. It is challenging to draw strong conclusions about the applicability of CFIR in humanitarian settings based on the minimal use of CFIR and IR frameworks within the papers reviewed, although Guan et al. provides a helpful model for how analysis can be structured around CFIR domains and constructs. It is worth considering that the minimal use of IR frameworks, and more specifically CFIR constructs, could be in part because that level of prescriptive categorization does not allow for enough fluidity in humanitarian settings. It also raises questions about the appropriate degree of standardization to pursue for research done in these settings.

The mapping of factors affecting SRH intervention implementation provides an example of how a modified CFIR framework could be used for IR in humanitarian contexts. This mapping exercise found factors that mapped to all five of the original CFIR domains as well as the sixth domain proposed by Means et al. All factors fit well within the definition for the selected domain, indicating an appropriate degree of fit between these existing domains and the factors identified as impacting SRH interventions in humanitarian settings. On a construct level, however, the findings were more variable, with one-quarter of factors not fully aligning with any construct. Furthermore, over 40% of the CFIR constructs (including the additional constructs from Means et al.) were not found to align with any factors cited by the papers in this review, also demonstrating some disconnect between the parameters posed by the CFIR constructs and the factors cited as relevant in a humanitarian context.

It is worth noting that while the CFIR as proposed in 2009 was used in this assessment, as well as in the included papers which used the CFIR, an update was published in 2022. Following a review of CFIR use since its publication, the authors provide updates to construct names and definitions to “make the framework more applicable across a range of innovations and settings.” New constructs and subconstructs were also added, for a total of 48 constructs and 19 subconstructs across the five domains [ 90 ]. A CFIR Outcomes Addendum was also published in 2022, based on recommendations for the CFIR to add outcomes and intended to be used as a complement to the CFIR determinants framework [ 91 ]. These expansions to the CFIR framework may improve applicability of the CFIR in humanitarian settings. Several constructs added to the Outer Setting domain could be of particular utility – critical incidents, local attitudes, and local conditions, each of which could help account for unique challenges faced in contexts of crisis. Sub-constructs added within the Inner Setting domain that seek to clarify structural characteristics and available resources would also be of high utility based on mapping of the factors identified in this review to the original CFIR constructs. As outcomes were not formally included in the CFIR until the 2022 addendum, a separate assessment of implementation outcomes was not undertaken in this review. However, analysis of the factors cited by papers in this review as affecting implementation was derived from the full text of the papers and thus captures content relevant to implementation determinants that is contained within the outcomes.

Given the demonstrated need for additional flexibility within an IR framework for humanitarian contexts, while not a focus of this review, it is worth considering whether a different framework could provide a better fit than the CFIR. Other frameworks have differing points of emphasis that would create different opportunities for flexibility but that do not seem to resolve the challenges experienced in applying the CFIR to a humanitarian context. As one example, the EPIS (Exploration, Preparation, Implementation, Sustainment) Framework considers the impact of inner and outer context on each of four implementation phases; while the constructs within this framework are broader than the CFIR, an emphasis on the intervention characteristics is missing, a domain where stronger alignment within the CFIR is also needed [ 92 ]. Alternatively, the PRISM (Practical, Robust Implementation and Sustainability Model) framework is a determinant and evaluation framework that adds consideration of context factors to the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) outcomes framework. It has a stronger emphasis on intervention aspects, with sub-domains to account for both organization and patient perspectives within the intervention. While PRISM does include aspects of context, external environment considerations are less robust and intentionally less comprehensive in scope, which would not provide the degree of alignment possible between the Characteristics of Systems and Outer Setting CFIR domains for the considerations unique to humanitarian environments [ 93 ].

Reflecting on their experience with the CFIR, Sarker et al. indicate that it can be a “great asset” in both evaluating current work and developing future interventions. They also encourage future research of humanitarian health interventions to utilize the CFIR [ 51 ]. The other papers that used the CFIR do not specifically reflect on their experience utilizing it, referring more generally to having felt that it was a useful tool [ 65 , 70 ]. On their use of an evaluation framework, Berg et al. reflected that it lent useful structure and helped to identify aspects affecting implementation that otherwise would have gone un-noticed [ 67 ]. The remaining studies that utilized an IR framework did not specifically comment on their experience with its use [ 72 , 83 ]. While a formal IR framework was not engaged by other studies, a number cite a desire for IR to contribute further detail to their findings [ 21 , 37 ].

In their recommendations for strengthening the evidence base for humanitarian health interventions, Ager et al. speak to the need for “methodologic innovation” to develop methodologies with particular applicability in humanitarian settings [ 7 ]. As IR is not yet routinized for SRH interventions, this could be opportune timing for the use of a standardized IR framework to gauge its utility. Using an IR framework to assess factors influencing implementation of the MISP in initial stages of a humanitarian response, and interventions to support more comprehensive SRH service delivery in protracted crises, could lend further rigor and standardization to SRH evaluations, as well as inform strategies to improve MISP implementation over time. Based on categorizing factors identified by these papers as relevant for intervention evaluation, there does seem to be utility to a modified CFIR approach. Given the paucity of formal IR framework use within SRH literature, it would be worth conducting similar scoping exercises to assess for explicit use of IR frameworks within the evidence base for other health service delivery areas in humanitarian settings. In the interim, the recommended approach from this review for future IR on humanitarian health interventions would be a modified CFIR approach with domain-level standardization and flexibility for constructs that may standardize over time with more use. This would enable use of a common analytical framework and vocabulary at the domain level for stakeholders to describe interventions and the factors influencing the effectiveness of implementation, with constructs available to use and customize as most appropriate for specific contexts and interventions.

This review had a number of limitations. As this was a scoping review and a two-part search strategy was used, the papers summarized here may not be comprehensive of those written pertaining to SRH interventions over the past 10 years. Papers from 2013 to 2017 that would have met this scoping review’s inclusion criteria may have been omitted due to being excluded from the systematic reviews. The review was limited to papers available in English. Furthermore, this review did not assess the quality of the papers included or seek to assess the methodology used beyond examination of the use of an IR framework. It does, however, serve as a first step in assessing the extent to which calls for implementation research have been addressed, and identify entry points for strengthening the science and practice of SRH research in humanitarian settings.

With one in 23 people worldwide in need of humanitarian assistance, and financing required for response plans at an all-time high, the need for evidence to guide resource allocation and programming for SRH in humanitarian settings is as important as ever [ 94 ]. Recent research agenda setting initiatives and strategies to advance health in humanitarian settings call for increased investment in implementation research—with priorities ranging from research on effective strategies for expanding access to a full range of contraceptive options to integrating mental health and psychosocial support into SRH programming to capturing accurate and actionable data on maternal and perinatal mortality in a wide range of acute and protracted emergency contexts [ 95 , 96 ]. To truly advance guidance in these areas, implementation research will need to be conducted across diverse humanitarian settings, with clear and consistent documentation of both intervention characteristics and outcomes, as well as contextual and programmatic factors affecting implementation.

Conclusions

Implementation research has potential to increase impact of health interventions particularly in crisis-affected settings where flexibility, adaptability and context-responsive approaches are highlighted as cornerstones of effective programming. There remains significant opportunity for standardization of research in the humanitarian space, with one such opportunity occurring through increased utilization of IR frameworks such as a modified CFIR approach. Investing in more robust sexual and reproductive health research in humanitarian contexts can enrich insights available to guide programming and increase transferability of learning across settings.

Availability of data and materials

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Availability, Accessibility, Acceptability and Quality

Centers for Disease Control and Prevention

Consolidated Framework for Implementation Research

Democratic Republic of the Congo

Exploration, Preparation, Implementation, Sustainment

• Implementation research

Low and middle income country

Minimum Initial Service Package

Practical, Robust Implementation and Sustainability Model

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Reach, Effectiveness, Adoption, Implementation, Maintenance

• Sexual and reproductive health

World Health Organization

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Cultural Relativity and Acceptance of Embryonic Stem Cell Research

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There is a debate about the ethical implications of using human embryos in stem cell research, which can be influenced by cultural, moral, and social values. This paper argues for an adaptable framework to accommodate diverse cultural and religious perspectives. By using an adaptive ethics model, research protections can reflect various populations and foster growth in stem cell research possibilities.

INTRODUCTION

Stem cell research combines biology, medicine, and technology, promising to alter health care and the understanding of human development. Yet, ethical contention exists because of individuals’ perceptions of using human embryos based on their various cultural, moral, and social values. While these disagreements concerning policy, use, and general acceptance have prompted the development of an international ethics policy, such a uniform approach can overlook the nuanced ethical landscapes between cultures. With diverse viewpoints in public health, a single global policy, especially one reflecting Western ethics or the ethics prevalent in high-income countries, is impractical. This paper argues for a culturally sensitive, adaptable framework for the use of embryonic stem cells. Stem cell policy should accommodate varying ethical viewpoints and promote an effective global dialogue. With an extension of an ethics model that can adapt to various cultures, we recommend localized guidelines that reflect the moral views of the people those guidelines serve.

Stem cells, characterized by their unique ability to differentiate into various cell types, enable the repair or replacement of damaged tissues. Two primary types of stem cells are somatic stem cells (adult stem cells) and embryonic stem cells. Adult stem cells exist in developed tissues and maintain the body’s repair processes. [1] Embryonic stem cells (ESC) are remarkably pluripotent or versatile, making them valuable in research. [2] However, the use of ESCs has sparked ethics debates. Considering the potential of embryonic stem cells, research guidelines are essential. The International Society for Stem Cell Research (ISSCR) provides international stem cell research guidelines. They call for “public conversations touching on the scientific significance as well as the societal and ethical issues raised by ESC research.” [3] The ISSCR also publishes updates about culturing human embryos 14 days post fertilization, suggesting local policies and regulations should continue to evolve as ESC research develops. [4]  Like the ISSCR, which calls for local law and policy to adapt to developing stem cell research given cultural acceptance, this paper highlights the importance of local social factors such as religion and culture.

I.     Global Cultural Perspective of Embryonic Stem Cells

Views on ESCs vary throughout the world. Some countries readily embrace stem cell research and therapies, while others have stricter regulations due to ethical concerns surrounding embryonic stem cells and when an embryo becomes entitled to moral consideration. The philosophical issue of when the “someone” begins to be a human after fertilization, in the morally relevant sense, [5] impacts when an embryo becomes not just worthy of protection but morally entitled to it. The process of creating embryonic stem cell lines involves the destruction of the embryos for research. [6] Consequently, global engagement in ESC research depends on social-cultural acceptability.

a.     US and Rights-Based Cultures

In the United States, attitudes toward stem cell therapies are diverse. The ethics and social approaches, which value individualism, [7] trigger debates regarding the destruction of human embryos, creating a complex regulatory environment. For example, the 1996 Dickey-Wicker Amendment prohibited federal funding for the creation of embryos for research and the destruction of embryos for “more than allowed for research on fetuses in utero.” [8] Following suit, in 2001, the Bush Administration heavily restricted stem cell lines for research. However, the Stem Cell Research Enhancement Act of 2005 was proposed to help develop ESC research but was ultimately vetoed. [9] Under the Obama administration, in 2009, an executive order lifted restrictions allowing for more development in this field. [10] The flux of research capacity and funding parallels the different cultural perceptions of human dignity of the embryo and how it is socially presented within the country’s research culture. [11]

b.     Ubuntu and Collective Cultures

African bioethics differs from Western individualism because of the different traditions and values. African traditions, as described by individuals from South Africa and supported by some studies in other African countries, including Ghana and Kenya, follow the African moral philosophies of Ubuntu or Botho and Ukama , which “advocates for a form of wholeness that comes through one’s relationship and connectedness with other people in the society,” [12] making autonomy a socially collective concept. In this context, for the community to act autonomously, individuals would come together to decide what is best for the collective. Thus, stem cell research would require examining the value of the research to society as a whole and the use of the embryos as a collective societal resource. If society views the source as part of the collective whole, and opposes using stem cells, compromising the cultural values to pursue research may cause social detachment and stunt research growth. [13] Based on local culture and moral philosophy, the permissibility of stem cell research depends on how embryo, stem cell, and cell line therapies relate to the community as a whole . Ubuntu is the expression of humanness, with the person’s identity drawn from the “’I am because we are’” value. [14] The decision in a collectivistic culture becomes one born of cultural context, and individual decisions give deference to others in the society.

Consent differs in cultures where thought and moral philosophy are based on a collective paradigm. So, applying Western bioethical concepts is unrealistic. For one, Africa is a diverse continent with many countries with different belief systems, access to health care, and reliance on traditional or Western medicines. Where traditional medicine is the primary treatment, the “’restrictive focus on biomedically-related bioethics’” [is] problematic in African contexts because it neglects bioethical issues raised by traditional systems.” [15] No single approach applies in all areas or contexts. Rather than evaluating the permissibility of ESC research according to Western concepts such as the four principles approach, different ethics approaches should prevail.

Another consideration is the socio-economic standing of countries. In parts of South Africa, researchers have not focused heavily on contributing to the stem cell discourse, either because it is not considered health care or a health science priority or because resources are unavailable. [16] Each country’s priorities differ given different social, political, and economic factors. In South Africa, for instance, areas such as maternal mortality, non-communicable diseases, telemedicine, and the strength of health systems need improvement and require more focus. [17] Stem cell research could benefit the population, but it also could divert resources from basic medical care. Researchers in South Africa adhere to the National Health Act and Medicines Control Act in South Africa and international guidelines; however, the Act is not strictly enforced, and there is no clear legislation for research conduct or ethical guidelines. [18]

Some parts of Africa condemn stem cell research. For example, 98.2 percent of the Tunisian population is Muslim. [19] Tunisia does not permit stem cell research because of moral conflict with a Fatwa. Religion heavily saturates the regulation and direction of research. [20] Stem cell use became permissible for reproductive purposes only recently, with tight restrictions preventing cells from being used in any research other than procedures concerning ART/IVF.  Their use is conditioned on consent, and available only to married couples. [21] The community's receptiveness to stem cell research depends on including communitarian African ethics.

c.     Asia

Some Asian countries also have a collective model of ethics and decision making. [22] In China, the ethics model promotes a sincere respect for life or human dignity, [23] based on protective medicine. This model, influenced by Traditional Chinese Medicine (TCM), [24] recognizes Qi as the vital energy delivered via the meridians of the body; it connects illness to body systems, the body’s entire constitution, and the universe for a holistic bond of nature, health, and quality of life. [25] Following a protective ethics model, and traditional customs of wholeness, investment in stem cell research is heavily desired for its applications in regenerative therapies, disease modeling, and protective medicines. In a survey of medical students and healthcare practitioners, 30.8 percent considered stem cell research morally unacceptable while 63.5 percent accepted medical research using human embryonic stem cells. Of these individuals, 89.9 percent supported increased funding for stem cell research. [26] The scientific community might not reflect the overall population. From 1997 to 2019, China spent a total of $576 million (USD) on stem cell research at 8,050 stem cell programs, increased published presence from 0.6 percent to 14.01 percent of total global stem cell publications as of 2014, and made significant strides in cell-based therapies for various medical conditions. [27] However, while China has made substantial investments in stem cell research and achieved notable progress in clinical applications, concerns linger regarding ethical oversight and transparency. [28] For example, the China Biosecurity Law, promoted by the National Health Commission and China Hospital Association, attempted to mitigate risks by introducing an institutional review board (IRB) in the regulatory bodies. 5800 IRBs registered with the Chinese Clinical Trial Registry since 2021. [29] However, issues still need to be addressed in implementing effective IRB review and approval procedures.

The substantial government funding and focus on scientific advancement have sometimes overshadowed considerations of regional cultures, ethnic minorities, and individual perspectives, particularly evident during the one-child policy era. As government policy adapts to promote public stability, such as the change from the one-child to the two-child policy, [30] research ethics should also adapt to ensure respect for the values of its represented peoples.

Japan is also relatively supportive of stem cell research and therapies. Japan has a more transparent regulatory framework, allowing for faster approval of regenerative medicine products, which has led to several advanced clinical trials and therapies. [31] South Korea is also actively engaged in stem cell research and has a history of breakthroughs in cloning and embryonic stem cells. [32] However, the field is controversial, and there are issues of scientific integrity. For example, the Korean FDA fast-tracked products for approval, [33] and in another instance, the oocyte source was unclear and possibly violated ethical standards. [34] Trust is important in research, as it builds collaborative foundations between colleagues, trial participant comfort, open-mindedness for complicated and sensitive discussions, and supports regulatory procedures for stakeholders. There is a need to respect the culture’s interest, engagement, and for research and clinical trials to be transparent and have ethical oversight to promote global research discourse and trust.

d.     Middle East

Countries in the Middle East have varying degrees of acceptance of or restrictions to policies related to using embryonic stem cells due to cultural and religious influences. Saudi Arabia has made significant contributions to stem cell research, and conducts research based on international guidelines for ethical conduct and under strict adherence to guidelines in accordance with Islamic principles. Specifically, the Saudi government and people require ESC research to adhere to Sharia law. In addition to umbilical and placental stem cells, [35] Saudi Arabia permits the use of embryonic stem cells as long as they come from miscarriages, therapeutic abortions permissible by Sharia law, or are left over from in vitro fertilization and donated to research. [36] Laws and ethical guidelines for stem cell research allow the development of research institutions such as the King Abdullah International Medical Research Center, which has a cord blood bank and a stem cell registry with nearly 10,000 donors. [37] Such volume and acceptance are due to the ethical ‘permissibility’ of the donor sources, which do not conflict with religious pillars. However, some researchers err on the side of caution, choosing not to use embryos or fetal tissue as they feel it is unethical to do so. [38]

Jordan has a positive research ethics culture. [39] However, there is a significant issue of lack of trust in researchers, with 45.23 percent (38.66 percent agreeing and 6.57 percent strongly agreeing) of Jordanians holding a low level of trust in researchers, compared to 81.34 percent of Jordanians agreeing that they feel safe to participate in a research trial. [40] Safety testifies to the feeling of confidence that adequate measures are in place to protect participants from harm, whereas trust in researchers could represent the confidence in researchers to act in the participants’ best interests, adhere to ethical guidelines, provide accurate information, and respect participants’ rights and dignity. One method to improve trust would be to address communication issues relevant to ESC. Legislation surrounding stem cell research has adopted specific language, especially concerning clarification “between ‘stem cells’ and ‘embryonic stem cells’” in translation. [41] Furthermore, legislation “mandates the creation of a national committee… laying out specific regulations for stem-cell banking in accordance with international standards.” [42] This broad regulation opens the door for future global engagement and maintains transparency. However, these regulations may also constrain the influence of research direction, pace, and accessibility of research outcomes.

e.     Europe

In the European Union (EU), ethics is also principle-based, but the principles of autonomy, dignity, integrity, and vulnerability are interconnected. [43] As such, the opportunity for cohesion and concessions between individuals’ thoughts and ideals allows for a more adaptable ethics model due to the flexible principles that relate to the human experience The EU has put forth a framework in its Convention for the Protection of Human Rights and Dignity of the Human Being allowing member states to take different approaches. Each European state applies these principles to its specific conventions, leading to or reflecting different acceptance levels of stem cell research. [44]

For example, in Germany, Lebenzusammenhang , or the coherence of life, references integrity in the unity of human culture. Namely, the personal sphere “should not be subject to external intervention.” [45]  Stem cell interventions could affect this concept of bodily completeness, leading to heavy restrictions. Under the Grundgesetz, human dignity and the right to life with physical integrity are paramount. [46] The Embryo Protection Act of 1991 made producing cell lines illegal. Cell lines can be imported if approved by the Central Ethics Commission for Stem Cell Research only if they were derived before May 2007. [47] Stem cell research respects the integrity of life for the embryo with heavy specifications and intense oversight. This is vastly different in Finland, where the regulatory bodies find research more permissible in IVF excess, but only up to 14 days after fertilization. [48] Spain’s approach differs still, with a comprehensive regulatory framework. [49] Thus, research regulation can be culture-specific due to variations in applied principles. Diverse cultures call for various approaches to ethical permissibility. [50] Only an adaptive-deliberative model can address the cultural constructions of self and achieve positive, culturally sensitive stem cell research practices. [51]

II.     Religious Perspectives on ESC

Embryonic stem cell sources are the main consideration within religious contexts. While individuals may not regard their own religious texts as authoritative or factual, religion can shape their foundations or perspectives.

The Qur'an states:

“And indeed We created man from a quintessence of clay. Then We placed within him a small quantity of nutfa (sperm to fertilize) in a safe place. Then We have fashioned the nutfa into an ‘alaqa (clinging clot or cell cluster), then We developed the ‘alaqa into mudgha (a lump of flesh), and We made mudgha into bones, and clothed the bones with flesh, then We brought it into being as a new creation. So Blessed is Allah, the Best of Creators.” [52]

Many scholars of Islam estimate the time of soul installment, marked by the angel breathing in the soul to bring the individual into creation, as 120 days from conception. [53] Personhood begins at this point, and the value of life would prohibit research or experimentation that could harm the individual. If the fetus is more than 120 days old, the time ensoulment is interpreted to occur according to Islamic law, abortion is no longer permissible. [54] There are a few opposing opinions about early embryos in Islamic traditions. According to some Islamic theologians, there is no ensoulment of the early embryo, which is the source of stem cells for ESC research. [55]

In Buddhism, the stance on stem cell research is not settled. The main tenets, the prohibition against harming or destroying others (ahimsa) and the pursuit of knowledge (prajña) and compassion (karuna), leave Buddhist scholars and communities divided. [56] Some scholars argue stem cell research is in accordance with the Buddhist tenet of seeking knowledge and ending human suffering. Others feel it violates the principle of not harming others. Finding the balance between these two points relies on the karmic burden of Buddhist morality. In trying to prevent ahimsa towards the embryo, Buddhist scholars suggest that to comply with Buddhist tenets, research cannot be done as the embryo has personhood at the moment of conception and would reincarnate immediately, harming the individual's ability to build their karmic burden. [57] On the other hand, the Bodhisattvas, those considered to be on the path to enlightenment or Nirvana, have given organs and flesh to others to help alleviate grieving and to benefit all. [58] Acceptance varies on applied beliefs and interpretations.

Catholicism does not support embryonic stem cell research, as it entails creation or destruction of human embryos. This destruction conflicts with the belief in the sanctity of life. For example, in the Old Testament, Genesis describes humanity as being created in God’s image and multiplying on the Earth, referencing the sacred rights to human conception and the purpose of development and life. In the Ten Commandments, the tenet that one should not kill has numerous interpretations where killing could mean murder or shedding of the sanctity of life, demonstrating the high value of human personhood. In other books, the theological conception of when life begins is interpreted as in utero, [59] highlighting the inviolability of life and its formation in vivo to make a religious point for accepting such research as relatively limited, if at all. [60] The Vatican has released ethical directives to help apply a theological basis to modern-day conflicts. The Magisterium of the Church states that “unless there is a moral certainty of not causing harm,” experimentation on fetuses, fertilized cells, stem cells, or embryos constitutes a crime. [61] Such procedures would not respect the human person who exists at these stages, according to Catholicism. Damages to the embryo are considered gravely immoral and illicit. [62] Although the Catholic Church officially opposes abortion, surveys demonstrate that many Catholic people hold pro-choice views, whether due to the context of conception, stage of pregnancy, threat to the mother’s life, or for other reasons, demonstrating that practicing members can also accept some but not all tenets. [63]

Some major Jewish denominations, such as the Reform, Conservative, and Reconstructionist movements, are open to supporting ESC use or research as long as it is for saving a life. [64] Within Judaism, the Talmud, or study, gives personhood to the child at birth and emphasizes that life does not begin at conception: [65]

“If she is found pregnant, until the fortieth day it is mere fluid,” [66]

Whereas most religions prioritize the status of human embryos, the Halakah (Jewish religious law) states that to save one life, most other religious laws can be ignored because it is in pursuit of preservation. [67] Stem cell research is accepted due to application of these religious laws.

We recognize that all religions contain subsets and sects. The variety of environmental and cultural differences within religious groups requires further analysis to respect the flexibility of religious thoughts and practices. We make no presumptions that all cultures require notions of autonomy or morality as under the common morality theory , which asserts a set of universal moral norms that all individuals share provides moral reasoning and guides ethical decisions. [68] We only wish to show that the interaction with morality varies between cultures and countries.

III.     A Flexible Ethical Approach

The plurality of different moral approaches described above demonstrates that there can be no universally acceptable uniform law for ESC on a global scale. Instead of developing one standard, flexible ethical applications must be continued. We recommend local guidelines that incorporate important cultural and ethical priorities.

While the Declaration of Helsinki is more relevant to people in clinical trials receiving ESC products, in keeping with the tradition of protections for research subjects, consent of the donor is an ethical requirement for ESC donation in many jurisdictions including the US, Canada, and Europe. [69] The Declaration of Helsinki provides a reference point for regulatory standards and could potentially be used as a universal baseline for obtaining consent prior to gamete or embryo donation.

For instance, in Columbia University’s egg donor program for stem cell research, donors followed standard screening protocols and “underwent counseling sessions that included information as to the purpose of oocyte donation for research, what the oocytes would be used for, the risks and benefits of donation, and process of oocyte stimulation” to ensure transparency for consent. [70] The program helped advance stem cell research and provided clear and safe research methods with paid participants. Though paid participation or covering costs of incidental expenses may not be socially acceptable in every culture or context, [71] and creating embryos for ESC research is illegal in many jurisdictions, Columbia’s program was effective because of the clear and honest communications with donors, IRBs, and related stakeholders.  This example demonstrates that cultural acceptance of scientific research and of the idea that an egg or embryo does not have personhood is likely behind societal acceptance of donating eggs for ESC research. As noted, many countries do not permit the creation of embryos for research.

Proper communication and education regarding the process and purpose of stem cell research may bolster comprehension and garner more acceptance. “Given the sensitive subject material, a complete consent process can support voluntary participation through trust, understanding, and ethical norms from the cultures and morals participants value. This can be hard for researchers entering countries of different socioeconomic stability, with different languages and different societal values. [72]

An adequate moral foundation in medical ethics is derived from the cultural and religious basis that informs knowledge and actions. [73] Understanding local cultural and religious values and their impact on research could help researchers develop humility and promote inclusion.

IV.     Concerns

Some may argue that if researchers all adhere to one ethics standard, protection will be satisfied across all borders, and the global public will trust researchers. However, defining what needs to be protected and how to define such research standards is very specific to the people to which standards are applied. We suggest that applying one uniform guide cannot accurately protect each individual because we all possess our own perceptions and interpretations of social values. [74] Therefore, the issue of not adjusting to the moral pluralism between peoples in applying one standard of ethics can be resolved by building out ethics models that can be adapted to different cultures and religions.

Other concerns include medical tourism, which may promote health inequities. [75] Some countries may develop and approve products derived from ESC research before others, compromising research ethics or drug approval processes. There are also concerns about the sale of unauthorized stem cell treatments, for example, those without FDA approval in the United States. Countries with robust research infrastructures may be tempted to attract medical tourists, and some customers will have false hopes based on aggressive publicity of unproven treatments. [76]

For example, in China, stem cell clinics can market to foreign clients who are not protected under the regulatory regimes. Companies employ a marketing strategy of “ethically friendly” therapies. Specifically, in the case of Beike, China’s leading stem cell tourism company and sprouting network, ethical oversight of administrators or health bureaus at one site has “the unintended consequence of shifting questionable activities to another node in Beike's diffuse network.” [77] In contrast, Jordan is aware of stem cell research’s potential abuse and its own status as a “health-care hub.” Jordan’s expanded regulations include preserving the interests of individuals in clinical trials and banning private companies from ESC research to preserve transparency and the integrity of research practices. [78]

The social priorities of the community are also a concern. The ISSCR explicitly states that guidelines “should be periodically revised to accommodate scientific advances, new challenges, and evolving social priorities.” [79] The adaptable ethics model extends this consideration further by addressing whether research is warranted given the varying degrees of socioeconomic conditions, political stability, and healthcare accessibilities and limitations. An ethical approach would require discussion about resource allocation and appropriate distribution of funds. [80]

While some religions emphasize the sanctity of life from conception, which may lead to public opposition to ESC research, others encourage ESC research due to its potential for healing and alleviating human pain. Many countries have special regulations that balance local views on embryonic personhood, the benefits of research as individual or societal goods, and the protection of human research subjects. To foster understanding and constructive dialogue, global policy frameworks should prioritize the protection of universal human rights, transparency, and informed consent. In addition to these foundational global policies, we recommend tailoring local guidelines to reflect the diverse cultural and religious perspectives of the populations they govern. Ethics models should be adapted to local populations to effectively establish research protections, growth, and possibilities of stem cell research.

For example, in countries with strong beliefs in the moral sanctity of embryos or heavy religious restrictions, an adaptive model can allow for discussion instead of immediate rejection. In countries with limited individual rights and voice in science policy, an adaptive model ensures cultural, moral, and religious views are taken into consideration, thereby building social inclusion. While this ethical consideration by the government may not give a complete voice to every individual, it will help balance policies and maintain the diverse perspectives of those it affects. Embracing an adaptive ethics model of ESC research promotes open-minded dialogue and respect for the importance of human belief and tradition. By actively engaging with cultural and religious values, researchers can better handle disagreements and promote ethical research practices that benefit each society.

This brief exploration of the religious and cultural differences that impact ESC research reveals the nuances of relative ethics and highlights a need for local policymakers to apply a more intense adaptive model.

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[34] Resnik, D. B., Shamoo, A. E., & Krimsky, S. (2006). Fraudulent human embryonic stem cell research in South Korea: lessons learned.  Accountability in research ,  13 (1), 101–109. https://doi.org/10.1080/08989620600634193 .

[35] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

[36] Association for the Advancement of Blood and Biotherapies.  https://www.aabb.org/regulatory-and-advocacy/regulatory-affairs/regulatory-for-cellular-therapies/international-competent-authorities/saudi-arabia

[37] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia.  BMC medical ethics ,  21 (1), 35. https://doi.org/10.1186/s12910-020-00482-6

[38] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia. BMC medical ethics , 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

Culturally, autonomy practices follow a relational autonomy approach based on a paternalistic deontological health care model. The adherence to strict international research policies and religious pillars within the regulatory environment is a great foundation for research ethics. However, there is a need to develop locally targeted ethics approaches for research (as called for in Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6), this decision-making approach may help advise a research decision model. For more on the clinical cultural autonomy approaches, see: Alabdullah, Y. Y., Alzaid, E., Alsaad, S., Alamri, T., Alolayan, S. W., Bah, S., & Aljoudi, A. S. (2022). Autonomy and paternalism in Shared decision‐making in a Saudi Arabian tertiary hospital: A cross‐sectional study. Developing World Bioethics , 23 (3), 260–268. https://doi.org/10.1111/dewb.12355 ; Bukhari, A. A. (2017). Universal Principles of Bioethics and Patient Rights in Saudi Arabia (Doctoral dissertation, Duquesne University). https://dsc.duq.edu/etd/124; Ladha, S., Nakshawani, S. A., Alzaidy, A., & Tarab, B. (2023, October 26). Islam and Bioethics: What We All Need to Know . Columbia University School of Professional Studies. https://sps.columbia.edu/events/islam-and-bioethics-what-we-all-need-know

[39] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[40] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[41] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[42] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[43] The EU’s definition of autonomy relates to the capacity for creating ideas, moral insight, decisions, and actions without constraint, personal responsibility, and informed consent. However, the EU views autonomy as not completely able to protect individuals and depends on other principles, such as dignity, which “expresses the intrinsic worth and fundamental equality of all human beings.” Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[44] Council of Europe. Convention for the protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine (ETS No. 164) https://www.coe.int/en/web/conventions/full-list?module=treaty-detail&treatynum=164 (forbidding the creation of embryos for research purposes only, and suggests embryos in vitro have protections.); Also see Drabiak-Syed B. K. (2013). New President, New Human Embryonic Stem Cell Research Policy: Comparative International Perspectives and Embryonic Stem Cell Research Laws in France.  Biotechnology Law Report ,  32 (6), 349–356. https://doi.org/10.1089/blr.2013.9865

[45] Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[46] Tomuschat, C., Currie, D. P., Kommers, D. P., & Kerr, R. (Trans.). (1949, May 23). Basic law for the Federal Republic of Germany. https://www.btg-bestellservice.de/pdf/80201000.pdf

[47] Regulation of Stem Cell Research in Germany . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-germany

[48] Regulation of Stem Cell Research in Finland . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-finland

[49] Regulation of Stem Cell Research in Spain . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-spain

[50] Some sources to consider regarding ethics models or regulatory oversights of other cultures not covered:

Kara MA. Applicability of the principle of respect for autonomy: the perspective of Turkey. J Med Ethics. 2007 Nov;33(11):627-30. doi: 10.1136/jme.2006.017400. PMID: 17971462; PMCID: PMC2598110.

Ugarte, O. N., & Acioly, M. A. (2014). The principle of autonomy in Brazil: one needs to discuss it ...  Revista do Colegio Brasileiro de Cirurgioes ,  41 (5), 374–377. https://doi.org/10.1590/0100-69912014005013

Bharadwaj, A., & Glasner, P. E. (2012). Local cells, global science: The rise of embryonic stem cell research in India . Routledge.

For further research on specific European countries regarding ethical and regulatory framework, we recommend this database: Regulation of Stem Cell Research in Europe . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-europe   

[51] Klitzman, R. (2006). Complications of culture in obtaining informed consent. The American Journal of Bioethics, 6(1), 20–21. https://doi.org/10.1080/15265160500394671 see also: Ekmekci, P. E., & Arda, B. (2017). Interculturalism and Informed Consent: Respecting Cultural Differences without Breaching Human Rights.  Cultura (Iasi, Romania) ,  14 (2), 159–172.; For why trust is important in research, see also: Gray, B., Hilder, J., Macdonald, L., Tester, R., Dowell, A., & Stubbe, M. (2017). Are research ethics guidelines culturally competent?  Research Ethics ,  13 (1), 23-41.  https://doi.org/10.1177/1747016116650235

[52] The Qur'an  (M. Khattab, Trans.). (1965). Al-Mu’minun, 23: 12-14. https://quran.com/23

[53] Lenfest, Y. (2017, December 8). Islam and the beginning of human life . Bill of Health. https://blog.petrieflom.law.harvard.edu/2017/12/08/islam-and-the-beginning-of-human-life/

[54] Aksoy, S. (2005). Making regulations and drawing up legislation in Islamic countries under conditions of uncertainty, with special reference to embryonic stem cell research. Journal of Medical Ethics , 31: 399-403.; see also: Mahmoud, Azza. "Islamic Bioethics: National Regulations and Guidelines of Human Stem Cell Research in the Muslim World." Master's thesis, Chapman University, 2022. https://doi.org/10.36837/ chapman.000386

[55] Rashid, R. (2022). When does Ensoulment occur in the Human Foetus. Journal of the British Islamic Medical Association , 12 (4). ISSN 2634 8071. https://www.jbima.com/wp-content/uploads/2023/01/2-Ethics-3_-Ensoulment_Rafaqat.pdf.

[56] Sivaraman, M. & Noor, S. (2017). Ethics of embryonic stem cell research according to Buddhist, Hindu, Catholic, and Islamic religions: perspective from Malaysia. Asian Biomedicine,8(1) 43-52.  https://doi.org/10.5372/1905-7415.0801.260

[57] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[58] Lecso, P. A. (1991). The Bodhisattva Ideal and Organ Transplantation.  Journal of Religion and Health ,  30 (1), 35–41. http://www.jstor.org/stable/27510629 ; Bodhisattva, S. (n.d.). The Key of Becoming a Bodhisattva . A Guide to the Bodhisattva Way of Life. http://www.buddhism.org/Sutras/2/BodhisattvaWay.htm

[59] There is no explicit religious reference to when life begins or how to conduct research that interacts with the concept of life. However, these are relevant verses pertaining to how the fetus is viewed. (( King James Bible . (1999). Oxford University Press. (original work published 1769))

Jerimiah 1: 5 “Before I formed thee in the belly I knew thee; and before thou camest forth out of the womb I sanctified thee…”

In prophet Jerimiah’s insight, God set him apart as a person known before childbirth, a theme carried within the Psalm of David.

Psalm 139: 13-14 “…Thou hast covered me in my mother's womb. I will praise thee; for I am fearfully and wonderfully made…”

These verses demonstrate David’s respect for God as an entity that would know of all man’s thoughts and doings even before birth.

[60] It should be noted that abortion is not supported as well.

[61] The Vatican. (1987, February 22). Instruction on Respect for Human Life in Its Origin and on the Dignity of Procreation Replies to Certain Questions of the Day . Congregation For the Doctrine of the Faith. https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_con_cfaith_doc_19870222_respect-for-human-life_en.html

[62] The Vatican. (2000, August 25). Declaration On the Production and the Scientific and Therapeutic Use of Human Embryonic Stem Cells . Pontifical Academy for Life. https://www.vatican.va/roman_curia/pontifical_academies/acdlife/documents/rc_pa_acdlife_doc_20000824_cellule-staminali_en.html ; Ohara, N. (2003). Ethical Consideration of Experimentation Using Living Human Embryos: The Catholic Church’s Position on Human Embryonic Stem Cell Research and Human Cloning. Department of Obstetrics and Gynecology . Retrieved from https://article.imrpress.com/journal/CEOG/30/2-3/pii/2003018/77-81.pdf.

[63] Smith, G. A. (2022, May 23). Like Americans overall, Catholics vary in their abortion views, with regular mass attenders most opposed . Pew Research Center. https://www.pewresearch.org/short-reads/2022/05/23/like-americans-overall-catholics-vary-in-their-abortion-views-with-regular-mass-attenders-most-opposed/

[64] Rosner, F., & Reichman, E. (2002). Embryonic stem cell research in Jewish law. Journal of halacha and contemporary society , (43), 49–68.; Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[65] Schenker J. G. (2008). The beginning of human life: status of embryo. Perspectives in Halakha (Jewish Religious Law).  Journal of assisted reproduction and genetics ,  25 (6), 271–276. https://doi.org/10.1007/s10815-008-9221-6

[66] Ruttenberg, D. (2020, May 5). The Torah of Abortion Justice (annotated source sheet) . Sefaria. https://www.sefaria.org/sheets/234926.7?lang=bi&with=all&lang2=en

[67] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[68] Gert, B. (2007). Common morality: Deciding what to do . Oxford Univ. Press.

[69] World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA , 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Declaration of Helsinki – WMA – The World Medical Association .; see also: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979).  The Belmont report: Ethical principles and guidelines for the protection of human subjects of research . U.S. Department of Health and Human Services.  https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html

[70] Zakarin Safier, L., Gumer, A., Kline, M., Egli, D., & Sauer, M. V. (2018). Compensating human subjects providing oocytes for stem cell research: 9-year experience and outcomes.  Journal of assisted reproduction and genetics ,  35 (7), 1219–1225. https://doi.org/10.1007/s10815-018-1171-z https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063839/ see also: Riordan, N. H., & Paz Rodríguez, J. (2021). Addressing concerns regarding associated costs, transparency, and integrity of research in recent stem cell trial. Stem Cells Translational Medicine , 10 (12), 1715–1716. https://doi.org/10.1002/sctm.21-0234

[71] Klitzman, R., & Sauer, M. V. (2009). Payment of egg donors in stem cell research in the USA.  Reproductive biomedicine online ,  18 (5), 603–608. https://doi.org/10.1016/s1472-6483(10)60002-8

[72] Krosin, M. T., Klitzman, R., Levin, B., Cheng, J., & Ranney, M. L. (2006). Problems in comprehension of informed consent in rural and peri-urban Mali, West Africa.  Clinical trials (London, England) ,  3 (3), 306–313. https://doi.org/10.1191/1740774506cn150oa

[73] Veatch, Robert M.  Hippocratic, Religious, and Secular Medical Ethics: The Points of Conflict . Georgetown University Press, 2012.

[74] Msoroka, M. S., & Amundsen, D. (2018). One size fits not quite all: Universal research ethics with diversity.  Research Ethics ,  14 (3), 1-17.  https://doi.org/10.1177/1747016117739939

[75] Pirzada, N. (2022). The Expansion of Turkey’s Medical Tourism Industry.  Voices in Bioethics ,  8 . https://doi.org/10.52214/vib.v8i.9894

[76] Stem Cell Tourism: False Hope for Real Money . Harvard Stem Cell Institute (HSCI). (2023). https://hsci.harvard.edu/stem-cell-tourism , See also: Bissassar, M. (2017). Transnational Stem Cell Tourism: An ethical analysis.  Voices in Bioethics ,  3 . https://doi.org/10.7916/vib.v3i.6027

[77] Song, P. (2011) The proliferation of stem cell therapies in post-Mao China: problematizing ethical regulation,  New Genetics and Society , 30:2, 141-153, DOI:  10.1080/14636778.2011.574375

[78] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[79] International Society for Stem Cell Research. (2024). Standards in stem cell research . International Society for Stem Cell Research. https://www.isscr.org/guidelines/5-standards-in-stem-cell-research

[80] Benjamin, R. (2013). People’s science bodies and rights on the Stem Cell Frontier . Stanford University Press.

Mifrah Hayath

SM Candidate Harvard Medical School, MS Biotechnology Johns Hopkins University

Olivia Bowers

MS Bioethics Columbia University (Disclosure: affiliated with Voices in Bioethics)

Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License .

This paper is in the following e-collection/theme issue:

Published on 17.5.2024 in Vol 26 (2024)

Utility of Large Language Models for Health Care Professionals and Patients in Navigating Hematopoietic Stem Cell Transplantation: Comparison of the Performance of ChatGPT-3.5, ChatGPT-4, and Bard

Authors of this article:

Original Paper

• Elisabetta Xue 1 , MD   ; 
• Dara Bracken-Clarke 1 , MD   ; 
• Giovanni Maria Iannantuono 2 , MD   ; 
• Hyoyoung Choo-Wosoba 3 , PhD   ; 
• James L Gulley 1 , MD   ; 
• Charalampos S Floudas 1 , MD  

1 Center for Immuno-Oncology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States

2 Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States

3 Biostatistics and Data Management Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States

Corresponding Author:

Elisabetta Xue, MD

Center for Immuno-Oncology

Center for Cancer Research, National Cancer Institute

National Institutes of Health

9000 Rockville Pike

Building 10, B2L312

Bethesda, MD, 20892

United States

Phone: 1 2403518904

Email: [email protected]

Background: Artificial intelligence is increasingly being applied to many workflows. Large language models (LLMs) are publicly accessible platforms trained to understand, interact with, and produce human-readable text; their ability to deliver relevant and reliable information is also of particular interest for the health care providers and the patients. Hematopoietic stem cell transplantation (HSCT) is a complex medical field requiring extensive knowledge, background, and training to practice successfully and can be challenging for the nonspecialist audience to comprehend.

Objective: We aimed to test the applicability of 3 prominent LLMs, namely ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Bard (Google AI), in guiding nonspecialist health care professionals and advising patients seeking information regarding HSCT.

Methods: We submitted 72 open-ended HSCT–related questions of variable difficulty to the LLMs and rated their responses based on consistency—defined as replicability of the response—response veracity, language comprehensibility, specificity to the topic, and the presence of hallucinations. We then rechallenged the 2 best performing chatbots by resubmitting the most difficult questions and prompting to respond as if communicating with either a health care professional or a patient and to provide verifiable sources of information. Responses were then rerated with the additional criterion of language appropriateness, defined as language adaptation for the intended audience.

Results: ChatGPT-4 outperformed both ChatGPT-3.5 and Bard in terms of response consistency (66/72, 92%; 54/72, 75%; and 63/69, 91%, respectively; P =.007), response veracity (58/66, 88%; 40/54, 74%; and 16/63, 25%, respectively; P <.001), and specificity to the topic (60/66, 91%; 43/54, 80%; and 27/63, 43%, respectively; P <.001). Both ChatGPT-4 and ChatGPT-3.5 outperformed Bard in terms of language comprehensibility (64/66, 97%; 53/54, 98%; and 52/63, 83%, respectively; P =.002). All displayed episodes of hallucinations. ChatGPT-3.5 and ChatGPT-4 were then rechallenged with a prompt to adapt their language to the audience and to provide source of information, and responses were rated. ChatGPT-3.5 showed better ability to adapt its language to nonmedical audience than ChatGPT-4 (17/21, 81% and 10/22, 46%, respectively; P =.03); however, both failed to consistently provide correct and up-to-date information resources, reporting either out-of-date materials, incorrect URLs, or unfocused references, making their output not verifiable by the reader.

Conclusions: In conclusion, despite LLMs’ potential capability in confronting challenging medical topics such as HSCT, the presence of mistakes and lack of clear references make them not yet appropriate for routine, unsupervised clinical use, or patient counseling. Implementation of LLMs’ ability to access and to reference current and updated websites and research papers, as well as development of LLMs trained in specialized domain knowledge data sets, may offer potential solutions for their future clinical application.

Introduction

The applications of large language model (LLM)–based chatbots, artificial intelligence tools trained to understand, interact with, and produce human-readable text, are garnering increasing interest in many fields. In medicine, LLMs are successfully passing board examinations [ 1 - 3 ] and show potential in information retrieval and finer conceptual application [ 4 ]. LLMs are accessible to health care professionals and patients; therefore, their ability to deliver complex medical information is of particular interest; so far, several applications have been explored, including patients’ education [ 5 ], patient-trial matching [ 6 ], administrative tasks, and training purposes [ 7 ].

Hematopoietic stem cell transplantation (HSCT) is a complex medical field requiring extensive knowledge, background, and training to practice successfully and can be challenging for the nonspecialist audience to comprehend. Here, we evaluated the performance of different chatbots in answering HSCT-related questions and assessed their reliability and verifiability, with the aim to determine which LLM can best assist the nonspecialists, including nonhematology medical professionals but also patients and caregivers, in navigating this field.

We compared the applicability to HSCT of 3 LLM chatbots, ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Bard (Google AI) that were prominent and widely available at the time of the study design (July 2023), by assessing their responses to HSCT-related questions. ChatGPT-3.5 and ChatGPT-4 share a similar architecture, but the former is a free, easily accessible platform, whereas the second is subscription only, was released more recently, and advertised for having better performance; therefore, we included both to examine the difference in their performance. We selected four HSCT-related topics: (1) drugs (mechanisms of action and toxicities), (2) transplant indications and conditioning platforms, (3) infectious, and (4) noninfectious complications. For each topic, we generated 18 open-ended questions, with 3 levels of difficulty ranging from “easy,” testing superficial factual knowledge (eg, drug toxicities), to “difficult,” testing complex clinical scenarios (eg, inferring causative drug from a toxicity and guiding subsequent patient-tailored management; see Materials and Methods section in Multimedia Appendix 1 for the complete list of submitted questions). All questions were submitted between July 14 and 18, 2023, responses were referenced from the 7th edition European Bone Marrow Transplantation Handbook [ 8 ], Lexicomp [ 9 ], and BeTheMatch [ 10 ] reviewed on July 13, 2023. Each question was submitted 3 times consecutively, without providing feedback to the chatbot: if the chatbot declined to answer any of the 3 submissions, no further evaluation was conducted for that question. If it responded all 3 times, we evaluated the responses for consistency, defined as the ability to convey the same information at each submission. If the 3 responses were consistent, we scored them together for (1) veracity, defined as correctness of the information, (2) language comprehensibility, defined as clarity of the output, and (3) specificity, defined as focus on the question: each variable was rated from “1” (low performance) to “3” (best performance); analyses were then conducted comparing ratings “1 and 2” versus “3.” Inconsistent answers were not analyzed further. In addition, answers that scored “2” or “3” in veracity were evaluated for completeness to assess for lack of relevant information. Finally, we assessed for hallucinations , defined as nonsensical, fabricated information [ 11 ], among incorrect (rated as “1” in veracity) and inconsistent answers ( Figure 1 ). For each step, 2 physicians (EX and DBC) independently graded the answers and reached consensus for any discrepancy; interrater reliability was evaluated through Cohen κ statistic. Average answer word count was also calculated.

Subsequently, after identifying the 2 overall best performing LLMs, we aimed to determine their utility as an information resource for nonspecialist audiences: we rechallenged them by resubmitting the “difficult” questions, prompting the chatbots to respond as if communicating with either a health care professional or a patient and to provide clear reference sources and concise responses. Answers were then regraded, as described above, and additionally assessed for language appropriateness, defined as language adaptation for the intended audience, to evaluate the LLMs’ ability to convey the same information using either simple or more technical terminology.

Fisher exact, Wilcoxon rank sum, and Kruskal-Wallis tests were used for categorical and continuous variables, respectively. All the statistical analyses were exploratory and performed using R (version 4.3.1; R Foundation Statistical Computing).

This study was exempt from ethical review since no human subjects were involved and 45 CFR part 46 did not apply.

Detailed questions and responses are included in Multimedia Appendix 1 . Cohen κ ranged between moderate to near perfect agreement (Table S1 in Multimedia Appendix 1 ). Figure 2 and Table S2 in Multimedia Appendix 1 display LLMs’ performances; ChatGPT-3.5 and ChatGPT-4 responded to all questions, whereas Bard did not answer 3 of 72 (4%; P =.12, reporting “I'm not able to help with that, as I'm only a language model”) questions. ChatGPT-4 had the highest rate of consistent responses (66/72, 92%) followed by Bard (63/69, 91%) and ChatGPT-3.5 (54/72, 75%; P =.007).

Consistent responses were evaluated for veracity, language comprehensibility, and specificity. ChatGPT-4 performed best in terms of veracity, with 58 of 66 (88%) of answers considered correct (rated as “3”), followed by ChatGPT-3.5 (40/54, 74%) and Bard (16/63, 25%; P <.001). For instance, when asked why a female patient who received an allogeneic HSCT from a male donor develops leukemia with 46XY karyotype, only ChatGPT-4 and Bard recognized this as donor-derived leukemia, whereas ChatGPT-3.5 wrongly suggested relapse of patient’s original disease. Bard had the highest rate of incorrect responses (rated as “1,” 21/63, 33%), especially among “moderate” and “difficult” questions. The LLMs also proved ineffective at calculating well-established risk scores (eg, Hematopoietic Cell Transplantation–specific Comorbidity Index). With respect to completeness, ChatGPT-3.5 and ChatGPT-4 answers were deemed complete in >80% of evaluable cases compared to approximately 60% for Bard answers ( P <.001).

For language comprehensibility, ChatGPT-3.5 and ChatGPT-4 performance was equivalent, with only <4% of the answers rated less than “3” (ie, less than fully comprehensible) compared to 18% (11/63) of Bard answers ( P =.002), especially among “moderate” and “difficult” questions (data not shown). Specifically, Bard exhibited a trend of repetitive language, yielding a less clear output.

Regarding specificity, ChatGPT-4 responses were rated as “3” (ie, very focused on the topic) in >90% of cases, followed by ChatGPT-3.5 (43/54, 80%) and Bard (27/63, 43%). For instance, when asked which drugs should be administered before antithymocyte globulin, ChatGPT-3.5 and ChatGPT-4 correctly listed the premedication and its purpose, whereas Bard listed all the premedication’s side effects. Bard provided more specific answers to the “easy” questions, whereas ChatGPT-3.5 and ChatGPT-4 performed similarly across the difficulty levels (data not shown).

All exhibited episodes of hallucinations, with ChatGPT-3.5, ChatGPT-4, and Bard showing at least 1 hallucinated answer in 7 of 24 (29%), 3 of 7 (43%), and 14 of 27 (52%) of evaluable cases, respectively. ChatGPT-4 provided shorter answers, with a median of 213 (IQR 191-261) words per answer, followed by ChatGPT-3.5 with 247 (IQR 212-307) words, and Bard with 303 (IQR 260-384) words ( P <.001).

Due to their overall better performance, ChatGPT-4 and ChatGPT-3.5 were selected for the assessment of audience-tailored information delivery. In the “health care professional-targeted” version, with respect to specificity, ChatGPT-3.5 more frequently yielded unfocused (ie, rated as “1” or “2”) answers compared to ChatGPT-4 (6/18, 33% and 0/21, 0% respectively); no noticeable differences were seen in other parameters, including language appropriateness (Table S3 in Multimedia Appendix 1 ). ChatGPT-3.5 did not provide verifiable information sources despite the prompt requiring them to do so, reporting “The information I provide is based on the knowledge I was trained on until September 2021”; ChatGPT-4 referenced scientific literature in 10 of 21 (48%) evaluable cases, frequently with relevant but out-of-date papers, or with inaccurate authorship, title, or Digital Object Identifier.

In the “patient-targeted” version (Table S4 in Multimedia Appendix 1 ), ChatGPT-4 yielded a higher rate of correct responses compared to ChatGPT-3.5, with 19 of 22 (86%) and 10 of 21 (48%) cases rated as “3” in veracity, respectively; however, ChatGPT-4 showed excessively technical language, with only 10 of 22 (46%) rated as “3” in language appropriateness compared to 17 of 21 (81%) for ChatGPT-3.5. No differences were seen in the other parameters. Both failed to return information sources but provided website resources targeted for patients in 62% (13/21) and 95% (21/22) of the cases, respectively. Notably, ChatGPT-3.5 returned several broken URLs, likely corresponding to no longer existing pages, while ChatGPT-4 tended to provide overly general websites for very specific queries (eg, American Cancer Society web page [ 12 ] for information on sinusoidal obstruction syndrome). Both consistently acknowledged the potential for case-to-case variability and recommended referring to the medical team for any case-specific questions.

The emergence of LLMs has expanded the accessibility of medical information to the general public [ 5 , 13 ]; however, their reliability remains of concern [ 14 ]. In our study, all 3 LLMs correctly answered most of the “easy” questions, but only ChatGPT-3.5 and ChatGPT-4 successfully addressed more complex scenarios, and both outperformed Bard in producing comprehensible and specific responses. However, all exhibited episodes of hallucinations; thus, the potential for mistakes in diagnosis and recommendations remains a major obstacle to their routine unsupervised use.

When testing LLMs as support learning tools for laypeople, ChatGPT-3.5 adopted a friendly tone and, interestingly, exhibited some degree of emotional support (eg, “I understand your concern” and “take care”), showing a greater ability in adjusting its language to the audience. Adapting language to the general community and avoiding technical jargon would be optimal tools for making complex information accessible to patients and caregivers. In our opinion, LLMs cannot replace effective patient-doctor communication but rather may potentially supplement it, eventually reducing the risk of misinformation from nonscientific websites and sources. However, in our experience, current LLMs failed to consistently provide specific and updated web-based references, likely due to ChatGPT’s then lack of real-time access to the internet, thus making their output frequently unverifiable by the reader.

From a physician’s perspective, LLMs cannot replace conferences or scientific literature but may effectively support personal learning. Unfortunately, limited access to current web-based data, errors in reporting peer-reviewed material, and failure to provide valid references severely compromise this application [ 15 ].

Our study has limitations, including the submission of each question 3 consecutive times, without opening a new chat session each time, potentially urging the chatbot to provide a different answer at each submission. Furthermore, we subjectively selected 3 among the most popular available LLMs, 2 of which are developed by the same company; as more are becoming available, our observation might not apply to other LLMs. Finally, this is a rapidly changing field: since the completion of our analysis, ChatGPT has gained access to real time internet data, and Bard was updated into Google Gemini, and thus might yield a different output if tested today.

In conclusion, our evaluation suggests that, given the higher rate of correct and focused responses provided, at the time of this analysis, ChatGPT-3.5 and ChatGPT-4 are not yet appropriate for routine, unsupervised clinical use for both the general population and health care providers, or patient counseling. Their use at present should only be considered under expert supervision or for research purposes. Nevertheless, because of the rapid progression and the clear potential of LLMs to revolutionize workflows in medicine, including specialized fields, we need to engage proactively with this technology. Implementation of LLMs’ ability to access and to reference current and updated websites and research papers, as well as the development of LLMs trained in specialized domain knowledge data sets, may offer potential solutions for their future clinical application.

Authors' Contributions

EX, DBC, GMI, and CSF designed the study; EX, GMI, and HCW performed the analysis and prepared the figure; EX, DBC, and CSF wrote the paper; JLG and CSF provided supervision. All authors accepted the final draft of the paper.

Conflicts of Interest

None declared.

Supplementary materials.

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Abbreviations

Edited by G Tsafnat; submitted 21.11.23; peer-reviewed by M Chatzimina, E Hermes-DeSantis, T Ma, R Yang, K Fitzner; comments to author 16.03.24; revised version received 22.03.24; accepted 22.03.24; published 17.05.24.

©Elisabetta Xue, Dara Bracken-Clarke, Giovanni Maria Iannantuono, Hyoyoung Choo-Wosoba, James L Gulley, Charalampos S Floudas. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Results are from logistic regression models controlling for age, Hispanic or Latina/x ethnicity, marital status, parity, tobacco use, prenatal visit utilization, stillbirth, and placental abruption. Other race includes Alaska Native, American Indian, Chinese, Filipino, Guam/Chamorro Hawaiian, Indian, Japanese, Korean, Other Asian/Pacific Islander, Samoan, and Vietnamese. In the sample, 4100 patients had a history of substance use, and 33 760 had no history of substance use; 4636 had a urine toxicology test, and 2199 had any positive test result at labor and delivery. Error bars indicate 95% CIs.

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Jarlenski M , Shroff J , Terplan M , Roberts SCM , Brown-Podgorski B , Krans EE. Association of Race With Urine Toxicology Testing Among Pregnant Patients During Labor and Delivery. JAMA Health Forum. 2023;4(4):e230441. doi:10.1001/jamahealthforum.2023.0441

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Association of Race With Urine Toxicology Testing Among Pregnant Patients During Labor and Delivery

• 1 Department of Health Policy and Management, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania
• 2 Friends Research Institute, Baltimore, Maryland
• 3 Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco
• 4 Department of Obstetrics, Gynecology & Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
• 5 Magee-Womens Research Institute, Pittsburgh, Pennsylvania

An estimated 16% of pregnant persons in the US use alcohol (10%) or an illicit substance (6%, including cannabis). 1 Urine toxicology testing (UTT) is often performed at the time of labor and delivery for pregnant patients to evaluate substance use. 2 , 3 We sought to elucidate associations between race and receipt of UTT and a positive test result among pregnant patients admitted to the hospital for delivery.

This cohort study followed the STROBE reporting guideline. Data were extracted from electronic medical records (EMRs) of patients with a live or stillbirth delivery between March 2018 and June 2021 in a large health care system in Pennsylvania. The study was approved by the University of Pittsburgh institutional review board. Informed consent was waived because the research constituted minimal risk. All patients presenting for delivery were verbally screened for substance use using questions adapted from the National Institute on Drug Abuse Quick Screen. 4 Policy specified UTT would be performed for those with a positive screen result, history of substance use in the year prior to delivery, few prenatal visits, or abruption or stillbirth without a clear medical explanation.

We studied 2 binary outcomes: the receipt of UTT (point of care presumptive testing) and a positive test result at delivery. The primary variable of interest, patient race, was conceptualized as a social construct that could manifest in biased or discriminatory delivery of health care. Self-reported race was categorized as Black, White, and other (Alaska Native, American Indian, Chinese, Filipino, Guam/Chamorro Hawaiian, Indian, Japanese, Korean, Other Asian/Pacific Islander, Samoan, and Vietnamese). Substance use history was defined as having a diagnosis of an alcohol, cannabis, opioid, or stimulant use or disorder during pregnancy in the EMR within 1 year prior through delivery. A positive UTT result was defined as at least 1 positive result of a test component, including amphetamines, barbiturates, benzodiazepines, buprenorphine, cocaine, cannabis, methadone, opiates, or phencyclidine. We used multivariable logistic regression models including race and substance use history, adjusting for age, Hispanic or Latina/x ethnicity, marital status, parity, tobacco use, prenatal visit utilization, stillbirth, and placental abruption. We derived mean predicted probabilities of outcomes by race and substance use history. 5 Analyses were conducted using Stata, version 17.

Among 37 860 patients (100% female; mean [SD] age, 29.8 [5.5] years), 16% Black, 76% were White, and 8% were other race ( Table ). Overall, 11% had a history of substance use; opioid use was more common among White patients (40% of all substance use), whereas cannabis use was most common among Black patients (86% of all substance use). The mean predicted probability of having a UTT at delivery was highest among Black patients compared with White patients and other racial groups regardless of history of substance use ( Figure ). For Black patients without a history of substance use, the mean predicted probability of receiving a UTT at delivery was 6.9% (95% CI, 6.4%-7.4%) vs 4.7% (95% CI, 4.4%-4.9%) among White patients. Among Black patients with a history of substance use, the mean predicted probability of receiving a UTT at delivery was 76.4% (95% CI, 74.8%-78.0%) vs 68.7% (95% CI, 67.3%-70.1%) among White patients. In contrast, among those with a history of substance use, the mean predicted probability of having a positive test result was 66.7% (95% CI, 64.8%-68.7%) among White patients and 58.3% (95% CI, 55.5%-61.1%) among Black patients.

In this cohort study, Black patients, regardless of history of substance use, had a greater probability of receiving a UTT at delivery compared with White patients and other racial groups. However, Black patients did not have a higher probability of a positive test result than other racial groups. Limitations of the study include a lack of a sufficient sample size to investigate other racial and ethnic minoritized groups, such as Alaska Native and American Indian patients, and that data were from a single geographical area and may not generalize nationally. To address racial biases, health care systems should examine drug testing practices and adhere to evidence-based practices.

Accepted for Publication: February 4, 2023.

Published: April 14, 2023. doi:10.1001/jamahealthforum.2023.0441

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Jarlenski M et al. JAMA Health Forum .

Corresponding Author: Marian Jarlenski, PhD, MPH, University of Pittsburgh School of Public Health, 130 DeSoto St, A619, Pittsburgh, PA 15261 ( [email protected] ).

Author Contributions: Dr Jarlenski and Mr Shroff had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Jarlenski, Terplan, Krans.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Jarlenski, Krans.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Shroff, Terplan, Brown-Podgorski, Krans.

Obtained funding: Jarlenski, Krans.

Administrative, technical, or material support: Krans.

Supervision: Jarlenski, Krans.

Conflict of Interest Disclosures: Dr Roberts reported receiving grants from the Foundation for Opioid Response Efforts and the University of California, San Francisco CSF Bixby Center for Global Reproductive Health and National Center of Excellence in Women's Health outside the submitted work. Dr Krans reported receiving grants from the National Institutes of Health, Merck, and Gilead outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by grant R01DA049759 from the National Institute on Drug Abuse (Dr Jarlenski and Krans).

Role of the Funder/Sponsor: The National Institute on Drug Abuse had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement .

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Assessing the impact of healthcare research: A systematic review of methodological frameworks

Samantha cruz rivera.

Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom

Derek G. Kyte

Olalekan lee aiyegbusi, thomas j. keeley, melanie j. calvert, associated data.

All relevant data are within the paper and supporting files.

Increasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix.

Methods and findings

Two independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched.

Conclusions

The measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Derek Kyte and colleagues systematically review approaches to the evaluation of health research.

Author summary

Why was this study done.

• There is a growing interest in demonstrating the impact of research in order to minimise research waste, allocate resources efficiently, and maximise the benefit of research. However, there is no consensus on which is the most appropriate tool to measure the impact of research.
• To our knowledge, this review is the first to synthesise existing methodological frameworks for healthcare research impact, and the associated impact metrics by which various authors have proposed impact should be measured, into a unified matrix.

What did the researchers do and find?

• We conducted a systematic review identifying 24 existing methodological research impact frameworks.
• We scrutinised the sample, identifying and summarising 5 proposed impact categories, 16 impact subcategories, and over 80 metrics into an impact matrix and methodological framework.

What do these findings mean?

• This simplified consolidated methodological framework will help researchers to understand how a research study may give rise to differing forms of impact, as well as in what ways and at which time points these potential impacts might be measured.
• Incorporating these insights into the design of a study could enhance impact, optimizing the use of research resources.

Introduction

In 2010, approximately US$240 billion was invested in healthcare research worldwide [ 1 ]. Such research is utilised by policy makers, healthcare providers, and clinicians to make important evidence-based decisions aimed at maximising patient benefit, whilst ensuring that limited healthcare resources are used as efficiently as possible to facilitate effective and sustainable service delivery. It is therefore essential that this research is of high quality and that it is impactful—i.e., it delivers demonstrable benefits to society and the wider economy whilst minimising research waste [ 1 , 2 ]. Research impact can be defined as ‘any identifiable ‘benefit to, or positive influence on the economy, society, public policy or services, health, the environment, quality of life or academia’ (p. 26) [ 3 ].

There are many purported benefits associated with the measurement of research impact, including the ability to (1) assess the quality of the research and its subsequent benefits to society; (2) inform and influence optimal policy and funding allocation; (3) demonstrate accountability, the value of research in terms of efficiency and effectiveness to the government, stakeholders, and society; and (4) maximise impact through better understanding the concept and pathways to impact [ 4 – 7 ].

Measuring and monitoring the impact of healthcare research has become increasingly common in the United Kingdom [ 5 ], Australia [ 5 ], and Canada [ 8 ], as governments, organisations, and higher education institutions seek a framework to allocate funds to projects that are more likely to bring the most benefit to society and the economy [ 5 ]. For example, in the UK, the 2014 Research Excellence Framework (REF) has recently been used to assess the quality and impact of research in higher education institutions, through the assessment of impact cases studies and selected qualitative impact metrics [ 9 ]. This is the first initiative to allocate research funding based on the economic, societal, and cultural impact of research, although it should be noted that research impact only drives a proportion of this allocation (approximately 20%) [ 9 ].

In the UK REF, the measurement of research impact is seen as increasingly important. However, the impact element of the REF has been criticised in some quarters [ 10 , 11 ]. Critics deride the fact that REF impact is determined in a relatively simplistic way, utilising researcher-generated case studies, which commonly attempt to link a particular research outcome to an associated policy or health improvement despite the fact that the wider literature highlights great diversity in the way research impact may be demonstrated [ 12 , 13 ]. This led to the current debate about the optimal method of measuring impact in the future REF [ 10 , 14 ]. The Stern review suggested that research impact should not only focus on socioeconomic impact but should also include impact on government policy, public engagement, academic impacts outside the field, and teaching to showcase interdisciplinary collaborative impact [ 10 , 11 ]. The Higher Education Funding Council for England (HEFCE) has recently set out the proposals for the REF 2021 exercise, confirming that the measurement of such impact will continue to form an important part of the process [ 15 ].

With increasing pressure for healthcare research to lead to demonstrable health, economic, and societal impact, there is a need for researchers to understand existing methodological impact frameworks and the means by which impact may be quantified (i.e., impact metrics; see Box 1 , 'Definitions’) to better inform research activities and funding decisions. From a researcher’s perspective, understanding the optimal pathways to impact can help inform study design aimed at maximising the impact of the project. At the same time, funders need to understand which aspects of impact they should focus on when allocating awards so they can make the most of their investment and bring the greatest benefit to patients and society [ 2 , 4 , 5 , 16 , 17 ].

Box 1. Definitions

• Research impact: ‘any identifiable benefit to, or positive influence on, the economy, society, public policy or services, health, the environment, quality of life, or academia’ (p. 26) [ 3 ].
• Methodological framework: ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ].
• Pathway: ‘a way of achieving a specified result; a course of action’ [ 19 ].
• Quantitative metrics: ‘a system or standard of [quantitative] measurement’ [ 20 ].
• Narrative metrics: ‘a spoken or written account of connected events; a story’ [ 21 ].

Whilst previous researchers have summarised existing methodological frameworks and impact case studies [ 4 , 22 – 27 ], they have not summarised the metrics for use by researchers, funders, and policy makers. The aim of this review was therefore to (1) identify the methodological frameworks used to measure healthcare research impact using systematic methods, (2) summarise common impact themes and metrics in an impact matrix, and (3) provide a simplified consolidated resource for use by funders, researchers, and policy makers.

Search strategy and selection criteria

Initially, a search strategy was developed to identify the available literature regarding the different methods to measure research impact. The following keywords: ‘Impact’, ‘Framework’, and ‘Research’, and their synonyms, were used during the search of the Medical Literature Analysis and Retrieval System Online (MEDLINE; Ovid) database, the Excerpta Medica Database (EMBASE), the Health Management Information Consortium (HMIC) database, and the Cumulative Index to Nursing and Allied Health Literature (CINAHL+) database (inception to May 2017; see S1 Appendix for the full search strategy). Additionally, the nonindexed Journal of Research Evaluation was hand searched during the same timeframe using the keyword ‘Impact’. Other relevant articles were identified through 3 Internet search engines (Google, Google Scholar, and Google Images) using the keywords ‘Impact’, ‘Framework’, and ‘Research’, with the first 50 results screened. Google Images was searched because different methodological frameworks are summarised in a single image and can easily be identified through this search engine. Finally, additional publications were sought through communication with experts.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (see S1 PRISMA Checklist ), 2 independent investigators systematically screened for publications describing, evaluating, or utilising a methodological research impact framework within the context of healthcare research [ 28 ]. Papers were eligible if they included full or partial methodological frameworks or pathways to research impact; both primary research and systematic reviews fitting these criteria were included. We included any methodological framework identified (original or modified versions) at the point of first occurrence. In addition, methodological frameworks were included if they were applicable to the healthcare discipline with no need of modification within their structure. We defined ‘methodological framework’ as ‘a body of methods, rules and postulates employed by a particular procedure or set of procedures (i.e., framework characteristics and development)’ [ 18 ], whereas we defined ‘pathway’ as ‘a way of achieving a specified result; a course of action’ [ 19 ]. Studies were excluded if they presented an existing (unmodified) methodological framework previously available elsewhere, did not explicitly describe a methodological framework but rather focused on a single metric (e.g., bibliometric analysis), focused on the impact or effectiveness of interventions rather than that of the research, or presented case study data only. There were no language restrictions.

Data screening

Records were downloaded into Endnote (version X7.3.1), and duplicates were removed. Two independent investigators (SCR and OLA) conducted all screening following a pilot aimed at refining the process. The records were screened by title and abstract before full-text articles of potentially eligible publications were retrieved for evaluation. A full-text screening identified the publications included for data extraction. Discrepancies were resolved through discussion, with the involvement of a third reviewer (MJC, DGK, and TJK) when necessary.

Data extraction and analysis

Data extraction occurred after the final selection of included articles. SCR and OLA independently extracted details of impact methodological frameworks, the country of origin, and the year of publication, as well as the source, the framework description, and the methodology used to develop the framework. Information regarding the methodology used to develop each methodological framework was also extracted from framework webpages where available. Investigators also extracted details regarding each framework’s impact categories and subgroups, along with their proposed time to impact (‘short-term’, ‘mid-term’, or ‘long-term’) and the details of any metrics that had been proposed to measure impact, which are depicted in an impact matrix. The structure of the matrix was informed by the work of M. Buxton and S. Hanney [ 2 ], P. Buykx et al. [ 5 ], S. Kuruvila et al. [ 29 ], and A. Weiss [ 30 ], with the intention of mapping metrics presented in previous methodological frameworks in a concise way. A consensus meeting with MJC, DGK, and TJK was held to solve disagreements and finalise the data extraction process.

Included studies

Our original search strategy identified 359 citations from MEDLINE (Ovid), EMBASE, CINAHL+, HMIC, and the Journal of Research Evaluation, and 101 citations were returned using other sources (Google, Google Images, Google Scholar, and expert communication) (see Fig 1 ) [ 28 ]. In total, we retrieved 54 full-text articles for review. At this stage, 39 articles were excluded, as they did not propose new or modified methodological frameworks. An additional 15 articles were included following the backward and forward citation method. A total of 31 relevant articles were included in the final analysis, of which 24 were articles presenting unique frameworks and the remaining 7 were systematic reviews [ 4 , 22 – 27 ]. The search strategy was rerun on 15 May 2017. A further 19 publications were screened, and 2 were taken forward to full-text screening but were ineligible for inclusion.

Methodological framework characteristics

The characteristics of the 24 included methodological frameworks are summarised in Table 1 , 'Methodological framework characteristics’. Fourteen publications proposed academic-orientated frameworks, which focused on measuring academic, societal, economic, and cultural impact using narrative and quantitative metrics [ 2 , 3 , 5 , 8 , 29 , 31 – 39 ]. Five publications focused on assessing the impact of research by focusing on the interaction process between stakeholders and researchers (‘productive interactions’), which is a requirement to achieve research impact. This approach tries to address the issue of attributing research impact to metrics [ 7 , 40 – 43 ]. Two frameworks focused on the importance of partnerships between researchers and policy makers, as a core element to accomplish research impact [ 44 , 45 ]. An additional 2 frameworks focused on evaluating the pathways to impact, i.e., linking processes between research and impact [ 30 , 46 ]. One framework assessed the ability of health technology to influence efficiency of healthcare systems [ 47 ]. Eight frameworks were developed in the UK [ 2 , 3 , 29 , 37 , 39 , 42 , 43 , 45 ], 6 in Canada [ 8 , 33 , 34 , 44 , 46 , 47 ], 4 in Australia [ 5 , 31 , 35 , 38 ], 3 in the Netherlands [ 7 , 40 , 41 ], and 2 in the United States [ 30 , 36 ], with 1 model developed with input from various countries [ 32 ].

Methodological framework development

The included methodological frameworks varied in their development process, but there were some common approaches employed. Most included a literature review [ 2 , 5 , 7 , 8 , 31 , 33 , 36 , 37 , 40 – 46 ], although none of them used a recognised systematic method. Most also consulted with various stakeholders [ 3 , 8 , 29 , 31 , 33 , 35 – 38 , 43 , 44 , 46 , 47 ] but used differing methods to incorporate their views, including quantitative surveys [ 32 , 35 , 43 , 46 ], face-to-face interviews [ 7 , 29 , 33 , 35 , 37 , 42 , 43 ], telephone interviews [ 31 , 46 ], consultation [ 3 , 7 , 36 ], and focus groups [ 39 , 43 ]. A range of stakeholder groups were approached across the sample, including principal investigators [ 7 , 29 , 43 ], research end users [ 7 , 42 , 43 ], academics [ 3 , 8 , 39 , 40 , 43 , 46 ], award holders [ 43 ], experts [ 33 , 38 , 39 ], sponsors [ 33 , 39 ], project coordinators [ 32 , 42 ], and chief investigators [ 31 , 35 ]. However, some authors failed to identify the stakeholders involved in the development of their frameworks [ 2 , 5 , 34 , 41 , 45 ], making it difficult to assess their appropriateness. In addition, only 4 of the included papers reported using formal analytic methods to interpret stakeholder responses. These included the Canadian Academy of Health Sciences framework, which used conceptual cluster analysis [ 33 ]. The Research Contribution [ 42 ], Research Impact [ 29 ], and Primary Health Care & Information Service [ 31 ] used a thematic analysis approach. Finally, some authors went on to pilot their framework, which shaped refinements on the methodological frameworks until approval. Methods used to pilot the frameworks included a case study approach [ 2 , 3 , 30 , 32 , 33 , 36 , 40 , 42 , 44 , 45 ], contrasting results against available literature [ 29 ], the use of stakeholders’ feedback [ 7 ], and assessment tools [ 35 , 46 ].

Major impact categories

1. primary research-related impact.

A number of methodological frameworks advocated the evaluation of ‘research-related impact’. This encompassed content related to the generation of new knowledge, knowledge dissemination, capacity building, training, leadership, and the development of research networks. These outcomes were considered the direct or primary impacts of a research project, as these are often the first evidenced returns [ 30 , 62 ].

A number of subgroups were identified within this category, with frameworks supporting the collection of impact data across the following constructs: ‘research and innovation outcomes’; ‘dissemination and knowledge transfer’; ‘capacity building, training, and leadership’; and ‘academic collaborations, research networks, and data sharing’.

1 . 1 . Research and innovation outcomes . Twenty of the 24 frameworks advocated the evaluation of ‘research and innovation outcomes’ [ 2 , 3 , 5 , 7 , 8 , 29 – 39 , 41 , 43 , 44 , 46 ]. This subgroup included the following metrics: number of publications; number of peer-reviewed articles (including journal impact factor); citation rates; requests for reprints, number of reviews, and meta-analysis; and new or changes in existing products (interventions or technology), patents, and research. Additionally, some frameworks also sought to gather information regarding ‘methods/methodological contributions’. These advocated the collection of systematic reviews and appraisals in order to identify gaps in knowledge and determine whether the knowledge generated had been assessed before being put into practice [ 29 ].

1 . 2 . Dissemination and knowledge transfer . Nineteen of the 24 frameworks advocated the assessment of ‘dissemination and knowledge transfer’ [ 2 , 3 , 5 , 7 , 29 – 32 , 34 – 43 , 46 ]. This comprised collection of the following information: number of conferences, seminars, workshops, and presentations; teaching output (i.e., number of lectures given to disseminate the research findings); number of reads for published articles; article download rate and number of journal webpage visits; and citations rates in nonjournal media such as newspapers and mass and social media (i.e., Twitter and blogs). Furthermore, this impact subgroup considered the measurement of research uptake and translatability and the adoption of research findings in technological and clinical applications and by different fields. These can be measured through patents, clinical trials, and partnerships between industry and business, government and nongovernmental organisations, and university research units and researchers [ 29 ].

1 . 3 . Capacity building , training , and leadership . Fourteen of 24 frameworks suggested the evaluation of ‘capacity building, training, and leadership’ [ 2 , 3 , 5 , 8 , 29 , 31 – 35 , 39 – 41 , 43 ]. This involved collecting information regarding the number of doctoral and postdoctoral studentships (including those generated as a result of the research findings and those appointed to conduct the research), as well as the number of researchers and research-related staff involved in the research projects. In addition, authors advocated the collection of ‘leadership’ metrics, including the number of research projects managed and coordinated and the membership of boards and funding bodies, journal editorial boards, and advisory committees [ 29 ]. Additional metrics in this category included public recognition (number of fellowships and awards for significant research achievements), academic career advancement, and subsequent grants received. Lastly, the impact metric ‘research system management’ comprised the collection of information that can lead to preserving the health of the population, such as modifying research priorities, resource allocation strategies, and linking health research to other disciplines to maximise benefits [ 29 ].

1 . 4 . Academic collaborations , research networks , and data sharing . Lastly, 10 of the 24 frameworks advocated the collection of impact data regarding ‘academic collaborations (internal and external collaborations to complete a research project), research networks, and data sharing’ [ 2 , 3 , 5 , 7 , 29 , 34 , 37 , 39 , 41 , 43 ].

2. Influence on policy making

Methodological frameworks addressing this major impact category focused on measurable improvements within a given knowledge base and on interactions between academics and policy makers, which may influence policy-making development and implementation. The returns generated in this impact category are generally considered as intermediate or midterm (1 to 3 years). These represent an important interim stage in the process towards the final expected impacts, such as quantifiable health improvements and economic benefits, without which policy change may not occur [ 30 , 62 ]. The following impact subgroups were identified within this category: ‘type and nature of policy impact’, ‘level of policy making’, and ‘policy networks’.

2 . 1 . Type and nature of policy impact . The most common impact subgroup, mentioned in 18 of the 24 frameworks, was ‘type and nature of policy impact’ [ 2 , 7 , 29 – 38 , 41 – 43 , 45 – 47 ]. Methodological frameworks addressing this subgroup stressed the importance of collecting information regarding the influence of research on policy (i.e., changes in practice or terminology). For instance, a project looking at trafficked adolescents and women (2003) influenced the WHO guidelines (2003) on ethics regarding this particular group [ 17 , 21 , 63 ].

2 . 2 . Level of policy impact . Thirteen of 24 frameworks addressed aspects surrounding the need to record the ‘level of policy impact’ (international, national, or local) and the organisations within a level that were influenced (local policy makers, clinical commissioning groups, and health and wellbeing trusts) [ 2 , 5 , 8 , 29 , 31 , 34 , 38 , 41 , 43 – 47 ]. Authors considered it important to measure the ‘level of policy impact’ to provide evidence of collaboration, coordination, and efficiency within health organisations and between researchers and health organisations [ 29 , 31 ].

2 . 3 . Policy networks . Five methodological frameworks highlighted the need to collect information regarding collaborative research with industry and staff movement between academia and industry [ 5 , 7 , 29 , 41 , 43 ]. A policy network emphasises the relationship between policy communities, researchers, and policy makers. This relationship can influence and lead to incremental changes in policy processes [ 62 ].

3. Health and health systems impact

A number of methodological frameworks advocated the measurement of impacts on health and healthcare systems across the following impact subgroups: ‘quality of care and service delivering’, ‘evidence-based practice’, ‘improved information and health information management’, ‘cost containment and effectiveness’, ‘resource allocation’, and ‘health workforce’.

3 . 1 . Quality of care and service delivery . Twelve of the 24 frameworks highlighted the importance of evaluating ‘quality of care and service delivery’ [ 2 , 5 , 8 , 29 – 31 , 33 – 36 , 41 , 47 ]. There were a number of suggested metrics that could be potentially used for this purpose, including health outcomes such as quality-adjusted life years (QALYs), patient-reported outcome measures (PROMs), patient satisfaction and experience surveys, and qualitative data on waiting times and service accessibility.

3 . 2 . Evidence-based practice . ‘Evidence-based practice’, mentioned in 5 of the 24 frameworks, refers to making changes in clinical diagnosis, clinical practice, treatment decisions, or decision making based on research evidence [ 5 , 8 , 29 , 31 , 33 ]. The suggested metrics to demonstrate evidence-based practice were adoption of health technologies and research outcomes to improve the healthcare systems and inform policies and guidelines [ 29 ].

3 . 3 . Improved information and health information management . This impact subcategory, mentioned in 5 of the 24 frameworks, refers to the influence of research on the provision of health services and management of the health system to prevent additional costs [ 5 , 29 , 33 , 34 , 38 ]. Methodological frameworks advocated the collection of health system financial, nonfinancial (i.e., transport and sociopolitical implications), and insurance information in order to determine constraints within a health system.

3 . 4 . Cost containment and cost-effectiveness . Six of the 24 frameworks advocated the subcategory ‘cost containment and cost-effectiveness’ [ 2 , 5 , 8 , 17 , 33 , 36 ]. ‘Cost containment’ comprised the collection of information regarding how research has influenced the provision and management of health services and its implication in healthcare resource allocation and use [ 29 ]. ‘Cost-effectiveness’ refers to information concerning economic evaluations to assess improvements in effectiveness and health outcomes—for instance, the cost-effectiveness (cost and health outcome benefits) assessment of introducing a new health technology to replace an older one [ 29 , 31 , 64 ].

3 . 5 . Resource allocation . ‘Resource allocation’, mentioned in 6frameworks, can be measured through 2 impact metrics: new funding attributed to the intervention in question and equity while allocating resources, such as improved allocation of resources at an area level; better targeting, accessibility, and utilisation; and coverage of health services [ 2 , 5 , 29 , 31 , 45 , 47 ]. The allocation of resources and targeting can be measured through health services research reports, with the utilisation of health services measured by the probability of providing an intervention when needed, the probability of requiring it again in the future, and the probability of receiving an intervention based on previous experience [ 29 , 31 ].

3 . 6 . Health workforce . Lastly, ‘health workforce’, present in 3 methodological frameworks, refers to the reduction in the days of work lost because of a particular illness [ 2 , 5 , 31 ].

4. Health-related and societal impact

Three subgroups were included in this category: ‘health literacy’; ‘health knowledge, attitudes, and behaviours’; and ‘improved social equity, inclusion, or cohesion’.

4 . 1 . Health knowledge , attitudes , and behaviours . Eight of the 24 frameworks suggested the assessment of ‘health knowledge, attitudes, behaviours, and outcomes’, which could be measured through the evaluation of levels of public engagement with science and research (e.g., National Health Service (NHS) Choices end-user visit rate) or by using focus groups to analyse changes in knowledge, attitudes, and behaviour among society [ 2 , 5 , 29 , 33 – 35 , 38 , 43 ].

4 . 2 . Improved equity , inclusion , or cohesion and human rights . Other methodological frameworks, 4 of the 24, suggested capturing improvements in equity, inclusion, or cohesion and human rights. Authors suggested these could be using a resource like the United Nations Millennium Development Goals (MDGs) (superseded by Sustainable Development Goals [SDGs] in 2015) and human rights [ 29 , 33 , 34 , 38 ]. For instance, a cluster-randomised controlled trial in Nepal, which had female participants, has demonstrated the reduction of neonatal mortality through the introduction of maternity health care, distribution of delivery kits, and home visits. This illustrates how research can target vulnerable and disadvantaged groups. Additionally, this research has been introduced by the World Health Organisation to achieve the MDG ‘improve maternal health’ [ 16 , 29 , 65 ].

4 . 3 . Health literacy . Some methodological frameworks, 3 of the 24, focused on tracking changes in the ability of patients to make informed healthcare decisions, reduce health risks, and improve quality of life, which were demonstrably linked to a particular programme of research [ 5 , 29 , 43 ]. For example, a systematic review showed that when HIV health literacy/knowledge is spread among people living with the condition, antiretroviral adherence and quality of life improve [ 66 ].

5. Broader economic impacts

Some methodological frameworks, 9 of 24, included aspects related to the broader economic impacts of health research—for example, the economic benefits emerging from the commercialisation of research outputs [ 2 , 5 , 29 , 31 , 33 , 35 , 36 , 38 , 67 ]. Suggested metrics included the amount of funding for research and development (R&D) that was competitively awarded by the NHS, medical charities, and overseas companies. Additional metrics were income from intellectual property, spillover effects (any secondary benefit gained as a repercussion of investing directly in a primary activity, i.e., the social and economic returns of investing on R&D) [ 33 ], patents granted, licences awarded and brought to the market, the development and sales of spinout companies, research contracts, and income from industry.

The benefits contained within the categories ‘health and health systems impact’, ‘health-related and societal impact’, and ‘broader economic impacts’ are considered the expected and final returns of the resources allocated in healthcare research [ 30 , 62 ]. These benefits commonly arise in the long term, beyond 5 years according to some authors, but there was a recognition that this could differ depending on the project and its associated research area [ 4 ].

Data synthesis

Five major impact categories were identified across the 24 included methodological frameworks: (1) ‘primary research-related impact’, (2) ‘influence on policy making’, (3) ‘health and health systems impact’, (4) ‘health-related and societal impact’, and (5) ‘broader economic impact’. These major impact categories were further subdivided into 16 impact subgroups. The included publications proposed 80 different metrics to measure research impact. This impact typology synthesis is depicted in ‘the impact matrix’ ( Fig 2 and Fig 3 ).

CIHR, Canadian Institutes of Health Research; HTA, Health Technology Assessment; PHC RIS, Primary Health Care Research & Information Service; RAE, Research Assessment Exercise; RQF, Research Quality Framework.

AIHS, Alberta Innovates—Health Solutions; CAHS, Canadian Institutes of Health Research; IOM, Impact Oriented Monitoring; REF, Research Excellence Framework; SIAMPI, Social Impact Assessment Methods for research and funding instruments through the study of Productive Interactions between science and society.

Commonality and differences across frameworks

The ‘Research Impact Framework’ and the ‘Health Services Research Impact Framework’ were the models that encompassed the largest number of the metrics extracted. The most dominant methodological framework was the Payback Framework; 7 other methodological framework models used the Payback Framework as a starting point for development [ 8 , 29 , 31 – 35 ]. Additional methodological frameworks that were commonly incorporated into other tools included the CIHR framework, the CAHS model, the AIHS framework, and the Exchange model [ 8 , 33 , 34 , 44 ]. The capture of ‘research-related impact’ was the most widely advocated concept across methodological frameworks, illustrating the importance with which primary short-term impact outcomes were viewed by the included papers. Thus, measurement of impact via number of publications, citations, and peer-reviewed articles was the most common. ‘Influence on policy making’ was the predominant midterm impact category, specifically the subgroup ‘type and nature of policy impact’, in which frameworks advocated the measurement of (i) changes to legislation, regulations, and government policy; (ii) influence and involvement in decision-making processes; and (iii) changes to clinical or healthcare training, practice, or guidelines. Within more long-term impact measurement, the evaluations of changes in the ‘quality of care and service delivery’ were commonly advocated.

In light of the commonalities and differences among the methodological frameworks, the ‘pathways to research impact’ diagram ( Fig 4 ) was developed to provide researchers, funders, and policy makers a more comprehensive and exhaustive way to measure healthcare research impact. The diagram has the advantage of assorting all the impact metrics proposed by previous frameworks and grouping them into different impact subgroups and categories. Prospectively, this global picture will help researchers, funders, and policy makers plan strategies to achieve multiple pathways to impact before carrying the research out. The analysis of the data extraction and construction of the impact matrix led to the development of the ‘pathways to research impact’ diagram ( Fig 4 ). The diagram aims to provide an exhaustive and comprehensive way of tracing research impact by combining all the impact metrics presented by the different 24 frameworks, grouping those metrics into different impact subgroups, and grouping these into broader impact categories.

NHS, National Health Service; PROM, patient-reported outcome measure; QALY, quality-adjusted life year; R&D, research and development.

This review has summarised existing methodological impact frameworks together for the first time using systematic methods ( Fig 4 ). It allows researchers and funders to consider pathways to impact at the design stage of a study and to understand the elements and metrics that need to be considered to facilitate prospective assessment of impact. Users do not necessarily need to cover all the aspects of the methodological framework, as every research project can impact on different categories and subgroups. This review provides information that can assist researchers to better demonstrate impact, potentially increasing the likelihood of conducting impactful research and reducing research waste. Existing reviews have not presented a methodological framework that includes different pathways to impact, health impact categories, subgroups, and metrics in a single methodological framework.

Academic-orientated frameworks included in this review advocated the measurement of impact predominantly using so-called ‘quantitative’ metrics—for example, the number of peer-reviewed articles, journal impact factor, and citation rates. This may be because they are well-established measures, relatively easy to capture and objective, and are supported by research funding systems. However, these metrics primarily measure the dissemination of research finding rather than its impact [ 30 , 68 ]. Whilst it is true that wider dissemination, especially when delivered via world-leading international journals, may well lead eventually to changes in healthcare, this is by no means certain. For instance, case studies evaluated by Flinders University of Australia demonstrated that some research projects with non-peer-reviewed publications led to significant changes in health policy, whilst the studies with peer-reviewed publications did not result in any type of impact [ 68 ]. As a result, contemporary literature has tended to advocate the collection of information regarding a variety of different potential forms of impact alongside publication/citations metrics [ 2 , 3 , 5 , 7 , 8 , 29 – 47 ], as outlined in this review.

The 2014 REF exercise adjusted UK university research funding allocation based on evidence of the wider impact of research (through case narrative studies and quantitative metrics), rather than simply according to the quality of research [ 12 ]. The intention was to ensure funds were directed to high-quality research that could demonstrate actual realised benefit. The inclusion of a mixed-method approach to the measurement of impact in the REF (narrative and quantitative metrics) reflects a widespread belief—expressed by the majority of authors of the included methodological frameworks in the review—that individual quantitative impact metrics (e.g., number of citations and publications) do not necessary capture the complexity of the relationships involved in a research project and may exclude measurement of specific aspects of the research pathway [ 10 , 12 ].

Many of the frameworks included in this review advocated the collection of a range of academic, societal, economic, and cultural impact metrics; this is consistent with recent recommendations from the Stern review [ 10 ]. However, a number of these metrics encounter research ‘lag’: i.e., the time between the point at which the research is conducted and when the actual benefits arise [ 69 ]. For instance, some cardiovascular research has taken up to 25 years to generate impact [ 70 ]. Likewise, the impact may not arise exclusively from a single piece of research. Different processes (such as networking interactions and knowledge and research translation) and multiple individuals and organisations are often involved [ 4 , 71 ]. Therefore, attributing the contribution made by each of the different actors involved in the process can be a challenge [ 4 ]. An additional problem associated to attribution is the lack of evidence to link research and impact. The outcomes of research may emerge slowly and be absorbed gradually. Consequently, it is difficult to determine the influence of research in the development of a new policy, practice, or guidelines [ 4 , 23 ].

A further problem is that impact evaluation is conducted ‘ex post’, after the research has concluded. Collecting information retrospectively can be an issue, as the data required might not be available. ‘ex ante’ assessment is vital for funding allocation, as it is necessary to determine the potential forthcoming impact before research is carried out [ 69 ]. Additionally, ex ante evaluation of potential benefit can overcome the issues regarding identifying and capturing evidence, which can be used in the future [ 4 ]. In order to conduct ex ante evaluation of potential benefit, some authors suggest the early involvement of policy makers in a research project coupled with a well-designed strategy of dissemination [ 40 , 69 ].

Providing an alternate view, the authors of methodological frameworks such as the SIAMPI, Contribution Mapping, Research Contribution, and the Exchange model suggest that the problems of attribution are a consequence of assigning the impact of research to a particular impact metric [ 7 , 40 , 42 , 44 ]. To address these issues, these authors propose focusing on the contribution of research through assessing the processes and interactions between stakeholders and researchers, which arguably take into consideration all the processes and actors involved in a research project [ 7 , 40 , 42 , 43 ]. Additionally, contributions highlight the importance of the interactions between stakeholders and researchers from an early stage in the research process, leading to a successful ex ante and ex post evaluation by setting expected impacts and determining how the research outcomes have been utilised, respectively [ 7 , 40 , 42 , 43 ]. However, contribution metrics are generally harder to measure in comparison to academic-orientated indicators [ 72 ].

Currently, there is a debate surrounding the optimal methodological impact framework, and no tool has proven superior to another. The most appropriate methodological framework for a given study will likely depend on stakeholder needs, as each employs different methodologies to assess research impact [ 4 , 37 , 41 ]. This review allows researchers to select individual existing methodological framework components to create a bespoke tool with which to facilitate optimal study design and maximise the potential for impact depending on the characteristic of their study ( Fig 2 and Fig 3 ). For instance, if researchers are interested in assessing how influential their research is on policy making, perhaps considering a suite of the appropriate metrics drawn from multiple methodological frameworks may provide a more comprehensive method than adopting a single methodological framework. In addition, research teams may wish to use a multidimensional approach to methodological framework development, adopting existing narratives and quantitative metrics, as well as elements from contribution frameworks. This approach would arguably present a more comprehensive method of impact assessment; however, further research is warranted to determine its effectiveness [ 4 , 69 , 72 , 73 ].

Finally, it became clear during this review that the included methodological frameworks had been constructed using varied methodological processes. At present, there are no guidelines or consensus around the optimal pathway that should be followed to develop a robust methodological framework. The authors believe this is an area that should be addressed by the research community, to ensure future frameworks are developed using best-practice methodology.

For instance, the Payback Framework drew upon a literature review and was refined through a case study approach. Arguably, this approach could be considered inferior to other methods that involved extensive stakeholder involvement, such as the CIHR framework [ 8 ]. Nonetheless, 7 methodological frameworks were developed based upon the Payback Framework [ 8 , 29 , 31 – 35 ].

Limitations

The present review is the first to summarise systematically existing impact methodological frameworks and metrics. The main limitation is that 50% of the included publications were found through methods other than bibliographic databases searching, indicating poor indexing. Therefore, some relevant articles may not have been included in this review if they failed to indicate the inclusion of a methodological impact framework in their title/abstract. We did, however, make every effort to try to find these potentially hard-to-reach publications, e.g., through forwards/backwards citation searching, hand searching reference lists, and expert communication. Additionally, this review only extracted information regarding the methodology followed to develop each framework from the main publication source or framework webpage. Therefore, further evaluations may not have been included, as they are beyond the scope of the current paper. A further limitation was that although our search strategy did not include language restrictions, we did not specifically search non-English language databases. Thus, we may have failed to identify potentially relevant methodological frameworks that were developed in a non-English language setting.

In conclusion, the measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise benefit, and to help minimise research waste. This review provides a collective summary of existing methodological impact frameworks and metrics, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research.

Supporting information

S1 appendix, s1 prisma checklist, acknowledgments.

We would also like to thank Mrs Susan Bayliss, Information Specialist, University of Birmingham, and Mrs Karen Biddle, Research Secretary, University of Birmingham.

Abbreviations

Funding statement.

Funding was received from Consejo Nacional de Ciencia y Tecnología (CONACYT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript ( http://www.conacyt.mx/ ).

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AgentClinic introduces four language agents: patient, doctor, measurement, and moderator. Each agent has specific roles and unique information for simulating clinical interactions. The patient agent provides symptom information without knowing the diagnosis, the measurement agent offers medical readings and test results, the doctor agent evaluates the patient and requests tests, and the moderator assesses the doctor’s diagnosis. AgentClinic also includes 24 biases relevant to clinical settings. The agents are built using curated medical questions from the USMLE and NEJM case challenges to create structured scenarios for evaluation using language models like GPT-4.

The accuracy of different language models (GPT-4, Mixtral-8x7B, GPT-3.5, and Llama 2 70B-chat) is evaluated on AgentClinic-MedQA, where each model acts as a doctor agent diagnosing patients through dialogue. GPT-4 achieved the highest accuracy at 52%, followed by GPT-3.5 at 38%, Mixtral-8x7B at 37%, and Llama 2 at 70B-chat at 9%. Comparison with MedQA accuracy showed weak predictability for AgentClinic-MedQA accuracy, similar to studies on medical residents’ performance relative to the USMLE.

To recapitulate,  this work researchers present AgentClinic, a benchmark for simulating clinical environments with 15 multimodal language agents and 107 unique language agents based on USMLE cases. These agents exhibit 23 biases, impacting diagnostic accuracy and patient-doctor interactions. GPT-4, the highest-performing model, shows reduced accuracy (1.7%-2%) with cognitive biases and larger reductions (1.5%) with implicit biases, affecting patient follow-up willingness and confidence. Cross-communication between patient and doctor models improves accuracy. Limited or excessive interaction time decreases accuracy, with a 27% reduction at N=10 interactions and a 4%-9% reduction at N>20 interactions. GPT-4V achieves around 27% accuracy in a multimodal clinical environment based on NEJM cases.

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Aswin AK is a consulting intern at MarkTechPost. He is pursuing his Dual Degree at the Indian Institute of Technology, Kharagpur. He is passionate about data science and machine learning, bringing a strong academic background and hands-on experience in solving real-life cross-domain challenges.

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COVID-19’s Impact on Eating Disorders Research Paper

Introduction, ed rate increase, lack of healthcare access, family support, lifestyle disruptions, lack of peer interactions, recommendations.

The prevalence of eating disorders worldwide, as well as the negative physical and emotional outcomes correlating with conditions such as anorexia and bulimia, exemplifies a major challenge that affects numerous individuals. Eating disorders (EDs) can also be exacerbated as a result of the external environment. Currently, such disruptions are often correlating with parental conflicts, peer pressure, and school problems. Nonetheless, the recent experience with COVID-19 and subsequent changes have highlighted how state and country-level regulations, as well as health risks, can affect people with eating disorders. It is essential to consider the restrictions that have impacted individuals, such as isolation measures, the closing of schools and businesses, and the lack of opportunities for interactions. It has negatively impacted a variety of individuals but had a particularly negative effect on people with the aforementioned conditions. In this paper, the correlation between COVID-19 and EDs will be assessed through the exemplification of increases in cases, lack of healthcare access, lifestyle disruptions, and a lack of peer interactions.

As mentioned previously, external factors do, indeed, correlate with risk maximization or reduction in the rate of eating disorders. A similar effect was observed during the COVID-19 pandemic. Namely, researchers mention an increase of more than 15% in ED cases (Zipfel et al., 2022). The challenge was primarily observed in adolescent girls with anorexia nervosa. Several potential triggers can be highlighted concerning the phenomenon. On the one hand, the government restrictions, which have led to a remote education process, may have maximized the focus on nutrition. Thus, without potential distractions, people who were already suffering from Eds and were in remission or have had mind symptoms without fully experiencing the conditions were more triggered by the change in their external environment. The fact that individuals with anorexia nervosa were most effective highlights the severity of the pattern in ED rate increase during COVID-19. Namely, anorexia is the condition most frequently referred to as a disorder linked to a high mortality rate, namely due to health problems, which are especially common among anorexia patients who purge (Mehler et al., 2022). As a result, it can be highlighted that COVID-19 negatively affected individuals with eating disorders and has facilitated an increase in negative health outcomes, especially when it comes to patients with anorexia.

The COVID-19 pandemic has caused major disruptions in many areas of life. Namely, schools have switched to a remote system, and multiple non-essential businesses have closed. Nonetheless, it is essential to acknowledge the burden the lockdown has had on the healthcare sector as a whole. The medical establishment was under major pressure to combat the negative consequences of the pandemic, which means that conditions other than COVID-19 have become less of a concern. However, the phenomenon has adversely impacted individuals with eating disorders who were unable to access the services and treatment they required to manage their conditions. Researchers highlight that while ED patients were more likely to access support from family members and partners, relationships with their nutritionists, therapists, and other medical personnel directly associated with their recovery were minimized (Haghshomar et al., 2022). This may correlate with the focus on telemedicine and the negative correlations that followed. As eating disorders are often perceived as shameful by those who have them, sharing experiences and symptoms and seeking advice via phone communication were perceived as less effective and ethical. As a result, people were less likely to address their disorders and more likely to suffer from the negative consequences of being isolated and not having the resources to combat their symptoms.

While correlating with negative aspects, stay-at-home orders also had a positive influence in certain regards. Namely, as mentioned previously, individuals with ED were more likely to receive support from their family members and partners, especially because they were enclosed in the same area for a long time (Haghshomar et al., 2022). For example, parents who would otherwise be at work had the opportunity to monitor their children’s food intake and ensure they were not showing signs of binging or restricting. A similar effect has happened concerning monitoring the progress of recovery. Individuals with ED were able to receive assistance from loved ones through moral support and encouragement to continue their healthy journey and avoid potential relapses.

Lifestyle disruptions occurred for most people during the COVID-19 stay-at-home order. Namely, as businesses and schools closed, people were operating remotely both in terms of their jobs and education. This majorly affected individuals with eating disorders as their lifestyles changed drastically without time to adapt to the alterations. For example, researchers mention that limited physical activities worsened self-perception (Miniati et al., 2021). Namely, the fact that gyms were closed resulted in disturbances in how people viewed themselves, which was especially critical for people with EDs. Since body dysmorphia is often a condition that aligns with eating disorders, the lack of opportunities to exercise adversely affects it. The same argument can be associated with the closing of restaurants and limited food access. Individuals with ED were not able to have the same lifestyle in regards to their preferred places where they eat, which is why worsening of the disorders may have occurred. Lifestyle disruptions, despite the direct effects such as limited food options and a lack of physical exercise opportunities, indirectly impacted people with eating disorders (Gao et al., 2022). Psychological well-being is minimized when an individual is put under stressful circumstances outside of one’s comfort zone

Based on the aforementioned phenomena, such as the closing of schools and businesses, social interactions were minimized. This affected individuals with eating disorders in two ways. On the one hand, adolescents with EDs specifically lost the factor of peer-to-peer comparison that may have triggered their eating disorder (Feinmann, 2021). These factors may also be associated with a positive implication as the lack of need for comparison could positively affect self-perception. However, on the other hand, people were more likely to emerge in social media due to the lack of in-person communication. As a result, unrealistic portrayals could generate more adverse outcomes for individuals with a distorted self-perception. Lack of peer interaction also had contrasting outcomes regarding its impact. On the one hand, individuals were more likely to avoid potential negative comments concerning their appearance, which often triggers EDs. However, another effect is the lack of additional support in case friends, classmates, and peers were supporting with one’s recovery.

The current literature, as highlighted previously, recognizes that the positive aspects of the COVID-19 lockdown impact on individuals with ED include maximization of family support and a lack of peer-to-peer comparison. On the other hand, lifestyle disruptions, lack of regular access to food, social media influence, and inability to access healthcare services have been highlighted as negative. Thus, the aspects are to be considered concerning potential recommendations. As mentioned previously, people with eating disorders were unable to access the healthcare resources they used to (Haghshomar et al., 2022). Thus, addressing the issue through the establishment of more accessible online platforms for patients to communicate with physicians concerning their eating disorders can address the barrier. Another aspect was the increased time people spent on social media, which can be addressed through monitoring. This intervention can be applied to parents and their approach to how minors are influenced by unrealistic body standards that trigger body dysmorphia and eating disorders. Such recommendations can be applied in case other health crises occur and lead to government restrictions.

COVID-19 and the stay-at-home order had a major impact on most people, but it is especially important to recognize its effects on individuals with eating disorders. Current literature highlights the major barriers correlating with disruptions in lifestyles, limited access to food, lack of peer support, limited health service provision, and increased use of social media. On the other hand, a lack of negative peer-to-peer comparisons as well as family support was mentioned as a positive aspect. For the care for ED patients to be maximized, it is vital to address the aforementioned challenges through monitoring of social media use as well as the establishment of more effective telemedicine measures. As a result, people who have eating disorders would be more likely to reach and maintain recovery from their conditions.

Feinmann, J. (2021). Eating disorders during the COVID-19 pandemic . BMJ . Web.

Gao, Y., Bagheri, N., & Furuya-Kanamori, L. (2022). Has the COVID-19 pandemic lockdown worsened eating disorder symptoms among patients with eating disorders? A systematic review . Journal of Public Health , 30 (11), 2743–2752. Web.

Haghshomar, M., Shobeiri, P., Brand, S., Rossell, S. L., Akhavan Malayeri, A., & Rezaei, N. (2022). Changes of symptoms of eating disorders (ed) and their related psychological health issues during the COVID-19 pandemic: A systematic review and meta-analysis . Journal of Eating Disorders , 10 (1). Web.

Mehler, P. S., Watters, A., Joiner, T., & Krantz, M. J. (2022). What accounts for the high mortality of anorexia nervosa? International Journal of Eating Disorders , 55 (5), 633–636. Web.

Miniati, M., Marzetti, F., Palagini, L., Marazziti, D., Orrù, G., Conversano, C., & Gemignani, A. (2021). Eating disorders spectrum during the COVID pandemic: A systematic review . Frontiers in Psychology , 12 . Web.

Zipfel, S., Schmidt, U., & Giel, K. E. (2022). The hidden burden of eating disorders during the COVID-19 pandemic . The Lancet Psychiatry , 9 (1), 9–11. Web.

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IvyPanda. (2024, May 18). COVID-19's Impact on Eating Disorders. https://ivypanda.com/essays/covid-19s-impact-on-eating-disorders/

"COVID-19's Impact on Eating Disorders." IvyPanda , 18 May 2024, ivypanda.com/essays/covid-19s-impact-on-eating-disorders/.

IvyPanda . (2024) 'COVID-19's Impact on Eating Disorders'. 18 May.

IvyPanda . 2024. "COVID-19's Impact on Eating Disorders." May 18, 2024. https://ivypanda.com/essays/covid-19s-impact-on-eating-disorders/.

1. IvyPanda . "COVID-19's Impact on Eating Disorders." May 18, 2024. https://ivypanda.com/essays/covid-19s-impact-on-eating-disorders/.

Bibliography

IvyPanda . "COVID-19's Impact on Eating Disorders." May 18, 2024. https://ivypanda.com/essays/covid-19s-impact-on-eating-disorders/.

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