Going digital: case study of an Italian insurance company

Journal of Business Strategy

ISSN : 0275-6668

Article publication date: 14 May 2020

Issue publication date: 18 March 2021

This paper aims to present the case of an Italian SME in the domain of insurance and how it approached its own digital transformation. Together with the founders of the SME, the author investigated the digital trends the company should adopt and identified where to intervene in the value chain of the company with new technologies available in the market. The research was focused on the following three sub-domains: a strategy for adoption of innovative digital solutions to improve the everyday operations of the company, platform connecting the company with the customers and analysis of cyber insurance policies to include in the portfolio of the company.

Design/methodology/approach

For the part on strategy for adoption of innovative digital solutions, the author performed literature review; for the part in which the study ideates new solution to better connect the company with the customers, the author relied on design thinking, creative facilitation and prototyping; and for the part on cyber insurance policies to include the portfolio, the author relied on data available from other insurance companies the SME collaborates with.

This paper presented the analysis on how an insurance SME can embrace digital innovation (via internal innovation, buying from startups, partnering with startups or investing in startups), how an SME can do internal innovation and come up with a simple tool to bring closer the insurers and their customers and types of new cyber risk policies to include in the portfolio to respond to the growing demand for cyber risk insurance. This paper provides useful insights and lessons learned from companies of similar size in the domain of insurance and discusses future extensions of inquiry.

Originality/value

Big insurance companies and incumbent for their digitization efforts rely on the freshly created InsurTechs wave of companies. In this paper, the author analyzes what small- and medium-sized insurance enterprises can do in this respect and showcases the approach an Italian SME took in this direction.

  • Digital transformation
  • Product innovation
  • Entrepreneurship

Pisoni, G. (2021), "Going digital: case study of an Italian insurance company", Journal of Business Strategy , Vol. 42 No. 2, pp. 106-115. https://doi.org/10.1108/JBS-11-2019-0225

Emerald Publishing Limited

Copyright © 2020, Galena Pisoni.

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

Since the first insurance contract, dating from over 600 years ago, the basic model of how insurance companies work has remained unchanged. Companies compensate clients financially against loss or damage from a specified event. Customers pay premiums to obtain such coverage. The long history of insurance has proven that the model works. People and businesses buy insurance products and studies show that consumers have a high level of confidence in the industry.

While insurance companies continue doing business the same way they have for years, they have failed to innovate. This failure to improve is most glaring with the customer experience. For example, many customers have difficulty understanding the differences between insurance policies and complicated insurance contracts. Many do not understand the conditions of the contracts and they are not confident the insurance product offered will satisfy their needs completely.

Customer expectations have changed. In the past, when buying goods or services, customers had few possibilities to compare prices between local agents or dealers. With the emergence of the internet, this has changed and readily accessible information allows for 24/7 active comparisons and searches for the best price via different channels.

Because of digital transformation, insurance consumers increasingly want to interact with companies anytime and anywhere. Therefore, companies constantly search for new ways to engage consumers digitally and where they want to be engaged. Companies are now expected to engage via channels such as text, chat, voice assistants, websites and mobile devices for customer acquisition and service. If a company does not offer a positive digital experience, many customers, particularly younger people, will move to industry competitors that offer better customer experiences digitally or they may turn to adjacent industries that offer the service as an “add on.”

Most insurance companies are left with systems, processes and practices that would be still recognizable by those in the industry in the 1980s. Though the insurance sector has stagnated, changes across other industries have been groundbreaking (invention of smartphones, social media, YouTube, Google and Amazon). Changes brought by digitalization of other industries exert even more pressure to the insurance industry.

A new era also brings new risks. For instance, organizations are increasingly concerned about their cyber risk exposure. As a result, companies have increasingly turned to the insurance industry with a need for cyber risk insurance products to better manage cyber threats and any resulting legal liability from data breaches. These new risks are opening the doors for insurance companies to provide new products and services, but the industry is immature and takes considerable resources to develop products and services in this new market. New technologies such as big data, cloud computing and social media increase the risks resulting from cybercrimes and cyber risks are now one of the top concerns of business leaders. Every organization is vulnerable to potential losses resulting from electronic data theft. Organizations want to protect themselves and their reputations from cyber risks as well as the data and records of their customers. For this, they turn to insurance companies, which in turn may not be prepared to help a company understand its cyber security risk profile.

leverage the most advanced technologies, as the most innovative solutions are at the core of the company;

focus on improving the experience to foster a user-centric approach, for instance, some focus on improving the purchasing stage, the underwriting process or streamlining claims management; and

develop an agile culture and approach, along with leveraging analytics to make faster business decisions.

In this paper, we analyze what small- and medium-sized insurance companies can do to leverage new technology and describe digital transformation in an Italian company that used this approach.

The case study company

Assinord Verona S.R.L offers insurance products in five different lines of business (all types of insurance products) and functioning relies on 15 people in the company and a number of collaborators from all over Italy that contribute to the growth of the company. Now, the company has more than 10,000 clients across Italy.

The study of the adoption strategy of innovative digital insurance solutions to improve everyday operations of the company.

Understanding the needs of the company and the customers with one-on-one sessions with team members and the development of first prototype for a platform to better connect the company with the customers.

The study of different cyber risk policies to enrich the product portfolio and company offering.

We explored the company offers and the established company processes to understand how to implement effective digital transformation. We studied the differences in complex insurance offers for existing clients and the author talked with the owners to understand how to make the whole process smoother.

The paper is organized as follows: in Section 2, we present the analysis of the different strategies to approach innovation and the one chosen by the company; in Section 3, we present the platform for connecting the company with the clients; and in Section 4, we present the analysis of cyber risks and the kind of insurance policies the company enriched the portfolio with to offer more up-to-date products. We conclude the paper with Section 5, in which we present the takeaways from the process and recommendations for potential next steps.

Strategy for adoption of new insurance technologies

Based on the literature, we researched how other companies of similar size have approached the digital transformation phenomenon and which approach best fits this company. We summarize the different strategies used by companies in the past: internal innovation, buy from startups, partner with startups and invest in startups. A brief summary of the different methods and the reaction to these by the company is discussed.

Internal innovation

One strategy that companies have been using to embrace digital trends is to foster innovation and the birth of new ideas internally. Different tools and methods for internal innovation have been described in the literature ( Wilson, 2017 ; O’Brien, 2004 ; Kohli and Melville, 2019 ). We present a summary of the possible tools to use in Figure 1 . With internal innovation, the idea is to encourage employees to generate ideas from their everyday experience and rely on simple methods such as researching current trends, applying customer intelligence, identifying customer needs, applying design thinking and user-driven innovation to implement process improvements and redesign business strategies. Key to this process is using brainstorming techniques to come up with innovative ideas.

The company’s ability to implement the ideas is limited and that is why ideas generated internally are usually implemented by dedicated teams.

Buy from startups

Other companies partner with tech startups by purchasing their services, licensing their solutions or co-developing solutions. Companies usually partner with highly specialized startups on new technologies that insurance companies can purchase to improve operations. One such example is Cognotekt, which provides software as a service for insurers to automate claims. Another example is Insurify, a personal insurance company that compares car insurance.

Partner with startups

Another strategy involves partnering with other companies to offer services to customers by bundling offers between two companies or by providing customers new “white brand” products. New technologies require special knowledge and expertise that insurance companies do not usually have. Existing insurers can partner with technology companies to innovate along different parts of the customer value chain.

Invest in startups

This strategy is most common for big incumbents but is occasionally used by smaller companies. This approach involves investing in a startup that may rely upon one incumbent for funding. The majority of the world’s largest insurers (AXA, Allianz, AIG, MetLife and Generali) have established their own in-house venture capital (VC) funds and committed investment in startups. These insurers select the areas where they want to innovate, and then look for startups in the field. In 2019, more than 140 traditional and corporate VC firms invested in an InsurTech startup.

What did the company decide?

The objectives when partnering with an InsurTech startup should be clearly aligned with the expected impact, which should be incremental, adjacent or transformative.

Business objectives should determine the model of cooperation and help to prioritize solutions and types of relationships to have with the InsurTech startups.

When choosing the companies to collaborate with, the expected impact on the company operations should be quite clear.

The different options were presented to the case study company and the decision was to do both: to try internal innovation (and in the following section, we show some examples of this approach) and to look for startups to buy products from or partner with in cases where there are mutual benefits. For the latter, we decided to rely on Plug and Play in Munich.

Partnership with Plug and Play in Munich: Plug and Play accelerates and invests in startups. It runs over 28 stage agnostic programs a year and invests in over 260 companies. Plug and Play matches startups and corporations worldwide. Our company chose to get in touch with the headquarters in Munich to start collaboration. Plug and Play supports three main lines of program: product innovation (startups working on digital transformation, improving user experience and risk assessments), new sources of risk (startups working on cyber security and other modern concerns) and disruptive ideas (startups working on industry changing business models such as the shared economy that are transforming current markets). The company decided to continue looking into startups coming from the production innovation track. In sequential meetings, different solutions will be presented to the business owners and will be made available for future analysis.

Platform for connecting the company with the clients

In this section, we show the results from pursuing internal innovation efforts to come up with a platform for connecting the company with its clients.

We started with underlining the actors involved in the system (customers and people inside the company) by using design thinking ( Brown and Wyatt, 2010 ; Kumar, 2009 ) and creative facilitation ( Van Boeijen et al. , 2014 ) as methods. The author performed one-to-one interviews with customers of the company to understand what kind of technology can better serve their needs in terms of insurance and can bring a better understanding of complicated insurance terms and contracts. The author performed 15 interviews (each lasting 1 h) with loyal clients the company selected. The questions were intended to help us understand how customers make buying insurance policy decisions. It also mapped the user experience from the time the individual started thinking about buying insurance all the way through the actual purchase of the insurance policy. In our search for the best ideas, we also involved employees. The author had three brainstorming sessions (each lasting 1 h) in which all the employees participated. In the brainstorming sessions, the employees were asked to reflect on the needs and situations in which the customers usually buy products (the customer interview data were given as input for each session) and they were asked to develop ideas for design concepts that were used later to develop a prototype.

From the interviews with the customers and employee brainstorming sessions, it became clear that life insurance policies presented a clear opportunity to simplify the user experience. We pursued this further by working on a prototype. After all the data was gathered and analyzed, we developed a prototype that would advise the customer about the ideal amount of life insurance coverage they should purchase. The prototype was based on demographic data entered by the consumer (age, income, partner’s income and children). This data made it possible to better understand the consumer’s life situation to present them with a recommended amount of life insurance. With this information, the yearly premium could be calculated and presented to the customer for the suggested amount of life insurance.

The prototype also helped with understanding the operations of the company. Usually only one employee in the company performs these tasks, recommending the amount of life insurance a person should have. This employee makes recommendations based on his own experience. With this prototype, the idea is to create a tool that optimizes the process to recommend and quote life insurance, by enabling all employees to do life insurance quotes, eliminating the need to rely on or wait for a single employee with such knowledge. It also allows better productivity by making it faster to do estimates.

Does the idea enhance the existing product offering?

Is the solution compliant with regulatory requirements or are they unknown or undefinedat the moment?

Is the idea something the customer wants and needs?

Is the idea commercially viable? Does it bring potential commercial benefits for the company and is it feasible to implement?

This process has been adapted from different approaches for service design presented in Kimbell (2014) . The idea seemed to satisfy all the above-mentioned aspects. After several iterations of the design, we came up with the prototype shown in Figures 2 – 4 .

We tested the prototype with several customers and several employees. The initial comments from both of sides were positive and the prototype yielded recognition and enjoyment by all involved parties. The users were curious to find out about the recommended amount that was proposed to them and were very interested in reading the detailed descriptions on why the suggested amount was suited for their particular case. The employees appreciated the ease of use the prototype provided for generating life insurance quotes for customers and the detailed descriptions of why the particular life insurance amount was recommended to them.

It was difficult to change the internal mind-set of the company. It took time and patience to get all the members of the organization on board with using the technology.

It was important to spread the knowledge and competency of the tool across the different key positions in the company and “energize” them about the solution. By having a shared language and communication daily, it allowed for a better understanding of the tool features.

Including multiple stakeholders in the design phase of the prototype allowed employers and customers to bring different perspectives to what different features pffered/

Cyber risk and digital insurance policies

The last area we investigated was the growing need for cyber security by businesses. As the pace of technological change continues unabated, organizations’ reliance on technology has become critical to their ability to offer products and services. Technology is critical to help companies interact with customers and employees. The significant economic impact of cyber risks on companies as well as the recent attention of the media has led companies to look for solutions from available insurance products. Because of this and the increased frequency of cyberattacks, cyber risks are now widely accepted as one of the top emerging risks. Cyber insurance policies provide twofold benefits for the companies buying them: they create awareness that cyberattacks might happen and the company looks for options to reduce these risks. The act of buying cyber insurance makes the company put a price on the potential damage in case the company is compromised by a cyberattack.

Liability risk – It provides compensation and legal support in the event of third-party claims, resulting from loss of personal and/or business data.

Crisis costs – It provides compensation to undertake forensic investigations, reputation public repair, customer notification costs, IT services and cyber incidents.

Fines – It provides compensation for research costs, legal assistance and administrative fines.

Digital media breaches – It covers compensation and defense costs related to third-party claims against the client company arising out of multimedia activities (e.g. defamation, allegation or plagiarism).

Cyber risk – It provides compensations for attacks from malicious software.

Insurance policy for network interruption – It provides protection for loss of revenues or net profits associated with network downtime.

With these products, the company enriched its portfolio of insurance products covering cyber products. With these new products in the portfolio, the sales team started actively promoting these products to businesses. The initial results are positive, at the moment of writing of this paper, the SME has issued five cyber risk policies.

Conclusions and future steps

In this paper, we presented the steps a small Italian insurance company took to respond to the wave of digital transformation. The technology-based organizational transformation required the organization to be flexible and responsive to changes. The article describes how an organization can embrace digital innovation via internal innovation, buying from startups, partnering with startups or investing in startups. It also discusses how the company decided to respond to the challenge by partnering with Plug and Play, an accelerator connecting corporations to startups. The company found the first contacts promising when presented with a number of product possibilities.

The article shows how a company can apply for internal innovation and come up with a simple tool to bring insurers closer to their customers. The approach presented in this paper shows how different design-based approaches and tools can find ways to improvise in a rapidly changing environment, especially at a stage where the needs of users are uncertain. At the moment, the developed platform supports the needs of the company and their everyday work in the office. Future steps foresee full development of the prototype into a working application including the expansion and support of other types of insurance policies, especially car and home insurance policies. The idea will be passed on and developed further by a small separate company that already works on other solutions for insurance incumbents.

The company enriched the portfolio with new insurance products, tackling diverse digital risks. The company can offer insurance products to other companies that go through digital transformation and need to respond to cyber risk. The company has already started selling these products with positive results.

The management of insurance claims continues to be painful for customers – both retail and corporate. Well-managed claims can create a number of efficiencies for an insurance company and help with client retention. Yet at this moment, this area is still under-served by new technology. We see a great opportunity for driving change here and will be exploring startups offering solutions in this space.

The company can potentially increase retention rates by offering benefits for loyal customers or customers who have multiple insurance policies with the company. The company will look for a solution through digital channels to build a behavior-linked loyalty program that tracks people’s activities and rewards them adequately. We plan to study reward mechanisms as a future extension of this work.

insurance case studies with solutions pdf

Different tools and methods a company can use for internal innovation based on the literature review used in different insurance companies of similar size

insurance case studies with solutions pdf

In the first screen ( Figure 2 ), the user is asked to enter key personal data points such as age, profession, presence of a partner, children, possession of a house and the income levels as well as level of coverage they would prefer to have

insurance case studies with solutions pdf

In this screen, the user is shown the ideal amount of insurance, as well as the yearly fee for the coverage amount

insurance case studies with solutions pdf

Third screen explains the proposed amount for the user’s specific case

Brown , T. and Wyatt , J. ( 2010 ), “ Design thinking for social innovation ”, Development Outreach , Vol. 12 No. 1 , pp. 29 - 43 .

Greineder , M. , Riasanow , T. , Böhm , M. and Krcmar , H. ( 2019 ), “ The generic InsurTech ecosystem and its strategic implications for the digital transformation of insurance industry ”, 40th GI EMISA .

Kimbell , L. ( 2014 ), The Service Innovation Handbook: Action-Oriented Creative Thinking Toolkit for Service Organizations , BIS publishers .

Kohli , R. and Melville , N.P. ( 2019 ), “ Digital innovation: a review and synthesis ”, Information Systems Journal , Vol. 29 No. 1 , pp. 200 - 223 .

Kumar , V. ( 2009 ), “ A process for practicing design innovation ”, Journal of Business Strategy , Vol. 30 No. 2/3 , pp. 91 - 100 .

Nicoletti , B. ( 2017 ), “ A business model for insurtech initiatives ”, The Future of FinTech , Palgrave Macmillan , Cham , pp. 211 - 249 .

O’Brien , C. ( 2004 ), “ Product innovation in financial services: a survey ”.

Van Boeijen , A. Daalhuizen , J. van der Schoor , R. and Zijlstra , J. ( 2014 ), “ Delft design guide: design strategies and methods ”.

Wilson , J.D. Jr. ( 2017 ), Creating Strategic Value through Financial Technology , John Wiley & Sons .

Further reading

Menhart , M. , Pyka , A. , Ebersberger , B. and Hanusch , H. ( 2004 ), “ Product innovation and population dynamics in the German insurance market ”, Zeitschrift Für Die Gesamte Versicherungswissenschaft , Vol. 93 No. 3 , pp. 477 - 519 .

Acknowledgements

The author would like to thank the company host Assinord Verona S.R.L. and all the employees for their time and effort dedicated to this project.

Corresponding author

About the author.

Galena Pisoni is based at the Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.

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Deliberately Digital pp 255–264 Cite as

Case Study 4: The Digital Transformation of Insurance

  • Hubert Tardieu 6 ,
  • David Daly 7 ,
  • José Esteban-Lauzán 8 ,
  • John Hall 9 &
  • George Miller 10  
  • First Online: 06 February 2020

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Part of the Future of Business and Finance book series (FBF)

Digital is providing great opportunities for insurance firms. The ability to collect and analyze ever-increasing quantities of data makes it possible to provide insurance that is highly personalised, and AI is enabling claims to be processed faster and more accurately than ever before. However, there are also threats for market incumbents, including the rise of aggregation platforms which can seize control of the customer relationship and relegate insurance providers to mere commodity suppliers. In this case study, we explore these trends, challenges, and opportunities, before finally covering how existing firms can leverage platform-based industry ecosystems.

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Digital start-ups specialized in the Insurance market (FinTech is used for those specialized in banking).

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A discipline that merges telecommunication engineering and computer science to gather and manage information that enables the tracking and controlling of remote objects.

Electronic devices—usually capable of some data gathering, storage and/or processing capability—which can be worn in clothes or as accessories.

Information Commissioner’s Office. (2019). Intention to fine British Airways £183.39m under GDPR for data breach. https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2019/07/ico-announces-intention-to-fine-british-airways/ . Accessed October 26, 2019.

Insurance Journal. (2019, October). Blockchain Initiative, B3i, Deploys Cat Excess of Loss Product for January Renewals. https://www.insurancejournal.com/news/international/2019/10/15/545507.htm . Accessed October 26, 2019.

International Fraud Bureau. (2018, February). IFB supports IFED’s warning about ghost broking scams. https://insurancefraudbureau.org/media-centre/news/2018/ifb-supports-ifed-s-warning-about-ghost-broking-scams/ . Accessed October 26, 2019.

International Fraud Bureau: Crash for cash. https://insurancefraudbureau.org/insurance-fraud/crash-for-cash/ . Accessed October 26, 2019.

Krebson Security. (2019, May). First American Financial Corp. Leaked hundreds of millions of title insurance records. https://krebsonsecurity.com/2019/05/first-american-financial-corp-leaked-hundreds-of-millions-of-title-insurance-records/comment-page-1/ . Accessed October 26, 2019.

Lemonade blog. (2017, January). Lemonade sets a new world record. https://www.lemonade.com/blog/lemonade-sets-new-world-record/ . Accessed October 26, 2019.

McKinsey. (2018, December). Friends or foes: The rise of European aggregators and their impact on traditional insurers. https://www.mckinsey.com/~/media/McKinsey/Industries/Financial%20Services/Our%20Insights/Friends%20or%20foes%20The%20rise%20of%20European%20aggregators%20and%20their%20impact%20on%20traditional%20insurers/Friends-or-foes-The-rise-of-European-aggregators.ashx . Accessed October 26, 2019.

McKinsey. (2019, May). A new industry model for insurtech. https://www.mckinsey.com/industries/financial-services/our-insights/insurance-blog/a-new-industry-model-for-insurtech . Accessed October 26, 2019.

The Actuary. (2017, December). Strength in numbers. https://www.theactuary.com/opinion/2017/12/strength-in-numbers/ . Accessed October 26, 2019.

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A case study of adapting a health insurance decision intervention from trial into routine cancer care

  • Miles E. Charles   ORCID: orcid.org/0000-0001-8381-6803 1 ,
  • Lindsay M. Kuroki 2 ,
  • Ana A. Baumann 1 ,
  • Rachel G. Tabak 3 , 4 ,
  • Aimee James 1 ,
  • Krista Cooksey 1 &
  • Mary C. Politi 1  

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This study adapted Improving Cancer Patients’ Insurance Choices ( I Can PIC), an intervention to help cancer patients navigate health insurance decisions and care costs. The original intervention improved knowledge and confidence making insurance decisions , however, users felt limited by choices provided in insurance markets. Using decision trees and frameworks to guide adaptations, we modified I Can PIC to focus on using rather than choosing health insurance. The COVID-19 pandemic introduced unforeseen obstacles, prompting changes to study protocols. As a result, we allowed users outside of the study to use I Can PIC (> 1050 guest users) to optimize public benefit. This paper describes the steps took to conduct the study, evaluating both the effectiveness of I Can PIC and the implementation process to improve its impact.

Although I Can PIC users had higher knowledge and health insurance literacy compared to the control group, results were not statistically significant. This outcome may be associated with systems-level challenges as well as the number and demographic characteristics of participants. The publicly available tool can be a resource for those navigating insurance and care costs, and researchers can use this flexible approach to intervention delivery and testing as future health emergencies arise.

Introduction

JL is a 66-year-old patient with progressive, recurrent ovarian cancer whose clinician recommended that she start on a targeted, oral cancer therapy based on genomic testing of her cancer. A month after receiving this recommendation, JL received a “summary of benefits” from her insurance company reflecting she owed a $3000 USD co-pay for a 30-day supply of this targeted therapy (the goal was to continue this therapy until her disease no longer responded to it, or she had intolerable side effects; her clinicians estimated this might take 1–2 years). As a full-time employed nurse, JL had health insurance. However, she did not qualify for the industry-sponsored financial assistance drug program because her annual income was slightly ($3500) over the allowed threshold. She would have to spend down 3% of her income on prescriptions that year in order to receive 100% coverage for the medication. Furthermore, because she had both government-sponsored and private insurance, her government-sponsored insurance made her ineligible for a “co-pay card” sponsored by the pharmaceutical company. JL was extremely distressed about this financial strain and considered whether and how she could take this therapy recommended by her doctor.

JL, like many under-insured patients, was inadvertently overlooked by her oncology team to be at risk for what scholars refer to as “financial toxicity,” or the material and psychosocial hardship from high costs of care. Yet, as many as 64% of patients report financial hardship following a cancer diagnosis [ 1 ], and many face barriers, like those described above, that prohibit affordable access to needed cancer therapies [ 2 , 3 ]. We use this case study to describe the critical steps we took to adapt and implement a health insurance decision intervention for cancer patients and survivors like JL, while balancing intervention testing and adaptation with real-world needs during a global pandemic.

Evidence supporting the intervention and the need for adaptation

Improving Cancer Patients’ Insurance Choices ( I Can PIC) is an interactive online decision tool originally designed to help cancer patients, like JL, think through their health insurance choices and identify ways to offset high costs of cancer and survivorship [ 4 ]. It provides tailored cost estimates across insurance plan types based on demographic and health characteristics and provides financial support resources.

In a randomized controlled trial of I Can PIC compared to an attention control group where participants were given an alternative intervention: a handout that lists financial resources along with brief definitions of health insurance terms, I Can PIC users knew more about health insurance and were more confident understanding insurance terms [ 4 ]. However, many I Can PIC users reported that their employer-based and marketplace insurance gave them limited choices [ 4 ]. This implied the potential to better align the tool within the current insurance landscape, even if it required adaptation before meeting all of its goals [ 5 ]. Therefore, the team elicited feedback from clinicians, patients, and policy experts on ways to emphasize using health insurance rather than focusing mostly on choosing health insurance (Additional file 2 : Table S1). This paper describes the adaptation process of I Can PIC to achieve these goals.

Intervention adaptation process

We used two guides to structure the adaptation process. The Iterative Decision-making for Evaluation of Adaptations (IDEA) decision tree informed the process of adaptation [ 6 ], and the Framework for Reporting Adaptation and Modifications-Expanded (FRAME) guided the tracking of adaptations (Fig.  1 ) [ 7 ]. To start the adaptation process, we first identified the core elements of the intervention that improved outcomes: health insurance educational resources, cost-of-care conversation guidance, and resources to offset costs which are critical to patients like JL (Additional file 1 : Fig S1). During this iterative process, we then added new elements to I Can PIC and made content, format, and functional improvements based on stakeholder feedback and the original trial results (Additional file 2 : Table S1).

figure 1

I Can PIC as Tracked and Adapted Using the FRAME Approach

Assessment of the adapted I Can PIC tool

After we adapted I Can PIC , we assessed its effectiveness among newly-diagnosed patients with gynecological, colorectal, or lung cancer, examining their health insurance knowledge, financial toxicity, health insurance literacy, and delayed or forgone care due to cost. We conducted a historic control survey assessing these constructs as well as whether and how treatment costs were discussed with patients at their most recent visit with their physicians. Next, we conducted a brief virtual/video conference training with fifteen medical or surgical oncologists to talk about screening for financial distress and discussing costs with patients. After the training, we conducted a pilot intervention study where patients were sent I Can PIC , completed a survey after their upcoming oncology appointment, and a follow-up survey 3–6 months after recruitment. Once during the study, we gave the oncologists feedback and reminded them to screen for financial distress and refer patients to I Can PIC. This study was approved by the Human Research Protection Office at Washington University in St. Louis (protocol number 202003033).

The COVID-19 pandemic introduced unforeseen obstacles to patient enrollment that shifted in-person recruitment to virtual methods (e.g., phone calls and emails) from secured, Health Insurance Portability and Accountability Act (HIPAA) compliant university-affiliated phone numbers and emails. We also partnered with the institutional review board and streamlined the consent script to be more succinct and engaging [ 8 ]. Prior to these changes and even after, many newly diagnosed cancer patients did not want to add a research commitment to their already busy or overextended lives. With recognition of these challenges and others, the revised version of I Can PIC was made available to the public while undergoing testing so that patients outside of eligibility criteria, like JL with recurrent cancer, or patients not interested in research, could still benefit from its health insurance and care costs information and support.

Eligible patients for both the historic control and intervention groups were English speaking, at least 18 years old, eligible for insurance through their employer or the federal marketplace, and diagnosed with a new lung, gynecological, or colorectal cancer within five months. Participants were recruited from a single site, NCI-designated cancer center where fifteen oncologists (five gynecologic oncologists, three colorectal surgeons, two medical oncologists treating colorectal cancer patients, and five thoracic surgeons treating lung cancer patients) gave the study team permission to review medical records and approach eligible patients for study participation. Recruitment into the historic control group began in May 2020. Starting in October 2020, we conducted our first provider training, since health insurance open enrollment was beginning, and we wanted at least some patients to use I Can PIC while they had options of changing insurance. Between October 2020 and February 2021, we trained the fifteen oncologists to screen for financial toxicity and discuss care costs with patients.

After clinicians were trained, we recruited patients into the intervention arm. Patients were asked to review I Can PIC before their upcoming appointment. After they met with their oncologist, the research team sent them a survey that could either be completed online or by phone. A three-month follow-up survey was also sent to patients in the intervention arm.

Patient socio-demographics were self-reported. As in the original trial, participant numeracy and health literacy were assessed using validated scales [ 9 , 10 , 11 , 12 ]. Primary outcomes included health insurance knowledge, health insurance literacy, frequency, and type of care cost conversations (including topics and strategies discussed), financial toxicity, and patient referrals to resources to further discuss costs [ 13 ].

Statistical analysis

Descriptive statistics were calculated for all sociodemographic variables and compared between groups using chi‐square analyses or Fisher's exact test for categorical variables, as appropriate, and the Kruskal–Wallis test for continuous variables. Baseline surveys for both the control and intervention groups were compared for one-way analysis of variance, Fisher's exact test to determine if there were nonrandom associations between two categorial variables, and Chi-Square tests to determine if two categorical variables were independent. To compare the intervention at the baseline survey and 3–6 month follow-up survey, paired t-tests for continuous variables and kappa statistics for categorical variables (discussed costs or not, discussed cost strategies or not, referral made or not, etc.)

During our study period, there were 1512 total logins on the I Can PIC website, of which 1058 (70%) were guest users. Guest users were treated in other facilities, ineligible due to cancer type, or not interested in participating in the research study, but wanted to access the information. Among the 136 consented and surveyed participants (68 historic controls; 68 intervention group), socio-demographics were similar except that the intervention group was slightly higher educated (Table 1 ). The intervention group had slightly higher health insurance knowledge (mean score 77.02 vs 72.45) and slightly higher health insurance literacy (mean score 34.71 vs 33.03) compared to controls; these differences were not statistically significant. Knowledge and health insurance literacy was sustained at the 3–6 month follow-up.

The frequency of cost discussions related to cancer care was similar between the intervention and control groups (57.4 vs 67.7%, p = 0.22), with the most common topics involving insurance, time off work, and costs of medications. Specific cost strategies that were discussed are detailed in Table 1 . Overall, a small proportion of patients received referrals (eg., I Can PIC website or any outside agency/office such as government assistance, community agency or charity, or hospital billing) from their oncologist to learn more about cancer costs and did not vary by group (controls, 16.2% vs intervention group, 20.6%).

Financial toxicity was reportedly low in both groups (17.7% in the control vs 16.1% in the intervention group), though decreased slightly within the intervention group during the study period (first survey average score was 16.06 vs. 14.17 at the 3–6 month follow-up). Unfortunately, 18% of individuals in the control group and 13% in the intervention group reported delaying care due to cost (p = 0.41).

Throughout the adaptation process, it is important to ensure that end users like JL can benefit from effective interventions, even if interventions require refinement and continued testing. Using systematic decision trees and guides such as IDEA and FRAME, we described one way to systematically track intervention adaptations while ensuring real-world access throughout the process to benefit patients. Strengths of our study include our diverse stakeholders which included patients, clinicians and policy experts who provided advice on I Can PIC , including patients across several cancer types, and modification of consent processes and tool access to optimize patient engagement and minimize burden.

This case example provides a guide for deploying low-risk interventions in routine care while continuing to generate evidence and improve on their public health impact. Of 1512 total logins on the I Can PIC website, 70% were guest users outside of the research study, and we hope many of them, like JL, benefited from I Can PIC access even if they were reluctant to join a study. JL ultimately made an informed decision with her oncology team and the support of her family to only work part-time to optimize her benefits in order to receive the targeted, oral cancer therapy through the pharmaceutical company’s financial assistance program.

Despite growing awareness of financial toxicity on underinsured patients, more interventions are needed to better integrate cost conversations into routine cancer care. Systems-level changes are needed to address this burden of care on patients. Future work will continue to build on the frameworks discussed to adapt content and delivery of I Can PIC so that patient-centered outcomes, such as financial toxicity, distress, quality-of-life, and adherence to treatment, are improved. This case study can provide guidance for other implementation studies, including those that might be conducted during future health emergencies.

Limitations

Due to rapid changes as a result of the COVID-19 pandemic, it is important to note limitations of our study design and execution of our protocol. Given this unprecedented time when unmet social and health needs were and still remain under constant threat and turmoil, we acknowledge our non-randomized study design and recruitment of historic controls are critical limitations to interpretation and generalizability of results. The timing of their recruitment could have exacerbated health or financial strain, although the pandemic was still ongoing even at the end of the study with new waves of health risks emerging. Future health emergencies could introduce similar issues without addressing the larger social and societal needs. Furthermore, COVID-19 and rapid transitions to virtual recruitment presented other challenges to this project, which was initially planned to be in person. Despite modifications to the protocol, consent documents, and workflow to reduce burden on participants, systemic issues remained that reduced the diversity of our sample in the research component of intervention implementation. These challenges are likely to remain without addressing systemic barriers to research and care more broadly. Consequently, these results may not be representative of the experiences of lower income and/or racially diverse patients experiencing financial toxicity due to their cancer diagnosis. Ongoing feedback from stakeholders will continue to ensure that the needs of various populations, including oncology providers are considered.

Availability of data and materials

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

Abbreviations

Improving Cancer Patients’ Insurance Choices

Iterative Decision-making for Evaluation of Adaptations

Framework for Reporting Adaptation and Modifications-Expanded

Health Insurance Portability and Accountability Act

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Acknowledgements

Not Applicable.

Financial support for this study was provided in part by a grant from the American Cancer Society (RSGI-17–018-01-CPHPS), the National Cancer Institute (P50CA244431), Washington University’s Institute for Clinical and Translational Sciences Dissemination and Implementation Research Core (UL1 TR002345) and The Foundation for Barnes Jewish Hospital. The content is solely the responsibility of the authors and does not necessarily represent the official views of the American Cancer Society, the National Institutes of Health, or Washington University. Ana Baumann and Rachel Tabak are funded by CTSA Grant UL1 TR002345 and P50 CA-19–006. Ana Baumann is also funded by 3U01HL13399403S1 and 5U24HL136790-02. LK was supported by the KL2 TR000450 and Doris Duke to Retain Clinical Scientists (2015215).

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Miles E. Charles, Ana A. Baumann, Aimee James, Krista Cooksey & Mary C. Politi

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All authors contributed to writing, revising, and approving the manuscript. MP led the decision intervention development and adaptation process and critically evaluated and revised the manuscript. MC and LK led the writing of the manuscript, its revisions, and final submission. AB and RT led the evaluation of the adaptation process and critically revised the manuscript for important implementation content. AJ contributed to the design and adaptation of the intervention and critically revised the manuscript. KC led the discussion of the changes to the study as a result of the COVID-19 pandemic, assisted with data analysis, and contributed to revisions of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Mary C. Politi .

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Ethics approval and consent to participate.

This study was approved by the Human Research Protection Office at Washington University in St. Louis (protocol number 202003033). All individuals that participated in this study provided informed consent.

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Supplementary Information

Additional file 1: figure s1..

I Can PIC as Adapted and Tracked Using the Iterative Decision-Making for Evaluation of Adaptations (IDEA) Framework Steps.

Additional file 2: Table S1.

Stakeholder- and Participant-Suggested Adaptations to I Can PIC.

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Charles, M.E., Kuroki, L.M., Baumann, A.A. et al. A case study of adapting a health insurance decision intervention from trial into routine cancer care. BMC Res Notes 15 , 298 (2022). https://doi.org/10.1186/s13104-022-06189-8

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