Value-based digital health: A systematic literature review of the value elements of digital health care

Abstract

Objective

To carry out a systematic literature review to identify the Value Based Digital Health elements.

Methods

The search for records was carried out for the period 2012 to January 2024, in two main databases, EMBASE and Scopus. Records were limited to studies published since 2012. Studies presented either a conceptual framework on the concept of Value-Based Digital Health (VBDH), or results from an empirical work to generate evidence on the value of digital health, or a combination of both. The characteristics of the study, the type of digital health and measures and/or their value elements included, are synthesized and interpreted. To synthetize information, the patient journey phases are used as themes, where the identified value elements are allocated. This is analyzed by type of agent (of the general healthcare framework).

Results

The search yielded a total of 606 abstracts for screening. In the end, 67 studies were finally included for data extraction. VBDH elements are presented by phases of the patient journey and by agent. Despite the high heterogeneity of patients, theoretical and empirical works converge in elements such as the value of patient-centered care, education (health literacy) in the use of new digital health technologies, mechanisms to increase patient confidence and their participation in the health decision-making processes, or the importance of collecting patients’ reported outcomes and experiences, among other elements. The review also found empty areas among theoretical and empirical studies, where no VBDH elements were identified.

Conclusion

This systematic review identified the theoretical and empirical elements to measure the value of digital health and provides a framework of VBDH elements. Some of the theoretical elements are actually used in practice. Others, however, despite being relevant from a theoretical point of view, do not seem to be measured empirically. This work provides researchers of a source, for consultation, of value based digital health elements. This is helpful for improving the measurement of the value of digital health.

Introduction

There are multiple definitions of Digital Health in the literature. Digital Health (DH) primarily focuses on healthcare in the digital world, bridging the evolution of advances in informatics and technology in medicine, health and all aspects of health care with the application of these developments in clinical practice, the patient experience, and their social, political and economic implications. The common element to all definitions of Digital Health is a digital intervention that examines the provision of healthcare and not only the use of the technology itself. ¹ When referring to value of health tools, generically, in the literature this has been defined as the outcomes that are important to patients, relative to the costs required to achieve those outcomes. ² In this study, the value of digital health will be explored, including not only looking for the value-based elements of health technologies, but any digital health resource used for the provision of health services. This is what this work understands by the concept of Value-Based Digital Health (VBDH).

The concept of Value-Based HealthCare (VBHC) was introduced by Michael Porter about fifteen years ago. ³ VBHC is currently being used as a guide for the healthcare practice towards those activities that generate the best possible health results or outcomes for each monetary unit of expenditure. The VBHC approach, thus, emphasizes the overall result of the health care process, that might be carried out on people or patients, considering the partial or complete recovery of the health state and the sustainability of that recovered health in relation to the efforts made. It has been argued, however, that the use of technology only leads to better health outcomes if the related cultural challenges are acknowledged and the new needs of patients are, actually, met. ⁴

Health systems are currently undergoing a digital transformation process that affects all areas of the health care process. The VBHC paradigm is evolving to a VBDH approach in many leading healthcare systems worldwide. VBHC and VBDH are closely related, both in the search for more effective and value-focused healthcare for patients. Paul Sonnier defined Digital Health as “that discipline where the digital and genomic revolutions converge with health and healthcare.” ⁵ However, defining the concept of VBDH and its implications is a work still in progress, but necessary, to give strength to this emerging concept, in order to help on this transformation process, by improving its quality, resilience, sustainability and innovative capacity. VBDH is, in the end, understood as the use of digital tools, solutions, strategies, and ecosystems to contribute to the generation of value in health. ⁶ Investigating the concept of VBDH is, thus, crucial because it optimizes healthcare and improves the efficiency of the healthcare system by focusing on outcomes that matter to patients and maximizing the use of resources.^7,8

Some literature reviews have attempted to identify how VBDH is conceptualized in the literature, but these were in very specific settings,^9,10 with the focus only on the economic value, and not on the effectiveness of VBDH, ¹¹ or was done for the perspective of health professionals only. ¹² No systematic literature review has, yet, approached, the concept of VBDH including all perspectives and all identifiable settings. Efforts have been also made trying to identify the degree of maturity of healthcare professionals on the use of digital health tools and technologies. Specifically, there is evidence of negative experiences due to the fact that the transformation process on this new digital changes the way some health services are provided. ²

The aim of this study is, thus, to carry out a systematic literature review on the identification of the VBDH elements.

The nature and extent of published evidence, that has linked both concepts, theoretically and empirically, is summarized into a conceptual framework of use in research and in the clinical practice.

Methods

Review design

A systematic literature review on the identification of value elements of digital health, VBDH, is the chosen method for gathering and analyze information regarding the concept of VBDH. Systematic literature reviews follow strict protocols. ¹³ First, the selection of at least two databases for the search was done and agreed by all authors, leading to the choice of EMBASE and Scopus as the two main databases for consultation for this work. The combination of these two databases led to a good number of records. It was considered that additional databases would need to be consulted if the selection of papers would not lead to an enough number of papers for the data extraction. This was not necessary in the end. The review method is qualitative, and targets the identification of value elements of digital health, and their description, in theoretical and empirical studies. To synthetize information, the patient journey phases (prevention, diagnosis, access to health care services, hospital/home care, specialist care, treatment and valuation/measurement of health outcomes) are used as themes, where the identified value elements will be allocated at the data extraction phase. This will, also, be analyzed by type of agent. Two researchers worked on the screening of abstracts and full texts. The data extraction method was also agreed by all authors, but was conducted by one researcher. A second researcher checked 10% of the screened abstracts, until there was agreement with the final selection and distribution among included and excluded records. This second researcher also checked the data extraction and the quality and bias assessment. The review was not registered in PROSPERO given that the initial idea was to conduct a scoping review (although in the end a full systematic review was conducted), which is a type of review that does not require registration in this system.

Search strategy

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. ¹⁴ The PICO/PECO method was applied to structure. ¹⁵ The framework of this systematic review according to PICO was: any place where the process has been studied and/or attempted to measure the value of digital transformation in the provision of health services (population); Digital transformation in health (Intervention); the elements identified in the theoretical literature vs. empirical works (comparison); theoretical elements to explain the value of digital health, and value elements of digital transformation in the provision of health services (outcomes).

The keywords of the search algorithm were combined using Boolean terms. The search strategy was conducted in EMBASE and Scopus:

((digital health OR digital healthcare OR digital health care OR digital health intervention* OR digital health technolog* OR telemedicine OR telehealth OR eHealth OR electronic health OR M-health OR mobile health) AND (value-based healthcare OR value-based health care OR value based healthcare OR value based health care)) NOT (Cost-effectiveness OR cost-utility OR cost-benefit)

Inclusion and exclusion criteria

Records were limited to any academic article or grey literature (e.g., reports from official and reliable institutions such as, for example the WHO) published since 2012 and available in full text format, in English or Spanish language, that provided relevant information about the theoretical model of the value of digital health transformation, as well as empirical works that actually measure the value of the said transformation, either for some specific digital technology, or for the set of digital health technologies in general. Articles that offered only a cost-effectiveness/utility analysis without any additional disruptive or novel measure of the value of new digital health technologies were not of interest, and were, thus, excluded.

Both purely theoretical and empirical descriptive studies, well designed, or with solid econometric analysis, were admitted. For qualitative studies, it was verified that the source of the information was reliable, or that the interviewed population had knowledge at an expert level. It was not filtered by age groups, but information was extracted in relation to age (when such information was presented in the study) in the full text review phase, in order to ensure including the results of the population in working age. No criteria were set in relation to the country of study. Included papers were classified into areas of application (using the categorization of the patient journey phases). All identified exclusion criteria are detailed in PRISMA flow diagram (Figure 1).

Figure 1.

PRISMA flow diagram result of the systematic literature review.

Data extraction

The identified references were imported into Zotero, the bibliographic software used for the selection and screening of studies. The screening of studies included the exploration of titles and abstracts in a first stage, and full-texts in a second stage. The search and final selection of references were carried out in January 2024 by two researchers, who did the work in parallel and independently. Any doubts or disagreements between the two researchers regarding the selection and exclusion criteria were discussed with a third researcher. The methodology followed for data extraction was reviewed and approved by all authors.

The following information was extracted for every paper:

1. Paper general information: authors and year of publication, title, journal, objective and country of study.

2. Information related with the study methods:

   • Type of study: depending on the methods used, such as qualitative study (for studies conducting focus groups, for example), Randomized Controlled Trials, Observational studies…
   • Area of study: depending on the type of patients or population involved in the study (e.g., chronicity, ophthalmology, infectious diseases…).
   • Target population description.
   • Randomization methods description (when applicable).
   • Data information: time horizon analyzed, number of participants, % female, age information, digital health technology analyzed and its description (and same for the comparator, if there is one).
   • Application or phase of the patient journey: Access to health care services, prevention, diagnosis, Hospital/clinical/home care, specialist care, Treatment, Valuing and measuring outcomes, behavioral health, and reimbursement decisions.
   • Primary outcomes: here the focus was on any outcomes related with the value of digital health technologies or interventions, including (but not just) Patient Reported Outcome Measures and Patient Reported Experience Measures (PROMs and PREMs). Also, when PROMs and PREMs were evaluated, such as instruments for the measurement of Health-Related Quality of Life or satisfaction instruments, an indicator of the instrument validity was also provided.
   • Methods for analysis: to make sure of the methods being appropriate for the results obtained, and make sure to keep only papers which methods are strong and analysis of enough (moderate or high) quality.
3. Information regarding the study results, discussion and conclusions:

   • Main results of the paper, with the focus on value elements of digital health technologies or interventions.
   • Discussion of the paper: relevant aspects of the discussion, explaining the contribution of the paper.
   • Paper strengths and limitations, as a complementary measure of the paper quality.
   • Conclusion: summary of the main results, as a complementary field that will help to make the interpretation of what are the main or the most important findings, and, sometimes, what is suggested for further research.

A supplementary file with the data extraction is available as supplementary material.

Analysis

Papers were classified and analyzed in three categories:

• Theoretical or conceptual frameworks: papers presenting a theoretical framework, discussion or commentary papers.

• Empirical works: for papers making some kind of evaluation of a novel Digital Health Technology.

• Theoretical and Empirical works: papers mixing theoretical and empirical methods.

Having extracted the information described above, the results regarding the value elements identified in the paper were classified, again, in three categories:

• Facilitators: elements that have demonstrated positive value.

• Barriers: elements that have demonstrated negative effect / value.

• Neutral or uncertain: elements for which value is neutral or still uncertain, but that literature suggests exploring due to its relevance.

The analysis conducted in this study identifies, for each type of study (theoretical papers: papers that offer pure theoretical analysis, empirical studies: studies involving qualitative or quantitative data analysis, or mixed methods: for studies combining theoretical and empirical analysis), the elements for VBDH for each phase of the patient journey (access to health care services, prevention, etc.), by type of digital health technology or intervention, and by agent involved. All agents were considered, as a societal perspective was used, and this includes: health centers, stakeholders, providers, patients, health care systems, researchers and society.

Quality and risk of bias assessment

The scale to assess quality of the studies and their risk of bias was specifically elaborated for this study, similar to previous studies (such as Parmar et al. ¹⁶ ) given that there are too many observational studies and none of the existing tools looked suitable formulating the precise questions to evaluate the quality of the different type of studies. This quality and bias assessment tool was developed based on various quality assessment tools for studies of different designs. The creation of adapted tools for quality and bias assessment is considered a valuable resource for evaluating study quality in systematic literature reviews. ¹⁷ The adapted tool used here, assesses internal validity (risk of bias), external validity (applicability/variability), and reporting quality based on a set of questions/statements:

• Selection bias: The sample is not representative of the population intended to be analyzed

• Ecological fallacy: Logical fallacy inherent in making causal inferences from group data to individual behaviors

• Confounding bias: The analysis does not account for those confounders that are expected to have an effect on the outcome and that have not been accounted for by the inclusion of other.

• Reporting bias: Inappropriate or inaccurate reporting of aspects of the study

• Time bias: Bias introduced owing to the timeframe considered

• Measurement error in exposure variable: Errors related to the exposure variable.

• Measurement error in health outcome: Errors related to the health outcome measurement.

For randomized control trials, selection bias was assessed by analyzing the appropriateness of sampling methods (power, sample size, blind design, and randomization). Those studies that provided information on the value of digital transformation in the most current context of study were considered to be of stronger quality and -at least- lesser temporal bias. The greater the distance (in years) between the time period analyzed and the time of publication, the greater was considered the risk of time bias. A greater measurement risk in our exposure variables (digital health and the value of transformation) was considered for those articles offering theoretical models not tested by empirical work, or for empirical works where the sample is too small (less than 100 for quantitative studies or less than 20 for focus groups and qualitative interviews).

For each publication, each domain of the adapted Parmar et al. scale was studied and given a score of 1 for a low risk of bias, 2 for a moderate risk and 3 for a high risk. The overall rating was calculated as follows: 1 (strong) is given if none of your domains are classified as weak, 2 (moderate) if up to two domains are classified as weak, or 3 (weak) if three or more domains are classified as weak. Records with an overall rating of 3 were finally excluded, leaving only records rated of strong and moderate quality for the final analysis.

Results

The search strategy (across titles and abstracts) identified 614 potential studies from EMBASE (452), Scopus (162). After removing duplicates 606 abstracts remained for review. Four hundred and ninety-four abstracts were excluded as a result of the titles and abstract screening phase and 112 were selected for full text screening. Documents were excluded in the data extraction phase, some because they did not offer results, others because the full text was not available, and others because they were not of enough quality or highly biased. Finally, 67 studies were included from which the detailed information was finally extracted.

A PRIMSA flowchart representing the process of selecting eligible sources for this study is presented in Figure 1. The reasons for exclusion in the screening and eligibility phases are detailed in the PRISMA. Reference lists of primary research reports and previously published systematic reviews with a focus on digital health and/or VBDH were also reviewed in an attempt to identify additional studies. Data extraction and final risk of bias and study quality assessment and scores are available as supplementary material.

Among the 67 studies fully extracted, there were 14 theoretical studies, 40 empirical studies and 13 studies combining theoretical and empirical methods. By agent type, and for each of the phases of the patient journey, the elements of VBDH were identified, and are now summarized. Each paper was allocated to one phase of the patient journey, depending, mainly, on the type of study, on main objective of the paper, and on the study setting. The areas of application identified in the analysis are now done, for each type of agent and by literature category.

Areas of VBDH identified for health care organizations

Only one paper of the theoretical literature identified VBDH elements of access to health care services for health organizations, analyzing the value of Health Information Technologies (HIT). ¹⁸

Areas of VBDH identified for stakeholders

One theoretical study found relevant VBDH elements, related to access to health care services, for stakeholders, analyzing the value of eHealth. ¹⁹

Two studies, within the mixed methods studies identified, found VBDH elements related to patients’ treatment for stakeholders, analyzing the value of Electronic Medication Adherence products and a disruptive Digital Health Technology.^20,21

Areas of VBDH identified for providers

Related with access to health care services, theoretical works found the following VBDH elements. Six studies found specific value elements for providers, related with access to health services, six of them finding elements of positive value. Different technologies were analyzed, including HIT, Emerging digital health care practices, smart nursing, AI and big data, and High Definition Video-to-Video services,^18,22–26 one of them finding an element of negative value, related with the burden of health care professionals. ¹⁸ Two mixed methods papers found VBDH elements, analyzing digital health communication technologies and Electronic Medical Records.^27,28 Related with prevention, one empirical study identified VBDH elements for providers, of analyzing a Quitline-like phone counseling and Telemedicine integrated into Primary Care. ²⁹ Related with diagnosis: One theoretical study analyzing imaging analysis technologies ³⁰ and two empirical studies, on CT, imaging were identified.^31,32 Related with hospital/clinical/home care, two theoretical studies identified VBDH elements for providers, analyzing mobile health and smart home devices and the Internet of Medical Things (IoMT).^33,34 In addition, one empirical study, studied the value of EHR. ³⁵ Related with specialist care, three empirical studies identified VBDH elements of telemedicine, a theory driven digital intervention, and Digital Health Monitoring Platforms for specialists.^36–38 Related with treatment, one empirical study analyzed digital health technologies in general, ³⁹ and one mixed method's paper analyzed the value of Electronic Medication Adherence products. ²⁰ Related with the valuation and measurement of outcomes, twelve studies were found identifying VBDH elements for providers, analyzing very different digital health technologies including a Virtual Reality App, Digital Monitoring Adherence systems, Social media-based health education, AI kit, mobile apps, online telehealth, Imaging related machine learning, teleconsultation, text messaging, Machine learning techniques applied to PROMs and Digital Communication Technologies.^40–51 In addition, one study, among the mixed methods included studies, identified negative VBDH elements of Electronic Health Records for this area. ⁴⁴

Areas of VBDH identified for patients

Related with access to health care services: nine theoretical studies were identified, discussing VBDH elements for different types of DH technologies, including HIT, Emerging Digital health care practices, Smart nursing, AI, High Definition Video-to-Video services and eHealth.^{18,19,22–26} Among the empirical studies identified within this area of access to health care services, two studies were found, both analyzing VBDH elements of mHealth.^52,53

Among the mixed methods studies, three studies were identified: One study, analyzing wearables, ⁵⁴ a second study, analyzing a cloud-based referral platform ⁵⁵ and the third study, analyzing an Integrated Digital Patient Education system. ⁵⁶

Related with prevention, two empirical works identified VBDH elements for patients: one study, analyzing teleophthalmology for screening. ⁵⁷ The second one, analyzing a Quitline-like phone counselling and Telemedicine integrated into Primary Care. ²⁹

Related with diagnosis, one theoretical work was identified on the VBDH for patients of imaging analysis. ³⁰ Empirically, one study, analyzed Breast Imaging technologies. ⁵⁸

Related with treatment, three empirical papers were identified. One studied integrated care with telemedicine support. ⁵⁹ The second paper, studying Digital Health Technologies. ³⁹ The third paper, analyzing an interactive telephone intervention. ⁶⁰

Related with the valuation and measurement of outcomes, thirteen empirical papers were found, identifying value elements, analyzing: a VR App, ⁴⁰ telerehabilitation, ⁶¹ telehealth education, ⁶² a digital intervention for online help seekers, ⁶³ a care coordination digital intervention, ⁶⁴ a Digital Monitoring Adherence system, ⁴¹ a mobile phone text messaging self-managing support system,^65,66 an evidence-based SDM aid, ⁶⁷ a computerized intervention, ⁶⁸ teleconsultation, ⁴⁸ an open medication system via iPad, ⁶⁹ and a Digital Communication Technology. ⁵¹ One paper among the mixed methods papers, analyzed a eHealth intervention of integrating self-report in Electronic Patient Records (eRAPID). ⁷⁰

Related with behavioral health, one study, analyzing the use of wearables. ⁷¹

Areas of VBDH identified for health care systems

Related with access to health care services, seven theoretical papers identified VBDH elements for health care systems, analyzing: smart nursing, ²³ Artificial Intelligence (AI), ²⁶ High Definition Video-to-Video Services,^25,72 eHealth in primary care in high and low-resource settings, ¹⁹ an electronic mindfulness-based intervention (eMBI). ⁷³ Among mixed methods papers, one paper analyzed a cloud-based referral platform. ⁵⁵

Related with diagnosis, one study among the mixed methods included studies, analyzed the value of telehealth services. ⁷⁴

Related with the valuation and measurement of outcomes, one empirical paper was found, analyzing a Digital Communication Technology. ⁵¹ In addition, two papers among the mixed methods papers, were found. One, analyzing an eHealth intervention of integrating self-report in Electronic Patient Records (eRAPID), ⁷⁰ and the second paper, also on Electronic Health Records. ⁴⁴

Areas of VBDH identified for researchers

Theoretical works identified some positive values in the area of access to health care services for researchers: One study, discussing smart nursing, ²³ the second paper, on smart home devices and the Internet of Medical Things, ³³ and the third paper, discussing an electronic mindfulness-based intervention (eMBI). ⁷³

Related with treatment, one empirical work studied the value of Digital health Technologies. ³⁹

Areas of VBDH identified for society

Theoretical works identified elements of VBDH for the society related with access to health care services. Technologies analyzed include learning health systems, ⁷⁵ emerging Digital health care practices, ²² telehealth services ⁷² and eHealth. ¹⁹

Related with the valuation and measurement of outcomes, three empirical papers were found, analyzing a telemedicine outreach system, ⁷⁶ an mHealth ROADMAP platform, ⁷⁷ an HPV Cancer Free App. ⁷⁸

Related with reimbursement decisions, one theoretical analyzed telehealth and the value of digital connectivity. ⁷⁹

Areas of VBDH identified for patients and providers

Theoretical works found some VBDH elements for both, patients and providers.

For the application area of access to health care services one study was found analyzing emerging digital health. ²² Another study, on Artificial Intelligence. ²⁶ The third paper identified, discussed high definition Video-to-Video services. ²⁵ Finally, the fourth and last identified paper, discussed eHealth. ¹⁹

Among empirical studies, some studies were found identifying VBDH elements for patient and providers in the application area of specialist care. These studies include the analysis of telemedicine (virtual visits), ³⁶ a theory driven digital intervention, ³⁷ or Digital Health Monitoring Platforms. ³⁸

One study among the mixed methods studies captured VBDH elements for patients and providers, in the application area of valuation and measurement of outcomes. The study analyzed EHR. ⁴⁴

Areas of VBDH identified for patients and health care systems

Theoretical works found VBDH elements for patients and health care systems, in the application area of access to health care services. One of the studies analyzed eHealth. ¹⁹ Another study, discussed health sensors, smart home devices and the Internet of Medical Things. ³³

Related with valuation and measurement of outcomes, theoretical works finding VBDH elements for both, patients and Health Care Systems, were also identified: One discussing an electronic mindfulness-based intervention (eMBI), ⁷³ a second one, analyzing a care coordination intervention, ⁶⁴ and a third study, analyzing a computerized intervention. ⁶⁸

Among the mixed methods studies, one paper also found VBDH elements of personal Digital Health Profile (DHP), for patients and health care systems, for the area of prevention. ⁸⁰ In the area of treatment, a study found VBDH elements for health care systems and researchers analyzing a digital self-management intervention. ⁸¹

Elements of VBDH identified by the literature and conceptual framework by literature category

The detailed results and analysis for theoretical studies, empirical studies and mixed methods studies are detailed in Table s1–3, respectively.

Tables 1–3 have also been of value for generating a conceptual framework, represented by Figures 2–4. The framework provides a list of VBDH elements, by type of agent and application area (phases of the patient journey) according to theoretical literature, empirical and, mixed methods literature, respectively.

Table 1.

Summary of theoretical included papers included: content and main results.

Authors (year)/country/ type of study	Application/population/therapeutic area	Digital health intervention/description	Objective	Primary outcome/s	Summary of results (value elements by agent type)
					Agents and positive or nonnegative effect/value (facilitators)	Agents and negative effect/value (barriers)	Agents and uncertain effect/value
Armstrong et al. (2019) US Recommendation document	Hospital. Clinical care. Veterans and military Several therapeutic areas	Mobile health (mHealth) Mobile health as a mechanism for technological culture	Describe provider- and patient-level cultural issues in the provision of clinical care in the military and veteran populations and how the development of cultural competency in technological culture can improve patient care.	Key factors are addressed when considering the cultural issues involved in the clinical integration of mobile health in the military and veteran populations.	Providers: Technological culture: demonstrate the application of cultural theoretical models to better develop provider competence.
Basatneh et al. (2018) US Discussion paper	Home care Patients with Lower Extremity Complications of Diabetes Chronicity	Health Sensors, Smart Home Devices, and the Internet of Medical Things The IoT covers many areas ranging from enabling technologies and components to several mechanisms that effectively integrate these low-level components.	To better understand potential opportunities and challenges associated with implementing Internet of Medical Things (IoMT) for management of DFUs.	Discussion regarding future direction for IoMT in the care of the diabetic foot.	Patients: Potential to empower patients to take care of their own health and thus promoting patients’ central role and responsibility in enabling an optimized health care ecosystem. Potential to not only keep patients safe and healthy. Providers: potential to improve how physicians deliver personalized and timely care. Researchers: Benefitting researchers with its ability to continuously and remotely monitor patients.	Patients: compliance, Technology issues (e.g., battery life), and security and privacy of patient information
Claikens et al. (2018) Belgium Discussion paper	Access to health care & health care services delivery Patients using hospital based medical imaging services Several Therapeutic areas	Health information technology (HIT) The goal of health information technology (HIT) is to improve the well-being of individuals and communities through the use of new technologies and deploying a resilient health information system (HIS), infrastructure and architecture.	In this article we focus and discuss on the benefits-based analysis of the use of information technology in hospital based medical imaging departments.	Benefits dependency network for an IT/IS investment	IT benefits of all the stakeholders. Hospital Organization: Customer attraction, loyalty and retention tool; Opportunity to generate cost-savings; Transparency, ownership, informed decision-making, care effectiveness; compliance; data sharing, relation based, platform independent. Clinical Imaging Centers: Remote access to clinical images, reports, lab results; Direct communication with referring physicians and patients; Electronic medical records with patient history; data sharing, relation based; platform independent. IT department: Hosted solution; Browser based, zero install, easy to deploy; Safe; Native applications; Standard based interfacing; Data sharing, relation based, `platform independent. Referring physicians: Fast access to images and clinical data; Data aggregation; Direct communication with peer physicians; Data sharing, relation based, platform independent. Patients: Empowering flexibility; Engagement in their care; Access anywhere, anytime, any device; Sharing clinical documents and images; Direct communication with physicians and experts, anywhere; Data sharing, relation based, platform independent.	Tendency to burden healthcare professionals with additional administrative responsibilities.
Deschamps et al. (2021) UK Commentary	Access to health care & health care services delivery (Secondary care) Podiatry patients Podiatry	Emerging digital foot health care practices A framework that helps in portraying and defining drivers of practice, actual practice as well potential barriers for current and future practice.	The objective of this commentary is to present a conceptual framework, namely the Value Based Digital Foot Care Framework, for use by podiatry students, lecturers, therapists, scientists and policymakers.	The proposed conceptual framework for podiatry practice utilizes the metaphor of an electronic circuit to reflect the vast and complex interconnections between factors that affect practice and professional behaviors. The framework helps in portraying and defining drivers of practice, actual practice as well potential barriers for current and future practice. The circuit emphasis the interconnectedness/interaction of three clusters: 1) internal factors, 2) interaction factors, 3) external factors.	Providers: Internal factors: Past experiences, perceived complexity of a health care and/ or support question and expertise of the podiatrist. As they learn and develop, student podiatrists are encouraged to embrace the principles of evidence based -practice and a person-centered approach. The term person centered approach is used specifically here in order encourage podiatrists to adopt a patient care perspective that is beyond the condition and tailored to the individual/patients’ wants and needs. In addition, learners, should be encouraged to develop knowledge by many ways of knowing: emotion, faith, imagination, intuition, language, memory, reasons, sense perception). Interaction patients and providers: Interaction factors. Focuses and define the interplay between the podiatrist and the person. this may warrant review of current podiatry education and practice, with training and research into the assessment of mental health, somatising traits and trends, health beliefs and behaviors. It is appropriate podiatrists employ methods to identify how multidimensional approaches are applied, for example use of clinical (red flags) and psychosocial flags (yellow, blue and black flags) that affect outcomes for patients. Patients: External factors. Value is placed on a patient as the expert in their own life and health. Society: The digital component. Societal expectations associated with digital experiences ask that health professionals and patients/clients integrate and use digital technologies in flexible ways.
Li and Tang (2020) China Theoretical & Discussion paper	Access to health care & health care services delivery Geriatric patients Geriatric care	Smart geriatric nursing Smart geriatric nursing is based on the IoT technologies, sensors, big data, artificial intelligence, and many other advanced technologies in information society.	Basing on the professional knowledge of geriatric nursing, this paper proposes a framework of smart geriatric nursing.	A framework of smart geriatric nursing that consists of three aspects of smart nursing: smart geriatric nursing in physical health using biosensor and advanced devices, smart geriatric nursing in mental health based on user profile, and smart geriatric nursing for daily life based on big data in health.	Providers: (1) More detailed optimization of relevant procedures, methods, and norms of intelligent elderly care, based on cloud computing platform;. Researchers: (2) integration, realization, and collection of related information of big data about geriatric daily nursing by artificial intelligence algorithm instead of the Internet of Things. Healthcare system: (3) continuation of the optimization of related technology and method.	Technology cannot replace the people to overcome the depression in older adults with chronic conditions. The economic effectiveness, accessibility, and practicality of some advanced technologies.	How to manage the privacy during using the big data is a challenge.
Lin et al. (2019) US Discussion paper	Access to health care & health care services delivery (GP/Primary Care) All patients Primary care	Artificial Intelligence Any technology designed to mimic human cognitive functions—including techniques such as classic machine learning, modern deep learning, natural language processing, and robotics.	This paper presents a current environmental scan, through the eyes of primary care physicians, of the top ten ways AI will impact primary care and its key stakeholders. We discuss ten distinct problem spaces and the most promising AI innovations in each, estimating potential market sizes and the Quadruple Aims that are most likely to be affected.	Top 10 ways artificial intelligence (AI) will impact primary care.	Patients & Providers: Risk prediction and Intervention: AI driven predictive modeling with EHR data can now outperform traditional predictive models in forecasting in-hospital mortality, 30-day unplanned readmission, prolonged length of stay, and all of a patient's final discharge diagnoses; Population health management: ability to identify and close care gaps, and to optimize their performance with the quality payment programs of MACRA. Practice Management: AI can also be used to automate certain aspects of pre-visit planning to make primary care encounters more efficient and rewarding for patients and physicians. Medical device and triage: AI doctors provide health advice directly to patients with common symptoms, freeing up primary care access for more complex care. AI support can be integrated into team-based care models that make it easier for primary care physicians to manage a patient panel. Risk adjusted paneling and resourcing: Risk-adjusted paneling can ensure that primary care physicians have adequate time to address the needs of each patient by increasing or decreasing panel sizes based on patient complexity, which can contribute to patient satisfaction and better work–life balance for physicians. In the future, Risk-adjusted panel models can be used to determine the level of staffing support needed for primary care practices (e.g., medical assistants, nurses, advanced practice providers, clinical pharmacists, social workers) based on the complexity and intensity of care provided. Device integration: Primary care physicians may be able to use data from such devices to diagnose or treat disease at earlier stages; however, the data's sheer volume and incompatibility with current EHRs make this unfeasible without the help of AI. Augmenting (not subverting) the patient-physician relationship: For AI to add the most value and for physicians to embrace it, these innovations should support, not supplant, the patient physician relationship. Implemented wisely, AI can free up physicians’ cognitive and emotional space for their patients, even helping them to become better at being human. Healthcare systems: Digital health coaching: Integrating digital health coaching programs inn health systems has shown reductions in cost per patient through reduced office and hospital visits. Providers: Chart review and documentation: Although still in its infancy, this type of AI carries the potential to unshackle physicians from the EHR and reduce burnout. (4) Patients: Diagnostics: In regions with lack of access to specialty care, these tools in the hands of primary care doctors can provide significant benefit to patients. The AI will generate possible diagnoses and suggest self-care versus a visit to their physician. This could reduce the need for unnecessary referrals, increase continuity with patients, and enhance mastery for primary care physicians. Clinical decision making: The next generation of clinical decision-making platforms must move beyond alerts and best practice advisories to provide analytics and evidence-based content in the clinical workflow to drive intended actions.	AI implemented poorly risks pushing humanity to the margins;
Marsch et al. (2020) US Discussion paper	Valuing outcomes (patients) Patients with addictive behaviors Mental health	An electronic mindfulness-based intervention (eMBI) ACHESS, a Smartphone-based recovery support system20 that is part of a larger suite of technology-based health systems known as CHESS. This tool is built on evidence-based principles of recovery support: long duration, assertive outreach, monitoring, prompts, action planning, peer and family support, and case management. Recovery support can improve health and reduce health-care use but is rarely used in addiction treatment due to limited resources for this type of care. Among ACHESS’ features focused on personalized monitoring and support to individuals in recovery are a global positioning system for letting individuals know when they are near environments that are historically high-risk areas for them, functionality to monitor one's behavior and risk factors, personal stories of recovery experiences, and links to recovery support groups.	To describe potential models for implementing technology-based interventions targeting substance use disorders. To discuss opportunities to optimize the effectiveness and impact of technology-based interventions targeting addiction and recovery, along with outstanding research needs.	Synthesis of evidence regarding the benefits of eMBI for society, Healthcare systems, Patients, Providers and Research.	Healthcare systems and Patients: Flexible models of deployment: new models of delivering behavioral health care. Technology based interventions offer tremendous opportunities to reach an unreached audience, and thus offer a considerable public health effect. Such tools offer considerable promise for ongoing recovery support. Technology-based therapeutic tools have the potential to enable individuals (and optionally an extended support network) to play leading roles in their own care management. Healthcare systems: Opportunities to Optimize the Effectiveness and Impact of Technology-Based Interventions Targeting Addiction and Recovery. Such a tool could offer a comprehensive and coordinated approach to the constellation of issues a given individual may be experiencing. Potential of technology for real-time monitoring of a given individual's trajectory and then provide ongoing tailoring of intervention content that is responsive to that trajectory. New model for interfacing with clinicians by offering much richer, detailed data about clients to their clinicians, which may affect the nature of the clinical care that is delivered. (3) Researchers: Research needs: a better understanding of the mechanisms of behavior change and health behavior promotion as a result of technology-based interventions relative to traditionally delivered interventions is needed. A significant opportunity and need exist in the research community for better understanding of how to optimally integrate empirically supported technology-based assessment and intervention programs into systems of care. Such data will be critical to promoting broad reach of evidence-based, technology-delivered interventions.
McElroy et al. (2020) US Commentary	Reimbursment decisions Patients with chronic conditions Chronicity	Telehealth and digital connectivity Telemedicine will refer to providing medical care at a distance, which includes audio–video care or audio care only.	In this commentary, we use telehealth as an umbrella term referring to telemedicine and other health-related virtual activities, such as distance continuing medical education, training, and patient portals. Telemedicine will refer to providing medical care at a distance, which includes audio–video care or audio care only.	Telehealth, in person visits and cancelled appointments before and during the pandemic.		Healthcare system: Disparities in telehealth adoption.
Menear et al. (2019) Canada Discussion paper	Access to health care & health care services delivery All patients All areas	Learning health systems Value-creating framework for learning healthcare systems	This paper presents a conceptual framework that has been developed in Canada to support the implementation of value-creating LHSs.	A conceptual framework for value-creating learning health systems	All agents of healthcare systems: Value is created when a more optimal and acceptable balance is achieved across four dimensions of health system performance: Patient experience; population Health; Provider Experience; Healthcare costs.
Molnar & Weerakkody (2013) Greece Qualitative (interviews)	Access to health care & health care services delivery (Secondary care) Glaucoma patients Ophthalmology	High Definition Video-to-Video Services LiveCity project: Essentially it is an application that manages bandwidth by avoiding interference from unwanted traffic and therefore ensuring that any loss of delivered data and delays are reduced. It therefore creates a virtual path over the public Internet to allow traffic (data, voice and video) to flow freely without losing quality. This ensures that signals are not interrupted by other external Internet traffic when the connection between two points of contact is established, therefore guaranteeing the transmission of high quality live video images between two points of contact.	This paper examines the process of developing key performance indicators (KPIs) for evaluating the use of high definition video (HD) to video (V2 V) communication for tele-monitoring patients in a healthcare setting. The obtained KPIs are used to analyze the potential of using V2 V services in the context of tele-monitoring glaucoma patients.	In this respect, it is important to find out what criteria are necessary to facilitate a successful adoption of HD V2 V communication as proposed in the LiveCity project in the context of a healthcare setting.	Patients: Improvement in the patients’ health Healthcare systems: Reduce the medication cost, Patient-providers interaction: Improved doctor/patient communication, Improved monitoring of patients by doctors, Improvement in the availability/easiness for patients to reach doctors, Improve patient perception of doctor availability, Improvement in patient management, Improvement in the number of patients treated, Improve the patient skills/knowledge in applying their eye drops, Patient reliability in getting the treatment correctly
Schwamm et al. (2017) US Policy statement	Access to health care & health care services delivery (Secondary care) Cardiovascular patients Cardiovascular & Stroke conditions	Telehealth Telehealth, as defined by Office for the Advancement of Telehealth, comprises the use of telecommunications and information technologies to share information and to provide clinical care, education, public health, and administrative services at a distance. Telehealth is a broad term that encompasses many digital health technologies, including telemedicine, eHealth, connected health, and mHealth. Telehealth is a new method of enabling care delivery that has the potential to help transform the healthcare system, to reduce costs, and to increase quality, patient-centeredness, and patient satisfaction.	The aim of this policy statement is to provide a comprehensive review of the scientific evidence evaluating the use of telemedicine in cardiovascular and stroke care and to provide consensus policy suggestions.	Summary of evidence regarding the following aspects: 1. Effective telehealth interventions that increase access to or enhance the quality of care for CVD patients; 2. Current barriers to implementation of telehealth interventions; 3. Emerging telehealth technologies; 4. Gaps in evidence and research needs; 5. Strategies to overcome the barriers to broad dissemination of telehealth.	Society: Telehealth interventions can reduce public health burden, making care more accessible and affordable while reducing many widespread disparities in access to care, particularly those attributable to geography or provider shortages. Regardless of how it is collected, stored, or analyzed, the practice of telehealth should be integrated into traditional healthcare delivery systems and electronic health records. When implemented, telehealth should optimize quality of care as defined by the Institute of Medicine and ensure that patient-centered care is never sacrificed to cut costs.	legal/regulatory, technological, and financial barriers.
van Biesen et al. (2021) Not specified Discussion paper	Access to health care & health care services delivery All patients All areas	Artificial Intelligence and big data A novel framework of justifiable health care and discussion on how AI and big data can contribute to achieving the goals of this framework	The current paper presents a novel framework of justifiable health care and explores how the use of artificial intelligence and big data can contribute to achieving the goals of this framework.	A conceptual framework of justifiable healthcare	Providers: Data can be used as feedback to the healthcare workers (formative evaluation) Patients: Data can be used to inform patients on the performance of different healthcare institutions/providers in domains that might be of interest and value to them.
van der Kleij et al. (2019) European countries Discussion paper	Access to health care & health care services delivery (GP/Primary Care) People with complex medical and social needs in primary care. Primary care areas	eHealth eHealth is not limited to mobile apps that can track a patient's behavior or symptoms. It encompasses communication technology that facilitates the exchange of information between primary and secondary care, or ‘big data’ research that informs the development of risk assessment tools. The first two categories of eHealth technologies, those that provide opportunities to monitor and inform and those that optimize communication, are perhaps most easily linked to the daily practice of primary care providers. However, electronic data collection to enable long-term monitoring, risk detection, and research are also essential to improve primary care. For example, a large data-mining study was able to include over 50 000 primary care patients and verify if the currently used Framingham indicators for heart failure were indeed able to predict eventual heart failure cases, years before diagnosis.	Providing insight into the role eHealth could play in primary care, is the aim of a series on eHealth in primary care, which we will publish in the European Journal of General Practice. The series covers seven themes: 1. eHealth for primary care (this paper): concepts, conditions, and challenges 2. eHealth for primary care: addressing the ethical implications 3. Evidence-based eHealth for primary care: inclusive, individualized and blended 4. Implications of eHealth for primary care in medical education and vocational training 5. Implementation of eHealth and integration into primary care routines 6. Developing an eHealth infrastructure: the role of primary care providers 7. Global perspective: eHealth for primary care in low resource settings	A set of Conditions to develop and implement safe and evidence-based eHealth in primary care.	Making available effective, safe and implementable eHealth applications for primary care: Stakeholders': Engagement and co-creation. To ensure an eHealth application is successfully implemented and its use is maintained is it important to pay sufficient attention to the interaction between user demands, technology, and the intended context. Patients and providers: Combining eHealth with regular care. Blended care has the potential to improve the quality and efficiency of care, while maintaining—or even improving—patient and provider satisfaction with care. Society: Individualized and inclusive: To individualized eHealth applications, the focus should also be on inclusiveness. eHealth has the potential to both increase and decrease health inequalities. Embedding iterative co-creation processes with vulnerable groups during eHealth development and implementation, and tailoring applications by the users’ level of (digital) literacy might help to reduce health inequalities. Health systems: eHealth in primary care in high and low-resource settings. Because of the major impact eHealth could have on healthcare systems in low resource settings, we argue that (research on) the upscaling of eHealth applications to those settings should be considered and prioritized. Health system and patients: Evidence-based: continuous research and educational guidance: integrating eHealth learning programs into the medical curriculum is suggested as a key factor in the successful implementation of eHealth in primary care. Patients: being attentive of ethical considerations, privacy and patient safety. The first rule of medicine is to ‘do no harm’. Hence, all healthcare providers need to ensure that the innovations or treatments they implement do not cause any adverse or unintended effects among the patient population. Offering technology that contains meaningful messages tailored towards low-literate populations can help to improve general accessibility, and screening tools on health (il)literacy might be able to enhance identification and handling of low-literacy from the outset. Taking these matters into account, the article advises (1) putting ‘do no harm’ high on the eHealth (research) agenda, for instance, by including a skilled ethicist in the team who is familiar with eHealth, and (2) to implement co-creation processes with vulnerable groups that can provide vital information on how to make eHealth applications suitable and feasible for all those in need.	Innovation complexity and lack of evidence on eHealth effectivity. The limited evidence available suggests, however, that practicing blended care may save a significant amount of (consultation) time, which can be redirected towards high-quality face-to-face care for those patients that need it the most.
Volynskaya et al. (2018) Canada Descriptive report	Diagnosis and Prognosis Patients from anatomic pathology and hematopathology at tertiary-care centers Personalized and Precision medicine	Imaging analysis Multisite pathology informatics platform to support high-quality surgical pathology and hematopathology using a sophisticated laboratory information system and whole slide imaging for histology and immunohistochemistry, integrated with ancillary tools, including electron microscopy, flow cytometry, cytogenetics, and molecular diagnostics.	To identify key requirements and establish a systematic approach to providing high-quality pathology in a publicly funded health care system that is responsible for services across a large geographic area that includes a heavily populated urban metropolis, large suburban communities, and remote towns that serve as hubs for large rural areas with scattered populations.	A model of subspecialty pathology that allows reporting of every specimen by the right pathologist at the right time.	Patients: 1-These tools enable patients in numerous geographic locations access to a model of subspecialty pathology that allows reporting of every specimen by the right pathologist at the right time; Providers: 2-Enables better communication among members of patient care teams.

Table 3.

Summary of papers mixing theoretical and empirical methods included: content and main results.

Authors (year)/country/type of study	Application/population/area	Objective	Time horizon of data analyzed	Number of participants/% female/age information	Technology/digital health intervention	Primary Outcome/s	PROMs or PREMs validated measures?	Summary of results (value elements by agent type)
								Positive or nonnegative effect/value (facilitators)	Negative effect/value (barriers)	Uncertain effect/value
Absolom et al. (2021) UK Prospective, randomized two-arm parallel group study	Valuing outcomes (patients) Cancer patients Oncology	Can we control better treatment- and disease-related symptoms during chemotherapy by introducing online monitoring with patient-reported outcome measures, uniquely combined with automated algorithm-driven severity-dependent patient advice?	January 22, 2015-June 11, 2018	508 79.9% mean = 56.0 (SD = 11.8)	eRAPID: eHealth Intervention During Chemotherapy. Immediate integration of the self-reports in electronic patient records (EPRs) facilitated clinical use.	PROMs: Symptom control measured by using the Functional Assessment of Cancer Therapy Scale-General Physical Well-Being subscale (FACT-PWB, scores 0–28, high scores 5 better symptoms)	YES	Patients: Improved physical well-being and self-efficacy in a patient population predominantly treated with curative intent, Healthcare system: does not increase hospital workload.
Burridge et al. (2017) UK Overview study	Home care Upper Limb poststroke spasticity patients Stroke care	This article addresses the need for cost-effective increased intensity of practice and suggests potential benefits of telehealth (TH) as an innovative model of care in physical therapy.	Not mentioned	Not applicable Not applicable Not applicable	A web supporting program (LifeCIT): wearable sensors; M-Mark	Feasibility was assessed by retention and adherence (duration of C-Mitt wear, activities, and use of the Web site). The Wolf Motor Function Test (WFMT) and the Motor Activity Log (MAL) (a self-assessment tool that measures about amount and quality of use of the affected upper limb) PREMs: Views and experience of using the Web site and wearing the CMitt. Safety: measured by documentation of adverse and serious adverse events and reported following standard procedures for each rehabilitation center. The economic impact (use of health and social care) of the LifeCIT intervention compared with usual care was examined using the Client Service Receipt Inventory.	YES	Patients: Improvement of intensity and adherence to rehabilitation regimens.
Chau et al. (2019) China Conceptual framework and survey	Access to health care & health care services delivery Respondents with experience of healthcare wearable technology Unspecified	To gain knowledge in this area and provide meaningful managerial implications for practitioners and wearable device developers.	October 1st 2018, to January 5th 2019	171 45% mean = 26–30 years (ranging from 18 to 65)	Healthcare wearable devices assisting users to check their health status as well as planning their exercises	Adoption intention of healthcare wearable technology	YES	Patients: better adoption intention of healthcare wearable technology (explained by perceived usefulness, which is expalined by perceived convenience and perceived irreplaceability).		Moderation effects of: gender, usage experience, education level and income of consumers
Curtis et al. (2018) US Narrative review and description	Valuing outcomes (population-based) Seriously ill patient population Palliative care	We examine use of the electronic health record (EHR) as a tool to assess quality of serious illness care through narrative review and description of a palliative care quality metrics program in a large healthcare system.	2010–2015	105 43% mean = 65.5 (SD = 15.1)	Electronic Health Records	A summary of the strengths and weaknesses of the EHR. A discussion of opportunities and challenges.	NOT ALL	Patients and providers: Offer opportunities to identify seriously ill patients and assess palliative care quality metrics across large numbers of patients with serious illness.	Healthcare systems: Lack of interoperability across healthcare systems and, at times, within a single system; Providers: absence of systematic documentation of patient- and family-centered outcomes; and difficulties in capturing quality metrics information from data that were collected for other purposes.
Dack et al. (2019) UK Focus groups and Interview	Treatment (self-management) Diabetes patients Chronic diseases	To describe the development process and content of a digital self-management intervention for people with type 2 diabetes (HeLP-Diabetes) that has been found to achieve its target clinical outcome, the reduction of HbA1c, a measure of glycemic control.	2011–2016	20 patients with T2DM and 18 Health care professionals Patients: 40% ; Healthcare professionals: 61.1% Patients: mean age = 56.8 (range 36–77) Healthcare professionals: mean age = 49 (range 32–64)	A digital self-management intervention (HeLP-Diabetes)	PREMs: Questions regarding patients understanding of the condition, how to self-manage it, where to get information or how to get treatment	YES	Research and Healthcare systems: The use of participatory design principles to integrate theory and evidence can create acceptable and effective digital health interventions.
Faisal et al. (2020) Canada Mixed-methods approach	Treatment (adherence) Older adults, caregivers, and health care providers Unspecified	The aim of this study is to examine user experience with electronic medication adherence products, with particular emphasis on features, usefulness, and preferences.	August-December 2018	37 (20 older adults; 5 caregivers; 11 HCP) Older adults: 52%; Caregivers: 20%; HCP: 82% Older adults: mean = 75 (range 65–87); Caregivers: mean = 73 (range 69–79); HCP: not reported	Electronic medication adherence products	PREMs: Identification of themes regarding users’ experiences with focus on features, usefulness, and preferences		Providers: Health care providers should consider patient-related factors such as cognitive and functional capability to operate a device, medication regimens, and product features to choose the right product for the right patient. Stakeholders (Manufacturers): should consider the involvement of users in the beginning stages of product development for these technologies to ensure high acceptability, user friendliness, and affordability for end users. Stakeholders (Policy makers): should consider subsidizing the cost of electronic medication adherence products to make them affordable for people who are chronically ill and are on long-term therapies		Patients: The initial impression of complexity or simplicity impacted the overall impression and projected use of the product. In particular, preference for a particular electronic medication adherence product depends on multiple factors, including, but not limited to, the storage capacity, security, cost, and size of the device.
Harrington and Burge (2018) UK Mix of literature review and qualitative study (interviews)	Treatment Patients with chronic conditions Chronicity	The aim of this paper is to define operating principles and protocols in a variety of healthcare contexts where there is a specific focus on service transformation, enabled by a series of emerging manufacturing paradigms and novel business model concepts.	2016	Not mentioned Not mentioned Not mentioned	Disruptive digital technologies	A Digital value network: conceptual model development based on results from a literature review and interviews. The conceptual model includes: Technology intervention perspective - connecting digital pharma networks and e-healthcare systems. Integrated digital value networks focus on flow of goods, services, revenue, and knowledge between actors. Refined criteria and emerging protocols.		Manufacturers: Potential to enable localized and personalized medication manufacture.
Jenssen et al. (2016) US National Survey	Access to health care & health care services delivery Primary care patients Primary care	We assessed patient attitudes regarding technology use to communicate with their primary care team or to engage with other patients outside typical office settings.	November - December 2012	3064 49.8% 18–60	Digital Communication technologies	Attitudes on the use of digital technology (email, text messaging, and social media such as Facebook and Twitter) to communicate with primary care teams about health behavior goals and test results. We also assessed attitudes toward the use of digital technologies to engage with other patients in activities such as peer coaching.	YES	Providers: identifying patients that may be willing to adopt these technologies for healthcare	Providers: Physicians should not use demographic factors like age, race/ethnicity, or socioeconomic status as a proxy for attitudes toward the use of technology
Kern et al. (2020) UK Retrospective cohort study	Access to health care & health care services delivery (Secondary care) Patients classified into the HES referral pathway by contributing optometrists have been included into this study. Ophtalmology	To report the implementation and initial results of a cloud-based referral platform to medical retina HES, which was developed to overcome the increasing demand on scarce ophthalmologist services by improving communication between opticians and ophthalmologists.	April 2018 to January 2019	107 53% mean =66.9 (SD = 18, range: 23–95)	A cloud-based referral platform	‘Urgent referral’ (HES appointment ≤4 weeks): all causes of choroidal neovascularisation including age related macular degeneration and myopia, referable oedema as in diabetic maculopathy and subretinal fluid as in central serous chorioretinopathy. “Routine referral” (HES appointment within 18 weeks referral to treatment time defined by the NHS13): all other nonurgent cases that needed further diagnostic or specialist evaluation and are not part of the category above. ‘No referral’ (yearly follow-up by optometrist): the absence of pathology classes described above.	Not applicable (does not measure PROMs or PREMs)	Healthcare systems: Can avoid unnecessary referrals	Health care system: High costs that accompany ophthalmic imaging equipment	Patients: Patient satisfaction is important and must be evaluated
Larsen et al. (2019) Denmark Intervention	Prevention Chronic patients at primary care Chronicity	The present paper examines individual characteristics and health-care usage of patients who took up the targeted preventive programs in response to their personal digital health profile.	May 2016 to December 2016.	2661 56% mean = 45.4	Personal digital health profile	Attendance to the preventive programs	YES	Patients and Healthcare systems: Motivating people with low self-efficacy to attend	Patients and healthcare systems: Not motivating patients who had not had a health check or visited their GP during the past years	Patients and Healthcare systems: May be beneficial for some groups of patients, but not for others, and should be supplemented by other options (such as waiting-room administered systems and outreach).
Mandelblatt et al. (2015) US Observational study	Access to health care & health care services delivery (Secondary care) Women with breast cancer Oncology	We developed and conducted preliminary evaluation of an algorithm to detect grade 3 and 4 toxicities using electronic data from a large integrated managed care organization.	2006–2009	1575 Not mentioned Most participants <65	Electronic medical records and administrative data	Having vs not having toxicity	Not applicable (does not measure PROMs or PREMs)	Providers and Patients: Identification of chemotherapy toxicity (potential to improving oncology care).
Schooley et al. (2020) US Mixed-methods approach (Survey and interviews)	Access to health care & health care services delivery (Secondary care) Patients who were counseled using a blended digital health education Chronicity	The goal of this study was to address the need for blended learning strategies combining technical and workflow integration of digital patient education systems for patients with chronic conditions within and across the regular process of care. Studies are needed to evaluate the utility and benefits of these technologies for providers and patients alike.	October 2017 to May 2018	178 56.7% Aged 18 years or older	Integrated Digital Patient Education (mobile devices, interactive media, 3-dimensional images, and multimedia educational content at the bedside.)	Patient understanding, patient motivation for self-care, patient confidence in care decision making, clinician-patient communication, and patient's intention to follow instructions.	YES	Patients: 1-Improved understanding of educational content and chronic health conditions; 2-More motivation for self-care at home; 3-Feeling capable of making health care decisions with their doctors and on their own; 4-Pastients more likely to report their intention to follow their doctor's instructions.
Zhao et al. (2020) US Cross-sectional analysis using census data from acute care hospitals	Hospital/Clinical care Acute care patients Acute care	The aim of this study was to examine the association between alternative payment models (APMs), market competition, and telehealth provisions in the hospital setting.	2018	4257 (hospitals) Not applicable Not applicable	Telehealth	Provision rates of Telehealth services	Not applicable (does not measure PROMs or PREMs)	Health care systems: Competitive advantage for hospital providing telehealth services

Figure 2.

Framework of value elements by agent type and application area of the patient journey according to theoretical literature.

Figure 4.

Value elements by agent type and application area of the patient journey according to mixed methods literature.

Figure 3.

Framework of value elements by agent type and application area of the patient journey according to empirical literature.

Table 2.

Summary of empirical papers included papers included: content and main results.

Authors (year)/country/type of study	Application/population/area	Time horizon of data analyzed	Number of participants/% female/age information	Technology/digital health intervention	PROMs or PREMs validated measures?	Summary of results (value elements by agent type)
						Positive or nonnegative effect/value (facilitators)	Negative effect/value (barriers)	Uncertain effect/value
Li et al. (2015) US Prospective Randomized Study	Prevention Population with suspected macular degeneration or stable but nonactive AMD Oftalmology	November 2011 - November 2012	106 61.1% mean age = 81.1	Teleoftalmology for screening of age related Macular degeneration	Not mentioned	Patients: Referral treatment time. No adverse outcomes.	Healthcare systems and Patients: Monitoring recurrence: increased waiting times.
Vlahiotis et al. (2018) US Projections	Diagnosis Adult (aged 18 + years) females who had at least one medical claim for a breast imaging or breast diagnostic procedure Breast procedures	2011–2015	875526 100% mean age = 52.5	Breast imaging and diagnostic procedures	Not applicable (does not measure PROMs or PREMs)	Patients and Healthcare system: Significant reduction in the number of procedures needed.
Kim et al. (2021) Korea Cohort study	Access to health care & health care services delivery 1) patients < 65 years with COVID-19, 2) patients without underlying medical comorbidities, and 3) COVID-19 disease severity of mild class. Infectious diseases	Mar 3, 2020, to Mar 26, 2020	290 64.1% median age = 37	Digital health (monitoring system): Smartphone app combined with personal health record platform	Not applicable (does not measure PROMs or PREMs)	Patients and Healthcare systems: Fast identification of the patients at possible risks of clinical deterioration
Serafica et al. (2019) Hawaii Qualitative: focus groups	Access to health care & health care services delivery Patients, their family supporters and significant others, and healthcare providers of a clinic that is the only federally qualified health center and safetynet service provider. Nursing and healthcare practice	Not mentioned	19 29% mean age = 59.43 (SD = 5.16)	Mobile health technologies	Not applicable (does not measure PROMs or PREMs)	Patients: Education (self-management of their care) and communication with providers
Bengtsson et al. (2018) Sweden Qualitative, explorative study	Access to health care & health care services delivery (GP/Primary Care) Patients being medically treated for hypertension; (2) over 30 years of age and (3) able to understand and read Swedish. Blood diseases	Not mentioned	20 55% median = 63.5 (range 43–72)	Mobile phone self-management support system	YES	Patients: Actively involved patients (more interactions with providers): results in patients with better understanding and self-management abilities
Ashry & Alsawy (2020) Egypt Clinical data collection & Questionnaire	Specialist Patients above 18 years and those who agreed to participate in telemedicine visits and patients who experienced a smooth postoperative course in the hospital and not expected to have complications. Neurosurgery	March-April 2020	32 37.5% mean age = 38.8 (range 18–65)	Telemedicine (virtual) visits	NO	Patients and Providers: Good level of satisfaction of patients and providers
Bucci et al. (2018) England Intervention: Implementation and test of efficacy and safety	Specialist Early psychosis patients. Mental health	2015–2017	36 50% Actissist: mean age = 20.21 (SD = 7.37) ClinTouch: mean age = 18.33 (SD = 7)	Theory-driven digital intervention: Actissist	YES	Patients: Feasibility; Acceptability; Safety; High user satisfaction level.
Zhen et al. (2021) US & Canada Survey analysis	Specialist IBD patients Digestive diseases	March 1, 2018 to September 30, 2018	32 37.5% median = 28.5 (IQR: 22–38)	Digital Health Monitoring platforms	YES	Patients: Positive: Improves patient's knowledge on the condition; Neutral: No changes in the relationship with providers or self-management (treatment or prevention) abilities.
Murphy et al. (2020) US Cross-sectional	Hospital care Acute care hospitals that attested to participation in the MU program and also participated in the Hospital Value-Based Purchasing Program (HVBP) Acute care	January 1 to December 31, 2016	2362 (hospitals) NA NA	Electronic health records	YES			Providers: Depends on EHR vendor
Inoriza et al. (2017) Spain Prospective study of two paralell cohorts	Treatment (integrated care) Diabetic patients Chronicity	Julio 2013-Septiembre 2014	126 35.70% Cases: mean age = 63.9 (SD = 14.9) Control: mean age = 63.4 (SD = 15.5)	Integrated care with telemedicine support program	YES	Patients: High satisfaction obtained by users.
Slevin et al. (2020) Ireland Qualitative study (interviews)	Treatment (provision) COPD patients Chronicity	Not mentioned	32 (clinicians) Not mentioned Not mentioned	Digital Health Technologies	NO	Patients: DH training and education; improving HCP digital literacy; Providers: personalized prescribing	Researchers: Data quality; evidence-based care; resource constraints.
Williams et al. (2012) Australia RCT	Treatment (provision) Type 2 diabetes patients Chronicity	July 2008 and December 2010	120 Not mentioned mean age = 57.4 (SD = 8.3)	Interactive telephone intervention	YES	Patients: Positive: Clinical outcomes; Mental HRQoL.		Patients: Physical HRQoL
Kip et al. (2019) The Netherlands Semistructured interviews (online questionnaire)	Valuing outcomes (patients) Mental health patients, therapists and other stakeholders Mental health	Not mentioned	146 60.95% Patients: mean age = 41.53 (SD = 7.37) Therapists working in forensic mental health: mean age = 38.79 (SD = 12.51) Other stakeholders: mean age = 43.95 (SD = 14.01)	Virtual Reality (VR) Application	YES	Providers: Good addition to therapy; practicing safely; good exercise; insight into behavior patient; cause for conversation; good addition to current treatment; Content: Realism of behavior; personalization of persons; personalization of scenario; personalization of environment; Patients: Improvement (future) behavior; insight own behavior; insight behavior others; treatment motivation; suitable for specific target group; supporting in reliving;	Providers: difficult to use in treatment; not of added value to current treatment; Content: Not enough options for adaptation of persons, scenarios and environment; not generalizable to real life; important element is lacking; no realistic behavior; use of VR is not necessary to reach same goal. Practical experience: No visual realism; hard to use; high costs; takes time during treatment; takes time to learn to use. Patients: Dishonesty about effect; dishonesty about input scenario; not suitable for specific group; no effect; elicitation negative feelings.	Providers: Apply for specific type of treatment; good training of therapist; input for VR scenario should be well-prepared; 3) Content: Combine ideas; use realistic situations; change an aspect of the application; 4) Practical: Combine with other technologies; improve visual realism; offer many choices in persons, scenarios & environments; context use VR; posture use VR; constant development Patients: Check whether VR is suitable for patient; cocreation of scenario with patient; apply to specific target group;
Tenforde et al. (2020) US Observational	Valuing outcomes (patients) Patients under telerehabilitation Rehabilitation patients	During COVID-19 pandemic (time not specified)	205 53.7% Most participants in age of working, although it also includes children and people >65.	Telerehabilitation techniques	NO (but survey developed with experts)	Patients: Patient centered outcomes; eliminating travel time, incorporating other healthcare advocates, and convenience delivering care in familiar environment to pediatric patients.
Liebmann et al. (2019) US RCT	Prevention Rural smokers Substance abuse disorders	June, 2009 through June, 2011	560 66.9% mean age = 47.51 (SD = 12.96)	Quitline-like phone counseling (QL) and telemedicine integrated into primary care (ITM)	YES	Providers: increases in perceived healthcare provider autonomy support at end of treatment Patients: enhanced perceived competence to quit smoking
Ashar et al. (2022) US RCT	Valuing outcomes (patients) Patients with chronic conditions Chronicity	August 2017 to November 2018, with 1-year follow-up completed by November 2019	151 54% mean age = 41.1 (SD = 15.6, range 21–70)	Telehealth education session	YES	Patients: Changes in pain beliefs
Bendtsen et al. (2022) Sweden RCT	Valuing outcomes Risky drinkers Substance abuse disorders	Between 25/04/2019 and 26/11/2020	2129 (intervention: 1063, control: 1066) 58% > = 18	Digital Intervention	YES	Patients: Behavior change among online help seekers (reduce alcohol harms in society)
Berinstein et al. (2022) US RCT	Valuing outcomes (patients) High risk IBD patients Chronicity	January 2019 to January 2020	205 (Intervention: n = 100; usual care: n = 105) 65.85% Usual care group: mean age = 48.35 (SD = 17.76) Care Coordination group: mean age = 46.69 (SD = 17.07) Range 18–90	Care coordination intervention: Proactive symptom monitoring and care coordinator-enabled triggered algorithms	YES	Patients: Patient symptoms improve Healthcare system and society: not added costs for high-risk patients with IBD
Brothers et al. (2022) US RCT	Valuing outcomes (Prevention) Young men that have sex with men PrEP	Not mentioned	100 3% (transgender) mean age = 21.9 (SD = 2.00, range 16–24)	Digital monitoring adherence system (Proteus discover)	YES	Providers: Confirms ingestion of treatment, monitors adherence both in real-time and longitudinally, and provides feedback mechanisms to promote enhanced adherence behaviors for YMSM.	Patients: Low adherence
Chaudhary et al. (2023) Nepal RCT	Valuing outcomes (patients) Pregnant women Postnatal care	May to August, 2021	229 100% mean age = 26.2 (SD = 3.93)	Digital education: Social media-based health education	YES	Providers: higher knowledge scores on how to provide care
Cheung et al. (2023) Australia RCT	Valuing outcomes (patients) Diabetes or Coronoary Heart Disease patients Chronic diseases	2014–2015	902 28.5% mean age = 61.5 (SD = 11.6, >=18)	Mobile phone text messaging self-management support system	YES	Patients: Improves experience and sense of engagement, which are important aspects of integrated care
Chu et al. (2021) China Cohort study	Valuing outcomes (patients) Gastrointestinal stromal tumor patients Oncology	June 2013–2019	292 56.1% mean age = 61 (SD = 12, range 29–90)	Artificial Intelligence kit	Not applicable (does not measure PROMs or PREMs)	Providers: Helps on the evaluation of risk status
Col et al. (2023) US RCT	Valuing outcomes (patients) Multiple sclerosis patients Multiple Sclerosis	November 11, 2019 and October 23, 2020	501 84.6% mean age = 48.4 years (SD = 11, range 19–78)	Evidence-based SDM aid	YES	Patients: 1-DMT Behaviors: Adherence, Continuation. 2-DMT decisions: Start/stop, adherence. 3-Health Outcomes: Quality of Life. 4-Decision processes: confidence, decision conflict, perceptions.		Patients: MS-SUPPORT may not be appropriate for all patients; modifications or assistance for those with lower health literacy may be needed
Collier et al. (2024) Sweden RCT	Valuing outcomes (patients) Swedish adults seeking help online to reduce their alcohol consumption. Substance abuse disorders	Between 25/04/2019 and 26/11/2020	1063 in the intervention group and 475 in the responders group 42% Intervention group: mean age = 45 (range 35–55) Responders group: mean age = 48 (range 40–57)	Digital Intervention: Text message	YES	Patients: Reported Experiences. Behavioral changes.
Creech et al. (2022) US RCT	Valuing outcomes (patients) Women with histories of sexual assault PTSD	May, 2017, through April, 2019	153 100% mean age = 43.55 (SD = 10.10)	Computerized Intervention	YES	Patients: Improve mental health treatment initiation and utilization; Acceptable at addressing sexual trauma and its consequences. Healthcare systems: Little cost or increase in provider time.
Curtis et al. (2022) US RCT	Valuing outcomes (health professionals) Health professions SBIRT trainees Substance abuse disorders	Two 10-week cohorts; one cohort in Fall 2016 and the second in Spring 2017	131 Not mentioned Not mentioned	SBIRT mobile app	YES	Providers: Potential value of the TPB model in explaining health care trainee behavior in the delivery of SBIRT
Fortier et al. (2022) US RCT	Valuing outcomes (veterans) Post-9/11 veterans PTSD	Not specified (but during COVID-19 pandemic).	74 17.56% mean age = 41.0 (SD = 11.5, range 24–65)	Online telehealth delivery	YES	Patients: Safety, feasibility, and greater treatment enrollment, adherence, group cohesion, and veteran-to-veteran support
Fortney et al. (2022) US RCT	Valuing outcomes (patients) Veterans with a PTSD diagnosis who were receiving care at a CBOC, but not engaged in specialty mental healthcare at the VAMC. PTSD	April 2016 to July 2019	447 9.7% mean age = 51.8 (SD = 15.0)	Telemedicine Outreach for PTSD	YES	Society: Reaching a substantial proportion of population with unmet need
Jia et al. (2021) China RCT	Valuing outcomes (patients) Diabetes patients Chronic diseases	2017–2019	17554 59.8% mean age = 60.5 (SD = 8.4)	mHealth ROADMAP platform	Not applicable (does not measure PROMs or PREMs)	Society: Disease control; Potential to be transferred to other chronic conditions management in similar contexts.
Kobayashi et al. (2022) France Cohort study	Valuing outcomes (patients) STANISLAS cohort Prevention	2011–2015	1705 51.8% mean age = 60.0 (SD = 5.0)	Cardiovascular Imaging-Related Machine Learning	Not applicable (does not measure PROMs or PREMs)	Providers: a new evidence-driven, integrated classification of individuals in preclinical settings with plausible circulating biomarkers and notable prognostic implications
Marx et al. (2022) Germany RCT	Valuing outcomes (patients) Adult Patients in intensive care with infectious diseases or possible infectious presentation Infectious diseases	February 2017 to January 2020	159424 Inpatient group: 53% Outpatient group: 45.3% Inpatient group: mean age = 69; Outpatient group: mean age = 42	Expert TeleconsultationsPlus Advanced Training Courses	Not applicable (does not measure PROMs or PREMs)	Providers: facilitates a direct interaction over broad distances between intensivists or infectious disease experts and physicians who care for patients in hospitals without ready access to these experts. Patients: increase guideline adherence and treatment quality in infectious disease and intensive care management, creating added value for critically ill patients.
Qiu et al. (2022) China Retrospective cohort study	Valuing outcomes (patients) Type 2 diabetes patients Chronic diseases	June 2021-December 2022	149 37.5% Training group. Normal bone mass group: mean = 54 (SD = 9.7); Abnormal bone mass group: mean = 65.5 (SD = 9.3); Validation group. Normal bone mass group: mean = 56.2 (SD = 8.6); Abnormal bone mass group: mean = 68.1 (SD = 11.7)	CT images	Not applicable (does not measure PROMs or PREMs)	Providers: Predictive value
Sacarny et al. (2022) US RCT	Valuing outcomes (patients) Patient with prescription of opioids or benzodiazepines Substance abuse disorders	June 2019 to March 2021	2237 57% mean age = 48% (SD = 16)	Text message	Not applicable (does not measure PROMs or PREMs)		Providers: Ineffective interventions take staff away from valuable clinical activities and can even harm the efficacy of beneficial interventions through alert fatigue
Shegog et al. (2022) US RCT	Valuing outcomes (behaviors) Parents of 11–17 year old male and female patients Vaccines	September 2017 through March 2019	57084 50.6% mean age = 13.5 (SD = 2.2)	HPVcancerFree app (HPVCF)	Not applicable (does not measure PROMs or PREMs)	Society: Contribute to mitigating the influence of social media misinformation.
Stuhlreyer et al. (2022) Germany RCT	Valuing outcomes (patients) Total knee Replacement patients Trauma surgery	October 2015 to March 2019	96 58% mean age = 68 (SD = 9)	APP: Open medication via the iPad app	YES	Patients: Combined with physician rounds led to stronger pain relief and less oxycodone consumption. Higher treatment satisfaction compared to usual care.
Verma et al. (2022) Denmark and The Netherlands RCT and Observational study	Valuing outcomes (patients) Nonspecific neck and/or low back pain Neck and Back pain	dataset 1: 2019–2020 dataset 2: 2012–2019	dataset 1: 218 ; dataset 2: 159 Not mentioned Not mentioned	ML methods applied to PROMs data	YES	Providers: Exploit the predictive value of PROMs and hence support clinical decision making, given that PROMs hold enough predictive power.
Wagner et al. (2021) US RCT	Valuing outcomes (patients) Asthma patients Respiratory diseases	February 2017 and August 2018	14978 Not mentioned Not mentioned	Digital Communication Technology	Not applicable (does not measure PROMs or PREMs)	Providers: Increase available healthcare personnel time to address asthma disease management. Healthcare systems: Opportunity to provide cost savings in a health system Patients: Not negatively impacting patient health.
Xiong et al. (2021) China Retrospective study	Diagnosis Patients evaluated for pulmonary ground-glass nodules Oncology	November 2012 and June 2018	198 60.1% median age = 61 (IQR = 54–66)	CT images	Not applicable (does not measure PROMs or PREMs)	Providers: Improves diagnostic performance
Zhart et al. (2023) US RCT	Behavior and health Community-dwelling adults Healthy individuals	September 2017 and September 2019	162 Not mentioned Not mentioned	Wearables: Apple Watch (Apple Inc.)/Meta-Mindset Intervention	YES	Patients: Change behavior to adopt a healthier diet; experiences of improved mental health (PROMIS-29) and aerobic capacity.
Zheng et al. (2023) China RCT	Diagnosis. (Predictive modeling) Patients diagnosed with combined hepatocellular-cholangiocarcinoma (cHCC-CC). Oncology	January 1, 2012, to December 31, 2020	143 28.6% Not mentioned	CT images	Not applicable (does not measure PROMs or PREMs)	Providers: Effectively predicts early postoperative recurrence

Discussion

This systematic literature review helped identifying, from the most relevant sources published in recent years, those elements that, according to theory, would be relevant to creating digital health value. Through the extraction of information from the reviewed sources, literature was synthetized in a way that VBDH elements can be identified at different levels (by phases of the patient journey), for different agents (in the society) and by type of digital health technology and intervention.

The review also helped identifying the elements that are actually being used to measure the value of digital health empirically. For example, to generate value in the access phase and provision of health services, theoretical and empirical literature coincide in elements such as research, education, patient-centered care, or trust. While theory proposes as an important element the government's regulatory and acting capacity, the empirical works reviewed do not present any type of measure that correspond to this approach. In relation to the diagnosis phase, for example, only the theoretical literature finds as key elements for this phase of the care process the importance of models by pathologies or pathological subspecialties, and of agile transmission, centered on the patient, ³⁰ something that has not yet been studied empirically. Related with the prevention of diseases, hospital care and treatment phases, this study did not find any elements in the theoretical literature, while empirically the existence of certain elements that serve to demonstrate the value of digital health in these areas is being demonstrated. On the valuation and measurement of health outcomes, theoretical literature suggests the need for restructuring of medical practice, which should tend towards more flexible implementation models, ⁷³ while the empirical literature reviewed does not find anything in this regard. However, coinciding elements in this phase are the importance of research (in this case focused on data collection, especially PROMs and PREMs), the importance of feasibility and confidence in new technologies, and the importance of information systems that allow identifying barriers to optimize the effectiveness of interventions based on new health technologies.

There were some common elements found between several of the different phases of the care cycle, such as the importance of patient-centered care. However, no theoretical article focused on the prevention and treatment phases has been identified, suggesting further research could focus on identifying such VBDH elements. Future research should study this context, as these phases of the patient's care cycle should be given the same importance than any other phase.

The main contribution of this study is to offer the VBDH elements identified in the literature (separately by theoretical and empirical works), by type of agent and for the different phases of a patient journey. Previous publication exists, providing a framework for defining Value-Based Health Care. A paper published recently, offers a strategic framework for VBHC implementation to achieve better patient outcomes. ⁸² An umbrella review was done for the value of mobile health applications used in patients with mental health problems. ⁸³ However, no framework has been proposed yet to identify specific elements of VBHC in the context of Digital Health. Another paper reviewed how digital transformation can help achieve VBHC, ⁸⁴ supporting the fact that Determining “value” may require a system-level model and framework to understand better the distributed components that are interrelated by complex processes within a healthcare system. Something that has already been a concern by researchers in the field. ⁸⁵ Therefore, this systematic review is a novel and useful piece of information for researchers who may want to develop, for example, new instruments of patient reported outcomes or experiences regarding the use of different digital health technologies. The framework proposed, result of the synthesis of information extracted, offered by this review, despite its complexity due to the multiple levels of analysis considered, identifies areas where no VBDH elements are identified, and therefore could also be useful for researchers who may be wondering what areas of application are still in need for further research in this sense. Finally, the data extraction could also be used as a data lake (easy to update) of VBDH interventions, for example, helping in the design of research projects in different areas of application.

This study has limitations. The main limitation is on the subjectivity of the interpretation of results offered by the literature, for example, when allocating the value of a digital technology to a specific phase of the patient journey or for a particular type of agent. However, the interpretation of the results was reviewed by all authors, and the final version has the agreement of all authors of this study. Any other limitations are natural of the methods for conducting any systematic literature review. It may be the case that relevant papers have been excluded in the screening phases of the review, and also it is possible that evaluating the quality and risk of bias of the selected studies, some papers qualified as weak (and thus, excluded) could be relevant papers for the purposes of this study.

Conclusion

This systematic literature review provides a framework composed of the main elements, for each phase of a patient's care cycle, that generate value for digital health technologies and interventions. Furthermore, contrasting the elements identified in theory and empirical work in the context of VBDH, the review shows a list of elements that coincide between both types of literature, as well as some existing gaps in the literature. This work, therefore, offers elements that build up a strategic framework, with empirically observed elements for VBDH, as well as suggests future lines of research that should be priorities for progress in VBDH. It provides researchers of a source, for consultation and development of future research projects, that might be useful for academic researchers as well as a tool to be used by clinicians in the clinical practice or by policy makers in decision making processes in the context of digital health. Conditioned to the existence of instruments to measure the value of these elements, this is a first step for improving the measurement of the value of digital health.