qualitative research in healthcare research

Qualitative research in healthcare: an introduction to grounded theory using thematic analysis

Affiliation.

• 1 AL Chapman, Department of Infectious Diseases, Monklands Hospital, Airdrie ML6 0JS, UK. Email [email protected].
• PMID: 26517098
• DOI: 10.4997/JRCPE.2015.305

In today's NHS, qualitative research is increasingly important as a method of assessing and improving quality of care. Grounded theory has developed as an analytical approach to qualitative data over the last 40 years. It is primarily an inductive process whereby theoretical insights are generated from data, in contrast to deductive research where theoretical hypotheses are tested via data collection. Grounded theory has been one of the main contributors to the acceptance of qualitative methods in a wide range of applied social sciences. The influence of grounded theory as an approach is, in part, based on its provision of an explicit framework for analysis and theory generation. Furthermore the stress upon grounding research in the reality of participants has also given it credence in healthcare research. As with all analytical approaches, grounded theory has drawbacks and limitations. It is important to have an understanding of these in order to assess the applicability of this approach to healthcare research. In this review we outline the principles of grounded theory, and focus on thematic analysis as the analytical approach used most frequently in grounded theory studies, with the aim of providing clinicians with the skills to critically review studies using this methodology.

Keywords: grounded theory; healthcare; inductive analysis; qualitative research; quality improvement; thematic analysis.

Publication types

• Data Collection*
• Grounded Theory*
• Health Services Research*
• Qualitative Research*
• Research Design*

• Open access
• Published: 13 December 2018

Using qualitative Health Research methods to improve patient and public involvement and engagement in research

• Danielle E. Rolfe 1 ,
• Vivian R. Ramsden 2 ,
• Davina Banner 3 &
• Ian D. Graham 1  

Research Involvement and Engagement volume  4 , Article number:  49 ( 2018 ) Cite this article

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Plain English summary

Patient engagement (or patient and public involvement) in health research is becoming a requirement for many health research funders, yet many researchers have little or no experience in engaging patients as partners as opposed to research subjects. Additionally, many patients have no experience providing input on the research design or acting as a decision-making partner on a research team. Several potential risks exist when patient engagement is done poorly, despite best intentions. Some of these risks are that: (1) patients’ involvement is merely tokenism (patients are involved but their suggestions have little influence on how research is conducted); (2) engaged patients do not represent the diversity of people affected by the research; and, (3) research outcomes lack relevance to patients’ lives and experiences.

Qualitative health research (the collection and systematic analysis of non-quantitative data about peoples’ experiences of health or illness and the healthcare system) offers several approaches that can help to mitigate these risks. Several qualitative health research methods, when done well, can help research teams to: (1) accurately incorporate patients’ perspectives and experiences into the design and conduct of research; (2) engage diverse patient perspectives; and, (3) treat patients as equal and ongoing partners on the research team.

This commentary presents several established qualitative health research methods that are relevant to patient engagement in research. The hope is that this paper will inspire readers to seek more information about qualitative health research, and consider how its established methods may help improve the quality and ethical conduct of patient engagement for health research.

Research funders in several countries have posited a new vision for research that involves patients and the public as co-applicants for the funding, and as collaborative partners in decision-making at various stages and/or throughout the research process. Patient engagement (or patient and public involvement) in health research is presented as a more democratic approach that leads to research that is relevant to the lives of the people affected by its outcomes. What is missing from the recent proliferation of resources and publications detailing the practical aspects of patient engagement is a recognition of how existing research methods can inform patient engagement initiatives. Qualitative health research, for example, has established methods of collecting and analyzing non-quantitative data about individuals’ and communities’ lived experiences with health, illness and/or the healthcare system. Included in the paradigm of qualitative health research is participatory health research, which offers approaches to partnering with individuals and communities to design and conduct research that addresses their needs and priorities.

The purpose of this commentary is to explore how qualitative health research methods can inform and support meaningful engagement with patients as partners. Specifically, this paper addresses issues of: rigour (how can patient engagement in research be done well?); representation (are the right patients being engaged?); and, reflexivity (is engagement being done in ways that are meaningful, ethical and equitable?). Various qualitative research methods are presented to increase the rigour found within patient engagement. Approaches to engage more diverse patient perspectives are presented to improve representation beyond the common practice of engaging only one or two patients. Reflexivity, or the practice of identifying and articulating how research processes and outcomes are constructed by the respective personal and professional experiences of researchers and patients, is presented to support the development of authentic, sustainable, equitable and meaningful engagement of patients as partners in health research.

Conclusions

Researchers will need to engage patients as stakeholders in order to satisfy the overlapping mandate in health policy, care and research for engaging patients as partners in decision-making. This paper presents several suggestions to ground patient engagement approaches in established research designs and methods.

Peer Review reports

Patient engagement (or patient and public involvement) in research involves partnering with ‘patients’ (a term more often used in Canada and the US, that is inclusive of individuals, caregivers, and/or members of the public) to facilitate research related to health or healthcare services. Rather than research subjects or participants, patients are engaged as partners in the research process. This partnership is intended to be meaningful and ongoing, from the outset of planning a research project, and/or at various stages throughout the research process. Engagement can include the involvement of patients in defining a research question, identifying appropriate outcomes and methods, collecting and interpreting data, and developing and delivering a knowledge translation strategy [ 1 ].

The concept of engaging non-researchers throughout the research process is not new to participatory health researchers, or integrated knowledge translation researchers, as the latter involves ongoing collaboration with clinicians, health planners and policy makers throughout the research process in order to generate new knowledge [ 2 , 3 ]. Patients, however, are less frequently included as partners on health research teams, or as knowledge users in integrated knowledge translation research teams compared to clinicians, healthcare managers and policy-makers, as these individuals are perceived as having “the authority to invoke change in the practice or policy setting.” (p.2) [ 2 ] Recent requirements for patient engagement by health research funders [ 4 , 5 , 6 ], ,and mandates by most healthcare planners and organizations to engage patients in healthcare improvement initiatives, suggest that it would be prudent for integrated knowledge translation (and indeed all) health researchers to begin engaging patients as knowledge users in many, if not all, of their research projects.

Training and tools for patient engagement are being developed and implemented in Canada via the Canadian Institutes for Health Research (CIHR) Strategy for Patient Oriented Research (SPOR) initiative, in the US via Patient Centered Outcomes Research Institute (PCORI), and very practical resources are already available from the UK’s more established INVOLVE Advisory Group [ 5 , 6 , 7 ]. What is seldom provided by these ‘get started’ guides, however, are rigorous methods and evidence-based approaches to engaging diverse patient perspectives, and ensuring that their experiences, values and advice are appropriately incorporated into the research process.

The purpose of this commentary is to stimulate readers’ further discussion and inquiry into qualitative health research methods as a means of fostering the more meaningfully engagement of patients as partners for research. Specifically, this paper will address issues of: rigour (how do we know that the interpretation of patients’ perspectives has been done well and is applicable to other patients?); representation (are multiple and diverse patient perspectives being sought?); and, reflexivity (is engagement being done ethically and equitably?). This commentary alone is insufficient to guide researchers and patient partners to use the methods presented as part of their patient engagement efforts. However, with increased understanding of these approaches and perhaps guidance from experienced qualitative health researchers, integrated knowledge translation and health researchers alike may be better prepared to engage patients in a meaningful way in research that has the potential to improve health and healthcare experiences and outcomes.

What can be learned from methods utilized in qualitative health research?

There is wide variation in researchers’ and healthcare providers’ openness to engaging patients [ 8 ]. Often, the patients that are engaged are a select group of individuals known to the research team, sometimes do not reflect the target population of the research, are involved at a consultative rather than a partnership level, and are more likely to be involved in the planning rather than the dissemination of research [ 9 , 10 , 11 ]. As a result, patient engagement can be seen as tokenistic and the antithesis of the intention of most patient engagement initiatives, which is to have patients’ diverse experiences and perspectives help to shape what and how research is done. The principles, values, and practices of qualitative health research (e.g., relativism, social equity, inductive reasoning) have rich epistemological traditions that align with the conceptual and practical spirit of patient engagement. It is beyond the scope of this commentary, however, to describe in detail the qualitative research paradigm, and readers are encouraged to gain greater knowledge of this topic via relevant courses and texts. Nevertheless, several qualitative research considerations and methods can be applied to the practice of patient engagement, and the following sections describe three of these: rigour, representation and reflexivity.

Rigour: Interpreting and incorporating patients’ experiences into the design and conduct of research

When patient engagement strategies go beyond the inclusion of a few patient partners on the research team, for example, by using focus groups, interviews, community forums, or other methods of seeking input from a broad range of patient perspectives, the diversity of patients’ experiences or perspectives may be a challenge to quickly draw conclusions from in order to make decisions about the study design. To make these decisions, members of the research team (which should include patient partners) may discuss what they heard about patients’ perspectives and suggestions, and then unsystematically incorporate these suggestions, or they may take a vote, try to achieve consensus, implement a Delphi technique [ 12 ], or use another approach designed specifically for patient engagement like the James Lind Alliance technique for priority setting [ 13 ]. Although the information gathered from patients is not data (and indeed would require ethical review to be used as such), a number of qualitative research practices designed to increase rigour can be employed to help ensure that the interpretation and incorporation of patients’ experiences and perspectives has been done systematically and could be reproduced [ 14 ]. These practices include member checking , dense description , and constant comparative analysis . To borrow key descriptors of rigour from qualitative research, these techniques improve “credibility” (i.e., accurate representations of patients’ experiences and preferences that are likely to be understood or recognized by other patients in similar situations – known in quantitative research as internal validity), and “transferability” (or the ability to apply what was found among a group of engaged patients to other patients in similar contexts – known in quantitative research as external validity) [ 15 ].

Member checking

Member checking in qualitative research involves “taking ideas back to the research participants for their confirmation” (p. 111) [ 16 ]. The objective of member checking is to ensure that a researcher’s interpretation of the data (whether a single interview with a participant, or after analyzing several interviews with participants) accurately reflects the participants’ intended meaning (in the case of a member check with a single participant about their interview), or their lived experience (in the case of sharing an overall finding about several individuals with one or more participants) [ 16 ]. For research involving patient engagement, member checking can be utilized to follow-up with patients who may have been engaged at one or only a few time points, or on an on-going basis with patient partners. A summary of what was understood and what decisions were made based on patients’ recommendations could be used to initiate this discussion and followed up with questions such as, “have I understood correctly what you intended to communicate to me?” or “do you see yourself or your experience(s) reflected in these findings or suggestions for the design of the study?”

Dense description

As with quantitative research, detailed information about qualitative research methods and study participants is needed to enable other researchers to understand the context and focus of the research and to establish how these findings relate more broadly. This helps researchers to not only potentially repeat the study, but to extend its findings to similar participants in similar contexts. Dense description provides details of the social, demographic and health profile of participants (e.g., gender, education, health conditions, etc.), as well as the setting and context of their experiences (i.e., where they live, what access to healthcare they have). In this way, dense description improves the transferability of study findings to similar individuals in similar situations [ 15 ]. To date, most studies involving patient engagement provide limited details about their engagement processes and who was engaged [ 17 ]. This omission may be done intentionally (e.g., to protect the privacy of engaged patients, particularly those with stigmatizing health conditions), or as a practical constraint such as publication word limits. Nonetheless, reporting of patient engagement using some aspects of dense description of participants (as appropriate), the ways that they were engaged, and recommendations that emanated from engaged patients can also contribute to greater transferability and understanding of how patient engagement influenced the design of a research study.

Constant comparative analysis

Constant comparative analysis is a method commonly used in grounded theory qualitative research [ 18 ]. Put simply, the understanding of a phenomenon or experience that a researcher acquires through engaging with participants is constantly redeveloped and refined based on subsequent participant interactions. This process of adapting to new information in order to make it more relevant is similar to processes used in rapid cycle evaluation during implementation research [ 19 ]. This method can be usefully adapted and applied to research involving ongoing collaboration and partnership with several engaged patient partners, and/or engagement strategies that seek the perspectives of many patients at various points in the research process. For example, if, in addition to having ongoing patient partners, a larger group of patients provides input and advice (e.g., a steering or advisory committee) at different stages in the research process, their input may result in multiple course corrections during the design and conduct of the research processes to incorporate their suggestions. These suggestions may result in refinement of earlier decisions made about study design or conduct, and as such, the research process becomes more iterative rather than linear. In this way, engaged patients and patient partners are able to provide their input and experience to improve each step of the research process from formulating an appropriate research question or objective, determining best approaches to conducting the research and sharing it with those most affected by the outcomes.

Representation: Gathering diverse perspectives to design relevant and appropriate research studies

The intention of engaging patients is to have their lived experience of health care or a health condition contribute to the optimization of a research project design [ 20 ]. Development of a meaningful and sustainable relationship with patient partners requires considerable time, a demonstrated commitment to partnership by both the patient partners and the researcher(s), resources to facilitate patient partners’ engagement, and often, an individual designated to support the development of this relationship [ 17 , 21 ]. This may lead some research teams to sustain this relationship with only one or two patients who are often previously known to the research team [ 17 ]. The limitation of this approach is that the experiences of these one or two individuals may not adequately reflect the diverse perspectives of patients that may be affected by the research or its outcomes. The notion of gaining ‘ the patient perspective’ from a single or only a few individuals has already been problematized [ 22 , 23 ]. To be sure, the engagement of a single patient is better than none at all, but the engagement of a broader and diverse population of patients should be considered to better inform the research design, and to help prevent further perpetuation of health disparities. Key issues to be considered include (1) how engagement can be made accessible to patients from diverse backgrounds, and (2) which engagement strategies (e.g., ranging from a community information forum to full partnership on the research team) are most appropriate to reach the target population [ 24 ].

Making engagement accessible

Expecting patient partner(s) to attend regular research team meetings held during working hours in a boardroom setting in a hospital, research institute or university limits the participation of many individuals. To support the participation and diversity of engaged patients, effort should be made to increase the accessibility and emotional safety of engagement initiatives [ 25 ]. A budget must be allocated for patient partners’ transportation, childcare or caregiving support, remuneration for time or time taken off work and, at the very least, covering expenses related to their engagement. Another consideration that is often made by qualitative health researchers is whether brief counselling support can be provided to patients should the sharing of their experiences result in emotional distress. There are some resources that can help with planning for costs [ 26 ], including an online cost calculator [ 27 ].

Engagement strategies

Patient partners can be coached to consider the needs and experiences of people unlike them, but there are other methods of engagement that can help to gain a more fulsome perspective of what is likely a diverse patient population that is the focus of the research study. In qualitative health research, this is known as purposeful or purposive sampling: finding people who can provide information-rich descriptions of the phenomenon under study [ 28 ]. Engagement may require different approaches (e.g., deliberative group processes, community forums, focus groups, and patient partners on the research team), at different times in the research process to reach different individuals or populations (e.g., marginalized patients, or patients or caregivers experiencing illnesses that inhibit their ability to maintain an ongoing relationship with the research team). Engagement strategies of different forms at different times may be required. For example, ongoing engagement may occur with patient partners who are members of the research team (e.g., co-applicants on a research grant), and intermittent engagement may be sought from other patients through other methods that may be more time-limited or accessible to a diverse population of patients (e.g., a one-time focus group, community forum, or ongoing online discussion) to address issues that may arise during various stages of the research or dissemination processes. The result of this approach is that patients are not only consulted or involved (one-time or low commitment methods), but are also members of the research team and have the ability to help make decisions about the research being undertaken.

Engagement can generate a wealth of information from very diverse perspectives. Each iteration of engagement may yield new information. Knowing when enough information has been gathered to make decisions with the research team (that includes patient partners) about how the research may be designed or conducted can be challenging. One approach from qualitative research that can be adapted for patient engagement initiatives is theoretical saturation [ 29 ], or “the point in analysis when…further data gathering and analysis add little new to the conceptualization, though variations can always be discovered.” (p. 263) [ 18 ]. That is, a one-time engagement strategy (e.g., a discussion with a single patient partner) may be insufficient to acquire the diverse perspectives of the individuals that will be affected by the research or its outcomes. Additional strategies (e.g., focus groups or interviews with several individuals) may be initiated until many patients identify similar issues or recommendations.

Engagement approaches should also consider: how patients are initially engaged (e.g., through known or new networks, posted notices, telephone or in-person recruitment) and whether involvement has been offered widely enough to garner multiple perspectives; how patients’ experiences are shared (e.g., community forums, formal meetings, individual or group discussions) and whether facilitation enables broad participation; and finally, how patients’ participation and experiences are incorporated into the research planning and design, with patients having equal decision-making capacity to other research team members. Several publications and tools are available that can help guide researchers who are new to processes of engaging patients in research [ 24 , 30 , 31 , 32 , 33 , 34 ], but unfortunately few address how to evaluate the effectiveness of engagement [ 35 ].

Reflexivity: Ensuring meaningful and authentic engagement

In qualitative research, reflexivity is an ongoing process of “the researcher’s scrutiny of his or her research experience, decisions, and interpretations in ways that bring the researcher into the process and allow the reader to assess how and to what extent the researcher’s interests, positions, and assumptions influenced inquiry. A reflexive stance informs how the researcher conducts his or her research, relates to the research participants, and represents them in written reports,” (p.188–189) [ 16 ]. The concept of reflexivity can be applied to research involving patient engagement by continually and explicitly considering how decisions about the research study were made. All members of the research team must consider (and perhaps discuss): (1) how patient partners are invited to participate in research planning and decision-making; (2) how their input is received relative to other team members (i.e., do their suggestions garner the same respect as researchers’ or providers’?); and, (3) whether engaged patients or patient partners feel sufficiently safe, able and respected to share their experiences, preferences and recommendations with the research team.

Ideally, reflexivity becomes a practice within the research team and may be operationalized through regular check-ins with patients and researchers about their comfort in sharing their views, and whether they feel that their views have been considered and taken onboard. Power dynamics should also be considered during patient engagement initiatives. For example, reflecting on how community forums, focus groups or interviews are to be facilitated, including a consideration of who is at the table/who is not, who speaks/who does not, whose suggestions are implemented/whose are not? Reflexivity can be practiced through informal discussions, or using methods that may allow more candid responses by engaged patients (e.g., anonymous online survey or feedback forms). At the very least, if these practices were not conducted throughout the research process, the research team (including patient partners) should endeavor to reflect upon team dynamics and consider how these may have contributed to the research design or outcomes. For example, were physicians and researchers seen as experts and patients felt less welcome or able to share their personal experiences? Were patients only engaged by telephone rather than in-person and did this influence their ability to easily engage in decision-making? Reflexive practices may be usefully supplemented by formal evaluation of the process of patient engagement from the perspective of patients and other research team members [ 36 , 37 ], and some tools are available to do this [ 35 ].

A note about language

One way to address the team dynamic between researchers, professional knowledge users (such as clinicians or health policy planners) and patients is to consider the language used to engage with patients in the planning of patient engagement strategies. That is, the term ‘patient engagement’ is a construction of an individual’s identity that exists only within the healthcare setting, and in the context of a patient-provider dynamic. This term does not consider how people make decisions about their health and healthcare within a broader context of their family, community, and culture [ 22 , 38 ]. This may be why research communities in some countries (e.g., the United Kingdom) use the term ‘patient and public involvement’. Additionally, research that involves communities defined by geography, shared experiences, cultural or ethnic identity, as is the case with participatory health research, may refer to ‘community engagement.’ Regardless of the term used, partnerships with patients, the public, or with communities need to be conceived instead as person-to-person interactions between researchers and individuals who are most affected by the research. Discussions with engaged patients should be conducted early on to determine how to best describe their role on the team or during engagement initiatives (e.g., as patient partners, community members, or people with lived experience).

Tokenism is the “difference between…the empty ritual of participation and having the real power needed to affect the outcome,” (p.2) [ 39 ]. Ongoing reflection on the power dynamic between researchers and engaged patients, a central tenet of critical qualitative health research [ 40 , 41 ], can increase the likelihood that engagement involves equitable processes and will result in meaningful engagement experiences by patients rather than tokenism [ 36 , 42 ]. Patient engagement initiatives should strive for “partnership” amongst all team members, and not just reflect a patient-clinician or researcher-subject dynamic [ 43 ]. To develop meaningful, authentic and sustainable relationships with engaged patients, methods used for participatory, action or community-based research (approaches that fall under the paradigm of qualitative inquiry) provide detailed experiential guidance [ 44 ]. For example, a realist review of community-based participatory research projects reported that gaining and maintaining trust with patient or community partners, although time-intensive, is foundational to equitable and sustainable partnerships that benefit communities and individuals [ 45 , 46 ]. Additionally, Chapter Nine of the Canadian Tri-Council Policy Statement on Research involving Humans, which has to date been applied to research involving First Nations, Inuit and, Métis Peoples in Canada [ 47 ], provides useful information and direction that can be applied to working with patient partners on research [ 48 ].

Authentic patient engagement should include their involvement at all stages of the research process [ 49 , 50 ], but this is often not the case [ 10 ]. .Since patient partners are not research subjects or participants, their engagement does not (usually) require ethics approval, and they can be engaged as partners as early as during the submission of grant applications [ 49 ]. This early engagement helps to incorporate patients’ perspectives into the proposed research before the project is wedded to particular objectives, outcomes and methods, and can also serve to allocate needed resources to support patient engagement (including remuneration for patient partners’ time). Training in research for patient partners can also support their meaningful engagement by increasing their ability to fully engage in decision-making with other members of the research team [ 51 , 52 ]. Patient partners may also thrive in co-leading the dissemination of findings to healthcare providers, researchers, patients or communities most affected by the research [ 53 ].

Patient engagement has gained increasing popularity, but many research organizations are still at the early stages of developing approaches and methods, many of which are based on experience rather than evidence. As health researchers and members of the public will increasingly need to partner for research to satisfy the overlapping mandate of patient engagement in health policy, healthcare and research, the qualitative research methods highlighted in this commentary provide some suggestions to foster rigorous, meaningful and sustained engagement initiatives while addressing broader issues of power and representation. By incorporating evidence-based methods of gathering and learning from multiple and diverse patient perspectives, we will hopefully conduct better patient engaged research, live out the democratic ideals of patient engagement, and ultimately contribute to research that is more relevant to the lives of patients; as well as, contribute to the improved delivery of healthcare services. In addition to the references provided in this paper, readers are encouraged to learn more about the meaningful engagement of patients in research from several key texts [ 54 , 55 , 56 ].

Abbreviations

Canadian Institutes for Health Research

Patient Centered Outcomes Research Institute

Strategy for Patient Oriented Research

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This paper was drafted in response to a call for concept papers related to integrated knowledge translation issued by the Integrated Knowledge Translation Research Network (CIHR FDN #143237).

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Rolfe, D.E., Ramsden, V.R., Banner, D. et al. Using qualitative Health Research methods to improve patient and public involvement and engagement in research. Res Involv Engagem 4 , 49 (2018). https://doi.org/10.1186/s40900-018-0129-8

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Handbook of Global Health pp 213–238 Cite as

Qualitative Research in Global Health Research

• Pranee Liamputtong 5 &
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This chapter discusses the contribution of qualitative research and its value in global health. Qualitative research alludes to “a broad approach” that qualitative researchers adopt as a means to examine social circumstances. The inquiry posits that people use “what they see, hear, and feel” to make sense of social experiences. There are many features that differentiate qualitative research from the quantitative approach. Fundamentally, it is interpretive. The meanings and interpretation of the participants are the essence of qualitative inquiry. Qualitative research is valuable in many ways. It offers researchers to hear silenced voices, to work with marginalized and vulnerable people, to address social justice issue, and to contribute to the person-centered healthcare and the design of clinical trials and plays an important role in evidence-based global health. Qualitative researchers are seen as constructivists who attempt to find answers in the real world. Fundamentally, qualitative researchers look for meanings that people have constructed. In this chapter, we discuss the value of qualitative research, qualitative inquiry in global health, qualitative research, and evidence-based practice in global health. The chapter also discusses in great depth some distinctiveness of the qualitative research, in particular the inductive nature of qualitative research, methodological frameworks, purposive sampling technique, saturation concept, qualitative data analysis, and the trustworthiness of a qualitative study. We also provide a concrete example of how a qualitative study was undertaken using details from the research project that we have conducted.

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Liamputtong, P., Rice, Z.S. (2021). Qualitative Research in Global Health Research. In: Kickbusch, I., Ganten, D., Moeti, M. (eds) Handbook of Global Health. Springer, Cham. https://doi.org/10.1007/978-3-030-45009-0_10

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Building public engagement and access to palliative care and advance care planning: a qualitative study

• Rachel Black   ORCID: orcid.org/0000-0001-8952-0501 1 ,
• Felicity Hasson   ORCID: orcid.org/0000-0002-8200-9732 2 ,
• Paul Slater   ORCID: orcid.org/0000-0003-2318-0705 3 ,
• Esther Beck   ORCID: orcid.org/0000-0002-8783-7625 4 &
• Sonja McIlfatrick   ORCID: orcid.org/0000-0002-1010-4300 5  

BMC Palliative Care volume  23 , Article number:  98 ( 2024 ) Cite this article

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Research evidence suggests that a lack of engagement with palliative care and advance care planning could be attributed to a lack of knowledge, presence of misconceptions and stigma within the general public. However, the importance of how death, dying and bereavement are viewed and experienced has been highlighted as an important aspect in enabling public health approaches to palliative care. Therefore, research which explores the public views on strategies to facilitate engagement with palliative care and advance care planning is required.

Exploratory, qualitative design, utilising purposive random sampling from a database of participants involved in a larger mixed methods study. Online semi-structured interviews were conducted ( n  = 28) and analysed using reflexive thematic analysis. Thematic findings were mapped to the social-ecological model framework to provide a holistic understanding of public behaviours in relation to palliative care and advance care planning engagement.

Three themes were generated from the data: “Visibility and relatability”; “Embedding opportunities for engagement into everyday life”; “Societal and cultural barriers to open discussion”. Evidence of interaction across all five social ecological model levels was identified across the themes, suggesting a multi-level public health approach incorporating individual, social, structural and cultural aspects is required for effective public engagement.

Conclusions

Public views around potential strategies for effective engagement in palliative care and advance care planning services were found to be multifaceted. Participants suggested an increase in visibility within the public domain to be a significant area of consideration. Additionally, enhancing opportunities for the public to engage in palliative care and advance care planning within everyday life, such as education within schools, is suggested to improve death literacy and reduce stigma. For effective communication, socio-cultural aspects need to be explored when developing strategies for engagement with all members of society.

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It is estimated that globally only 14% of patients who require palliative support receive it [ 1 ]. The World Health Organisation (WHO) advocates for palliative care (PC) to be considered a public health issue and suggests earlier integration of PC services within the wider healthcare system is required [ 2 ]. However, research has shown that a lack of public knowledge and misconceptions about PC may deter people from accessing integrative PC services early in a disease trajectory [ 3 ]. Integral to good PC is the facilitation of choice and decision-making, which can be facilitated via advance care planning (ACP). Evidence suggests that ACP can positively impact the quality of end of life care and increase the uptake of palliative care services [ 4 ]. While ACP is commonly associated with end of life (EOL) care, it provides the opportunity for adults of any age to consider their wishes for future care and other financial and personal planning. However, there is evidence of a lack of active engagement in advance care planning (ACP) [ 5 ]. Recent research exploring knowledge and public attitudes towards ACP found just 28.5% of participants had heard the term and only 7% had engaged in ACP [ 6 ]. Barriers to engagement in ACP discussions have been found to include topics such as death and dying are considered a social taboo, posing an increased risk of distress for loved ones; and [ 6 ] a misconception that ACP is only for those at the end of life rather than future planning [ 7 ]. Therefore, there is a need for a public health approach to ACP, to enable and support individuals to engage in conversations about their wishes and make decisions surrounding their future care.

The need for a public health approach to PC, to tackle the challenges of equity and access for diverse populations, was noted in a recent Lancet paper [ 8 ]. This is further supported in a recent review, exploring inequalities in hospice care in the UK, Australia, New Zealand, and Canada which reported that disadvantaged groups such as those with non-cancer illnesses, people living in rural locations and homeless individuals had unequal access to palliative care [ 9 ]. They postulated that differing levels of public awareness in what hospice care provides, and to whom, was an influencing factor with variations in health literacy and knowledge of health services being present in both minority and socioeconomic groups [ 9 ].

Changes in how we experience death and dying have resulted in a shift away from family and community settings into healthcare settings. The Lancet commission exploring the ‘Value of Death’, suggests it has created an imbalance where the value of death is no longer recognised [ 10 ]. The commission’s report posits the need to rebalance death, dying and grieving, where changes across all death systems are required. This needs to consider how the social, cultural, economic, religious, and political factors that determine how death, dying, and bereavement are understood, experienced, and managed [ 10 ].

New public health approaches that aim to strengthen community action and improve death literacy, through increased community responsibility are reflected in initiatives, such as ‘Compassionate Communities’ and ‘Last Aid’ [ 11 , 12 ]. However, a suggested challenge is the management of potential tensions that are present when attempting to conceptualise death in a way that mobilises a whole community [ 13 ]. Whilst palliative care education (PCE) can be effective in improving knowledge and reducing misconceptions, many PCE intervention studies, have focused on carers and healthcare professionals [ 14 ]. Initiatives such as ‘Last Aid’ attempt to bridge this gap by focusing on delivering PCE to the public, however, they are not embedded into the wider social networks of communities. It can be argued that public health campaigns, such as these are falling short by neglecting to use the full range of mass media to suit different ages, cultures, genders and religious beliefs [ 15 ]. Consequently, to understand what is required to engage the public successfully, the voice of the public must lead this conversation. Therefore, this study sought to explore public views on strategies and approaches to enable engagement with palliative care and advance care planning to help share future debate and decision making.

Within the last decades the delivery of PC and ACP have been increasingly medicalised and viewed as a specialist territory, however in reality, the care of those with life-limiting conditions occurs not only within clinical settings but within a social structure that affects the family and an entire community [ 16 ]. Therefore, death, dying and bereavement involve a combination of social, physical, psychological and spiritual events, therefore, to frame PC and ACP within a public health approach the response requires a shift from the individual to understanding the systems and culture within which we live. The Social Ecological Model (SEM) recognises the complex interplay between individual behaviours, and organisational, community, and societal factors that shape our acceptance and engagement. SEM provides a framework to understand the influences affecting engagement with PC and ACP and has been utilised as a lens through which the data in this study is explored.

Qualitative research, using semi-structured interviews were adopted as this enabled an in-depth understanding of public views on strategies to enable engagement with PC. This research was part of a larger mixed-methods study [ 17 ]. Comprehensive Consolidated Criteria for Reporting Qualitative research (COREQ) were used [ 18 ](See Supplementary file 1 ).

A purposive random sampling method, using a random number generator, was adopted to recruit participants who consented to be contacted during data collection of a larger mixed methods study. Selected individuals were contacted by telephone and email to invite them to participate. Inclusion and exclusion criteria are outlined in Table  1 . Interested individuals were provided with a participant information sheet detailing the aims of the study and asked to complete a consent form and demographic questionnaire.

A total of 159 participants were contacted, 105 did not respond, 21 declined and three were ineligible to participate. A total of thirty participants consented, however, two subsequently opted to withdraw prior to the interview.

Data collection

Data was collected from December 2022 to March 2023 by RB. The qualitative interview schedule comprised four broad topic areas: (1) participants’ knowledge of PC and ACP; (2) sources of information on PC and ACP and current awareness of local initiatives for public awareness; (3) knowledge of accessibility to PC and ACP and (4) future strategies for promoting public awareness of PC and ACP, with a consideration of supporting and inhibiting factors. The interview schedule was adapted from a previous study on palliative care to incorporate the topic of ACP [ 3 ] (See Supplementary file 2 ). This paper reports on future strategies.

Participants were asked to complete a short demographic questionnaire prior to the interview to enable the research team to describe the characteristics of those who participated. These questions included variables such as age, gender, religion, marital status, behaviour relating to ACP and experience of PC.

Data was collected via online interviews conducted using the videoconferencing platform Microsoft Teams. Interviews lasted between 20 and 60 min and were recorded with participant consent. Data were stored on a secure server and managed through NVivo 12 Software.

Data analysis

Qualitative data were transcribed verbatim automatically by Microsoft Teams and the transcripts were reviewed and mistakes corrected by the interviewer. All identifying information was removed. Transcripts were analysed using reflexive thematic analysis which involved a six-step process: familiarisation, coding, generating initial themes, developing and reviewing themes, refining, defining and naming themes, and writing up [ 19 ]. Themes were derived by exploring patterns, similarities and differences within and across the data in relation to participant’s views on the promotion of PC and ACP and the best ways to engage the public in open discussions.

The study explored the data through a SEM lens to provide a holistic framework for understanding the influences surrounding health behaviour change in relation to palliative care and advance care planning by mapping the findings to each of the SEM constructs.

The SEM for public health was conceptualised by McLeroy et al. [ 20 ]., and was based on previous work by Bronfenbrenner’s ecological systems theory [ 21 ]. The SEM looks to identify social-level determinants of health behaviours [ 22 ]. Five factor levels have been identified within the SEM; (1) Intrapersonal factors (2) Interpersonal processes (3) Institutional factors (4) Community factors and (5) Public policy [ 20 ]. In short, the SEM suggests that the social factors that influence health behaviours on an individual level are nestled within a wider complex system of higher levels. Current research literature has explored SEM as a model for understanding barriers and facilitators to the delivery of PC, adults’ preferences for EOL care and older adults’ knowledge and attitudes of ACP within differing socioeconomic backgrounds [ 23 , 24 , 25 ]. It has demonstrated the importance of a multilevel approach within these populations. However, there is a scarcity of research exploring strategies for public engagement with PC and/or ACP which are underpinned by SEM theory.

To ensure rigour in the analysis four members of the research team (RB, SM, FH, EB) independently reviewed the transcripts and were involved in the analysis and development of themes as a method of confirmability [ 26 ].

Ethical approval was gained from the University Research Ethics Filter Committee prior to commencing data collection. Participants provided written informed consent prior to the commencement of the interviews. They were advised of their right to withdraw, and the confidentiality and anonymity of all data were confirmed. All data was kept in accordance with the Data Protection Act (2018) [ 27 ].

All participants were white; 70% were female (n-19) and 70% were either married or cohabiting (n-19). The largest proportion of the sample 44% was aged under 50 years (n-12), with 22% aged between 50 and 59 (n-6) and 33% (n-9) aged between 60 and 84. Over half of the sample was employed (n-15), 15% were self-employed [ 4 ] whilst 26% were retired (n-7). Demographic data were missing for one of the included participants (see Table  2 ).

Responses to questions relating to ACP knowledge and behaviours found just 12 participants had heard of the term ACP prior to completing the Northern Ireland Life and Times Survey. Furthermore, none of the participants had been offered the opportunity to discuss ACP and none had prepared a plan of their wishes and preferences.

Main findings

Three overarching themes were generated from the data: ‘Visibility and relatability’; ‘Embedding opportunities for engagement into everyday life’; ‘Societal and cultural barriers. These findings were then mapped to the five social ecological model (SEM) levels ( individual; interpersonal; institutional; community; and policy ) to demonstrate the importance of a multilevel approach when seeking to engage the public around PC and ACP. See Fig.  1 for SEM construct mapping.

Theme 1: visibility and relatability

This theme relates to the suggestion that social taboo was a barrier to awareness and the mechanism to ameliorate this was visibility – in turn promoting reduction of stereotypes and promoting understanding and engagement. This posits the idea that the lack of understanding of PC is the root cause of much of the stigma surrounding it. The SEM construct mapping suggests a multilevel approach is required with intrapersonal (increased individual understanding), interpersonal (openness in discussion with friends and family through media normalisation) and institutional (health service policies for promotion and support) levels being identified.

Participants discussed how there is a lack of knowledge on what PC is, with many assuming that it was for people in the latter stages of life or facing end of life care. This highlighted the lack of individual education with participants suggesting that there should be more visibility and promotion on PC and ACP so that individuals are better informed.

“So, it’s really um there needs to be more education, maybe, I think around it. So that people can view it maybe differently or you know talk about it a bit more. Yeah, probably demystifying what it is. This is this is what it is. This isn’t what it is. You know, this isn’t about um, ending your life for you, you know. And this is about giving you choices and ensuring that you know, you know people are here looking after you”(P37538F45) .

However, there was a recognition that individual differences play a part in whether people engage in discussions. A number of participants explored the idea that some people just don’t want to talk about death and that for some it was not a subject that they want to approach. Despite this, there was a sense that increasing visibility was considered important as there will still be many people who are willing to increase their knowledge and understanding of PC and ACP.

“I can talk about it, for example, with one of my sisters, but not with my mom and not with my other sister or my brothers. They just refuse point blank to talk about it…. some of them have done and the others have started crying and just shut me. Shut me off. And just. No, we don’t want to talk about that. So, it just depends on the personality, I suppose” (P14876F59) .

The lack of knowledge and awareness of PC and ACP was suggested to be the attributed to the scarcity of information being made available at a more institutional level. For some participant’s, this was felt to be the responsibility of the health service to ensure the knowledge is out there and being promoted.

“I think people are naive and they know they’re not at that stage and they don’t know what palliative care is, you know. It’s all like it’s ignorance. But our health service is not promoting this. Well in my eyes, they’re not promoting it whatsoever. And they should, they should, because it would help a hell of a lot of people ” (P37172M61). “I and I think it needs to be promoted by the point of contact, whether it’s a GP, National Health, whatever it might be, I think when they’re there, there needs to be a bit more encouragement to have that conversation” (P26495M43) .

The lack of visibility within the general practice was discussed by several participants who said that leaflets and posters would be helpful in increasing visibility. One participant went as far as to say that a member of staff within a GP surgery would be beneficial.

“I suppose the palliative care because it is a bit more personal. There should be even maybe a professional that you could talk to in your GP practice, or you know, like they have mental health practitioners now in GP practices. Maybe there are I don’t know if there is or not, but there should be maybe a palliative health practitioner that talks to people when they are at that stage of their life” (P21647F29).

Participants also noted how there were generational differences in how people accessed health information. Many of the participants suggested that they would turn to the internet and ‘google’ for information, however, the suggestion was made that care should be taken to target awareness campaigns to different age groups via different methods to reduce disparities in technology skills such as those with less computer literacy.

“ I think a certain proportion of society need the visibility because they’re not always going to be self-sufficient enough to jump on the Internet even though you know we’re getting to the point now where the generational thing is. The generation have been brought up with the Internet and they’re obviously they go to it as the first point of call. But we still got the generation at the moment that don’t”. (P25046F-)

One of the participants talked about ways to increase visibility via the use of the media, including social media, and the utilisation of famous faces.

“Yeah, I think you know, they need to discuss it on Loose Women. You know, morning TV need to get on the bandwagon….But you know. It only takes like that one celebrity to mention it and then the whole media is jumping on the bandwagon.” (P19874F-) .

The UK media coverage of other successful campaigns such as those highlighting mental health and bowel cancer were noted to have been particularly helpful in raising awareness.

“if I think myself about the whole exposure that we have and as a society at the minute about mental health in general, you know a lot of the work on that has been done via social media. You know, celebrities hash tagging and talked about their experience. It’s OK to not be OK etcetera. And I feel like that is responsible for a lot of people who are now discussing their mental health” (P21647F29) .

The sentiment expressed in the above quotes regarding increased visibility in the media also suggests that unless a topic seems relevant to an individual then they won’t engage with health promotion. This concept of relatability pertains to those aspects of human empathy where they can place themselves within a situation leading it to become more relevant to them.

Many participants discussed that using real-life stories on television and in campaigns would be an effective way to connect with the public and it would make PC and ACP more relatable and highlight the importance of thinking about it.

“I think always what tends to be most effective is when it’s somebody that we could all relate to telling their own story (…)……I’m not too sure I know enough about what it involves, but really, like the consequences, that the consequences that people have suffered from not having done that. Not knowing what the wishes were, not having planned for it”. (P32288F62)

Several participants discussed how the topics of PC and ACP were not something they would identify with as being relevant to them. The suggestion was that without it being an immediate concern, for example, if they were not approaching a certain age, then they would assume that they did not need to increase their knowledge about what PC or ACP involves.

“You have to be able to relate to it in order to think, oh, yeah, that applies to me. You need to have something in which you identify with”. (P13697F51)

Theme 2: embedding opportunities for engagement into everyday life

Throughout the discussions, there was evidence that participants felt that death literacy could be increased by providing more opportunities to gain knowledge about planning for future care and what PC involves. Education was highlighted as a potential pathway to engaging the public by targeting appropriate age groups and professions with relevant knowledge and skills. For some, this was thought to be best achieved through educating the youth and for others, the importance of educating those who are working in the healthcare profession was particularly salient. In addition, almost half of the participants suggested they would approach charity organisations for information, with participants advocating for education within secondary and tertiary levels and within community organisations. This data reflects an institutional-level construct within the SEM framework.

Educating younger generations on the topics of PC and ACP through open discussions in schools, and providing skills on how to have difficult conversations with loved ones were seen to be a valuable strategies.

“ young people don’t have that ability to accept and admit and bring it out into the open and I think they need to be perhaps encouraged more to do that through some kind of teaching in the school environment when they’re at a young and impressionable age ”. (P25046F-)

Due to the difficulties around having conversations about death, it was suggested that different healthcare professionals should be trained to have conversations with their patients.

“Yeah, it’s like you think the discussions are difficult to broach for maybe health care professionals that you know, a difficult topic even for them to bring up. Well, if you’re working with someone who’s you know with a family and where things are quite distressed and very often it can be either, it can be the stress can cause a lot of friction and you know, decision making can be very difficult for people…., but just at every level, there’s, you know, possibility to be having conversations like that with people.”(P37538F45) . “I suppose you could think about training some care professionals (…) there may be some way that as a second part of the person’s job or whatever that they’re trained so that people could go along and discuss ” (P19265F76) .

Further to education for young people and healthcare professionals, there was the recognition that community organisations are perfectly positioned to educate the public in PC and ACP. One participant highlighted the missed opportunity to educate family members and carers through an existing programme on dementia. This is particularly pertinent to ACP due to the impact of cognitive decline on decision-making.

“I went to zoom meetings for four weeks in a row with the Alzheimer’s Society. Umm regarding things to do with dementia. And you know, there was a week about your finances and things like that. I suppose. They never really talked about end of life care you know that sort of thing. Um I suppose it would have been useful had they you know, broached that subject as well. But it wasn’t, you know, there was more about looking after yourself, looking after the person. The symptoms of dementia and all this sort of thing, Alzheimer’s and then you know, um the financial and the help available to you. You know, but they didn’t mention about the end-of-life care and like the end result of dementia, I guess, is death. So, you know that that subject, you know, I was, I suppose to just those organizations that deal with um the issues of, like dementia or, as you say, all the rest for, you know, the rest of the diseases and that. You know to be up front and honest and say you know where this can lead and to make people you know, make people aware that there is a palliative care process that can be gone through.” (P19874F-) .

Furthermore, the option of a helpline was suggested with reference made to other successful charity helplines such as the National Society for the Prevention of Cruelty to Children (NSPCC) and The Samaritans. Whilst it was acknowledged there were specialist support services available for people living with a terminal diagnosis, there was a sense that a generic information support helpline would be helpful.

“You know, people need to be (aware)… they’re not alone. There is help out there. You know, you see your helpline, your children, NSPCC, your Samaritans. All on all these helplines, I have to think, I’ve never seen a helpline for palliative care or who you can contact. You don’t see things like that.” (P22964F52) .

In addition to education, the suggestion that embedding ACP discussions into other more common aspects of future planning such as will making, and organ donation was postulated as a potential way to engage the public. This demonstrated clear links to the policy and institutional level constructs within SEM. Changes within organisational policies and public law to support individuals to consider future planning would promote better engagement on a wider societal level.

Participants suggested that they would like to see some of the conversations surrounding ACP introduced into workplace policies and guidelines, as well as through other legal discussions. They noted that conversations surrounding future planning already occur when discussing legal wills and workplace pensions. The potential to expand these discussions to include ACP with solicitors and in workplaces was seen to be a missed opportunity.

“So maybe um around people who are making their wills and you know, you get will making services advertised and things like that. And I think once you get into your 30s and 40s, people start thinking about a will and things like that. So maybe aligned to something like that, you know would get younger people.” (P13790F67) . “when people talk about the pension, you know so retirement, you know, to make people aware about this as well. You know, I would say that’s probably good ways to reach people” (P21263M54) .

Current legislation and promotion regarding organ donation were discussed as being successful in engaging the public and therefore implied that the government should take a more active role in the promotion of ACP.

“If you look at the way, sort of, government have been promoting like organ donation and that. You know that sort of thing. And then people have really bought into it and you know, and there’s a lot of positivity around it. So, I think that’s sort of similar approach would be good”. (P29453F40) “You know, people talking about pension, pension plans and so on and it’s part of the natural life circle, you know. So, if it’s in connection with this, you know, so think about your future, make your plans …so probably in connection with organ donation and so on, you know, so I think they could be trigger points, you know,, people talking about this.” (P21263M54) .

Theme 3: societal and cultural barriers to open discussion

In conversations surrounding why there was a lack of openness in discussion, participants postulated that a potential factor was the influence of cultural and societal norms. This was found to overlap in the SEM levels of intrapersonal, interpersonal and community.

Rural farming communities were highlighted as potentially being more isolated and traditional in their views around death and dying, whilst those with strong religious beliefs were seen to be less likely to engage in discussions.

“You know, and there is a, I’m out in the countryside. Well, it’s (place name). So it’s a relatively rural sort of conservative place and it and it’s that… you know you’re tied to the land, you’re tied to the farm. This is your home and sending you away from it early it is seen as a bit of a shameful thing. So yeah, to try and educate folk and to try and speak into that I think would be really helpful because again, I’ve seen situations where probably the individual’s life, the end of life, has been made tougher because the family have fought to keep that person at home.…. We’re going to try and do it ourselves” (P26495M43) . “I think it’s probably a lot to do with religion more than likely because people just like to hear, because we still are very much, you know, a lot is dictated by religion. So, people just want to leave stuff in God’s hands so they don’t want to have like…that would be the kind of where my mom and dad are coming from, you know, don’t interfere with it, blah blah blah. So, it’s a very gentle like kind of reminding them, you know, well, I think we do need to think about it. And I think people are afraid that they’ll make again that um it’s kind of euthanasia you know what I mean” (P37538F45) .

Cultural differences between countries were also considered with some seeing other global communities approaching death as a celebration rather than something to shy away from.

“I think in Northern Ireland we are, and the UK and probably the world in general. We are really poor at talking about, about death, and it has to be a positive thing to be able to talk more about death. You look at other cultures where you know. Death is treated differently, you know, even in Africa things where, you know, it’s a real celebration, whereas it’s seen so differently in in Northern Ireland” (P31154F35) .

Furthermore, regions and countries that have experienced war and conflict were perceived by one participant as a potential barrier to engaging in subject matters which involve death.

“I don’t I don’t know about other countries, but those that grew up maybe during the conflict here, maybe it’s just something that, you know it’s it’s a completely different mindset to them” (P23609M39) .

Whilst it is unclear from the data why the participant felt the conflict might inhibit engagement with the subject of dying, it could be interpreted that the participant was suggesting that death is too morbid to engage with following a conflict period which saw numerous deaths. An alternative interpretation could be that people are desensitised to death and do not see death as something that an individual has autonomy over.

It was noted that in many modern societies communities are changing. People no longer interact with their neighbours in the way they used to which results in a reduced sense of community responsibility.

“I don’t know that everyone is as neighbourly as they used to be. Northern Ireland I always perceived as being open door policy and everybody looked out for everyone else. But I think as a society has changed and it has changed in Northern Ireland, and it’s become becoming more closed (…) So I think we need to try and encourage that community um experience back again so that people are mindful of their neighbours and share that responsibility and making sure that everyone’s OK” (P25046F) .

This sense of social isolation was discussed by one participant who referred to homeless people. They reflected on how current social structures may not be providing the information and support to this minority group and therefore should be considered when developing public health approaches to engaging them in PC and ACP education.

“And if you look at homeless people on the streets, where do they get the information from? Where do they get the care, the information, the attention. I know there are Street workers that work, but I don’t know again to what extent in Northern Ireland compared with the likes of London” (P25046F) .

Mapping the findings to a social ecological model framework

Following thematic analysis each of the resulting themes were mapped to the five socioecological levels identified by McLeroy et al. (1988) for health promotion programmes. Construct mapping can be found in Fig.  1 below.

Thematic interaction within the Social Ecological Model levels

The findings from this study highlight the complexity of current public perceptions of palliative care and their views on effective engagement with PC and ACP. Within medicalised western culture there is a tendency to focus on the preservation of life, with conversations about death avoided. This has resulted in death becoming a taboo, raising fear and stigma where death is equated with failure. These social taboos that exist around death, dying and bereavement are posited to stem from the lack of awareness and understanding of PC and ACP and the resulting stigma of approaching these discussions. There was evidence of influencing factors on all SEM levels, which demonstrates the need for a multifaceted public health approach that uses not only behaviour change communication but also social change communication, social mobilisation and advocacy. It can be argued this reflects the key aspects outlined in Lancet Commission report on ‘Valuing Death’, which advocated for a ‘systems approach’ [ 10 ]. This systems approach is aligned to differing levels within the SEM and the different approaches the public have identified when seeking to build public engagement and access to palliative care. Three key aspects were noted: visibility, embedding opportunities for engagement in everyday life and societal and cultural influences.

It was clear from the analysis that a major factor associated with poor public engagement was the lack of visibility within the public domain, which was hindering both the normalisation of death and understanding that PC was more than just end of life care. The findings demonstrated different ways to address the lack of visibility, such as the use of targeted social media and wider publicity campaigns. Research to date has demonstrated that palliative care education is a useful tool in improving knowledge of, confidence in and attitudes towards palliative care amongst healthcare professionals and carers [ 14 ]. Similar results have been noted for the public when exploring the potential to promote palliative care through various media challenges such as YouTube and social media [ 28 ]. This does, however, raise questions around the quality and accuracy of information offered via the media, taking cognisance of whether some of the messaging may inadvertently be adding to misunderstanding, and thus a lack of public engagement.

Secondly, the findings indicated that experience at the individual level within a social context was noted as an important element when seeking ways to increase public engagement with PC and ACP. The experience of illness, dying and loss is often overlooked, therefore, this points to the potential value of community-based education approaches, with peers enabling experience-based exchange. Such interventions have been noted in the literature on the role of volunteers and education [ 29 ]. This reflects the need for an overall public health palliative care approach that seeks to empower individuals, families and communities to draw on their own resources and community supports to adapt and cope with death and dying [ 6 , 30 ].

Thirdly, the findings from this study indicated the need for enhancing opportunities for engagement in PC and ACP within everyday life. Research indicates there is an appetite for people to talk about death, for example, in the UK, a recent YouGov ‘daily question’ survey reported 67% of adults who responded think the subject of death and dying should be talked about in schools [ 31 ]. This speaks to the need to consider schools, workplaces and key trigger points in life as times to consider engagement with PC and ACP. This reflects the overall need for death literacy in society to improve experiences at the end of life [ 10 ].

Finally, the importance of socio-cultural aspects for the public cannot be underestimated. Therefore, effective communication strategies need to be tailored to individuals, and communities and be culturally appropriate. This has been noted as an important aspect for specific communities, such as the Chinese diaspora, for example, but nuances around this for specific ethnic, political, religious, and geographical aspects need further consideration [ 32 ]. Cultural competence, defined as an understanding of how culture affects an individual’s beliefs, values and behaviour, is an important consideration [ 33 ]. A meta-analysis of 19 review articles, concluded that interventions to increase cultural competence in healthcare were effective in enhancing the knowledge, skills and attitudes of healthcare providers, leading to clinical benefits for patients/clients through improved access and utilization of healthcare [ 34 ]. The translation of such reviews for public engagement in PC and ACP warrants further exploration. It has been advocated that elements of cultural systems should be analysed with a socio-ecological framework [ 35 ]. Such consideration and integration of salient contextual cultural factors could assist public messaging and cultural communication, which would enhance more effective and sustainable public engagement in PC and ACP.

Limitations

When considering potential limitations, it is pertinent to note that due to the sensitive nature of the topic the exclusion criteria restricted the sample to those who had not experienced a recent bereavement. This may have limited the ability to gain a wider perspective, as the views of the recently bereaved may have provided further nuanced insights into how best to engage the public. Furthermore, the participant sample was limited to those involved in a larger mixed-methods study. This may have introduced bias in relation to true knowledge and attitudes due to the participants having completed the survey questionnaire prior to the interviews.

In conclusion, this qualitative study has provided insights into how the public would like to be engaged in PC and ACP. The findings highlighted that to build public engagement and access to palliative care and advance care planning a multifaceted public health approach is required. Discussions of death and dying remain difficult for many members of society, therefore, an increase in death literacy across all systems to reduce misperceptions surrounding PC and APC is needed, by increasing visibility and providing opportunities for the public to engage with PC and ACP within everyday life. Finally, socio-cultural aspects need consideration when developing strategies to ensure effective communication and engagement with all members of the community.

Data availability

The datasets analysed are not publicly available but are available from the corresponding author upon reasonable request.

Abbreviations

Advance care plan

• Palliative care

Palliative care education

Social ecological model

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Acknowledgements

The authors would like to thank all interviewees for their participation in the research.

This study was funded by HSC R&D Division of Public Health Agency in Northern Ireland.

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Black, R., Hasson, F., Slater, P. et al. Building public engagement and access to palliative care and advance care planning: a qualitative study. BMC Palliat Care 23 , 98 (2024). https://doi.org/10.1186/s12904-024-01420-8

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• Advance care planning
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Published on 11.4.2024 in Vol 26 (2024)

Patients’ Experiences With Digitalization in the Health Care System: Qualitative Interview Study

Authors of this article:

Original Paper

• Christian Gybel Jensen 1 * , MA   ; 
• Frederik Gybel Jensen 1 * , MA   ; 
• Mia Ingerslev Loft 1, 2 * , MSc, PhD  

1 Department of Neurology, Rigshospitalet, Copenhagen, Denmark

2 Institute for People and Technology, Roskilde University, Roskilde, Denmark

*all authors contributed equally

Corresponding Author:

Mia Ingerslev Loft, MSc, PhD

Department of Neurology

Rigshospitalet

Inge Lehmanns Vej 8

Phone: 45 35457076

Email: [email protected]

Background: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients’ digital practices and experiences with digital health services.

Objective: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them?

Methods: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis.

Results: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out.

Conclusions: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Introduction

In 2022, the fourth most googled question in Denmark was, “Why does MitID not work?” [ 1 ]. MitID (My ID) is a digital access tool that Danes use to enter several different private and public digital services, from bank accounts to mail from their municipality or the state. MitID is a part of many Danish citizens’ everyday lives because the public sector in Denmark is digitalized in many areas. In recent decades, digitalization has changed how governments and people interact and has demonstrated the potential to change the core functions of public sectors and delivery of public policies and services [ 2 ]. When public sectors worldwide become increasingly digitalized, this transformation extends to the public health sectors as well, and some studies argue that we are moving toward a “digital public health era” that is already impacting the health systems and will fundamentally change the future of health systems [ 3 ]. While health systems are becoming more digitalized, it is important that both patients and digitalized systems adapt to changes in accordance with each other. Digital practices of people can be understood as what people do with and through digital technologies and how people relate to technology [ 4 ]. Therefore, it is relevant to investigate digital practices and how patients perceive and experience their own use of digital tools and services, especially in relation to existing digital health services. In our study, we highlight a broad perspective on experiences with digital practices and particularly add insight into the challenges with digital practices faced by patients who have acute or chronic illness, with some of them also experiencing physical, communicative, or cognitive difficulties.

An international Organization for Economic Cooperation and Development report indicates that countries are digitalized to different extents and in different ways; however, this does not mean that countries do not share common challenges and insights into the implementation of digital services [ 2 ].

In its global Digital Government Index, Denmark is presented as one of the leading countries when it comes to public digitalization [ 2 ]. Recent statistics indicate that approximately 97% of Danish families have access to the internet at home [ 5 ]. The Danish health sector already offers many different digital services, including web-based delivery of medicine, e-consultations, patient-related outcome questionnaires, and seeking one’s own health journal or getting test results through; “Sundhed” [ 6 ] (the national health portal) and “Sundhedsjournalen” (the electronic patient record); or the apps “Medicinkortet” (the shared medication record), “Minlæge” (My Doctor, consisting of, eg, communication with the general practitioner), or “MinSP” (My Health Platform, consisting of, eg, communication with health care staff in hospitals) [ 6 - 8 ].

The Danish Digital Health Strategy from 2018 aims to create a coherent and user-friendly digital public sector for everyone [ 9 ], but statistics indicate that certain groups in society are not as digitalized as others. In particular, the older population uses digital services the least, with 5% of people aged 65 to 75 years and 18% of those aged 75 to 89 years having never used the internet in 2020 [ 5 ]. In parts of the literature, it has been problematized how the digitalization of the welfare state is related to the marginalization of older citizens who are socially disadvantaged [ 10 ]. However, statistics also indicate that the probability of using digital tools increases significantly as a person’s experience of using digital tools increases, regardless of their age or education level [ 5 ].

Understanding the digital practices of patients is important because they can use digital tools to engage with the health system and follow their own health course. Researching experiences with digital practices can be a way to better understand potential possibilities and barriers when patients use digital health services. With patients becoming more involved in their own health course and treatment, the importance of patients’ health literacy is being increasingly recognized [ 11 ]. The World Health Organization defines health literacy as the “achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions” [ 12 ]. Furthermore, health literacy can be described as “a person’s knowledge and competencies to meet complex demands of health in modern society, ” and it is viewed as a critical step toward patient empowerment [ 11 , 12 ]. In a digitalized health care system, this also includes the knowledge, capabilities, and resources that individuals require to use and benefit from eHealth services, that is, “digital health literacy (eHealth literacy)” [ 13 ]. An eHealth literacy framework created by Norgaard et al [ 13 ] identified that different aspects, for example, the ability to process information and actively engage with digital services, can be viewed as important facets of digital health literacy. This argument is supported by studies that demonstrate how patients with cognitive and communicative challenges experience barriers to the use of digital tools and require different approaches in the design of digital solutions in the health sector [ 14 , 15 ]. Access to digital services and digital literacy is becoming increasingly important determinants of health, as people with digital literacy and access to digital services can facilitate improvement of health and involvement in their own health course [ 16 ].

The need for a better understanding of eHealth literacy and patients’ capabilities to meet public digital services’ demands as well as engage in their own health calls for a deeper investigation into digital practices and the use of digital tools and services from the perspective of patients with varying digital capabilities. Important focus areas to better understand digital practices and related challenges have already been highlighted in various studies. They indicate that social support, assessment of value in digital services, and systemic assessment of digital capabilities are important in the use and implementation of digital tools, and they call for better insight into complex experiences with digital services [ 13 , 17 , 18 ]. Therefore, we aimed to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients, addressing the following research questions: how do patients use digital services and digital tools, and how do they experience them?

We aimed to investigate digital practices and experiences with digital health services and digital tools; therefore, we used a qualitative design and adopted a hermeneutic approach as the point of departure, which means including preexisting knowledge of digital practices but also providing room for new comprehension [ 19 ]. Our interpretive approach is underpinned by the philosophical hermeneutic approach by Gadamer et al [ 19 ], in which they described the interpretation process as a “hermeneutic circle,” where the researcher enters the interpretation process with an open mind and historical awareness of a phenomenon (preknowledge). We conducted semistructured interviews using an interview guide. This study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist [ 20 ].

Setting and Participants

To gain a broad understanding of experiences with public digital health services, a purposive sampling strategy was used. All 31 participants were hospitalized or formerly hospitalized patients in a large neurological department in the capital of Denmark ( Table 1 ). We assessed whether including patients from the neurological field would give us a broad insight into the experiences of digital practices from different perspectives. The department consisted of, among others, 8 inpatient units covering, for example, acute neurology and stroke units, from which the patients were recruited. Patients admitted to a neurological department can have both acute and transient neurological diseases, such as infections in the brain, stroke, or blood clot in the brain from which they can recover completely or have persistent physical and mental difficulties, or experience chronic neurological and progressive disorders such as Parkinson disease and dementia. Some patients hospitalized in neurological care will have communicative and cognitive difficulties because of their neurological disorders. Nursing staff from the respective units helped the researchers (CGJ, FGJ, and MIL) identify patients who differed in terms of gender, age, and severity of neurological illness. Some patients (6/31, 19%) had language difficulties; however, a speech therapist assessed them as suitable participants. We excluded patients with severe cognitive difficulties and those who were not able to speak the Danish language. Including patients from the field of neurology provided an opportunity to study the experience of digital health practice from various perspectives. Hence, the sampling strategy enabled the identification and selection of information-rich participants relevant to this study [ 21 ], which is the aim of qualitative research. The participants were invited to participate by either the first (CGJ) or last author (MIL), and all invited participants (31/31, 100%) chose to participate.

All 31 participants were aged between 40 to 99 years, with an average age of 71.75 years ( Table 1 ). Out of the 31 participants, 10 (32%) had physical disabilities or had cognitive or communicative difficulties due to sequela in relation to neurological illness or other physical conditions.

Data Collection

The 31 patient interviews were conducted over a 2-month period between September and November 2022. Of the 31 patients, 20 (65%) were interviewed face-to-face at the hospital in their patient room upon admission and 11 (35%) were interviewed on the phone after being discharged. The interviews had a mean length of 20.48 minutes.

We developed a semistructured interview guide ( Table 2 ). The interview questions were developed based on the research aim, findings from our preliminary covering of literature in the field presented in the Introduction section, and identified gaps that we needed to elaborate on to be able to answer our research question [ 22 ]. The semistructured interview guide was designed to support the development of a trusting relationship and ensure the relevance of the interviews’ content [ 22 ]. The questions served as a prompt for the participants and were further supported by questions such as “please tell me more” and “please elaborate” throughout the interview, both to heighten the level of detail and to verify our understanding of the issues at play. If the participant had cognitive or communicative difficulties, communication was supported using a method called Supported Communication for Adults with Aphasia [ 23 ] during the interview.

The interviews were performed by all authors (CGJ, FGJ, and MIL individually), who were skilled in conducting interviews and qualitative research. The interviewers are not part of daily clinical practice but are employed in the department of neurology from where the patients were recruited. All interviews were audio recorded and subsequently transcribed verbatim by all 3 authors individually.

a PRO: patient-related outcome.

Data Analysis

The text from each transcribed interview was analyzed using manifest content analysis, as described by Graneheim and Lundman [ 24 ]. Content analysis is a method of analyzing written, verbal, and visual communication in a systematic way [ 25 ]. Qualitative content analysis is a structured but nonlinear process that requires researchers to move back and forth between the original text and parts of the text during the analysis. Manifest analysis is the descriptive level at which the surface structure of the text central to the phenomenon and the research question is described. The analysis was conducted as a collaborative effort between the first (CGJ) and last authors (MIL); hence, in this inductive circular process, to achieve consistency in the interpretation of the text, there was continued discussion and reflection between the researchers. The transcriptions were initially read several times to gain a sense of the whole context, and we analyzed each interview. The text was initially divided into domains that reflected the lowest degree of interpretation, as a rough structure was created in which the text had a specific area in common. The structure roughly reflected the interview guide’s themes, as guided by Graneheim and Lundman [ 24 ]. Thereafter, the text was divided into meaning units, condensed into text-near descriptions, and then abstracted and labeled further with codes. The codes were categorized based on similarities and differences. During this process, we discussed the findings to reach a consensus on the content, resulting in the final 4 categories presented in this paper.

Ethical Considerations

The interviewees received oral and written information about the study and its voluntary nature before the interviews. Written informed consent was obtained from all participants. Participants were able to opt of the study at any time. Data were anonymized and stored electronically on locked and secured servers. The Ethics Committee of the Capitol Region in Denmark was contacted before the start of the study. This study was registered and approved by the ethics committee and registered under the Danish Data Protection Agency (number P2021-839). Furthermore, the ethical principles of the Declaration of Helsinki were followed for this study.

The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools.

Social Resources as a Digital Lifeline

Throughout the analysis, it became evident that access to both material and social resources was of great importance when using digital tools. Most participants already possessed and had easy access to a computer, smartphone, or tablet. The few participants who did not own the necessary digital tools told us that they did not have the skills needed to use these tools. For these participants, the lack of material resources was tied particularly to a lack of knowledge and know-how, as they expressed that they would not know where to start after buying a computer—how to set it up, connect it to the internet, and use its many systems.

However, possessing the necessary material resources did not mean that the participants possessed the knowledge and skill to use digital tools. Furthermore, access to material resources was also a question of having access to assistance when needed. Some participants who had access to a computer, smartphone, and tablet and knew how to use these tools still had to obtain help when setting up hardware, updating software, or getting a new device. These participants were confident in their own ability to use digital devices but also relied on family, friends, and neighbors in their everyday use of these tools. Certain participants were explicitly aware of their own use of social resources when expressing their thoughts on digital services in health care systems:

I think it is a blessing and a curse. I think it is both. I would say that if I did not have someone around me in my family who was almost born into the digital world, then I think I would be in trouble. But I feel sorry for those who do not have that opportunity, and I know quite a few who do not. They get upset, and it’s really frustrating. [Woman, age 82 years]

The participants’ use of social resources indicates that learning skills and using digital tools are not solely individual tasks but rather continuously involve engagement with other people, particularly whenever a new unforeseen problem arises or when the participants want a deeper understanding of the tools they are using:

If tomorrow I have to get a new ipad...and it was like that when I got this one, then I had to get XXX to come and help me move stuff and he was sweet to help with all the practical stuff. I think I would have cursed a couple of times (if he hadn’t been there), but he is always helpful, but at the same time he is also pedagogic so I hope that next time he showed me something I will be able to do it. [Man, age 71 years]

For some participants, obtaining assistance from a more experienced family member was experienced as an opportunity to learn, whereas for other participants, their use of public digital services was even tied directly to assistance from a spouse or family member:

My wife, she has access to mine, so if something comes up, she can just go in and read, and we can talk about it afterwards what (it is). [Man, age 85 years]

The participants used social resources to navigate digital systems and understand and interpret communication from the health care system through digital devices. Another example of this was the participants who needed assistance to find, answer, and understand questionnaires from the health care department. Furthermore, social resources were viewed as a support system that made participants feel more comfortable and safer when operating digital tools. The social resources were particularly important when overcoming unforeseen and new challenges and when learning new skills related to the use of digital tools. Participants with physical, cognitive, and communicative challenges also explained how social resources were of great importance in their ability to use digital tools.

Possessing the Necessary Capabilities

The findings indicated that possessing the desire and knowing how to use digital tools are not always enough to engage with digital services successfully. Different health issues can carry consequences for motor skills and mobility. Some of these consequences were visibly affecting how our participants interacted with digital devices, and these challenges were somewhat easy to discover. However, our participants revealed hidden challenges that posed difficulties. In some specific cases, cognitive and communicative inabilities can make it difficult to use digital tools, and this might not always be clear until the individual tries to use a device’s more complex functions. An example of this is that some participants found it easy to turn on a computer and use it to write but difficult to go through security measures on digital services or interpret and understand digital language. Remembering passwords and logging on to systems created challenges, particularly for those experiencing health issues that directly affect memory and cognitive abilities, who expressed concerns about what they were able to do through digital tools:

I think it is very challenging because I would like to use it how I used to before my stroke; (I) wish that everything (digital skills) was transferred, but it just isn’t. [Man, age 80 years]

Despite these challenges, the participants demonstrated great interest in using digital tools, particularly regarding health care services and their own well-being. However, sometimes, the challenges that they experienced could not be conquered merely by motivation and good intentions. Another aspect of these challenges was the amount of extra time and energy that the participants had to spend on digital services. A patient diagnosed with Parkinson disease described how her symptoms created challenges that changed her digital practices:

Well it could for example be something like following a line in the device. And right now it is very limited what I can do with this (iPhone). Now I am almost only using it as a phone, and that is a little sad because I also like to text and stuff, but I also find that difficult (...) I think it is difficult to get an overview. [Woman, age 62 years]

Some participants said that after they were discharged from the hospital, they did not use the computer anymore because it was too difficult and too exhausting , which contributed to them giving up . Using digital tools already demanded a certain amount of concentration and awareness, and some diseases and health conditions affected these abilities further.

Big Feelings as Facilitators or Barriers

The findings revealed a wide range of digital practices in which digital tools were used as a communication device, as an entertainment device, and as a practical and informative tool for ordering medicine, booking consultations, asking health-related questions, or receiving email from public institutions. Despite these different digital practices, repeating patterns and arguments appeared when the participants were asked why they learned to use digital tools or wanted to improve their skills. A repeating argument was that they wanted to “follow the times, ” or as a participant who was still not satisfied with her digital skills stated:

We should not go against the future. [Woman, age 89 years]

The participants expressed a positive view of the technological developments and possibilities that digital devices offered, and they wanted to improve their knowledge and skills related to digital practice. For some participants, this was challenging, and they expressed frustration over how technological developments “moved too fast ,” but some participants interpreted these challenges as a way to “keep their mind sharp. ”

Another recurring pattern was that the participants expressed great interest in using digital services related to the health care system and other public institutions. The importance of being able to navigate digital services was explicitly clear when talking about finding test answers, written electronic messages, and questionnaires from the hospital or other public institutions. Keeping up with developments, communicating with public institutions, and taking an interest in their own health and well-being were described as good reasons to learn to use digital tools.

However, other aspects also affected these learning facilitators. Some participants felt alienated while using digital tools and described the practice as something related to feelings of anxiety, fear, and stupidity as well as something that demanded “a certain amount of courage. ” Some participants felt frustrated with the digital challenges they experienced, especially when the challenges were difficult to overcome because of their physical conditions:

I get sad because of it (digital challenges) and I get very frustrated and it takes a lot of time because I have difficulty seeing when I look away from the computer and have to turn back again to find out where I was and continue there (...) It pains me that I have to use so much time on it. [Man, age 71 years]

Fear of making mistakes, particularly when communicating with public institutions, for example, the health care system, was a common pattern. Another pattern was the fear of misinterpreting the sender and the need to ensure that the written electronic messages were actually from the described sender. Some participants felt that they were forced to learn about digital tools because they cared a lot about the services. Furthermore, fears of digital services replacing human interaction were a recurring concern among the participants. Despite these initial and recurring feelings, some participants learned how to navigate the digital services that they deemed relevant. Another recurring pattern in this learning process was repetition, the practice of digital skills, and consistent assistance from other people. One participant expressed the need to use the services often to remember the necessary skills:

Now I can figure it out because now I’ve had it shown 10 times. But then three months still pass... and then I think...how was it now? Then I get sweat on my forehead (feel nervous) and think; I’m not an idiot. [Woman, age 82 years]

For some participants, learning how to use digital tools demanded time and patience, as challenges had to be overcome more than once because they reappeared until the use of digital tools was more automatized into their everyday lives. Using digital tools and health services was viewed as easier and less stressful when part of everyday routines.

Life Without Digital Tools: Not a Free Choice

Even though some participants used digital tools daily, other participants expressed that it was “too late for them.” These participants did not view it as a free choice but as something they had to accept that they could not do. They wished that they could have learned it earlier in life but did not view it as a possibility in the future. Furthermore, they saw potential in digital services, including digital health care services, but they did not know exactly what services they were missing out on. Despite this lack of knowledge, they still felt sad about the position they were in. One participant expressed what she thought regarding the use of digital tools in public institutions:

Well, I feel alright about it, but it is very, very difficult for those of us who do not have it. Sometimes you can feel left out—outside of society. And when you do not have one of those (computers)...A reference is always made to w and w (www.) and then you can read on. But you cannot do that. [Woman, age 94 years]

The feeling of being left out of society was consistent among the participants who did not use digital tools. To them, digital systems seemed to provide unfair treatment based on something outside of their own power. Participants who were heavily affected by their medical conditions and could not use digital services also felt left out because they saw the advantages of using digital tools. Furthermore, a participant described the feelings connected to the use of digital tools in public institutions:

It is more annoying that it does not seem to work out in my favour. [Woman, age 62 years]

These statements indicated that it is possible for individuals to want to use digital tools and simultaneously find them too challenging. These participants were aware that there are consequences of not using digital tools, and that saddens them, as they feel like they are not receiving the same treatment as other people in society and the health care system.

Principal Findings

The insights from our findings demonstrated that our participants had different digital practices and different experiences with digital tools and services; however, the analysis also highlighted patterns related to how digital services and tools were used. Specific conditions were important for the possibility of digital practice, including having access to social resources; possessing the necessary capabilities; and feeling motivated, secure, and comfortable . These prerequisites were necessary to have positive experiences using digital tools in the health care system, although some participants who lived up to these prerequisites were still skeptical toward digital solutions. Others who did not live up to these prerequisites experienced challenges and even though they were aware of opportunities, this awareness made them feel left out. A few participants even viewed the digital tools as a threat to their participation in society. This supports the notion of Norgaard et al [ 13 ] that the attention paid to digital capability demands from eHealth systems is very important. Furthermore, our findings supported the argument of Hjeltholt and Papazu [ 17 ] that it is important to better understand experiences related to digital services. In our study, we accommodate this request and bring forth a broad perspective on experiences with digital practices; we particularly add insight into the challenges with digital practices for patients who also have acute or chronic illness, with some of them also experiencing physical, communicative, and cognitive difficulties. To our knowledge, there is limited existing literature focusing on digital practices that do not have a limited scope, for example, a focus on perspectives on eHealth literacy in the use of apps [ 26 ] or intervention studies with a focus on experiences with digital solutions, for example, telemedicine during the COVID-19 pandemic [ 27 ]. As mentioned by Hjeltholt et al [ 10 ], certain citizens are dependent on their own social networks in the process of using and learning digital tools. Rasi et al [ 28 ] and Airola et al [ 29 ] argued that digital health literacy is situated and should include the capabilities of the individual’s social network. Our findings support these arguments that access to social resources is an important condition; however, the findings also highlight that these resources can be particularly crucial in the use of digital health services, for example, when interpreting and understanding digital and written electronic messages related to one’s own health course or when dealing with physical, cognitive, and communicative disadvantages. Therefore, we argue that the awareness of the disadvantages is important if we want to understand patients’ digital capabilities, and the inclusion of the next of kin can be evident in unveiling challenges that are unknown and not easily visible or when trying to reach patients with digital challenges through digital means.

Studies by Kayser et al [ 30 ] and Kanoe et al [ 31 ] indicated that patients’ abilities to interpret and understand digital health–related services and their benefits are important for the successful implementation of eHealth services—an argument that our findings support. Health literacy in both digital and physical contexts is important if we want to understand how to better design and implement services. Our participants’ statements support the argument that communication through digital means cannot be viewed as similar to face-to-face communication and that an emphasis on digital health literacy demonstrates how health systems are demanding different capabilities from the patients [ 13 ]. We argue that it is important to communicate the purposes of digital services so that both the patient and their next of kin know why they participate and how it can benefit them. Therefore, it is important to make it as clear as possible that digital health services can benefit the patient and that these services are developed to support information, communication, and dialogue between patients and health professionals. However, our findings suggest that even after interpreting and understanding the purposes of digital health services, some patients may still experience challenges when using digital tools.

Therefore, it is important to understand how and why patients learn digital skills, particularly because both experience with digital devices and estimation of the value of digital tools have been highlighted as key factors for digital practices [ 5 , 18 ]. Our findings indicate that a combination of these factors is important, as recognizing the value of digital tools was not enough to facilitate the necessary learning process for some of our participants. Instead, our participants described the use of digital tools as complex and continuous processes in which automation of skills, assistance from others, and time to relearn forgotten knowledge were necessary and important facilitators for learning and understanding digital tools as well as becoming more comfortable and confident in the use of digital health services. This was particularly important, as it was more encouraging for our participants to learn digital tools when they felt secure, instead of feeling afraid and anxious, a point that Bailey et al [ 18 ] also highlighted. The value of digital solutions and the will to learn were greater when challenges were viewed as something to overcome and learn from instead of something that created a feeling of being stupid. This calls for attention on how to simplify and explain digital tools and services so that users do not feel alienated. Our findings also support the argument that digital health literacy should take into account emotional well-being related to digital practice [ 32 ].

The various perspectives that our participants provided regarding the use of digital tools in the health care system indicate that patients are affected by the use of digital health services and their own capabilities to use digital tools. Murray et al [ 33 ] argued that the use of digital tools in health sectors has the potential to improve health and health delivery by improving efficacy, efficiency, accessibility, safety, and personalization, and our participants also highlighted these positive aspects. However, different studies found that some patients, particularly older adults considered socially vulnerable, have lower digital health literacy [ 10 , 34 , 35 ], which is an important determinant of health and may widen disparities and inequity in health care [ 16 ]. Studies on older adult populations’ adaptation to information and communication technology show that engaging with this technology can be limited by the usability of technology, feelings of anxiety and concern, self-perception of technology use, and the need for assistance and inclusive design [ 36 ]. Our participants’ experiences with digital practices support the importance of these focus areas, especially when primarily older patients are admitted to hospitals. Furthermore, our findings indicate that some older patients who used to view themselves as being engaged in their own health care felt more distanced from the health care system because of digital services, and some who did not have the capabilities to use digital tools felt that they were treated differently compared to the rest of society. They did not necessarily view themselves as vulnerable but felt vulnerable in the specific experience of trying to use digital services because they wished that they were more capable. Moreover, this was the case for patients with physical and cognitive difficulties, as they were not necessarily aware of the challenges before experiencing them. Drawing on the phenomenological and feministic approach by Ahmed [ 37 ], these challenges that make patients feel vulnerable are not necessarily visible to others but can instead be viewed as invisible institutional “walls” that do not present themselves before the patient runs into them. Some participants had to experience how their physical, cognitive, or communicative difficulties affected their digital practice to realize that they were not as digitally capable as they once were or as others in society. Furthermore, viewed from this perspective, our findings could be used to argue that digital capabilities should be viewed as a privilege tied to users’ physical bodies and that digital services in the health care system are indirectly making patients without this privilege vulnerable. This calls for more attention to the inequities that digital tools and services create in health care systems and awareness that those who do not use digital tools are not necessarily indifferent about the consequences. Particularly, in a context such as the Danish one, in which the digital strategy is to create an intertwined and user-friendly public digital sector for everyone, it needs to be understood that patients have different digital capabilities and needs. Although some have not yet had a challenging experience that made them feel vulnerable, others are very aware that they receive different treatment and feel that they are on their own or that the rest of the society does not care about them. Inequities in digital health care, such as these, can and should be mitigated or prevented, and our investigation into the experiences with digital practices can help to show that we are creating standards and infrastructures that deliberately exclude the perspectives of those who are most in need of the services offered by the digital health care system [ 8 ]. Therefore, our findings support the notions that flexibility is important in the implementation of universal public digital services [ 17 ]; that it is important to adjust systems in accordance with patients’ eHealth literacy and not only improve the capabilities of individuals [ 38 ]; and that the development and improvement of digital health literacy are not solely an individual responsibility but are also tied to ways in which institutions organize, design, and implement digital tools and services [ 39 ].

Limitations

This qualitative study provided novel insights into the experiences with public digital health services from the perspective of patients in the Danish context, enabling a deeper understanding of how digital health services and digital tools are experienced and used. This helps build a solid foundation for future interventions aimed at digital health literacy and digital health interventions. However, this study has some limitations. First, the study was conducted in a country where digitalization is progressing quickly, and people, therefore, are accustomed to this pace. Therefore, readers must be aware of this. Second, the study included patients with different neurological conditions; some of their digital challenges were caused or worsened by these neurological conditions and are, therefore, not applicable to all patients in the health system. However, the findings provided insights into the patients’ digital practices before their conditions and other challenges not connected to neurological conditions shared by patients. Third, the study was broad, and although a large number of informants was included, from a qualitative research perspective, we would recommend additional research in this field to develop interventions that target digital health literacy and the use of digital health services.

Conclusions

Experiences with digital tools and digital health services are complex and multifaceted. The advantages in communication, finding information, or navigating through one’s own health course work as facilitators for engaging with digital tools and digital health services. However, this is not enough on its own. Furthermore, feeling secure and motivated and having time to relearn and practice skills are important facilitators. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges that require assistance. Digitalization of the health care system means that patients do not have the choice to opt out of using digital services without having consequences, resulting in them receiving a different treatment than others. To ensure digitalization does not create inequities in health, it is necessary for developers and the health institutions that create, design, and implement digital services to be aware of differences in digital health literacy and to focus on simplifying communication with patients and next of kin through and about digital services. It is important to focus on helping individuals meet the necessary conditions and finding flexible solutions for those who do not have the same privileges as others if the public digital sector is to work for everyone.

Acknowledgments

The authors would like to thank all the people who gave their time to be interviewed for the study, the clinical nurse specialists who facilitated interviewing patients, and the other nurses on shift who assisted in recruiting participants.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 14.03.23; peer-reviewed by G Myreteg, J Eriksen, M Siermann; comments to author 18.09.23; revised version received 09.10.23; accepted 27.02.24; published 11.04.24.

©Christian Gybel Jensen, Frederik Gybel Jensen, Mia Ingerslev Loft. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

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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Qualitative research in health care

Assessing quality in qualitative research, nicholas mays.

a Social Policy Branch, The Treasury, PO Box 3724, Wellington, New Zealand, b Department of Social Medicine, University of Bristol, Bristol BS8 2PR

Catherine Pope

The views expressed in this paper are those of the authors and do not necessarily reflect the views of the New Zealand Treasury, in the case of NM. The Treasury takes no responsibility for any errors or omissions in, or for the correctness of the information contained in this article.

In the past decade, qualitative methods have become more commonplace in areas such as health services research and health technology assessment, and there has been a corresponding rise in the reporting of qualitative research studies in medical and related journals. 1 Interest in these methods and their wider exposure in health research has led to necessary scrutiny of qualitative research. Researchers from other traditions are increasingly concerned to understand qualitative methods and, most importantly, to examine the claims researchers make about the findings obtained from these methods.

The status of all forms of research depends on the quality of the methods used. In qualitative research, concern about assessing quality has manifested itself recently in the proliferation of guidelines for doing and judging qualitative work. 2 – 5 Users and funders of research have had an important role in developing these guidelines as they become increasingly familiar with qualitative methods, but require some means of assessing their quality and of distinguishing “good” and “poor” quality research. However, the issue of “quality” in qualitative research is part of a much larger and contested debate about the nature of the knowledge produced by qualitative research, whether its quality can legitimately be judged, and, if so, how. This paper cannot do full justice to this wider epistemological debate. Rather it outlines two views of how qualitative methods might be judged and argues that qualitative research can be assessed according to two broad criteria: validity and relevance.

Summary points

• Qualitative methods are now widely used and increasingly accepted in health research, but quality in qualitative research is a mystery to many health services researchers
• There is considerable debate over the nature of the knowledge produced by such methods and how such research should be judged
• Antirealists argue that qualitative and quantitative research are very different and that it is not possible to judge qualitative research by using conventional criteria such as reliability, validity, and generalisability
• Quality in qualitative research can be assessed with the same broad concepts of validity and relevance used for quantitative research, but these need to be operationalised differently to take into account the distinctive goals of qualitative research

Two opposing views

There has been considerable debate over whether qualitative and quantitative methods can and should be assessed according to the same quality criteria. Extreme relativists hold that all research perspectives are unique and each is equally valid in its own terms, but this position means that research cannot derive any unequivocal insights relevant to action, and it would therefore command little support among applied health researchers. 6 Other than this total rejection of any quality criteria, it is possible to identify two broad, competing positions, for and against using the same criteria. 7 Within each position there is a range of views.

Separate and different: the antirealist position

Advocates of the antirealist position argue that qualitative research represents a distinctive paradigm and as such it cannot and should not be judged by conventional measures of validity, generalisability, and reliability. At its core, this position rejects naive realism—a belief that there is a single, unequivocal social reality or truth which is entirely independent of the researcher and of the research process; instead there are multiple perspectives of the world that are created and constructed in the research process. 8

Relativist criteria for quality 7

• Degree to which substantive and formal theory is produced and the degree of development of such theory
• Novelty of the claims made from the theory
• Consistency of the theoretical claims with the empirical data collected
• Credibility of the account to those studied and to readers
• Extent to which the description of the culture of the setting provides a basis for competent performance in the culture studied
• Extent to which the findings are transferable to other settings
• Reflexivity of the account—that is, the degree to which the effects of the research strategies on the findings are assessed or the amount of information about the research process that is provided to readers

Those relativists who maintain that assessment criteria are feasible but that distinctive ones are required to evaluate qualitative research have put forward a range of different assessment schemes. In part, this is because the choice and relative importance of different criteria of quality depend on the topic and the purpose of the research. Hammersley has attempted to pull together these quality criteria (box). 7 These criteria are open to challenge (for example, it is arguable whether all research should be concerned to develop theory). At the same time, many of the criteria listed are not exclusive to qualitative research.

Using criteria from quantitative research: subtle realism

Other authors agree that all research involves subjective perception and that different methods produce different perspectives, but, unlike the anti-realists, they argue that there is an underlying reality which can be studied. 9 , 10 The philosophy of qualitative and quantitative researchers should be one of “subtle realism”—an attempt to represent that reality rather than to attain “the truth.” From this position it is possible to assess the different perspectives offered by different research processes against each other and against criteria of quality common to both qualitative and quantitative research, particularly those of validity and relevance. However, the meansof assessment may be modified to take account of the distinctive goals of qualitative research. This is our position.

Assessing the validity of qualitative research

There are no mechanical or “easy” solutions to limit the likelihood that there will be errors in qualitative research. However, there are various ways of improving validity, each of which requires the exercise of judgment on the part of researcher and reader.

Triangulation

Triangulation compares the results from either two or more different methods of data collection (for example, interviews and observation) or, more simply, two or more data sources (for example, interviews with members of different interest groups). The researcher looks for patterns of convergence to develop or corroborate an overall interpretation. This is controversial as a genuine test of validity because it assumes that any weaknesses in one method will be compensated by strengths in another, and that it is always possible to adjudicate between different accounts (say, from interviews with clinicians and patients). Triangulation may therefore be better seen as a way of ensuring comprehensiveness and encouraging a more reflexive analysis of the data (see below) than as a pure test of validity.

Respondent validation

Respondent validation, or “member checking,” includes techniques in which the investigator's account is compared with those of the research subjects to establish the level of correspondence between the two sets. Study participants' reactions to the analyses are then incorporated into the study findings. Although some researchers view this as the strongest available check on the credibility of a research project, 8 it has its limitations. For example, the account produced by the researcher is designed for a wide audience and will, inevitably, be different from the account of an individual informant simply because of their different roles in the research process. As a result, it is better to think of respondent validation as part of a process of error reduction which also generates further original data, which in turn requires interpretation. 11

Clear exposition of methods of data collection and analysis

Since the methods used in research unavoidably influence the objects of inquiry (and qualitative researchers are particularly aware of this), a clear account of the process of data collection and analysis is important. By the end of the study, it should be possible to provide a clear account of how early, simpler systems of classification evolved into more sophisticated coding structures and thence into clearly defined concepts and explanations for the data collected. Although it adds to the length of research reports, the written account should include sufficient data to allow the reader to judge whether the interpretation proffered is adequately supported by the data.

Reflexivity

Reflexivity means sensitivity to the ways in which the researcher and the research process have shaped the collected data, including the role of prior assumptions and experience, which can influence even the most avowedly inductive inquiries. Personal and intellectual biases need to be made plain at the outset of any research reports to enhance the credibility of the findings. The effects of personal characteristics such as age, sex, social class, and professional status (doctor, nurse, physiotherapist, sociologist, etc) on the data collected and on the “distance” between the researcher and those researched also needs to be discussed.

Attention to negative cases

As well as exploration of alternative explanations for the data collected, a long established tactic for improving the quality of explanations in qualitative research is to search for, and discuss, elements in the data that contradict, or seem to contradict, the emerging explanation of the phenomena under study. Such “deviant case analysis” helps refine the analysis until it can explain all or the vast majority of the cases under scrutiny.

Fair dealing

The final technique is to ensure that the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of one group is never presented as if it represents the sole truth about any situation.

Research can be relevant when it either adds to knowledge or increases the confidence with which existing knowledge is regarded. Another important dimension of relevance is the extent to which findings can be generalised beyond the setting in which they were generated. One way of achieving this is to ensure that the research report is sufficiently detailed for the reader to be able to judge whether or not the findings apply in similar settings. Another tactic is to use probability sampling (to ensure that the range of settings chosen is representative of a wider population, for example by using a stratified sample). Probability sampling is often ignored by qualitative researchers, but it can have its place. Alternatively, and more commonly, theoretical sampling ensures that an initial sample is drawn to include as many as possible of the factors that might affect variability of behaviour, and then this is extended, as required, in the light of early findings and emergent theory. 2 The full sample, therefore, attempts to include the full range of settings relevant to the conceptualisation of the subject.

Some questions about quality that might be asked of a qualitative study

• Worth or relevance—Was this piece of work worth doing at all? Has it contributed usefully to knowledge?
• Clarity of research question — If not at the outset of the study, by the end of the research process was the research question clear? Was the researcher able to set aside his or her research preconceptions?
• Appropriateness of the design to the question—Would a different method have been more appropriate? For example, if a causal hypothesis was being tested, was a qualitative approach really appropriate?
• Context—Is the context or setting adequately described so that the reader could relate the findings to other settings?
• Sampling—Did the sample include the full range of possible cases or settings so that conceptual rather than statistical generalisations could be made (that is, more than convenience sampling)? If appropriate, were efforts made to obtain data that might contradict or modify the analysis by extending the sample (for example, to a different type of area)?
• Data collection and analysis—Were the data collection and analysis procedures systematic? Was an “audit trail” provided such that someone else could repeat each stage, including the analysis? How well did the analysis succeed in incorporating all the observations? To what extent did the analysis develop concepts and categories capable of explaining key processes or respondents' accounts or observations? Was it possible to follow the iteration between data and the explanations for the data (theory)? Did the researcher search for disconfirming cases?
• Reflexivity of the account—Did the researcher self consciously assess the likely impact of the methods used on the data obtained? Were sufficient data included in the reports of the study to provide sufficient evidence for readers to assess whether analytical criteria had been met?

Is there any place for quality guidelines?

Whether quality criteria should be applied to qualitative research, which criteria are appropriate, and how they should be assessed is hotly debated. It would be unwise to consider any single set of guidelines as definitive. We list some questions to ask of any piece of qualitative research (box); the questions emphasise criteria of relevance and validity. They could also be used by researchers at different times during the life of a particular research project to improve its quality.

Although the issue of quality in qualitative health and health services research has received considerable attention, a recent paper was able to argue, legitimately, that “quality in qualitative research is a mystery to many health services researchers.” 12 However, qualitative researchers can address the issue of quality in their research. As in quantitative research, the basic strategy to ensure rigour, and thus quality, in qualitative research is systematic, self conscious research design, data collection, interpretation, and communication. Qualitative research has much to offer. Its methods can, and do, enrich our knowledge of health and health care. It is not, however, an easy option or the route to a quick answer. As Dingwall et al conclude, “qualitative research requires real skill, a combination of thought and practice and not a little patience.” 12

Further reading

Murphy E, Dingwall R, Greatbatch D, Parker S, Watson P. Qualitative research methods in health technology assessment: a review of the literature . Health Technology Assessment 1998;2.

Dingwall R, Murphy E, Watson P, Greatbatch D, Parker S. Catching goldfish: quality in qualitative research. Journal of Health Services Research and Policy 1998;3:167-72.

Acknowledgments

We acknowledge the contribution of the HTA report on qualitative research methods by Elizabeth Murphy, Robert Dingwall, David Greatbatch, Susan Parker, and Pamela Watson to this paper. We thank these authors for their careful exposition of a tangled series of debates, and their timely publication of this literature review.

This is the first in a series of three articles

Series editors: Catherine Pope and Nicholas Mays

Competing interests: None declared.

This article is taken from the second edition of Qualitative Research in Health Care , edited by Catherine Pope and Nicholas Mays, published by BMJ Books

• Open access
• Published: 03 April 2024

Capturing artificial intelligence applications’ value proposition in healthcare – a qualitative research study

• Jasmin Hennrich 1 ,
• Eva Ritz 2 ,
• Peter Hofmann 1 , 4 &
• Nils Urbach 1 , 3  

BMC Health Services Research volume  24 , Article number:  420 ( 2024 ) Cite this article

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Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications’ potential.

We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.

Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.

We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.

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Applications based on artificial intelligence (AI) have the potential to transform the healthcare (HC) industry [ 1 ]. AI applications can be characterized as applications or agents with capabilities that typically demand intelligence [ 2 , 3 ]. In our context, we understand AI as a collection of technological solutions from the field of applied computer science, in which algorithms are trained on medical and HC data to perform tasks that are normally associated with human intelligence (i.e., medical decision-making) [ 4 ]. AI is not a single type of technology, instead, it encompasses a diverse array of technologies spread across various application areas in HC, such as diagnostics (e.g., [ 5 ], biomedical research (e.g., [ 6 ], clinical administration (e.g., [ 7 ], therapy (e.g., [ 8 ], and intelligent robotics (e.g., [ 9 ]. These areas are expected to benefit from AI applications’ capabilities, such as accuracy, objectivity, rapidity, data processing, and automation [ 10 , 11 ]. Accordingly, AI applications are said to have the potential to drive business value and enhance HC [ 12 ], paving the way for transformative innovations in the HC industry [ 13 ]. There are already many promising AI use cases in HC that are expected to improve patient care and create value for HC organizations. For instance, AI applications can advance the quality of patient care by supporting radiologists with more accurate and rapid diagnosis, compensating for humans’ limitations (e.g., data processing speeds) and weaknesses (e.g., inattention, distraction, and fatigue) [ 10 , 14 ]. Klicken oder tippen Sie hier, um Text einzugeben.While the use of AI applications in HC has the overarching goal of creating significant value for patients through improved care, they also come with the potential for business value creation and the opportunity for HC organizations to gain a competitive edge (e.g., [ 15 , 16 ]).

Despite the promised advantages, AI applications’ implementation is slow, and the full realization of their potential within the HC industry is yet to be achieved [ 11 , 17 ]. With just a handful of practical examples of AI applications in the HC industry [ 13 , 18 ], the adoption of AI applications is still in its infancy. The AI in Healthcare Survey Report stated that in 2021, only 9% of respondents worldwide have reached a sophisticated adoption of AI Models, while 32% of respondents are still in the early stages of adopting AI models. According to the survey, the majority of HC organizations (60%) are not actively considering AI as a solution, or they are currently evaluating AI use cases and experimenting with the implementation [ 19 ]. Nevertheless, HC startups are increasingly entering the market [ 20 ], pressuring incumbent HC organizations to evaluate and adopt AI applications. Existing studies already investigate AI technologies in various use cases in HC and provide insights on how to design AI-based services [ 21 ], explain in detail the technical functions and capabilities of AI technologies [ 10 , 11 ], or take on a practical perspective with a focus on concrete examples of AI applications [ 14 ]. However, to foster the adoption of AI applications, HC organizations should understand how they can unfold AI applications’ capabilities into business value to ensure effective investments. Previous studies on the intersection of information systems and value creation have expressed interest into how organizations can actually gain value through the use of technology and thus, enhance their adoption [ 22 , 23 ]. However, to the best of our knowledge, a comprehensive investigation of the value creation of AI applications in the context of HC from a managerial level is currently missing. Thus, our study aims to investigate AI applications’ value creation and capture mechanisms in the specific HC context by answering the following question: How can HC organizations create and capture AI applications’ value?

We conduct a systematic literature analysis and semi structured expert interviews to answer this research question. In the systematic literature analysis, we identify and analyze a heterogeneous set of 21 AI use cases across five different HC application fields and derive 15 business objectives and six value propositions for HC organizations. We then evaluate and refine the categorized business objectives and value propositions with insights from 11 expert interviews. Our study contributes to research on the value creation mechanism of AI applications in the HC context. Moreover, our results have managerial implications for HC organizations since they can draw on our results to evaluate AI applications, assess investment decisions, and align their AI application portfolio toward an overarching strategy.

In what follows, this study first grounds on relevant work to gain a deeper understanding of the underlying constructs of AI in HC. Next, we describe our qualitative research method by describing the process of data collection and analysis, followed by our derived results on capturing AI applications’ value proposition in HC. Afterward, we discuss our results, including this study’s limitations and pathways for further research. Finally, we summarize our findings and their contribution to theory and practice in the conclusion.

Relevant work

In the realm of AI, a thorough exploration of its key subdiscipline, machine learning (ML), is essential [ 24 , 25 ]. ML is a computational model that learns from data without explicitly programming the data [ 24 ] and can be further divided into supervised, unsupervised, and reinforcement learning [ 26 ]. In supervised learning, the machine undergoes training with labeled data, making it well-suited for tasks involving regression and classification problems [ 27 ]. In contrast, unsupervised learning is designed to automatically identify patterns within unlabeled datasets [ 28 ], with its primary utility lying in the extraction of features [ 11 ]. Reinforcement learning, characterized as a method of systematic experimentation or trial and error, involves a situated agent taking specific actions and observing the rewards it gains from those actions, facilitating the learning of behavior in a given environment [ 29 ]. The choice of which type of ML will be used in the different application areas depends on the specific problem, the availability of labeled data, and the nature of the desired outcome.

In recent years, the rapid advances in AI have triggered a revolution in various areas, with numerous impressive advantages. In the financial sector, AI applications can significantly improve security by detecting anomalies and preventing fraud [ 30 ]. Within education, AI has emerged as a powerful tool for tailoring learning experiences, aiming to enhance engagement, understanding, and retention [ 31 ]. In the energy market, the efficacy of AI extends to fault detection and diagnosis in building energy systems, showcasing its robust capabilities in ensuring system integrity [ 32 ]. Moreover, the HC industry is expected to be a promising application area for AI applications. The HC sector is undergoing a significant transformation due to the increasing adoption of digital technologies, with AI technologies at the forefront of this shift. The increasing relevance of AI technologies in HC is underlined by a growing and multidisciplinary stream of AI research, as highlighted by Secinaro et al. [ 33 ]. Taking a closer look at the different application areas in HC, AI applications offer promising potential, as demonstrated by the following exemplary AI use cases. In diagnosis, AI applications can identify complex patterns in medical image data more accurately, resulting in precise and objective disease recognition. This can improve patient safety by reducing the risks of misinterpretation [ 5 ]. Another use case can be found in biomedical research. For example, AI technology is commonly used for de novo drug design. AI can rapidly browse through molecule libraries to detect nearly \({10}^{60}\) drug-like molecules, accelerating the drug development process [ 6 ]. Furthermore, AI applications are used in clinical administration. They enable optimized operation room capacities by automating the process and by including information about absence or waiting times, as well as predicting interruptions [ 34 ]. Furthermore, AI applications are used in therapy by predicting personalized medication dosages. As this helps to reduce the mortality risk, it leads to enhanced patient outcomes and quality of care [ 35 ]. Intelligent prostheses by which patients can improve interactions are another use case. The AI algorithm continuously detects and classifies myoelectric signal patterns to predict movements, leading to reduced training expenditure and more self-management by the patient [ 36 ]. In summary, envisioning that AI applications successfully address persisting challenges, such as lack of transparency (e.g., [ 37 ], bias (e.g., [ 38 ], privacy concerns, and trust issues (e.g., [ 39 ], the potential of AI applications is vast. The conceivable benefits extend to individual practitioners and HC organizations, including hospitals, enabling them to harness AI applications for creating business value and ultimately enhancing competitiveness. Thereby, we follow Schryen’s (p. 141) revisited definition of business value of technologies: “the impact of investments on the multidimensional performance and capabilities of economic entities at various levels, complemented by the ultimate meaning of performance in the economic environment” [ 40 ]. His perspective includes all kinds of tangible value (such as an increase in productivity or reduced costs) to intangible value (such as service innovation or customer satisfaction), as well as internal value for the HC organizations and external value for stakeholders, shareholders, and customers. To create business value, it is essential to have a clear understanding of how the potential of AI applications can be captured. The understanding of how information systems, in general, create value is already covered in the literature. For example, Badakhshan et al. [ 31 ] focus on how process mining can pave the way to create business value. Leidner et al. [ 32 ] examine how enterprise social media adds value for new employees, and Lehrer et al. [ 33 ] answer the question of how big data analytics can enable service. There are also studies focusing on the value creation of information systems in the context of HC. For instance, the study by Haddad and Wickramasinghe [ 41 ] shows that information technology in HC can capture value by improving the quality of HC delivery, increasing safety, or offering additional services. Strong et al. [ 42 ] analyze how electronic health records afford value for HC organizations and determine goal-oriented actions to capture this potential. There is even literature on how machine learning adds value within the discipline of radiology (e.g., [ 43 ].

However, these studies either do not address the context of HC, consider technologies other than AI or information systems in general, or focus only on a small area of HC (e.g., radiology) and a subset of AI technology (e.g., machine learning). Although these studies deliver valuable insights into the value creation of information systems, a comprehensive picture of how HC organizations can capture business value with AI applications is missing.

To answer our research question, we adopted a qualitative inductive research design. This research design is consistent with studies that took a similar perspective on how technologies can create business value [ 44 ]. In conducting our structured literature review, we followed the approach of Webster and Watson [ 45 ] and included recommendations of Wolfswinkel et al. [ 46 ] when considering the inclusion and exclusion criteria. We started by collecting relevant data on different successful AI use cases across five application areas in HC. Siggelkow [ 47 ] argued that use cases are able to provide persuasive arguments for causal relationships. In an initial literature screening, we identified five promising application domains focusing on AI applications for patients and HC providers: disease diagnostics (DD) (e.g., [ 5 ], biomedical research (BR) (e.g., [ 6 ], clinical administration (CA) (e.g., [ 7 ], therapy (T) (e.g., [ 8 ], and intelligent robotics (IR) (e.g., [ 9 ]. Second, to sample AI use cases, we aimed to collect a heterogeneous set of AI use cases within these application domains and consider the heterogeneity in AI applications, underlying data, innovation types, and implementation stages when selecting 21 AI use cases for our in-depth analysis. The AI use case and an exemplary study for each use case are listed in Table  1 .

After sampling the AI use cases, we used PubMed to identify papers for each use case. PubMed is recognized as a common database for biomedical and medical research for HC topics in the information systems domain (e.g., [ 62 , 63 ]. Our search included journal articles, clinical conferences, clinical studies, and comparative studies in English as of 2010. Based on the AI use case sample, we derived a search string based on keywords [ 45 ] considering titles and abstracts by following Shepherd et al. [ 62 ] guidelines. It was aimed to narrow and specific selection to increase data collection replicability for the use cases. Boolean operators (AND, OR) are used to improve results by combining search terms [ 62 ].

((artificial intelligence AND (radiology OR (cancer AND imaging) OR (radiology AND error) OR (cancer AND genomics) OR (speech AND cognitive AND impairment) OR (voice AND parkinson) OR EEG OR (facial AND analysis) OR (drug AND design) OR (Drug AND Biomarker) OR De-identification OR Splicing OR (emergency AND triage) OR (mortality AND prediction) OR (operating AND room) OR text summarization OR (artificial AND pancreas) OR vasopressor OR Chatbot OR (myoelectric prosthesis) OR (automated surgery task) OR (surgery AND workflow)))

The initial search led to 877 results (see Fig.  1 ). After title screening, we eliminated 516 papers that are not relevant (i.e., not covering a specific AI application, only including the description of AI algorithm, or not including a managerial perspective and the value created by AI applications). We further excluded 162 papers because their abstract is not concurrent with any specific use case (e.g., because they were literature reviews on overarching topics and did not include a specific AI application). We screened the remaining 199 papers for eligibility through two content-related criteria. First, papers need to cover an AI use case’s whole value proposition creation path, including information on data, algorithms, functions, competitive advantage, and business value of a certain AI application. The papers often only examine how a certain application works but lack the value proposition perspective, which leads to the exclusion of 63 articles. Second, we removed 89 papers that do not match any of our use cases. This step led to a remaining set of 47 relevant papers. During a backward-forward search according to Webster and Watson [ 45 ] and Levy and Ellis [ 64 ], we additionally included 35 papers. We also incorporated previous and subsequent clinical studies of the same researcher, resulting in an additional six papers. The final set contains 88 relevant papers describing the identified AI use cases, whereby at least three papers describe each AI use case.

Search strategy

In the second step, we engaged in open, axial, and selective coding of the AI use cases following analysis techniques of grounded theory [ 65 ]. We focused on extracting business objectives, detailing how each AI application drives value. We documented these for each AI use case by recording codes of business objectives and value propositions and assigning relationships among the open codes. For example, from the following text passage of Berlyand et al. [ 56 ], who investigate the use case CA1: “Rapidly interpreting clinical data to classify patients and predict outcomes is paramount to emergency department operations, with direct impacts on cost, efficiency, and quality of care”, we derived the code rapid task execution.

After analyzing the AI use cases, we revised the documented tuples to foster consistency and comparability. Then, we iteratively coded the identified tuples by relying on selective coding techniques which is a process to identify and refine categories at a highly generalizable degree [ 65 ]. In all 14 coding iterations, one author continuously compares, relates, and associates categories and properties and discusses the coding results with another author. We modified some tuples during the coding process in two ways. First, we equalized small phrasing disparities for homogenous and refined wording. Second, we carefully adjusted the tuples regarding coherency. Finally, we reviewed the coding schema for internal validity through a final comparison with the data [ 66 ]. Then, we set the core variables “business objectives” and “value propositions”. We refer to business objectives as improvements through implementing the technology that drives a value proposition. We define value proposition as the inherent commitment to deliver reciprocal value to the organization, its customers, and/or partners [ 67 ].

In the third step following Schultze and Avital [ 68 ], we conducted semi structured expert interviews to evaluate and refine the value propositions and business objectives. We developed and refined an interview script following the guidelines of Meyers and Newman [ 69 ] for qualitative interviews. An additional file shows the used interview script (see Additional file 1 ). We conducted expert sampling to select suitable interviewees [ 70 ]. Due to the interdisciplinarity of the research topic, we chose experts in the two knowledge areas, AI and HC. In the process of expert selection, we ensured that interviewees possessed a minimum of two years of experience in their respective fields. We aimed for a well-balanced mix of diverse professions and positions among the interviewees. Additionally, for those with a primary background in HC, we specifically verified their proficiency and understanding of AI, ensuring a comprehensive perspective across the entire expert panel. Table 2 provides an overview of our expert sample. The interviewees were recruited in the authors’ networks and by cold calling. Identified experts were first contacted by email, including some brief information regarding the study. If there was no response within two weeks, they were contacted again by telephone to arrange an interview date. In total, we conducted 11 interviews that took place in a time range between 40 and 75 min. The expert interviews are transcribed verbatim using the software f4. As a coding aid, we use the software MAXQDA—a tool for qualitative data analysis which is frequently used in the analyses of qualitative data in the HC domain (e.g., [ 38 , 71 , 72 ]).

To systematically decompose how HC organizations can realize value propositions from AI applications, we identified 15 business objectives and six value propositions (see Fig.  2 ). These business objectives and value propositions resulted from analyzing the collected data, which we derived from the literature and refined through expert interviews. In the following, we describe the six value propositions and elaborate on how the specific AI business objectives can result in value propositions. This will be followed by a discussion of the results in the discussion of the paper.

Business objectives and value propositions risk-reduced patient care

This value proposition follows business objectives that may identify and reduce threats and adverse factors during medical procedures. HC belongs to a high-risk domain since there are uncertain external factors (E4), including physicians’ fatigue, distractions, or cognitive biases [ 73 , 74 ]. AI applications can reduce certain risks by enabling precise decision support, detecting misconduct, reducing emergent side effects, and reducing invasiveness.

Precise decision support stems from AI applications’ capability to integrate various data types into the decision-making process, gaining a sophisticated overview of a phenomenon. Precise knowledge about all uncertainty factors reduces the ambiguity of decision-making processes [ 49 ]. E5 confirms that AI applications can be seen as a “perceptual enhancement”, enabling more comprehensive and context-based decision support. Humans are naturally prone to innate and socially adapted biases that also affect HC professionals [ 14 ]. Use Case CA1 highlights how rapid decision-making by HC professionals during emergency triage may lead to overlooking subtle yet crucial signs. AI applications can offer decision support based on historical data, enhancing objectivity and accuracy [ 56 ].

Detection of misconduct is possible since AI applications can map and monitor clinical workflows and recognize irregularities early. In this context, E10 highlights that “one of the best examples is the interception of abnormalities.” For instance, AI applications can assist in allocating medications in hospitals (Use case T2). Since HC professionals can be tired or distracted in medication preparation, AI applications may avoid serious consequences for patients by monitoring allocation processes and patients’ reactions. Thus, AI applications can reduce abuse and increase safety.

Reduction of emergent side effects is enabled by AI applications that continuously monitor and process data. If different treatments and medications are combined during a patient’s clinical pathway, it may cause overdosage or evoke co-effects and comorbidities, causing danger for the patient [ 75 ]. AI applications can prevent these by detecting and predicting these effects. For instance, AI applications can calculate the medication dosage for the individual and predict contraindications (Use case T2) [ 76 ]. E3 adds that the reduction of side effects also includes “cross-impacts between medications or possible symptoms that only occur for patients of a certain age or disease.” Avoidable side effects can thus be detected at an early stage, resulting in better outcomes.

Reduction of invasiveness of medical treatments or surgeries is possible by allowing AI applications to compensate for and overcome human weaknesses and limitations. During surgery, AI applications can continuously monitor a robot’s position and accurately predict its trajectories [ 77 ]. Intelligent robots can eliminate human tremors and access hard-to-reach body parts [ 60 ]. E2 validates, “a robot does not tremble; a robot moves in a perfectly straight line.” The precise AI-controlled movement of surgical robots minimizes the risk of injuring nearby vessels and organs [ 61 ]. Use cases DD5 and DD7 elucidate how AI applications enable new methods to perform noninvasive diagnoses. Reducing invasiveness has a major impact on the patient’s recovery, safety, and outcome quality.

Advanced patient care

Advanced patient care follows business objectives that extend patient care to increase the quality of care. One of HC’s primary goals is to provide the most effective treatment outcome. AI applications can advance patient care as they enable personalized care and accurate prognosis.

Personalized care can be enabled by the ability of AI technologies to integrate and process individual structured and unstructured patient data to increase the compatibility of patient and health interventions. For instance, by analyzing genome mutations, AI applications precisely assess cancer, enabling personalized therapy and increasing the likelihood of enhancing outcome quality (Use case DD4). E11 sums up that “we can improve treatment or even make it more specific for the patient. This is, of course, the dream of healthcare”. Use case T1 exemplifies how the integration of AI applications facilitates personalized products, such as an artificial pancreas. The pancreas predicts glucose levels in real time and adapts insulin supplementation. Personalized care allows good care to be made even better by tailoring care to the individual.

Accurate prognosis is achieved by AI applications that track, combine, and analyze HC data and historical data to make accurate predictions. For instance, AI applications can precisely analyze tumor tissue to improve the stratification of cancer patients. Based on this result, the selection of adjuvant therapy can be refined, improving the effectiveness of care [ 48 ]. Use case DD6 shows how AI applications can predict seizure onset zones to enhance the prognosis of epileptic seizures. In this context, E10 adds that an accurate prognosis fosters early and preventive care.

Self-management

Self-management follows the business objectives that increase disease controllability through the support of intelligent medical products. AI applications can foster self-management by self-monitoring and providing a new way of delivering information.

Self-monitoring is enhanced by AI applications, which can automatically process frequently measured data. There are AI-based chatbots, mobile applications, wearables, and other medical products that gather periodic data and are used by people to monitor themselves in the health context (e.g., [ 78 , 79 ]. Frequent data collection of these products (e.g., using sensors) enables AI applications to analyze periodic data and become aware of abnormalities. While the amount of data rises, the applications can improve their performance continuously (E2). Through continuous tracking of heartbeats via wearables, AI applications can precisely detect irregularities, notify their users in the case of irregularities, empower quicker treatment (E2), and may reduce hospital visits (E9). Self-monitoring enhances patient safety and allows the patient to be more physician-independent and involved in their HC.

Information delivery to the patient is enabled by AI applications that give medical advice adjusted to the patient’s needs. Often, patients lack profound knowledge about their anomalies. AI applications can contextualize patients’ symptoms to provide anamnesis support and deliver interactive advice [ 59 ]. While HC professionals must focus on one diagnostic pathway, AI applications can process information to investigate different diagnostic branches simultaneously (E5). Thus, these applications can deliver high-quality information based on the patient’s feedback, for instance, when using an intelligent conversational agent (use case T3). E4 highlights that this can improve doctoral consultations because “the patient is already informed and already has information when he comes to talk to doctors”.

Process acceleration

Process acceleration comprises business objectives that enable speed and low latencies. Speed describes how fast one can perform a task, while latency specifies how much time elapses from an event until a task is executed. AI applications can accelerate processes by rapid task execution and reducing latency.

Rapid task execution can be achieved by the ability of AI applications to process large amounts of data and identify patterns in a short time. In this context, E4 mentions that AI applications can drill diagnosis down to seconds. For instance, whereas doctors need several minutes for profound image-based detection, AI applications have a much faster report turnaround time (use case DD1). Besides, rapid data processing also opens up new opportunities in drug development. AI applications can rapidly browse through molecule libraries to detect nearly 10^60 molecules, which are synthetically available (use case BR1). This immense speed during a discovery process has an essential influence on the business potential and can enormously decrease research costs (E10).

Latency reduction can be enabled by AI technologies monitoring and dynamically processing information and environmental factors. By continuously evaluating vital signs and electrocardiogram records, AI applications can predict the in-house mortality of patients in real time [ 57 ]. The AI application can detect an increased mortality risk faster than HC professionals, enabling a more rapid emergency intervention. In this case, AI applications decrease the time delay between the cause and the reaction, which positively impacts patient care. E7 emphasizes the importance of short latencies: “One of the most important things is that the timeframe between the point when all the data is available, and a decision has been made, […] must be kept short.”

Resource optimization

Resource optimization follows the business objectives that manage limited resources and capacities. The HC industry faces a lack of sufficient resources, especially through a shortage of specialists (E8), which in turn negatively influences waiting times. AI applications can support efficient resource allocation by optimizing device utilization, organizational capacities and unleashing personnel capabilities.

Optimized device utilization can be enhanced by AI applications that track, analyze, and precisely predict load of times of medical equipment in real-time. For instance, AI applications can maximize X-Ray or magnetic resonance tomography device utilization (use case CA3). Besides, AI applications can enable a dynamic replanning of device utilization by including absence or waiting times and predicting interruptions. Intelligent resource optimization may include various key variables (e.g., the maximized lifespan of a radiation scanner) [ 48 ]. Optimized device utilization reduces the time periods when the device is not utilized, and thus, losses are made.

Optimized organizational capacities are possible due to AI applications breaking up static key performance indicators and finding more dynamic measuring approaches for the required workflow changes (E5, E10). The utilization of capacities in hospitals relies on various known and unknown parameters, which are often interdependent [ 80 ]. AI applications can detect and optimize these dependencies to manage capacity. An example is the optimization of clinical occupancy in the hospital (use case CA3), which has a strong impact on cost. E5 adds that the integration of AI applications may increase the reliability of planning HC resources since they can predict capacity trends from historical occupancy rates. Optimized planning of capacities can prevent capacities from remaining unused and fixed costs from being offset by no revenue.

Unleashing personnel capabilities is enabled by AI applications performing analytical and administrative tasks, relieving caregivers’ workload (E8, E10, E11). E7 validates that “our conviction is […] that administrational tasks generate the greatest added value and benefit for doctors and caregivers.” Administrative tasks include the creation of case summaries (use case CA4) or automated de-identification of private health information in electronic health records (use case BR2) [ 54 ]. E8 says that resource optimization enables “more time for direct contact with patients.”

Knowledge discovery

Knowledge discovery follows the business objectives that increase perception and access to novel and previously unrevealed information. AI applications might synthesize and contextualize medical knowledge to create uniform or equalized semantics of information (E5, E11). This semantics enables a translation of knowledge for specific users.

Detection of similarities is enabled by AI applications identifying entities with similar features. AI applications can screen complex and nonlinear databases to identify reoccurring patterns without any a priori understanding of the data (E3). These similarities generate valuable knowledge, which can be applied to enhance scientific research processes such as drug development (use case BR1). In drug development, AI applications can facilitate ligand-based screening to detect new active molecules based on similarities compared with already existing molecular properties. This increases the effectiveness of drug design and reduces risks in clinical trials [ 6 ].

Exploration of new correlations is facilitated by AI applications identifying relationships in data. In diagnostics, AI applications can analyze facial photographs to accurately identify genotype–phenotype correlations and, thus, increase the detection rate of rare diseases (use case DD7). E8 states the potential of AI applications in the field of knowledge discovery: “Well, if you are researching in any medical area, then everybody aims to understand and describe phenomena because science always demands a certain causation.” However, it is crucial to develop transparent and intelligible inferences that are comprehensible for HC professionals and researchers. Exploring new correlations improves diagnoses of rare diseases and ensures earlier treatment.

After describing each business objective and value proposition, we summarize the AI use cases’ contributions to the value propositions in Table  3 .

By revealing 15 business objectives that translate into six value propositions, we contribute to the academic discourse on the value creation of AI (e.g. [ 81 ] and provide prescriptive knowledge on AI applications' value propositions in the HC domain. Our discourse also emphasizes that our findings are not only relevant to the field of value creation research but can also be helpful for adoption research. The value propositions we have identified can be a good starting point to accelerate the adoption of AI in HC, as the understanding of potential value propositions that we foster could mitigate some of the current obstacles to the adoption of AI applications in HC. For example, our findings may help to mitigate the obstacle “added value”, which is presented in the study by Hennrich et al.38 [ 38 ] as users’ concerns that AI might create more burden than benefits.

Further, we deliver valuable implications for practice and provide a comprehensive picture of how organizations in the context of HC can achieve business value with AI applications from a managerial level, which has been missing until now. We guide HC organizations in evaluating their AI applications or those of the competition to assess AI investment decisions and align their AI application portfolio toward an overarching strategy. These results will foster the adoption of AI applications as HC organizations can now understand how they can unfold AI applications’ capabilities into business value. In case a hospital’s major strategy is to reduce patient risks due to limited personal capacities, it might be beneficial for them to invest in AI applications that reduce side effects by calculating medication dosages (use case T2). If an HC organization currently faces issues with overcrowded emergency rooms, the HC organization might acquire AI applications that increase information delivery and help patients decide if and when they should visit the hospital (use case T3) to increase patients’ self-management and, in turn, improve triage. Besides, our findings also offer valuable insights for AI developers. Addressing issues such as transparency and the alignment of AI applications with the needs of HC professionals is crucial. Adapting AI solutions to the specific requirements of the HC sector ensures responsible integration and thus the realization of the expected values.

A closer look at the current challenges in the HC sector reveals that new solutions to mitigate them and improve value creation are needed. Given that a nurse, for example, dedicates a substantial 25% of their working hours to administrative tasks [ 17 ], the rationale behind the respondents’ (E7) recognition of “the greatest added value” in utilizing AI applications for administrative purposes becomes evident. The potential of AI applications in streamlining administrative tasks lies in creating additional time for meaningful patient interactions. Acknowledging the significant impact of the doctor-patient interpersonal relationship on both the patient’s well-being and the processes of diagnosis and healing, as elucidated by Buck et al. [ 82 ] in their interview study, the physicians interviewed emphasized that the mere presence of the doctor in the same room often alleviates the patient’s problems. Consequently, it becomes apparent that the intangible value of AI applications plays a crucial role in the context of HC and is an important factor in the investment decision as to where an AI application should be deployed.

The interviews also indicate that the special context of the HC sector leads to concerns regarding the use of AI applications. For example, one interviewee emphasized a fundamental characteristic of medical staff by pointing out that physicians have a natural desire to understand all phenomena (E8). AI applications, however, are currently struggling with the challenge of transparency. This challenge is described by the so-called black box problem, a phenomenon that makes it impossible to decipher the underlying algorithms that lead to a particular recommendation [ 37 ]. The lack of transparency and the resulting lack of intervention options for medical staff can lead to incorrect decisions by the AI application, which may cause considerable damage. Aware of these risks, physicians are currently struggling with trust issues in AI applications [ 72 ]. The numerous opportunities for value creation through AI applications in HC are offset by the significant risk of causing considerable harm to patients if the technology is not yet fully mature. Ultimately, it remains essential to keep in mind that there are many ethical questions to be answered [ 83 ], and AI applications are still facing many obstacles [ 38 ] that must be overcome in order to realize the expected values and avoid serious harm. One important first step in mitigating the obstacles is disseminating the concerns and risks to relevant stakeholders, emphasizing the urgency for collaborative scientific and public monitoring efforts [ 84 ]. However, keeping these obstacles in mind, by providing prescriptive knowledge, we enhance the understanding of AI’s value creation paths in the HC industry and thus help to drive AI integration forward. For example, looking at the value proposition risk reduced patient care , we demonstrate that this value proposition is determined by four business objectives: precise decision support , detection of misconduct, reduction of side effects, and reduction of invasiveness . Similarly, the AI application’s capability to analyze data more accurately in diagnosis (use case DD1) enables the business objective precise decision support , thereby reducing risks in patient care. Another mechanism can be seen, for example, considering the business objective task execution , which leads to the value proposition process acceleration . The ability of AI applications to rapidly analyze large amounts of data and recognize patterns in biomedical research (use case BR1) allows a faster drug development process.

Further research

By investigating the value creation mechanism of AI applications for HC organizations, we not only make an important contribution to research and practice but also create a valuable foundation for future studies. While we have systematically identified the relations between the business objectives and value propositions, further research is needed to investigate how the business objectives themselves are determined. While the examination of AI capabilities was not the primary research focus, we found first evidence in the use cases that indicates AI technology’s unique capabilities (e.g., to make diagnoses accurate, faster, and more objective) that foster one or several business objectives (e.g., rapid task execution, precise decision support) and unlock one or several value propositions (e.g., Risk-reduced patient care, process acceleration ). In subsequent research, we aim to integrate these into the value creation mechanism by identifying which specific AI capabilities drive business objectives, thereby advancing the understanding of how AI applications in HC create value propositions.

Limitations

This study is subject to certain limitations of methodological and conceptual nature. First, while our methodological approach covers an in-depth analysis of 21 AI use cases, extending the sample of AI use cases would foster the generalizability of the results. This is especially important regarding the latest developments on generative AI and its newcoming use cases. However, our results demonstrate that these AI use cases already provide rich information to derive 15 business objectives, which translate into six value propositions. Second, while many papers assume the potential of AI applications to create value propositions, only a few papers explicitly focus on the value creation and capture mechanisms. To compensate for this paucity of appropriate papers, we used 11 expert interviews to enrich and evaluate the results. Besides, these interviews ensured the practical relevance and reliability of the derived results. Third, we acknowledge limitations of conceptual nature. Our study predominantly takes an optimistic perspective on AI applications in medicine. While we discuss the potential benefits and value propositions in detail, it is important to emphasize that there are still significant barriers and risks currently associated with AI applications that need to be addressed before the identified values can be realized. Furthermore, our investigation is limited because we derive the expected value of AI applications without having extensive real-world use cases to evaluate. It is important to emphasize that our findings are preliminary, and critical reassessment will be essential as the broader implementation of AI applications in medical practice progresses. These limitations emphasize the need for ongoing research and monitoring to understand the true value of AI applications in HC fully.

Conclusions

This study aimed to investigate how AI applications can create value for HC organizations. After elaborating on a diverse and comprehensive set of AI use cases, we are confident that AI applications can create value by making HC, among others, more precise, individualized, self-determined, faster, resource-optimized, and data insight-driven. Especially with regard to the mounting challenges of the industry, such as the aging population and the resulting increase in HC professionals’ workloads, the integration of AI applications and the expected benefits have become more critical than ever. Based on the systematic literature review and expert interviews, we derived 15 business objectives that translate into the following six value propositions that describe how HC organizations can capture the value of AI applications: risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery .

By presenting and discussing our results, we enhance the understanding of how HC organizations can unlock AI applications’ value proposition. We provide HC organizations with valuable insights to help them strategically assess their AI applications as well as those deployed by competitors at a management level. Our goal is to facilitate informed decision-making regarding AI investments and enable HC organizations to align their AI application portfolios with a comprehensive and overarching strategy. However, even if various value proposition-creating scenarios exist, AI applications are not yet fully mature in every area or ready for widespread use. Ultimately, it remains essential to take a critical look at which AI applications can be used for which task at which point in time to achieve the promised value. Nonetheless, we are confident that we can shed more light on the value proposition-capturing mechanism and, therefore, support AI application adoption in HC.

Availability of data and materials

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

Abbreviations

Artificial Intelligence

Machine Learning

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The authors thank all physicians who participated in this study.

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JH initially composed the introduction, background, and discussion sections; PH contributed to the analysis design, supported data analysis, and critically reviewed the manuscript; ER conducted data collection and analysis and produced the initial draft of the methodology section; NU edited and critically reviewed the manuscript and provided guidance. All authors read and approved the final manuscript.

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Hennrich, J., Ritz, E., Hofmann, P. et al. Capturing artificial intelligence applications’ value proposition in healthcare – a qualitative research study. BMC Health Serv Res 24 , 420 (2024). https://doi.org/10.1186/s12913-024-10894-4

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DOI : https://doi.org/10.1186/s12913-024-10894-4

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