Skip to main content

Bridging the voice of healthcare to digital transformation in practice – a holistic approach



Digital transformation is key for healthcare to meet future needs and expectations and compete equally with new actors on the market. Medical digital diagnosis tools and clinical decision support systems (CDSS) are improving and offer new opportunities. To introduce new technology in healthcare can however be a challenging mission, too often ending in failure, with excessive costs or the actual transformation work not being carried out at all. It is unclear how to drive the establishment to reach desired results in this environment, and how industrial experiences can be used to support healthcare.


The objective of this study was to develop a holistic approach for introducing new information technology (IT), such as a CDSS, into a primary healthcare organization supported by industry best practices for digital transformation.


This qualitative study used a combined inductive and deductive method where the perceptions and beliefs of selected primary healthcare stakeholders were used as directions for developing an approach that could utilize existing industry best practices for digital transformation.


A holistic healthcare-ified approach including 20 requirements was developed, that meet the needs of healthcare. The voice of healthcare was used as prism to healthcare-ify the industry practices and adapt it to match specific healthcare conditions. An example was provided showing how the research could be put into practice.


This study proposed a holistic approach, based on industry best practices, but adapted to healthcare using the voice of healthcare as a bridge, that may be used to introduce CDSS and other IT into a primary healthcare organization and step up the needed digitalization.

Peer Review reports


Digital transformation in general

Digital transformation means a major change to an organization and is driven or enabled by digital technology [1]. It brings a disruption that changes the rules of business. It is about how an organization adapts to the new environment that new digital technologies bring [2]. Transformation and digital transformation can be very challenging, and many organizations fail to perform a sustainable change [3] Several models explain how to perform a change [4], e.g., Kotter’s change model [5] covering the following steps: create a sense of urgency to realize a change is needed, establish a steering committee, develop, and communicate a vision, enable action, and remove barriers, leverage quick wins, build on the flow, and extend it, make change part of the culture.

Since digitalization covers more than the actual implementation of new IT [6, 7], the transformation actions, going from a current to a future state [8], need to cover a variety of dimensions. Stoyanova identifies top-down, bottom-up or combined approaches for driving large- or small-scale digital change [6]. Regardless of approach the key to success is said to depend on how well the initiatives are planned, prioritized, and executed [9]. The focus should be to increase client value and thereby drive the business while the technology is seen as a “favourable factor” [6]. The practices mentioned are leadership, building of digital capabilities, opportunity for employees, new technology, and communication. Further Stoyanova [6] proposes the following success factors for digital transformation: Develop a digitalization strategy, rethink the workplace, change the operational model, communicate, focus client experience, and build digital platform.

While academic research may focus on different aspects of digital transformation, the availability of integrated frameworks is limited, and existing frameworks lack academic support [10]. Ivancic et al. presents a digital transformation framework resulting from a holistic investigation [11]. It covers seven dimensions: strategy, organization, client, people, technology, innovation, and eco-system. The framework is cross-industry generic, but not healthcare specific. Greenhalgh et al. proposes a seven-domain framework for considering influences on the adoption, non-adoption, abandonment, scale-up, spread and sustainability of technology in healthcare [12]. Lean covers the three main elements: management system, operating system, and mindsets with related behaviours [13]. Osmundsen et al. suggests eight success factors for digital transformation: culture for change, transformation management, build and leverage skills, engage managers and people, grow capabilities, develop a digital business strategy, and alignment with the information system [1]. A report from McKinsey mentions 21 success factors that fall into the categories of leadership, building of capabilities, practitioner empowerment, upgrade of tools and communication [3].

When a change is dependent on people's adoption, the complexity increases, and organizational culture becomes an important part of the transformation [14]. The transformation changes or even threatens established structures [10]. People’s views, standards, understanding, and acceptance will therefore decide the success of a change [9, 14, 15]. Motivation is key to successful transformation to make people engaged [4]. Many transformation practices emanate from quality management theories where the methods are orchestrated by project- and change management approaches. Improvement frameworks have since long been used by industry to drive organizational performance improvements, including IT digitalization. There is a variety of methods and tools for improving and managing quality. Some well-known frameworks for quality management are Total Quality Management [16], Lean [13, 16,17,18], and Lean six sigma [5, 17, 19]. These can be used on daily basis to drive improvements. There are several themes of these initiatives, all with a similar intention [16], to make the organization focus on continuous improvements and thereby create value for the client [17]. Strategies (digital strategies [20]and many tools and methods support these frameworks, e.g., business cases [21], flowcharts [16, 17], workshops, prioritization matrices [5, 16], progress reporting [22] and SWOT (Strength, Weakness, Opportunity, Threat) diagrams [17].

Primary healthcare and the challenge of performing digital transformation

Primary healthcare manages basic medical treatments and care that do not require medical skills provided by hospitals or other specialized institutions [23, 24]. In many countries (e.g., Sweden) the healthcare is predominantly politically governed, highly regulated, and the cost mainly covered by the tax system [23, 25, 26].

The primary healthcare population (i.e., the practitioners such as doctors and nurses) is a closed community, indeed capable but fostered to be sceptical to new unless there is convincing scientific evidence [27]. The professionals are under hard pressure and time is not sufficient for performing all demanded tasks [27, 28]. The reaction to change can therefore be that there is more to lose than to win. A change can also be seen as a threat since it changes existing structures [10, 27]. To drive change in this environment, there is advantageous to have medical background and be clinically active [27]. In healthcare, the power comes with the white coat [27]. In primary healthcare there is commonly less teaming, and each doctor is more self-governing. It is therefore key to convince a lead doctor to sponsor the change or even act as a role model [27] to make the powerful frontline move in the desired direction.

The exploration of Information Technology (IT) in healthcare is lagging behind and digital transformation can be described as “overdue” compared to other industries that have come further [7]. This is due to many reasons, e.g., resistance to change, not having the time, unawareness of the opportunity, or not knowing how to drive digital change effectively in this environment [27]. The non-exploring or failure of implementation can be costly. Both healthcare providers and users suffer the consequences of this non-usage of new technology. This has become obvious when interviewing healthcare stakeholders [27].

Primary healthcare needs to improve its ability to drive digital transformation in a successful way. Healthcare has similarities to other industries however some factors differ which need to be considered when driving change. The holistic conditions for introducing new IT, such as an Artifical Intelligence (AI) based digital Clinical Decision Support Systems (CDSS), into a primary healthcare organization were previously investigated [27]. The analysis found that it requires a multidimensional perspective and handling and provided a holistic and healthcare-ified view of those conditions, including to “learn from previous mistakes, adopt success factors for change, adjust according to healthcare differentiators, consider the level of IT maturity and quality management maturity of an organization, identify specific improvement areas (e.g., related to malignant melanoma diagnosis) and reflect on new challenges and prerequisites that will appear when changing the way of working. It also encompasses the needed commitment and trust from the stakeholders involved and impacted by the change” [27]. The result of the content analysis was summarized in a table including 78 codes, 33 subcategories, 7 categories, and one central theme. Additional file 1 (A1) provides a copy of that table along with numbering of the codes. That study provided “a view of the perceptions and beliefs of selected primary healthcare stakeholders” related to the introduction of new IT [27]. It is however still unclear how the specific conditions for primary healthcare can be covered by an approach to introduce new IT and how experiences from other industries can be used to support healthcare.


The objective of this qualitative study was to develop a holistic approach for introducing new information technology, such as digital decision support systems, into a primary healthcare organization supported by industry best practices for digital transformation.

The specific research questions were: What can an approach for introducing new information technology into a primary healthcare organization look like?; How can the conditions in healthcare, expressed through the perceptions and beliefs of selected primary healthcare stakeholders, be catered for by the approach?; and How can industrial experiences and best practices support healthcare?

Materials and methods

This qualitative study was conducted through a combined inductive and deductive (hybrid) approach. A qualitative inductive approach is useful when the aim is to search for knowledge in the format of personal views and perception of the environment, whereas a qualitative deductive approach can be used when there are existing theories to start from [29, 30].

Materials used in this study

The study was based on the qualitative content analysis of semi-structured interviews performed by Frisinger et al. [27] which resulted in ~ 600 meaning units that were coded into 78 codes (A1, second column).

A selection of digital transformation best practice components was performed by the lead author (A.F.), a combination of those that the author was familiar with, guided by literature and findings from a previous interview study [27]. The set does not represent a full coverage of all components (frameworks, settings, techniques, methods, tools) that exist. The selection of components should be seen as an example and components and those are exchangeable by others as long as those address the requirement. A user of the approach may thus use another component that this person is more familiar with, as long as it matches the need.

Methods used in this study

The following two steps were performed to identify themes representing requirements that could be solutioned by healthcare-ified industry best practices: 1) The 78 codes were analysed through an inductive thematic approach [31] which resulted in healthcare-ified requirements (Additional file 2). The requirements were thus induced from the interviews with primary healthcare stakeholders. Originating from codes, one code could, in combination with other codes, be induced to one or several requirements (i.e., a many to many combination). See Additional file 3 (A3) for a schematic view of the induction of codes. The induction was iterated until (according to the authors) no more relevant combinations remained. The requirements were mapped deductively into an existing 7-dimensions framework to confirm a holistic coverage [11, 32]. The dimensions can on high level be described as follows, which is also in line with change related aspects discussed in a previous interview study [27], and in literature [1,2,3, 6, 13]: strategy (e.g., digital strategy, business strategy); organization (e.g., organizational structure, management system, leadership, project management); client (e.g., the market and the clients); people (e.g., people management, skills, resources, performance management, organizational culture and inclusion); technology (e.g., the requirements and usage of technology); innovation (e.g., creation and management of new ideas); and eco-system (e.g., cooperation, inspiration, sharing of knowledge and success-stories). Additional file 4 (A4) shows an example of the abstraction path. 2). A sample of industry best practices for digital transformation (including examples of industry best practice components) formed, according to a deductive approach [30, 32], solutions that addressed the requirements. The solutions were thus derived from a set of already existing best practice components. One best practice component could, in combination with others, be part of solutions and thus address one or several requirements (i.e., a many to many combination), see Additional file 5 for a schematic view of the deduction of best practice components. Additional file 6 (A6) shows an example of how best practice components form solutions that address requirements.

The induction and deduction in steps 1 and 2 were iterated to confirm all requirements could be solutioned. Figure 1 illustrates the combination of the inductive/deductive methodology providing the requirements and solutions for an approach.

Fig. 1
figure 1

A combined inductive and deductive method resulting in requirements and addressing solutions for an approach

Information from the actual interviews in Frisinger et al. [27] served as a prism to further "healthcare-ify" (adapt to healthcare) the requirement—digital transformation best practices package. The healthcare-ification was performed to adapt to something that according to healthcare stakeholders were more probable to work in a healthcare environment. See Fig. 2 for an example of the concept of healthcare-ification where digital transformation best practice components (the input) are turned into healthcare-ified digital transformation best practice components (the output) using the voice of healthcare as a prism (a catalyst).

Fig. 2
figure 2

The healthcare-ification concept turning unhealthcare-ified to healthcare-ified using the voice of healthcare as a prism

Further, an action-oriented way of working was used, meaning that the research iterated in cycles of Plan, Act, Observe and Reflect, where one cycle provided input to the next with intention to make the background and requirements for the approach clearer for every cycle and refine the results [33]. In addition, a cross-disciplinary perspective [5] was used throughout the study, utilizing the authors’ prior experiences in the fields of IT, management and medicine.


A holistic approach for digital transformation in primary healthcare

In this study, a holistic approach for digital transformation emerged from an integration of requirements and a variety of components (frameworks, settings, techniques, methods, tools) which formed a proposed solution going from start to end, as described in the method. The transformation includes the phases: understand current state and areas for improvement, decide future state, the plan and perform of the transformation, the hand-over (from project to line organization) and follow-up of results.

Following action-oriented research, a couple of iterations (two major and ~ five minor adjustment generating) led to the given results.

A multidimensional, holistic perspective was needed to cover all aspects mentioned in interviews, represented by the 78 codes (Additional file 1).

Table 1 presents the approach constructed from the resulting 20 requirements (second column), addressed by best practice components (third column and Additional file 7), mapped into a seven-dimension framework (first column). The selection of best practice components is exchangeable to other components provided those address the healthcare-ified requirements.

Table 1 A holistic approach for introducing digital decision support systems into a primary healthcare organization. It includes healthcare-ified requirements, best practice components addressing those, all mapped into a 7-dimension framework. The framework is adapted from Ivancic et al. [11]


The following Fig. 3 exemplifies the healthcare-ification process, where the original digital transformation best practices commonly used in other industries (light grey in background of figure) serves as input and is turned in to the output—a healthcare-ified digital transformation best practice that meets healthcare needs and values (seen clearly in black when looking through the prism), by using the voice of healthcare (what healthcare stakeholders said in the interviews) as a catalyst.

Fig. 3
figure 3

Examples of healthcare-ification, using the voice of healthcare (interviews with selected primary healthcare stakeholders) as prism (here using interpretations of selected quotations). A Allocate time and resources to run pilot. B Use role model from the clinical practise. C Involve frontline employees. D Meet healthcare requirements: safe, IT-secure, trusted provider

Application of the approach in practice

All of the 20 requirements in the approach (Table 1) are intended to be considered when performing a transformation, however some might need more attention. The focus can vary and depends on the goals, the complexity of the change, and the experience of the organization. For the introduction of new IT, such as a digital CDSS, a pilot is suggested to be run as a first step. The focus would then initially be on a selection of the requirements with corresponding best practice components, some in lighter format (as decided by the stakeholders). The pilot serves as a preparation for a broader deployment, provided the trial is successful, otherwise it should be valued as lessons learned. Success-stories could also be shared with others including do’s and don’ts. Figure 4 exemplifies the application of the approach. It shows how selected best practice components, representing the 20 requirements, becomes activities that are executed throughout a transformation initiative. Some activities have been given higher priority (marked with a solid line in the figure) whereas others have been given lower priority (marked with dotted line in the figure). The actual priorities, set by the involved team, may vary from time to time. On the top of the figure, the transformation phases are shown with continued iterations. To make the change effective the journey continues after the transformation or there is risk for erosion. After each cycle of change the result should be evaluated and new opportunities for improvement identified, a never-ending story for those who want to stay relevant since the environment is continuously changing.

Fig. 4
figure 4

Example of how to apply the holistic approach during transformation The lines surrounding the best practice components represent the priority where solid line means high priority and dotted line means lower priority or activity in lighter format. The actual priorities should be set by the involved team and varies from time to time


The present study identified 20 healthcare-ified requirements with accompanying best practice components (Table 1). Those are intended to be used as a holistic transformation approach to introduce new IT, e.g., digital decision support systems, into a primary healthcare organization. The results aim to advance the field of digital transformation for primary healthcare by covering the specific conditions for primary healthcare in an approach. The application of the approach was also exemplified (Fig. 4).

The introduction of new IT, such as digital decision support systems, into a primary healthcare organization can be complex [27] as it covers a lot more than just plugging in a new IT device. The challenges are many times non-technical [7]. A holistic approach can thus help cover a variety of needed aspects, e.g., business, organization, client, people, technology, innovation, quality. If one aspect is omitted, there is a risk this for failure and unnecessary cost. To become holistic a number of dimensions need to be covered. One can discuss how many dimensions there are. This study found that the twenty requirements induced from the interviews span over the seven dimensions suggested by a study by Ivancic et al. [11], and each can be related to (at least) one of the dimensions given. All twenty requirements are important to consider. If ignoring one there is risk to miss an important aspect of the problem and thereby reach an unsatisfying solution. Depending on the situation, some requirements can be more important to focus than the others, however all should be considered. Together, the components responding to the requirements form a solution, thus addressing the multidimensional area of implementing new IT into a primary healthcare organization.

This study did not find any evident reason not to use industry practices for digital transformation in healthcare, even though previous research identified conditions that are unique for healthcare [27], The best practices should however be “healthcare-ified”, i.e., customized to give special attention to certain areas, preserve healthcare values and to earn trust from stakeholders.

There is not one single best practice for digital transformation, but there are a variety of useful components. In this study a selection was considered, seen by the lead author (A.F.) as the most relevant, but guided by findings from the interviews [27] and literature [2, 4, 5, 11, 13,14,15,16,17,18,19,20,21,22, 27, 33] (Additional file 7).

The presented approach is based on well proven industry best practices adapted to healthcare. Based on the authors’ own healthcare experiences, primary healthcare (the interviewed population [27]) does not, related to digital transformation, differ substantially from other healthcare environments. It is therefore the authors’ view that it, to a high extent, is healthcare generic and can help healthcare (a variety of healthcare environments) accelerate the needed digital transformation.

The approach was designed for primary healthcare and ready to pilot and use in the clinical practice, in contrast to previously presented digital transformation frameworks that are mainly generic [11] or academic [12]—a sign that this study covers unexplored territory. In addition, a cross-disciplinary perspective [5] was used, which seems to differentiate from prior performed studies. A cross-disciplinary perspective (e.g., by having a mixed background or diversity in the team) can be advantageous when working with complex problems [34] as it may bring new and different perspectives to the table. By seeing different things and details, or experience things differently, depending on prior background, new solutions may come up never seen before. In the effort of advancing digital transformation in healthcare, a background in IT, management and medicine showed to be valuable. In addition, being part of the medical community made it possible to understand the innermost conditions of healthcare and gain trust from this closed community [27] to take part of their thoughts.

Strengths and limitations

The mixed method of inductive/deductive reasoning were useful approaches to go from objective to result. This study also used an action-oriented way of working which increased the understanding of the subject for every cycle performed [5, 17]. The voice of healthcare sample is currently based on interviews with 16 primary health care stakeholders [27]. The limited number can be seen as small for developing an approach. The way the approach was built was however intended to make it open and scalable. The current result, with requirements and corresponding best practices, may serve as a baseline where additional information, coming from real transformation-cases, can confirm or refine the approach (e.g., add, change or remove a requirement) by performing new iterations. Any additional or redundant group of requirements or dimensions will appear as more piloting and usage of the approach is performed and more data will be available. The approach will then get closer to right, however, the level of perfect might never be reached because all situations differ, and the environment will also change over time. The design aim to make the approach fault tolerant and adaptive.

Digital transformation in healthcare is not a new occurrence. Healthcare organizations have performed digital initiatives before, successful or non-successful and will not start from a zero experience. The perceptions of these experiences (what goes right and wrong) and what differentiates healthcare from other industries that have come further in their digitalization were captured in a previous study [27] The current study did not consider the degree of digital maturity when describing the approach as such but aimed to present a healthcare generic approach. The aspect of digital maturity and digital transformation experience will however be of importance when using the approach.

The approach can be viewed as a tools portfolio to perform digital transformation in healthcare. As with any tool, the usefulness and result depend on the skill and experience of the user.

The requirements of the approach were directly derived from healthcare stakeholder’s statements [27]. The approach was adapted to healthcare (healthcare-ified) by using the voice of healthcare as a prism. The prism can be seen as a metaphor for the adaption of the requirements—digital transformation best practices package to something that, according to what healthcare stakeholders say / the voice of healthcare (the catalysator) is more likely to work. It thereby preserved the needs and values important to consider for driving change in this specific environment [35]. By taking this knowledge into consideration, previous mistakes are more likely to be avoided, success-stories can be built on, and healthcare specifics can be catered for. It may turn the transformation journey to something that healthcare professionals feel relates to them. Since the approach is anchored in healthcare it might reach acceptance in the healthcare community. This may increase the chance to reach successful sustainable results and avoid waisted cost for abandoned initiatives [36].

Practical application

To stay relevant, any organization should continuously look for improvements [17] e.g., related to activities where IT can make a difference, i.e., not unique for primary healthcare environment. When a need has been identified, it has been confirmed there is a budget, available resources, and the timing is right, a lighter pilot may evaluate the appropriateness and function as a tollgate for broader deployment. This could especially be relevant for a business with several primary healthcare centres before broader deployment, but also for a single unit that would like to perform a smaller, non-committed trial (“toe in the water”) before final deployment with larger impact on the business and surrounding environment. That would minimize risk for implementation failure. After a full deployment, the journey should continue with iterated cycles (similar to Plan-Do-Check-Act [17]). This is to avoid erosion or becoming irrelevant for the purpose. In addition to improve the actual solution, each time the approach is run, more learning is fed into the approach itself, with potential of making it better.

Initial use of the proposed approach is expected to face challenges. Even though the approach may serve as a guide to run digital transformation in healthcare, certain skills and experience will still be a prerequisite, e.g., related to IT and change management. Organizations will build this type of skills as they move forward, but will need some to get started. Another related problem is then that the closed healthcare community might be hesitant to consult people outside their own organization [27]. There might also be a variation in attitude and the degree of trust in new technology such as AI based applications that may be seen as a threat [28].

Future work

As a next step, the approach should be applied to real cases. There is a variety of needs out there e.g., Artificial Intelligence based [37]. CDSS for malignant melanoma [27] and other application systems for diagnostics, decision support systems, or systems related to medical records [7]. Such initiatives would evaluate and further explore the application of the approach. A real-world initiative is planned to evaluate the systematic approach’s ability to perform successful digital transformation in the clinic using a 1st generation machine learning clinical system as example of new IT. This will evaluate the approach and likely—by using this systematic, holistic approach—also explore new research areas related to the introduction of AI in healthcare.


A holistic approach, based on industry best practices but adapted to healthcare can be used to introduce new information technology, such as digital decision support systems, into a primary healthcare organization and may help healthcare in general to accelerate the needed digitalization. Industry has long experience of driving digital change and practices can be transferred to support digital transformation in primary healthcare but needs to be healthcare-ified to adapt to the specific healthcare environment to preserve values and earn trust from stakeholders. The specific conditions and values in healthcare can be catered for by using the voice of healthcare as a bridge to digitalization in practice.

Availability of data and materials

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



Clinical Decision Support System


Information Technology


Plan, Do, Check, Act


  1. Osmundsen K, Iden J, Bygstad B. Digital Transformation: Drivers, Success Factors, and Implications. MCIS 2018 Proceedings. 2018.

  2. Kane G. The Technology Fallacy: People Are the Real Key to Digital Transformation. Res Technol Manag. 2019;62:44–9.

    Article  Google Scholar 

  3. The keys to a successful digital transformation | McKinsey. Accessed 30 May 2023.

  4. Hallin A., Olsson A., Widström A. Förändringsledning (Change management). Studentlitteratur AB; 2019.

  5. Johnson JK, Sollecito WA. McLaughlin and Kaluzny’s continuous quality improvement in health care. 5th edition. Jones & Bartlett Learning; 2019.

  6. Stoyanova M. Good Practices and Recommendations for Success in Construction Digitalization. TEM J. 2020;9:42–7.

    Google Scholar 

  7. Promoting an overdue digital transformation in healthcare | McKinsey. Accessed 30 May 2023.

  8. Hölscher K, Wittmayer JM, Loorbach D. Transition versus transformation: What’s the difference? Environ Innov Soc Transit. 2018;27:1–3.

    Article  Google Scholar 

  9. Gjellebæk C, Svensson A, Bjørkquist C, Fladeby N, Grundén K. Management challenges for future digitalization of healthcare services. Futures. 2020;124.

  10. Buck C, Probst Marques C, Rosemann M. Eight Building Blocks for Managing Digital Transformation. International Journal of Innovation and Technology Management. 2021;18.

  11. Ivančić L, Vukšić VB, Spremić M. Mastering the Digital Transformation Process: Business Practices and Lessons Learned. Technol Innov Manag Rev. 2019;9:36–50.

    Article  Google Scholar 

  12. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, et al. Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. J Med Internet Res. 2017;19.

  13. Drew J, McCallum B, Roggenhofer S. Journey to Lean: Making Operational Change Stick. 2004.

  14. Arthur J. Lean six sigma for hospitals: improving patient safety, patient flow and the bottom line. 2nd edition. McGraw-Hill Education; 2016.

  15. Payne A, Frow P, Eggert A. The customer value proposition: evolution, development, and application in marketing. J Acad Mark Sci. 2017;45:467–89.

    Article  Google Scholar 

  16. Walshe K, Smith J. Healthcare management. Open University Press; 2016.

  17. Karuppan CM, Dunlap NE, Waldrum MR. Operations Management in Healthcare. New York, NY: Springer Publishing Company; 2016.

    Google Scholar 

  18. Moriates C, Arora V, Shah N. Understanding Value-Based Healthcare. McGraw Hill / Medical; 2015.

  19. Silver SA, Harel Z, McQuillan R, Weizman AV, Thomas A, Chertow GM, et al. How to Begin a Quality Improvement Project. Clin J Am Soc Nephrol. 2016;11:893–900.

    Article  PubMed  PubMed Central  Google Scholar 

  20. OECD Reviews of Digital Transformation: Going Digital in Sweden. OECD; 2018.

  21. Ricciardi W, Pita Barros P, Bourek A, Brouwer W, Kelsey T, Lehtonen L, et al. How to govern the digital transformation of health services. Eur J Public Health. 2019;29(Supplement_3):7–12.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Harrison JS, Tompson SM. Strategic Management of Healthcare Organizations. Business Expert Press; 2014.

  23. Ministry of Health and Social Affairs. Swedish Health Care Act (2017:30). 2017. Accessed 7 Nov 2021.

  24. Lindgren, Emma. Primärvårdens uppdrag (The mission of Primary Care). 2016.

  25. Ministry of Health and Social Affairs. Swedish Patient Safety Act (2010:659). 2010. Accessed 7 Nov 2021.

  26. Ministry of Health and Social Affairs. Swedish Patient Act (2014:821). 2014.

  27. Frisinger A, Papachristou P. The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study. BMC Primary Care. 2023;24:1–15.

    Article  Google Scholar 

  28. Blease C, Kaptchuk TJ, Bernstein MH, Mandl KD, Halamka JD, DesRoches CM. Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners’ Views. J Med Internet Res. 2019;21:e12802.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Proudfoot K. Inductive/Deductive Hybrid Thematic Analysis in Mixed Methods Research. J Mix Methods Res. 2022;2023:308–26.

    Google Scholar 

  30. Fereday J, Muir-Cochrane E. Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. Int J Qual Methods. 2006;5:80–92.

    Article  Google Scholar 

  31. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101.

    Article  Google Scholar 

  32. Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13:1–8.

    Article  Google Scholar 

  33. Frisinger A. A generic security evaluation method for open distributed systems. 2001.

  34. Loeb SJ, Penrod J, Kolanowski A, Hupcey JE, Haidet KK, Fick DM, et al. Creating Cross-Disciplinary Research Alliances to Advance Nursing Science. J Nurs Scholarsh. 2008;40:195–201.

    Article  PubMed  Google Scholar 

  35. Fletcher M, Read C, -Adderio LD. Nurse Leadership Post COVID Pandemic-A Framework for Digital Healthcare Innovation and Transformation.

  36. Dowey A. Timeline: What happened with the NHS Covid-19 app. 2020. Accessed 1 Jun 2023.

  37. Buck C, Doctor E, Hennrich J, Jöhnk J, Eymann T. General Practitioners’ Attitudes Toward Artificial Intelligence–Enabled Systems: Interview Study. J Med Internet Res. 2022;24(1):e28916.

    Article  PubMed  PubMed Central  Google Scholar 

  38. World Medical Association Declaration of Helsinki. ethical principles for medical research involving human subjects. JAMA. 2013;310:2191–4.

    Article  Google Scholar 

  39. Ministry of Education. Swedish Ethical Review Act (2003:460). Accessed 10 Apr 2022.

Download references


Not applicable.


Open access funding provided by Karolinska Institute. No external funding was available.

Author information

Authors and Affiliations



AF and PP analysed and interpreted the data. AF was the major contributor in writing the manuscript. Both authors read, revised and approved the final manuscript.

Corresponding authors

Correspondence to Ann Frisinger or Panagiotis Papachristou.

Ethics declarations

Ethics approval and consent to participate

This study has been conducted according to the principles expressed in the Declaration of Helsinki, referring to the ethical principles for medical research involving human subject [38]. According to the study protocol, it has not collected any sensitive information from the human subjects that falls under the obligations of the Swedish Ethical Review Act [39] All participating human subjects have provided their written informed consent prior to study participation. The institutional review board at the department of NVS, Karolinska Institutet, Sweden has provided their ethical statement regarding the present study.

Consent for publication

Not applicable.

Competing interests

The author P. Papachristou is a co-founder of the CDSS (Dermalyser). No conflicts of interest are declared for other authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: A1 Table.

Qualitative content analysis result.

Additional file 2: A2 Table.

Going from code to requirement

Additional file 3: A3 Figure.

Schematic view of the induction of codes to identify requirements. This is an example (not showing the actual induction)

Additional file 4: A4 Table.

Example of abstraction path going code to requirement, and then mapped to dimension

Additional file 5: A5 Figure.

Schematic view of the deduction of best practice components to form solutions that address the requirements. This is an example (not showing the actual deduction).

Additional file 6: A6 Table.

Example of how best practice components formsolutions that address requirements.

Additional file 7: A7 Table.

Description of best practices components ("the toolbox”).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Frisinger, A., Papachristou, P. Bridging the voice of healthcare to digital transformation in practice – a holistic approach. BMC Digit Health 2, 12 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: