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

Digital Health Reviews (1289) Registered Report (560) Prevention and Health Promotion (986) Innovative Methods in Public Health and Surveillance (332)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53927, first published .
Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review

Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review

Mapping Digital Public Health Interventions Among Existing Digital Technologies and Internet-Based Interventions to Maintain and Improve Population Health in Practice: Scoping Review

Authors of this article:

Laura Maaß1, 2, 3 Author Orcid Image ;   Konstantinos Angoumis4, 5 Author Orcid Image ;   Merle Freye2, 6 Author Orcid Image ;   Chen-Chia Pan2, 5, 7 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Review

    1University of Bremen, SOCIUM Research Center on Inequality and Social Policy, Bremen, Germany

    2Leibniz ScienceCampus Digital Public Health Bremen, Bremen, Germany

    3Digital Health Section, European Public Health Association - EUPHA, Utrecht, Netherlands

    4University of Bielefeld, Bielefeld, Germany

    5Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany

    6University of Bremen, Institute for Information, Health and Medical Law - IGMR, Bremen, Germany

    7University of Bremen, Institute for Public Health and Nursing Research – IPP, Bremen, Germany

    Corresponding Author:

    Laura Maaß, MA

    University of Bremen, SOCIUM Research Center on Inequality and Social Policy

    Mary-Somerville-Straße 3

    Bremen, 28359

    Germany

    Phone: 49 421 218 58610

    Email: laura.maass@uni-bremen.de


    Background: The rapid progression and integration of digital technologies into public health have reshaped the global landscape of health care delivery and disease prevention. In pursuit of better population health and health care accessibility, many countries have integrated digital interventions into their health care systems, such as web-based consultations, electronic health records, and telemedicine. Despite the increasing prevalence and relevance of digital technologies in public health and their varying definitions, there has been a shortage of studies examining whether these technologies align with the established definition and core characteristics of digital public health (DiPH) interventions. Hence, the imperative need for a scoping review emerges to explore the breadth of literature dedicated to this subject.

    Objective: This scoping review aims to outline DiPH interventions from different implementation stages for health promotion, primary to tertiary prevention, including health care and disease surveillance and monitoring. In addition, we aim to map the reported intervention characteristics, including their technical features and nontechnical elements.

    Methods: Original studies or reports of DiPH intervention focused on population health were eligible for this review. PubMed, Web of Science, CENTRAL, IEEE Xplore, and the ACM Full-Text Collection were searched for relevant literature (last updated on October 5, 2022). Intervention characteristics of each identified DiPH intervention, such as target groups, level of prevention or health care, digital health functions, intervention types, and public health functions, were extracted and used to map DiPH interventions. MAXQDA 2022.7 (VERBI GmbH) was used for qualitative data analysis of such interventions’ technical functions and nontechnical characteristics.

    Results: In total, we identified and screened 15,701 records, of which 1562 (9.94%) full texts were considered relevant and were assessed for eligibility. Finally, we included 185 (11.84%) publications, which reported 179 different DiPH interventions. Our analysis revealed a diverse landscape of interventions, with telemedical services, health apps, and electronic health records as dominant types. These interventions targeted a wide range of populations and settings, demonstrating their adaptability. The analysis highlighted the multifaceted nature of digital interventions, necessitating precise definitions and standardized terminologies for effective collaboration and evaluation.

    Conclusions: Although this scoping review was able to map characteristics and technical functions among 13 intervention types in DiPH, emerging technologies such as artificial intelligence might have been underrepresented in our study. This review underscores the diversity of DiPH interventions among and within intervention groups. Moreover, it highlights the importance of precise terminology for effective planning and evaluation. This review promotes cross-disciplinary collaboration by emphasizing the need for clear definitions, distinct technological functions, and well-defined use cases. It lays the foundation for international benchmarks and comparability within DiPH systems. Further research is needed to map intervention characteristics in this still-evolving field continuously.

    Trial Registration: PROSPERO CRD42021265562; https://tinyurl.com/43jksb3k

    International Registered Report Identifier (IRRID): RR2-10.2196/33404

    J Med Internet Res 2024;26:e53927

    doi:10.2196/53927

    Keywords

    digital public health; digital health; public health; telemedicine; electronic health records; e-prescription; e-referral; e-consultation; e-surveillance; e-vaccination registries; scoping review 


    Background

    Digital technologies have become ubiquitous in all facets of life and work [1]. Within health care, digital solutions offer a promising opportunity to make processes and operational methods more efficient and effective [2,3]. This advancement in health care and public health will empower individuals to engage in self-management and actively manage their overall well-being and health [4]. Similarly, digital technologies can facilitate the cost-efficient implementation of health promotion, prevention, monitoring, and surveillance measures, making them easy to access [5-11]. These technologies also foster novel care possibilities, enabling sustainable and equitable access to health services across entire populations [12-16]. Therefore, adopting digital health solutions presents a valuable opportunity to strengthen national health care systems. Eventually, these solutions will transform traditional clinical care and public health structures, fundamentally reshaping health systems on a global scale.

    However, to our knowledge, no study has yet attempted to comprehensively map out the heterogeneous landscape of digital public health (DiPH) and digital public health interventions (DiPHIs). Despite existing reviews focusing on selected intervention categories [17], specific target groups such as adolescents [18,19] or patients who are chronically ill with specific medical conditions [20], a comprehensive mapping of interventions in digital health is still missing. In addition, in all existing reviews, the studies analyzed digital health interventions but not DiPHIs, thereby limiting these interventions to their medical and clinical usability. Therefore, this scoping review aims to fill the research gap and comprehensively map out the heterogeneous landscape of DiPHIs. The development, implementation, integration, and evaluation of evidence- and needs-based DiPH interventions require a clear and shared understanding of the specific characteristics of digital health technologies for public health purposes [21]. Conversely, the lack of a standardized lexicon creates communication challenges between the subdisciplines of DiPH [22,23], while concurrently hindering comparability between national health systems.

    Definition of DiPH

    For this review, DiPH is defined as using information and communication technologies (ICTs) such as digital technologies, services, or tools to achieve traditional public health goals [24] with a population health impact [25]. According to Winslow [26], public health aims at “preventing disease, prolonging life, and promoting physical and mental health and efficiency through organized community efforts...” This definition is still used nowadays by the World Health Organization (WHO) to define essential public health functions. This places public health as the leading discipline in health governance, financing, health information systems, communication, coverage, care, education, and health regulations to protect and promote the health of populations [27].

    Hence, we define DiPHI as interventions that address “at least one essential public health function through digital means.” [26]. To enhance acceptance among the population, DiPHI should consistently follow a needs- and evidence-based design with a participatory and human-centric approach [21,25,28,29]. The impetus behind their development should not stem solely from technological capabilities but from DiPHIs potential to mitigate disparities and improve access to health services among different groups of a population [22,25,27].

    Unlike the domain of digital health, which involves the application of health ICT for personalized medicine and telemedicine [30] and its subfield mobile health, which primarily focuses on patient monitoring and the use of personal digital assistants in the clinical setting [31], DiPHIs encompass a broader spectrum of services and interventions. These include those in health promotion, such as wearables to promote physical activity; in prevention, such as web-based vaccination registries; in health care, such as telemedicine or electronic health records (EHRs); and in surveillance systems, such as dashboards or tracing systems for infectious disease monitoring on a national level. The goal of DiPHIs is to improve the health and well-being of groups considered vulnerable rather than individuals [25].

    By combining the foundational goals of public health with the capabilities of ICT applications, DiPHIs have the potential to substantially strengthen population health, promote well-being, and advance public health objectives. This impact can be realized at the individual, community, and national levels [32].

    Study Aims and Objective

    This scoping review primarily aims to outline a comprehensive range of proposed to implemented DiPHI, including different levels of prevention, health care, and public health research initiatives. Following the aforementioned definition of DiPHI, the second objective is to map the landscape of the existing DiPHI, shedding light on their self-reported digital health functions (based on the evidence standards framework [ESF] for digital health technologies from the National Institute for Health and Care Excellence [NICE] [33]) and the addressed essential public health functions as defined by the WHO [27]. Our scoping review will guide future research to address underexplored DiPHI types by identifying research gaps (eg, underrepresented intervention types). The multinational and interdisciplinary comparative analysis of interventions will generate a data set on various interventions, their characteristics, functions, and use cases, which can support researchers or policy makers to make informed decisions for future DiPHIs planning and upscaling.

    It is vital to acknowledge that this scoping review, while intended to illustrate the distinctive characteristics of DiPHI, does not seek to provide exhaustive analyses on cost-effectiveness, efficacy, or barriers and facilitators of implementation. The inherent heterogeneity of DiPHI renders it impractical to address these multifaceted aspects within a single literature review [13,14].


    Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we registered our review protocol with the PROSPERO database (CRD42021265562) on August 2, 2021, and details and rationale of the protocol were published in 2022 [34].

    Eligibility Criteria

    The standards for eligibility were constructed in the participants, intervention, and study design format. An overview of the inclusion and exclusion criteria is presented in Textbox 1. Accompanying these criteria, the general requirements of full-text accessibility and the publication language were also considered. The review expanded the publication language limitation to English, German, and Chinese based on the language skills of the reviewer team. Given the scope of our study, which focuses on mapping the characteristics of DiPHI rather than comparing the effectiveness or costs, the review process refrains from specifically incorporating reported outcome variables.

    Textbox 1. Eligibility criteria for the scoping review.

    Inclusion criteria

    • Population
      • The study focuses on the community level or above (ie, regional or national) of the general population.
    • Intervention
      • The paper describes a concrete digital public health intervention (DiPHI) that addresses at least 1 essential public health function through digital means as defined by the World Health Organization (WHO).
      • The DiPHI matches our definition of digital public health.
      • The DiPHI addresses at least 1 essential public health function as defined by the WHO.
      • The DiPHI is paid or reimbursed by the government or health insurance.
      • The DiPHI uses the internet or Bluetooth to provide its core function or service.
      • The DiPHI is the central research object of the paper.
    • Study design
      • All original peer-reviewed studies, reports, books, book chapters, or peer-reviewed conference papers that have a description of a DiPHI as their primary intervention component.
    • Full text
      • Full text was available.

    Exclusion criteria

    • Population
      • The study population consists of veterans, armed forces, prisoners, inmates, refugees, or asylum seekers.
    • Intervention
      • The intervention needs to be privately bought by the user without reimbursement by the government or health insurance.
      • The intervention focuses on background management processes.
    • Study design
      • Study protocols, editorials, commentaries, conference proceedings, or reviews (ie, narrative reviews, scoping reviews, systematic reviews, or meta-analyses).
    • Full text
      • Full text was not available.

    Literature Search

    The initial search followed a comprehensive search strategy conducted on February 19, 2021, in CENTRAL, PubMed, and Web of Science. We ran an additional search following the same search strategy on December 1, 2021, for 2 additional databases: ACM Full-Text Collection and IEEE Xplore. The search results for all 5 databases were updated on October 5, 2022. We applied a consistent search string to all databases with adaption according to index and syntax specifications differences. Given the substantial volume of publications aligning with our inclusion criteria, supplementary manual searches were not undertaken. However, we conducted reference list searches for reviews and meta-analyses describing DiPHIs. No additional references were identified through this approach, as all references were already identified through our systematic search.

    The search strategy for all databases was structured around 3 themes connected through “AND.” Terms within the themes are combined through “OR” (refer to Figure 1 for the general overview and Multimedia Appendix 1 for the complete search strategy for all databases, including results per term of the initial search). We decided against searching for concrete interventions such as EHRs, specific health or medical apps, or other interventions to reduce the risk of confirmation bias.

    We additionally used Medical Subject Headings wherever appropriate during database searches. All other terms were limited to title, abstract, or keywords. No search limits were applied.

    ‎
    Figure 1. The fundamental concept of the search strategy.

    Screening Process

    We applied a 3-stage screening strategy (ie, titles, abstracts, and full texts) to identify suitable publications (Figure 2). To reduce the list of reasons for full-text exclusion, the first 3 exclusion criteria mentioned in Textbox 1 were summarized under “wrong setting.” Three authors (LM, MF, and KA) were involved in the screening process for eligibility, and each stage had 2 authors partake in the screening independently. After each screening stage, another author (CCP) resolved any disagreements on study selection with independent decisions. The screening was conducted through the free web and mobile app Rayyan (Rayyan Systems Inc) [35]. References published in another language than English, Chinese, or German were excluded during full-text screening. Chinese references were screened only by CCP. Cohen κ for full-text screening was 0.77 (identifying a substantial agreement rate according to the study by Landis and Koch [36]). Full texts were included if the article was published open access, if it was published in a subscription journal licensed by our institution, or if the corresponding author provided a copy. An overview of all excluded full texts, along with the reasons for their exclusion, is included in Multimedia Appendix 2 [37-39].

    ‎
    Figure 2. Flowchart of the search and screening process.

    Data Extraction and Qualitative Content Analysis

    Two authors (LM and KA) independently extracted data in an Excel 2019 spreadsheet (Microsoft Corp). The extraction sheet was piloted with the first 10 included publications. The extraction for all data except for the technical functions and nontechnical intervention characteristics (which were extracted as free texts) followed a predefined coding table, which was extended if necessary (Table 1). The extraction for all data was based on qualitative description directly provided by the included texts. Discrepancies in data extraction were resolved through discussion between the 2 authors. Missing data for the included publications or data where only a vaguely formulated description was given in the text were requested from authors via email. Where multiple interventions were described within 1 publication, data for each intervention was extracted in a new row. Target groups were defined and extracted based on the digital health application directory by Lantzsch et al [37] due to their precise differentiation between healthy users and patient users. Additional target groups (eg, policy makers, health professionals, or researchers) were added in an iterative process. The digital health functions and the intervention types followed the 2019 edition of the NICE ESF for digital health technologies [33]. The most updated ESF from 2022 reduced its number of individual technical functions and was changed toward a more clinical setting. However, the 2019 framework still included 10 different functions, for example, intervention types covering health promotion, health care, communication, education, and system services. Therefore, we deemed the 2019 version more suitable for our mapping approach. In addition, these functions were supported by the work of Lantzsch et al [37]. The public health functions were based on the 11 WHO functions [27]. Building on our previous research, ESF and ESPHF are merged to map DiPH for this study [29]. Extracting authors selected the option “other” where multiple target groups, functions, or prevention or health care levels appealed.

    Target groups were defined and extracted based on the digital health application directory by Lantzsch et al [37] due to their precise differentiation between healthy users and patient users. Extracting authors selected the option “other” where multiple functions or target groups appealed. This decision displays the only deviation from the registered study protocol for this review [34] where we did not define different categories within target groups (the study by Lantzsch et al [37] was published after our study protocol, which underlines the rapidly evolving nature of DiPH). The definitions for prevention and health care (ie, last column in Table 2) were based on the WHO definitions (Table 2).

    In addition to the previously mentioned topics, we also extracted information on the study design, publication year and type, intervention name, technical features, and nontechnical characteristics. We did not extract data on outcome measures or type of data collection in the included publications because this review aims to conduct an intervention mapping focusing on their characteristics and not on their effectiveness or costs.

    Table 1. Coding table for data extraction.
    Study typeCountryType of interventionImplementation statusPrimary target groupDigital health functionEssential public health functionLevel of prevention, health care, and research
    Case reportAfghanistanHealth or medical appProposed interventionHealth insurancesActive monitoringGovernancePrimary prevention
    Case-control studyAlbaniaDisease surveillance systemPlanned interventionHealth professionalsSimple monitoringFinancingSecondary prevention
    Longitudinal studyAlgeriaEarly warning softwarePilot studyResearchersCalculateHuman resourcesTertiary prevention
    Cross-sectional studyAndorraElectronic health recordLocally implementedPolicy makersCommunicateHealth information systemPrimary health care
    Economical studyAngolaElectronic consultationRegionally implementedHealthy without known risk factorsDiagnoseResearchSecondary health care
    Mixed methods studyAntigua and BarbudaElectronic medication planNationally implementedHealthy with known risk factorsInformSocial participation and health communicationTertiary health care
    Trial or Experimental studyArgentinaElectronic prescribingInternationally implementedAcute ill not life threateningPreventative behavior changeHealth promotionResearch
    Qualitative studyArmeniaElectronic referralWebsiteChronically ill stableSelf-manageHealth protection—
    —aAustriaElectronic disease registry—Highly vulnerable or unstable health statusSystem serviceDisease prevention—
    —AzerbaijanElectronic vaccination registry—Disaster management professionalsTreatmentPreparedness for public health emergencies—
    —BahamasHealth information system———health care—
    —BahrainImplants—————
    —BangladeshInformation website—————
    —BarbadosPatient portal—————
    —BelarusPatient to provider communication portal—————
    —BelgiumProvider to provider communication portal—————
    —BelizeTelemedicine—————
    —BeninWearable—————

    aNot applicable.

    Table 2. Applied definitions for prevention and health care.
    Level of prevention or health careDefinitionSource
    Primary preventionAvoiding new diseases, disabilities, or injuriesWHOa [40] and Baumann and Ylinen [41]
    Secondary preventionEarly detection of diseases, disabilities, or injuriesWHO [40,42] and Baumann and Ylinen [41]
    Tertiary preventionReducing complication risk of existing diseases, disabilities, or injuriesBaumann and Ylinen [41]
    Primary health careCare for acute mild illness, injuries, or medical problems at a primary care provider with a whole-of-society approach (eg, through a general practitioner)WHO [40,42], Hopayian [43], and Bodenheimer and Grumbach [44]
    Secondary health careSpecialist or emergency medical care through specialized physiciansHopayian [43] and Bodenheimer and Grumbach [44]
    Tertiary health careHighly specialized medical health care over an extended period in stationary settings, usually in referral hospitalsHopayian [43] and Bodenheimer and Grumbach [44]

    aWHO: World Health Organization.

    For the geographic analysis of interventions, we counted every intervention multiple times if it was implemented internationally. For instance, Adler et al [45] described an intervention that was implemented in 7 countries. Weighting these interventions per country by the number of countries they were implemented in (here 1/7) would have weakened internationally planned or implemented interventions compared to interventions only targeting 1 country. Therefore, we decided to give full weight to each intervention and count the intervention as 1 for every targeted country. While this approach resulted in an artificially higher number of included interventions, it also ensured that international interventions were not discriminated against based on the geographic analysis.

    For the qualitative analysis, we grouped the identified references by intervention types based on our study protocol. We then summarized the extracted data for each intervention type, as illustrated in Table 2. Additional intervention types were added in an inductive procedure where needed. In addition, we developed an inductive category system based on 5 randomly selected publications per intervention type to describe the technical functions and nontechnical characteristics of different intervention types. The qualitative content analysis through iterative coding was then conducted for all references of 1 intervention type in MAXQDA 2022.7 (VERBI GmbH) by LM. The procedure followed the inductive content analysis procedure described by Vears and Gillam [46].


    Overview

    In total, we identified 15,701 different publications published until October 5, 2022 (24,767 before deduplication), through our systematic search in 5 scientific databases. Of these, 2987 (19.02%) were assessed for abstract screening and 1609 (10.25%) for full-text screening. The process excluded 1414 records, primarily due to unavailable full-text content and a lack of specificity in describing interventions. Of the screened full texts, the remaining 11.5% (185/1609) were considered eligible for this review. Some of these publications described multiple interventions, whereas, in other cases, the same intervention was presented by >1 publication. Of the 185 publications, 96 (51.9%) were case reports that focused on a detailed intervention design or implementation description, while the aim of the remaining 89 (48.1%) publications lay on the intervention evaluation, most often evaluated through cross-sectional studies (n=37, 20%), trials (n=19, 10.3%), or longitudinal studies (n=18, 9.7%). The characteristics and extracted information of all included 185 references are displayed in Multimedia Appendix 3 [38,45,47-229] instead of the main manuscript. As sometimes multiple references are reported on the same intervention, this scoping review mapped the characteristics of 179 different DiPHI for the included 185 publications.

    Distribution of Interventions Globally

    Figure 3 displays the distribution of the included interventions by country. For this analysis, all interventions were weighted equally regardless of the number of countries they were implemented in. As several interventions were implemented in >1 country, this analysis artificially increased the number of observed interventions to 199. Although most interventions (36/199, 18.1%) were from the United States, continent -wise, Europe took the lead with 38.2% (76/199) reported interventions. Ranked third was Asia with 17.6% (35/199), followed by Africa (26/199, 13.1%), Oceania and Australia (9/199, 4.5%), and South America (6/199, 3%). From a gross domestic product point of view (ie, grouped in high, middle, or low-income countries as classified by the World Bank [230]), most interventions were planned for or implemented in high-income countries (129/199, 64.8%) and were set-up in middle-income countries (58/199, 29.1%). Of the 10 interventions in low-income countries, 3 (30%) targeted Malawi [73-76,82,218], and 2 (20%) each were applied in Afghanistan and Ethiopia [73-76,91,175,222]. Furthermore, of the 10 interventions, 1 (10%) each was planned or implemented in Mali, Sierra Leone, and Uganda [56,111,190].

    ‎
    Figure 3. World map for the number of included interventions by country, continent, and high-income to low-income countries.

    Intervention Types and Implementation Status at the Time of Publication

    Under the classification of intervention by NICE ESF, most interventions (49/179, 27.4%) were centered on telemedical services, including telestroke, telecare, and telemonitoring. According to Timpel et al [231], these interventions are characterized using ICT to cover a geographic distance in health care delivery from a health professional to a patient or group of patients. The second most frequently counted interventions were health apps and medical apps (28/179, 15.6%), followed by EHR (23/179, 12.8%). The complete overview of intervention types is displayed in Figure 4, together with their implementation status at the reported time from the included references. As 4 publications reported on the same intervention but at different time phases, all publications were included for this display (eg, Austria’s national EHR ELGA was only planned when Ströher and Honekamp [199] reported about it, but was already nationally implemented when Herbek et al [113] and Schaller et al [177] described the platform). This results in a total of 181 displayed interventions. Interventions in >1 country were categorized as internationally implemented.

    ‎
    Figure 4. Overview of intervention types and implementation status. Some interventions were described during earlier phases and then again in later periods. Therefore, they are listed once for every reported intervention life cycle in this figure.

    Target Population and Intervention Setting

    Our analysis showed that DiPHIs tend to focus on an average of 3 distinct groups within the population. These include health professionals, policy makers, public insurance executives, researchers, disaster management professionals, patients, and healthy individuals. Unsurprisingly, health professionals were the most often targeted group, with 140 (78%) of 179 interventions. They were followed by patients who were chronically ill but stable (101/179, 56.4%). Of all interventions, 51.9% (93/179) were aimed at healthy people with or without risk factors for certain health conditions.

    Parallel to the observation of the target population, each intervention affected, on average, 2 different settings, that is, level of prevention, health care, or research as defined previously. Most interventions were health care applications (ie, 100/179, 55.9% for primary, 89/179, 49.7% for secondary, and 55/179, 30.7% for tertiary health care). Out of the 179 interventions, while 16 (8.9%) interventions included a research component [57,84,93,104,128,139,154,163,180,220,221,225], only 3 (1.7%) focused exclusively on research: the Lone Star Stroke Consortium Telestroke Registry, the SAI Databank for electronic health research and evaluation, and the German COVID-19 data donation app (ie, Corona Datenspende) [57,93,128]. For prevention, of the 179 interventions, 13 (7%) exclusively targeted primary prevention (ie, through vaccination registries, behavior change programs, or health education in healthy individuals) [55,62,96,98,101,106,126,140,143,188, 208,223,226]. Of the 179 interventions, 8 (4%) focused only on secondary prevention, as digital screening and surveillance programs [48,61,153,161,171,194,201,219], and 3 (2%) improved the health of patients who were chronically ill on a tertiary prevention level [103,147,202]. In contrast, of all included 179 interventions, 10 (6%) were established for primary health care purposes only [49,67,79,109,124,172,178, 186,210,212,228,229], 17 (9%) were designed for secondary health care through specialists or emergency use cases [52,54,60,65,78,83,88,95,134,135,173-175,179,189,193,216], and 5 (3%) aimed at tertiary health care in hospital settings [70,109,132,157,200]. Figure 5 gives an overview of the relative size of each target group and the addressed level of prevention, health care, or research per intervention setting.

    ‎
    Figure 5. Addressed target groups and level of prevention, health care, or research in relative distribution per intervention type.

    The Intersection of Digital Health Technologies and Essential Public Health Functions in DiPHIs

    The analysis of the distribution of EPHFs and digital health technology functions matches the aforementioned focus on health care in DiPHIs. The heat map, displayed in Figure 6, highlights that most interventions were found in the cross-section of health care and diagnostic (64/179, 35.8%), treatment (63/179, 35.2%), communication (61/179, 34.1%), and information (57/179, 31.8%) or as system services in health care (42/179, 23.5%). Relatively few interventions, in contrast, were used for calculation (11/179, 6.1%), preventive behavior change (40/179, 22.3%), or public health research (48/179, 26.8%). A subgroup analysis for all intervention types with at least 20 included references is included in Multimedia Appendix 4. This analysis covers telemedicine, EHR, and health or medical apps, offering an overview of the diversity of topics addressed even within the same intervention category.

    ‎
    Figure 6. Heat map of the essential public health functions and digital health technologies to map digital public health interventions.

    Technological Functions and Nontechnical Intervention Characteristics

    Given the sample size, we only provide details on the technical functions and nontechnical characteristics of telemedical interventions and EHRs. We chose not to include health apps or medical apps in this analysis due to the considerable heterogeneity in the wide range of purposes and functions of these apps, as discussed in a previous article [232].

    Telemedicine

    According to a review by Sood et al [233], which included 104 publications, telemedicine “...uses communications networks for delivery of health care services and medical education from one geographical location to another, primarily to address challenges like uneven distribution and shortage of infrastructural and human resources.” The category system includes 5 mayor categories and subcategories for which we assessed all 49 telemedical interventions included in our review [45,47,50,51,60,61,63,65,66,71,85,92,100,101,109,110,115,116,118, 119,124,129,134,141,142,145,146,149,151,155,156,159,164,165,174, 176,178,179,184,189,193,200,204,206,209-212,227]. The results are displayed in Table 3. Each intervention may exhibit a combination of these features, showcasing how telemedicine is applied in different settings. The most dominant characteristic for included interventions was that the software program was supported by hardware to facilitate clinical care. Besides computers and webcams, this included high-quality cameras, sensors (eg, for blood pressure or blood sugar), ultrasounds, or digital stethoscopes. Nearly half of the interventions (25/49, 51%) described how they used the internet or satellite connections to process and store data. The share of interventions that included a provider-to-provider communication option was slightly lower (17/49, 34%) than those that allowed patient-to-provider communication (23/49, 47%). Nevertheless, 20 (41%) of 49 interventions were characterized by their focus on individual patients, sometimes even through personalized intervention interfaces. However, telemedical interventions may not only focus on clinical care. As stated in the definition, they are also applied to the education context (ie, for patients and health care workers), health promotion, and improving self-efficacy in patients who were chronically ill.

    In summary, telemedical interventions exhibit core characteristics that include the use of supporting hardware during consultations, data storage through databases, internet connectivity, bidirectional communication between users, educational modules, user interfaces, and the ability to plan and adjust clinical care plans when needed.

    Table 3. Technical functions and nontechnical characteristics of the telemedical interventions (n=49).
    CategoryInterventions, n (%)References
    Technical infrastructure

    		Software supported through hardware tools41 (84)[45,47,50,60,61,63,65,66,71,85,92,100,101,109,115,116,118,124,129,134,141, 145,146,151,155,156,159,164,174,176,178,184,189,193, 200,204,209-212,227]a-c

    		Data acquisition, transmission, and storage in a database33 (67)[45,50,51,60,61,63,65,71,85,109,110,116,118,119,124,134,135,145,146,151,155,156,164, 174,176, 179,184,189,193,200,206,210]a

    		Internet network and connectivity25 (51)[47,50,60,61,71,85,100,101,115,118,124,146,151,159,174,178,179,184,189,193,200,206, 209,210,212]

    		Web-based user interfaces22 (45)[47,50,51,60,65,85,92,101,110,119,145,149,151,156,159,174,178,184,200,206,210,212]

    		Data security procedures18 (37)[45,47,60,61,66,85,100,101,149,155,159,174,179,189,193,204,206,211,212,227]a

    		Centralized server application16 (33)[47,50,51,61,85,92,100,109,110,116,119,142,155,156,176,210]b

    		Multiaccess, multiprofile web application16 (33)[60,66,85,92,100,101,110,118,135,151,156,174,178,184,204,211,212,227]a

    		Clinician modules and units14 (29)[47,50,51,60,66,92,100,116,124,145,176,189,193,209,211,227]a

    		Patient modules and units6 (12)[85,92,109,179,209,210]b
    Patient-provider interaction

    		Bidirectional communication (patient-provider)23 (47)[60,63,92,100,109,116,119,124,135,142,145,146,149,151,156,174,176,178,184,189, 200,204,212]c

    		Clinical care treatment planning and adjustments22 (45)[51,60,61,63,66,85,92,100,109,110,116,118,142,146,149,151,176,189,193,200,204,211,227]a-c

    		Bidirectional communication (provider-provider)17 (35)[50,71,85,92,116,118,119,124,129,134,145,149,156,176,189,193,210]

    		Remote monitoring of vital signs and clinical status through monitoring kits and sensors17 (35)[50,71,85,92,116,118,119,124,129,134,145,149,156,176,189,193,210]

    		Compliance and adherence monitoring11 (22)[92,100,116,118,124,145,146,149,178,189,204]

    		Custom and predefined questionnaires6 (12)[85,116,145,156,176,204]
    User-centered features

    		Patient-centered interactions possible20 (41)[45,51,60,85,92,109,110,142,145,149,151,155,156,159,164,165,174,204,212]b,c

    		Self-monitoring and tracking10 (20)[92,101,116,119,124,145,149,156,176,210]

    		Personalization (eg, avatars, screen names, communication preferences, reminders, and alerts)8 (16)[65,85,92,101,149,151,204,210]

    		Alarm detection and notification6 (12)[85,92,116,119,184,206]
    Use of patient data

    		Data interpretation and analysis12 (24)[50,51,60,61,63,65,85,92,100,145,151,156]

    		Data visualization tool13 (27)[45,50,51,66,85,92,101,110,151,176,204,209-211,227]a

    		Data- and guideline-driven decision support11 (22)[50,66,71,85,92,145,149,151,156,176,193,211,227]a
    Support and engagement

    		Education and counseling modules22 (45)[45,51,71,92,101,109,116,118,134,135,145,146,149,156,159,164,165,178,189, 200,204,206]c

    		Counseling and motivation module10 (20)[45,92,116,118,141,146,149,155,189,204]

    		Patient empowerment and self-efficacy5 (10)[85,141,145,146,189]

    a3 studies [66,211,227] described the same intervention.

    bIntervention 1 by Hartvigsen et al [109].

    cIntervention 2 by Hartvigsen et al [109].

    EHRs

    Following the broadly accepted definition by the International Organization for Standardization (2019), an EHR is “a data repository regarding the health and health care of a subject of care where all information is stored on electronic media” [234]. According to this terminology, we developed a category system with 4 major topics and subcategories, which was applied to the 26 publications that described 23 different EHR systems [67,69,81,83,87,89-91,104,105,107,113,114,123,125,162,166,177, 181,182,185,195,199,205,215,221,224]. The results are displayed in Table 4. Nearly all interventions (18/23, 78%) allowed health care professionals access to the EHR after the patient consented or an active relationship (eg, due to treatment of chronic diseases) existed between the 2 parties. Although not as often described, most EHR systems (14/23, 61%) stated that patients were able to access their data. Data sharing was possible in 61% (14/23) of the interventions, and another 61% (14/23) described data protective procedures for accessing and sharing data. Although fewer times, interoperability with other health care systems was described (11/23, 48%). We observed differences regarding the stored and accessible data. Unsurprisingly, nearly all interventions (21/23, 91%) reported on storing health and clinical data. However, only 30% (7/23) of the interventions included personal data on the record holder or their immunization records. Only 17% (4/23) of the interventions allowed clinicians to upload their notes to the system.

    Table 4. Technical functions and nontechnical characteristics of the electronic health records (EHRs; n=23).
    CategoryInterventions, n (%)References
    Technical infrastructure

    		Integration and interoperability with external health care systems11 (48)[69,87,91,104,107,113,123,162,177,181,195,199,205,215,221]a,b

    		Mobile platform7 (30)[87,90,113,166,177,185,199,205,221]b,c

    		Centralized server6 (26)[81,104,113,114,162,177,182,195,199,215]a,b

    		Communication protocols6 (26)[69,81,113,177,181,182,199,221]b,c

    		Cloud-based system4 (17)[87,90,185,205,221]c

    		Data synchronization3 (13)[81,185,205,221]
    Data transfer and privacy

    		Data sharing possible14 (61)[67,69,81,87,91,104,105,107,113,123,162,177,181,182,199,215,221]a-c

    		Data security protocols embedded14 (61)[69,87,89,90,104,107,113,123,125,162,166,177,181,195,199,205,215,221]a-c

    		Opt-out approach4 (17)[67,104,113,162,177,199,215,221]a-c

    		Opt-in approach4 (17)[69,83,182,205,221]c
    Stored data

    		Medical history (eg, diagnoses, medical procedures, laboratory results, and treatments)21 (91)[69,81,83,87,89,91,104,105,107,113,114,123,125,162,166,177,181,182,185,195,199, 205,215,221,224]a-c

    		Medication plan14 (61)[69,83,89,91,105,107,113,114,123,166,177,181,182,199,205,221]b,c

    		Patient demographic data7 (30)[69,89,91,113,125,166,177,195,199]b

    		Immunization records7 (30)[69,91,105,125,185,195,205,221]c

    		Financial and administrative data related to health care services5 (22)[91,114,205,221,224]c

    		Clinician notes4 (17)[105,107,224]
    EHR system features and modules

    		Health professional access to patient data18 (78)[67,69,87,89,104,105,107,113,123,125,162,166,177,181,182,185,195,199,205,215,221,224]a-c

    		Patient access to data14 (61)[69,87,89-91,104,105,113,162,166,177,181,182,199,205,215,221,224]a-c

    		User interfaces11 (48)[69,87,89,90,113,123,166,177,181,185,195,199,205,221]b,c

    		Clinical case–finding features8 (35)[67,104,107,113,114,123,162,177,181,195,199,215]a,b

    		Patient management modules3 (13)[114,166,195]

    		Data used for real-time surveillance and monitoring3 (13)[81,87,195]

    a3 studies [104,162,215] described the same intervention.

    b3 studies [113,177,199] described the same intervention.

    c2 studies [205,221] described the same intervention.


    Principal Findings

    Our scoping review has identified a substantial number of publications, underscoring the substantial interest and activity in DiPH. The wealth of publications illustrates the broad spectrum of interventions being explored and implemented worldwide. This diversity reflects the evolving landscape of health care delivery and disease prevention in the digital age.

    The distribution of DiPHIs across countries and continents showcases the worldwide interest in leveraging technology to enhance health care but also points out significant differences in implementation adaptation between high-income and low-income countries. A reason for this imbalance negatively impacting the global digital divide might lie in the inequality regarding the digital advance among developed countries compared to developing countries [235,236]. The prevalence of interventions in high-income countries may partly be attributed to more significant resources and infrastructure [237]. This divide is highlighted by a lack of funding, inadequate computer availability, and limited internet skills that limit the expansion and applicability of ICT and digital (public) health [238]. Still, interventions in low-income countries signal the potential for digital technologies to bridge health care gaps in resource-constrained settings and aging societies [237]. Indeed, the Global Digital Health Monitor covers our findings by displaying overall scores for countries regarding their digital health maturity. Developing countries (especially in Africa) rank lower than their developed counterparts in Europe or North America [239]. More rigorous public health research and practice efforts are needed to empower developing countries to apply digital tools for health-related purposes. The WHO’s Digital Health Atlas can serve as such an effort where project leads can voluntarily register their digital health intervention free of charge [240]. Maps such as these will support public health policy, research, and practice in better resource allocation (eg, the map can display already existing subnational initiatives that could be scaled up to the national or international level without creating double-infrastructures), benchmarking between interventions and further developing intervention types. However, the explicit distinction between medically focused digital health interventions and population-centric DiPHIs will be needed for future versions of the atlas.

    Our analysis shows that DiPHIs often target multiple population groups and settings. Health professionals are a primary target, reflecting technology integration into health care practice and education. The emphasis on patients who are chronically ill but stable underscores the potential of digital interventions to improve care for this population. Moreover, interventions in diverse prevention, health care, and research settings illustrate their versatility across the health care continuum. This observation was supported by the heat- map representation of EPHFs and ESFs, highlighting a significant concentration of digital interventions in health care–related functions such as diagnostics, treatment, communication, and information. This reflects the predominant role of digital interventions in enhancing clinical care and health care management. However, there is room for further exploration of interventions addressing prevention and public health research functions.

    Health apps, telemedical services, and EHRs emerged as the dominant intervention types in our review. Health apps are well known for their heterogeneity [232], which provides the potential to address all areas of public health, including health promotion, clinical care, and rehabilitation [241]. Telemedical services stood out as a substantial focus of our included references, aligning with their capacity to extend universal health coverage reach across geographic boundaries and increase health care delivery speed while lowering treatment costs and response times to control disease outbreaks [242]. EHRs, in contrast, can be essential tools for public health surveillance in providing data on the population’s health status and the health care system’s performance [39,114,243]. These findings highlight the multifaceted nature of DiPHIs and their adaptability to various health care needs and contexts in public health.

    Overall, the analysis underscores the heterogeneity within DiPHIs, even within the same intervention category, as we have displayed through a qualitative analysis of included publications for telemedical interventions and EHRs. This diversity necessitates precise definitions and standardized terminologies to facilitate effective communication, collaboration, and evaluation. Clear definitions can also aid in benchmarking and comparing interventions across different health care systems. The detailed analysis of technological functions and nontechnical characteristics of both intervention types revealed the complexity and variation within each intervention type. Identifying core characteristics and added features can guide intervention development and enhance our understanding of their capabilities. In 2019, WHO published their recommendations on digital interventions for the clinical setting, which provides a definition for each intervention (eg, client-to-provider telemedicine), synonyms for these tools, and recommendations regarding their use cases [244]. While this overview serves as a good starting point, it requires extensions for nonclinical settings and an overview of core functions and technical features to allow benchmarking. The heterogeneity in DiPHI forces us to rethink how national health system assessments should be conducted to allow for comparable results. Instead of asking, Does your country have an EHR system?” it is critical to ask for the availability of an intervention with specific characteristics: “Does your country have an intervention with the following characteristics, targeting these population groups, and being applied for at least one of the following settings?”

    As the field continues to evolve, it is essential to prioritize rigorous research and evaluation to assess these interventions’ effectiveness, safety, and impact on population health and health care accessibility. This review serves as a valuable resource for policy makers, researchers, and health care practitioners seeking to navigate the dynamic and diverse field of DiPHIs.

    Strengths and Limitations

    Originally conceived as a systematic review, our research protocol was registered with PROSPERO to conduct a comprehensive synthesis of the existing literature on DiPHIs. The systematic approach is typically characterized by a rigorous process involving reviewing evidence to answer narrowly defined research questions. However, as our study progressed, we recognized that our research question and the evolving DiPH landscape were better suited for a scoping review methodology. While systematic reviews focus on specific research questions and outcomes, the strengths of scoping reviews for effectively mapping and characterizing interdisciplinary and comprehensive topics (such as the diverse spectrum of DiPHIs in our case, including their characteristics, settings, and target populations) [245] were more applicable for our study aim. This change allowed us to adapt to the dynamic nature of the field. It provided a broader perspective essential for understanding the multifaceted nature of digital interventions. This transition underscores the importance of selecting the most suitable methodology to achieve comprehensive insights into the subject matter. The selection of our information sources demonstrated the interdisciplinarity of DiPH by combining literature databases from public health and computer science. This rigorous strategy mitigates the risk of missing essential publications. The broader inclusion of publications from 3 languages (ie, English, German, and Chinese) is another strength of our study, given the rising number of publications on digital health and DiPH from German-speaking and Chinese-speaking countries. Throughout the process, a minimum of 2 researchers independently screened all references, adhering to the 4-eye principle. Any disagreements in decisions regarding inclusion or exclusion were resolved by another author (CCP) to ensure the reliability of the results. This study followed the PRISMA-ScR [245] to provide high-quality, transparent research results. The filled checklist is provided in Multimedia Appendix 5.

    Nevertheless, while this scoping review exhibits numerous strengths, it also presents certain limitations. We did not create publication alerts for the selected databases, limiting our results to references published before October 6, 2022. However, we conducted manual reference list checks for reviews and meta-analyses to identify additional publications that were potentially missed in our search. As no further relevant findings were added from the manual search, we are confident that our scoping review draws a holistic picture of the landscape of DiPHIs globally. Furthermore, we are convinced that the included references are sufficient for an initial intervention mapping of DiPHIs. Nonetheless, it is worth noting that certain intervention types might be underrepresented, such as early warning software, electronic medication plans, electronic prescribing, or emerging intervention types that were not yet reported during our search (eg, artificial intelligence tools). Here, we invite other researchers to build upon our first results and continue the mapping process. Following the PRISMA-ScR [245], assessing the quality of the included publications was not applicable, which could lead to questions regarding the reporting accuracy of the included publications. Nevertheless, as we focused solely on the functions and use cases of the interventions and not on their outcomes, this limitation remains minor.

    Furthermore, the challenge of intervention specificity emerged as a notable consideration. The varying levels of detail in the descriptions of digital interventions in the literature posed difficulties in discerning their precise nature. This lack of specificity hindered the ability to categorize and analyze interventions effectively, as it often left critical questions unanswered. For instance, understanding the core technological functions, nontechnical attributes, determined use cases, and defined user groups of a given digital intervention is essential for its accurate classification and evaluation. Although in our case, data extraction was conducted by 2 authors (LM and KA) independently and authors were contacted in case of uncertainty, the absence of such information generally can impede efforts to compare interventions, assess their impact, and make informed decisions regarding their implementation or scalability. This is why we advocate for clearer reporting standards for digital health and DiPH interventions. Finally, we must point out that although the volume of included publications was substantial, a distinct portion could not be included in our review due to factors such as unavailable full-text content and a lack of specificity in intervention descriptions. This highlights challenges in accessing relevant literature and emphasizes the need for more detailed reporting in published studies, particularly regarding the specifics of digital interventions. In an era where timely access to information is critical, the inaccessibility of full-text articles poses an obstacle to researchers and stakeholders seeking to explore and build upon existing knowledge in DiPHIs.

    Further Research

    Due to the heterogeneity of DiPHIs, it was not feasible for this scoping review to report detailed information for every intervention type. Therefore, we encourage other researchers to uptake our methodology, conduct reviews on specific intervention types, and refine our initial intervention mapping results. More critically, a shared and multidisciplinary understanding of key terminology in digital health and DiPH is needed. Consequently, consensus finding methods (eg, Delphi approaches) need to be applied to internationally create a shared understanding of DiPHI.

    In addition, assessments of the sustainability of DiPHIs are crucial to map the implementation status. As Iwelunmor et al [246] and Kaboré et al [247] already highlighted, the sustainability of DiPHIs is an essential component of the whole life cycle of implemented interventions. Therefore, it is crucial to consider whether an intervention has been sustainably implemented or stopped after the initial pilot funding phase has ended. Further research should be conducted to analyze, through a longitudinal design or pre-post comparison, how sustainably DiPHIs are implemented and whether differences between intervention types exist.

    Conclusions

    This scoping review has shown that DiPHIs are distinctly diverse in their use cases between individual intervention groups and within such groups. Therefore, using precise terminology when planning, implementing, or evaluating DiPHIs is crucial. For instance, instead of phrasing an intervention as an EHR, one should ask for specific technological functions and nontechnical characteristics, determine use cases (from a digital health and public health perspective), and define user groups. This approach facilitates cross-disciplinary collaboration in intervention development and research and fosters international benchmarks and comparability across global DiPH systems. These insights offer valuable guidance for policy makers, researchers, and health care practitioners interested in using the potential of digital interventions to improve population health and health care accessibility.

    Acknowledgments

    The authors gratefully acknowledge the support of the Leibniz ScienceCampus Bremen Digital Public Health. The study is part of the Leibniz ScienceCampus Bremen Digital Public Health, which is jointly funded by the Leibniz Association (W4/2018), the Federal State of Bremen, and the Leibniz Institute for Prevention Research and Epidemiology (BIPS).

    Data Availability

    The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

    Authors' Contributions

    LM conceptualized and designed the study, including its methodology, with input from CCP. LM served as the principal investigator and project administrator for the entire project. LM, KA, and MF conducted the literature review (data collection), with CCP settling disagreements during the screening process. LM and KA independently conducted the data analysis, with support from CCP. LM and CCP conducted the interpretation of the study data. LM drafted the manuscript, and this paper was revised critically for important intellectual content by all authors. All authors approved the final version of the manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Complete search strategies for the PubMed, Web of Science, CENTRAL, IEEE Xplore, and ACM Full-Text Collection databases.

    PDF File (Adobe PDF File), 272 KB
    Multimedia Appendix 2

    Publications excluded during full-text screening.

    PDF File (Adobe PDF File), 825 KB
    Multimedia Appendix 3

    Overview of the included interventions.

    XLSX File (Microsoft Excel File), 42 KB
    Multimedia Appendix 4

    Heat maps of the selected intervention types.

    PDF File (Adobe PDF File), 154 KB
    Multimedia Appendix 5

    PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for this manuscript.

    PDF File (Adobe PDF File), 221 KB
    1. Susskind R, Susskind D. The Future of the Professions: How Technology Will Transform the Work of Human Experts. Oxford, UK. Oxford University Press; 2015.
    2. Moller AC, Merchant G, Conroy DE, West R, Hekler E, Kugler KC, et al. Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential. J Behav Med. Feb 2017;40(1):85-98. [FREE Full text] [CrossRef] [Medline]
    3. Németh O, Simon F, Benhamida A, Kivovics M, Gaál P. eHealth, teledentistry and health workforce challenges: results of a pilot project. BMC Oral Health. Dec 01, 2022;22(1):552. [FREE Full text] [CrossRef] [Medline]
    4. Greenwood DA, Gee PM, Fatkin KJ, Peeples M. A systematic review of reviews evaluating technology-enabled diabetes self-management education and support. J Diabetes Sci Technol. Sep 2017;11(5):1015-1027. [FREE Full text] [CrossRef] [Medline]
    5. Strain T, Wijndaele K, Brage S. Physical activity surveillance through smartphone apps and wearable trackers: examining the UK potential for nationally representative sampling. JMIR Mhealth Uhealth. Jan 29, 2019;7(1):e11898. [FREE Full text] [CrossRef] [Medline]
    6. Zeeb H, Pigeot I, Schüz B. [Digital public health-rapid technological progress, but many open public health questions]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. Feb 2020;63(2):135-136. [FREE Full text] [CrossRef] [Medline]
    7. Bailey JV, Murray E, Rait G, Mercer CH, Morris RW, Peacock R, et al. Interactive computer-based interventions for sexual health promotion. Cochrane Database Syst Rev. Sep 08, 2010;(9):CD006483. [FREE Full text] [CrossRef] [Medline]
    8. Murray E, Burns J, See TS, Lai R, Nazareth I. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev. Oct 19, 2005;(4):CD004274. [CrossRef] [Medline]
    9. Dalton CB. Enablers of innovation in digital public health surveillance: lessons from Flutracking. Int Health. May 01, 2017;9(3):145-147. [FREE Full text] [CrossRef] [Medline]
    10. Desai AN, Kraemer MU, Bhatia S, Cori A, Nouvellet P, Herringer M, et al. Real-time epidemic forecasting: challenges and opportunities. Health Secur. 2019;17(4):268-275. [FREE Full text] [CrossRef] [Medline]
    11. Shakeri Hossein Abad Z, Kline A, Sultana M, Noaeen M, Nurmambetova E, Lucini F, et al. Digital public health surveillance: a systematic scoping review. NPJ Digit Med. Mar 03, 2021;4(1):41. [FREE Full text] [CrossRef] [Medline]
    12. Alwashmi M, Hawboldt J, Davis E, Marra C, Gamble JM, Abu Ashour W. The effect of smartphone interventions on patients with chronic obstructive pulmonary disease exacerbations: a systematic review and meta-analysis. JMIR Mhealth Uhealth. Sep 01, 2016;4(3):e105. [FREE Full text] [CrossRef] [Medline]
    13. Gunasekeran DV, Tseng RM, Tham YC, Wong TY. Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ Digit Med. Feb 26, 2021;4(1):40. [FREE Full text] [CrossRef] [Medline]
    14. Azzopardi-Muscat N, Sørensen K. Towards an equitable digital public health era: promoting equity through a health literacy perspective. Eur J Public Health. Oct 01, 2019;29(Supplement_3):13-17. [FREE Full text] [CrossRef] [Medline]
    15. Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M, et al. Digital technologies in the public-health response to COVID-19. Nat Med. Aug 07, 2020;26(8):1183-1192. [CrossRef] [Medline]
    16. Wynn M, Garwood-Cross L, Vasilica C, Griffiths M, Heaslip V, Phillips N. Digitizing nursing: a theoretical and holistic exploration to understand the adoption and use of digital technologies by nurses. J Adv Nurs. Oct 02, 2023;79(10):3737-3747. [CrossRef] [Medline]
    17. Xue J, Zhang B, Zhao Y, Zhang Q, Zheng C, Jiang J, et al. Evaluation of the current state of chatbots for digital health: scoping review. J Med Internet Res. Dec 19, 2023;25:e47217. [FREE Full text] [CrossRef] [Medline]
    18. Kouvari M, Karipidou M, Tsiampalis T, Mamalaki E, Poulimeneas D, Bathrellou E, et al. Digital health interventions for weight management in children and adolescents: systematic review and meta-analysis. J Med Internet Res. Feb 14, 2022;24(2):e30675. [FREE Full text] [CrossRef] [Medline]
    19. Yun J, Shin J, Lee H, Kim DJ, Choi IY, Kim M. Characteristics and potential challenges of digital-based interventions for children and young people: scoping review. J Med Internet Res. Apr 14, 2023;25:e45465. [FREE Full text] [CrossRef] [Medline]
    20. Fournier V, Duprez C, Grynberg D, Antoine P, Lamore K. Are digital health interventions valuable to support patients with cancer and caregivers? An umbrella review of web-based and app-based supportive care interventions. Cancer Med. Dec 08, 2023;12(23):21436-21451. [FREE Full text] [CrossRef] [Medline]
    21. Zeeb H, Pigeot I, Schüz B, Leibniz-WissenschaftsCampus Digital Public Health Bremen. [Digital public health-an overview]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. Feb 09, 2020;63(2):137-144. [CrossRef] [Medline]
    22. Darmann-Finck I, Rothgang H, Zeeb H. [Digitalization and health sciences - white paper digital public health]. Gesundheitswesen. Jul 22, 2020;82(7):620-622. [FREE Full text] [CrossRef] [Medline]
    23. Taj F, Klein MC, van Halteren A. Digital health behavior change technology: bibliometric and scoping review of two decades of research. JMIR Mhealth Uhealth. Dec 13, 2019;7(12):e13311. [FREE Full text] [CrossRef] [Medline]
    24. Odone A, Buttigieg S, Ricciardi W, Azzopardi-Muscat N, Staines A. Public health digitalization in Europe: EUPHA vision, action and role in digital public health. Eur J Public Health. Oct 01, 2019;29(Supplement_3):28-35. [FREE Full text] [CrossRef] [Medline]
    25. Wong BL, Maaß L, Vodden A, van Kessel R, Sorbello S, Buttigieg S, et al. European Public Health Association (EUPHA) Digital Health Section. The dawn of digital public health in Europe: implications for public health policy and practice. Lancet Reg Health Eur. Mar 2022;14:100316. [FREE Full text] [CrossRef] [Medline]
    26. Winslow CE. The untilled fields of public health. Science. Jan 09, 1920;51(1306):23-33. [CrossRef] [Medline]
    27. Essential public health functions, health systems and health security. Developing conceptual clarity and a WHO roadmap for action. World Health Organization. 2018. URL: https://iris.who.int/bitstream/handle/10665/272597/9789241514088-eng.pdf?sequence=1 [accessed 2024-04-29]
    28. van Kessel R, Hrzic R, O'Nuallain E, Weir E, Wong BL, Anderson M, et al. Digital health paradox: international policy perspectives to address increased health inequalities for people living with disabilities. J Med Internet Res. Feb 22, 2022;24(2):e33819. [FREE Full text] [CrossRef] [Medline]
    29. Wienert J, Jahnel T, Maaß L. What are digital public health interventions? First steps toward a definition and an intervention classification framework. J Med Internet Res. Jun 28, 2022;24(6):e31921. [FREE Full text] [CrossRef] [Medline]
    30. What is digital health? U.S. Food & Drug Administration. Sep 22, 2020. URL: https://www.fda.gov/medical-devices/digital-health-center-excellence/what-digital-health [accessed 2024-06-09]
    31. mHealth: new horizons for health through mobile technologies: second global survey on eHealth. WHO Global Observatory for eHealth. 2011. URL: https://iris.who.int/bitstream/handle/10665/44607/9789241564250_eng.pdf?sequence=1&isAllowed=y [accessed 2024-04-29]
    32. Murray CJ, Alamro NM, Hwang H, Lee U. Digital public health and COVID-19. Lancet Public Health. Sep 2020;5(9):e469-e470. [CrossRef]
    33. Evidence standards framework for digital health technologies. National Institute for Health and Care Excellence (NICE). 2019. URL: https:/​/www.​nice.org.uk/​Media/​Default/​About/​what-we-do/​our-programmes/​evidence-standards-framework/​digital-evidence-standards-framework.​pdf [accessed 2024-06-09]
    34. Maaß L, Pan CC, Freye M. Mapping digital public health interventions among existing digital technologies and internet-based interventions to maintain and improve population health in practice: protocol for a scoping review. JMIR Res Protoc. Mar 31, 2022;11(3):e33404. [FREE Full text] [CrossRef] [Medline]
    35. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. Dec 05, 2016;5(1):210. [FREE Full text] [CrossRef] [Medline]
    36. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. Mar 1977;33(1):159. [CrossRef]
    37. Lantzsch H, Panteli D, Martino F, Stephani V, Seißler D, Püschel C, et al. Benefit assessment and reimbursement of digital health applications: concepts for setting up a new system for public coverage. Front Public Health. Apr 21, 2022;10:832870. [FREE Full text] [CrossRef] [Medline]
    38. Andriuskevicius E, Dobravolskas E, Punys V, Sinciene V, Valentinavicius A. Architecture for national e-health infrastructure in Lithuania. In: Hasman A, Haux R, VanderLei J, DeClercq E, France FH, editors. Ubiquity: Technologies for Better Health in Aging Societies. Amsterdam, the Netherlands. Ios Press; 2006:421.
    39. Elliott AF, Davidson A, Lum F, Chiang MF, Saaddine JB, Zhang X, et al. Use of electronic health records and administrative data for public health surveillance of eye health and vision-related conditions in the United States. Am J Ophthalmol. Dec 2012;154(6 Suppl):S63-S70. [FREE Full text] [CrossRef] [Medline]
    40. Health promotion and disease prevention through population-based interventions, including action to address social determinants and health inequity. World Health Organization. 2023. URL: https://www.emro.who.int/about-who/public-health-functions/health-promotion-disease-prevention.html [accessed 2024-06-09]
    41. Baumann LC, Ylinen A. Prevention: primary, secondary, tertiary. In: Gellman MD, editor. Encyclopedia of Behavioral Medicine. New York, NY. Springer New York; 2017:1-3.
    42. Primary health care. World Health Organization. 2023. URL: https://www.who.int/health-topics/primary-health-care#tab=tab_1 [accessed 2024-06-09]
    43. Hopayian K. Health care systems: primary, secondary, tertiary and quaternary care. J Fam Med Med Sci Res. 2022;11(4):1000133. [CrossRef]
    44. Bodenheimer T, Grumbach K. How health care is organized—I: primary, secondary, and tertiary care. In: Bodenheimer T, Grumbach K, editors. Understanding Health Policy: A Clinical Approach. New York, NY. McGraw Hill; 2020.
    45. Adler E, Alexis C, Ali Z, Allen U, Bartels U, Bick C, et al. Bridging the distance in the Caribbean: telemedicine as a means to build capacity for care in paediatric cancer and blood disorders. Stud Health Technol Inform. 2015;209:1-8. [Medline]
    46. Vears DF, Gillam L. Inductive content analysis: a guide for beginning qualitative researchers. Focus Health Prof Educ. Mar 31, 2022;23(1):111-127. [CrossRef]
    47. Agarwal S, Day DJ, Sibson L, Barry PJ, Collas D, Metcalf K, et al. Thrombolysis delivery by a regional telestroke network--experience from the U.K. National Health Service. J Am Heart Assoc. Feb 26, 2014;3(1):e000408. [FREE Full text] [CrossRef] [Medline]
    48. Akintoye S, Ogoh G, Krokida Z, Nnadi J, Eke D. Understanding the perceptions of UK COVID-19 contact tracing app in the BAME community in Leicester. J Inf Commun Ethics Soc. Dec 07, 2021;19(4):521-536. [CrossRef]
    49. Alharbi A. Knowledge, attitude and practice toward the mHealth app Mawid: a cross-sectional study. Int Health. May 02, 2023;15(3):342-350. [FREE Full text] [CrossRef] [Medline]
    50. Alis C, del Rosario C, Buenaobra B, Mar Blanca C. Lifelink: 3G-based mobile telemedicine system. Telemed J E Health. Apr 2009;15(3):241-247. [CrossRef] [Medline]
    51. Alkmim MB, Silva CB, Figueira RM, Santos DV, Ribeiro LB, da Paixão MC, et al. Brazilian national service of telediagnosis in electrocardiography. Stud Health Technol Inform. Aug 21, 2019;264:1635-1636. [CrossRef] [Medline]
    52. Anderson D, Villagra V, Coman EN, Zlateva I, Hutchinson A, Villagra J, et al. A cost-effectiveness analysis of cardiology eConsults for medicaid patients. Am J Manag Care. Jan 01, 2018;24(1):e9-16. [FREE Full text] [Medline]
    53. Ando T, Mori R, Takehara K, Asukata M, Ito S, Oka A. Correction: effectiveness of pediatric teleconsultation to prevent skin conditions in infants and reduce parenting stress in mothers: randomized controlled trial. JMIR Pediatr Parent. Apr 12, 2022;5(2):e38059. [FREE Full text] [CrossRef] [Medline]
    54. Angileri FF, Cardali S, Conti A, Raffa G, Tomasello F. Telemedicine-assisted treatment of patients with intracerebral hemorrhage. Neurosurg Focus. Apr 2012;32(4):E6. [CrossRef] [Medline]
    55. Angula N, Dlodlo N. A mobile application for health information dissemination: a Namibian context. In: Proceedings of the 2016 International Conference on Advances in Computing and Communication Engineering. 2016. Presented at: ICACCE '16; November 28-29, 2016:461-466; Durban, South Africa. URL: https://ieeexplore.ieee.org/document/8073792
    56. Arnold K, Mugisha GA, Uzoka FN, Imanirakiza S, Muhumuza C, Bukenya JN. Development of an e-health system for improving health-care access in developing countries. In: Proceedings of the 2021 Conference on Future Technologies Conference. 2021. Presented at: FTC '21; October 28-29, 2021:607-606; Vancouver, BC. URL: https://link.springer.com/chapter/10.1007/978-3-030-89880-9_45 [CrossRef]
    57. Astudillo C, Ankrom C, Trevino A, Malazarte RM, Bambhroliya AB, Savitz S, et al. Rationale and design of a statewide telestroke registry: lone star stroke consortium telestroke registry (LeSteR). BMJ Open. Sep 04, 2019;9(9):e026496. [FREE Full text] [CrossRef] [Medline]
    58. Balco P, Linhardt P, Drahosova M. The e-health solution in Slovak Republic: cloud concept for centralised national solution. In: Proceedings of the 2017 IEEE International Symposium on Signal Processing and Information Technology. 2017. Presented at: ISSPIT '17; December 18-20, 2017:434-438; Bilbao, Spain. URL: https://ieeexplore.ieee.org/abstract/document/8388682 [CrossRef]
    59. Barnett ML, Yee Jr HF, Mehrotra A, Giboney P. Los Angeles safety-net program eConsult system was rapidly adopted and decreased wait times to see specialists. Health Aff (Millwood). Mar 01, 2017;36(3):492-499. [CrossRef] [Medline]
    60. Bartnik SE, Copeland SP, Aicken AJ, Turner AW. Optometry-facilitated teleophthalmology: an audit of the first year in Western Australia. Clin Exp Optom. Sep 2018;101(5):700-703. [CrossRef] [Medline]
    61. Bastos de Carvalho A, Ware SL, Lei F, Bush HM, Sprang R, Higgins EB. Implementation and sustainment of a statewide telemedicine diabetic retinopathy screening network for federally designated safety-net clinics. PLoS One. 2020;15(11):e0241767. [FREE Full text] [CrossRef] [Medline]
    62. Becker ER, Shegog R, Savas LS, Frost EL, Coan SP, Healy CM, et al. Parents' experience with a mobile health intervention to influence human papillomavirus vaccination decision making: mixed methods study. JMIR Pediatr Parent. Feb 21, 2022;5(1):e30340. [FREE Full text] [CrossRef] [Medline]
    63. Bladin CF, Kim J, Bagot KL, Vu M, Moloczij N, Denisenko S, et al. Improving acute stroke care in regional hospitals: clinical evaluation of the Victorian Stroke Telemedicine program. Med J Aust. May 2020;212(8):371-377. [CrossRef] [Medline]
    64. Bracale M, Cesarelli M, Pepino A, Bifulco P. Telemedicine-islands project: cost-effectiveness and cost-comparison analysis. In: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2000. Presented at: EMBS '00; July 23-28, 2000:1364-1367; Chicago, IL. URL: https://ieeexplore.ieee.org/document/897992 [CrossRef]
    65. Brauchli K, O'Mahony D, Banach L, Oberholzer M. iPath - a telemedicine platform to support health providers in low resource settings. In: Marsh A, Bos L, Laxminarayan S, editors. Medical and Care Compunetics 2. Amsterdam, the Netherlands. Ios Press; 2005:2-7.
    66. Bruno A, Lanning KM, Gross H, Hess DC, Nichols FT, Switzer JA. Timeliness of intravenous thrombolysis via telestroke in Georgia. Stroke. Sep 2013;44(9):2620-2622. [CrossRef] [Medline]
    67. Buendia O, Shankar S, Mahon H, Toal C, Menzies L, Ravichandran P, et al. Is it possible to implement a rare disease case-finding tool in primary care? A UK-based pilot study. Orphanet J Rare Dis. Feb 16, 2022;17(1):54. [FREE Full text] [CrossRef] [Medline]
    68. Bunnell BE, Davidson TM, Dewey D, Price M, Ruggiero KJ. Rural and urban/suburban families' use of a web-based mental health intervention. Telemed J E Health. May 2017;23(5):390-396. [FREE Full text] [CrossRef] [Medline]
    69. Cabrnoch M, Hasić B. Electronic health book—a unique Czech solution for eHealth. Health Technol. Jul 20, 2011;1(2-4):57-69. [CrossRef]
    70. Camara G, Diallo AH, Lo M, Tendeng JN, Lo S. A national medical information system for Senegal: architecture and services. In: Hoerbst A, Hackl WO, DeKeizer N, Prokosch HU, HercigonjaSzekeres M, DeLusignan S, editors. Exploring Complexity in Health: An Interdisciplinary Systems Approach. Amsterdam, the Netherlands. Ios Press; 2016:43-47.
    71. Cheung ST, Davies RF, Smith K, Marsh R, Sherrard H, Keon WJ. The Ottawa telehealth project. Telemed J. Jan 1998;4(3):259-266. [CrossRef] [Medline]
    72. Conway NT, Wotayan RA, Alkuzam A, Al-Refaei FF, Badawi D, Barake R, et al. The Kuwait-Scotland eHealth Innovation Network (KSeHIN): a sustainable approach to quality improvement in healthcare. Qual Prim Care. 2014;22(1):43-51. [Medline]
    73. Cunningham M, Cunningham PM. mHealth4Afrika pilot validation in healthcare facilities in Ethiopia, Kenya and Malawi. In: Proceedings of the 2019 IEEE Global Humanitarian Technology Conference. 2019. Presented at: GHTC '19; October 17-20, 2019:297-304; Seattle, WA. URL: https://ieeexplore.ieee.org/document/9033052 [CrossRef]
    74. Cunningham P, Cunningham M. mHealth4Afrika – challenges when co-designing a cross-border primary healthcare solution. In: Proceedings of the 2018 IEEE Conference on International Symposium on Technology and Society. 2018. Presented at: ISTAS '18; November 13-14, 2018:32-36; Washington, DC. URL: https://ieeexplore.ieee.org/document/8638279 [CrossRef]
    75. Cunningham P, Cunningham M. mHealth4Afrika - lessons learnt and good practices from validation of a comprehensive healthcare solution for resource constrained environments. In: Proceedings of the 2019 IEEE International Symposium on Technology and Society. 2019. Presented at: ISTAS '19; November 15-16, 2019:1-8; Medford, MA. URL: https://ieeexplore.ieee.org/document/8937991 [CrossRef]
    76. Cunningham PM, Cunningham M. mHealth4Afrika-Co-designing a standards based solution for use in resource constrained primary healthcare facilities. In: Proceedings of the 41st Annual International Conference of the Ieee Engineering in Medicine and Biology Society. 2019. Presented at: EMBC '19; July 23–27, 2019:4289-4292; Berlin, Germany. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8856860 [CrossRef]
    77. Dash S, Aarthy R, Mohan V. Telemedicine during COVID-19 in India-a new policy and its challenges. J Public Health Policy. Sep 2021;42(3):501-509. [FREE Full text] [CrossRef] [Medline]
    78. de Bustos EM, Berthier E, Chavot D, Bouamra B, Moulin T. Evaluation of a French regional telemedicine network dedicated to neurological emergencies: a 14-year study. Telemed J E Health. Feb 2018;24(2):155-160. [CrossRef] [Medline]
    79. de Melo MD, Nunes MV, Resende RF, Figueiredo RR, Ruas SS, Dos Santos AF, et al. Belo Horizonte telehealth: incorporation of teleconsultations in a health primary care system. Telemed J E Health. Aug 2018;24(8):631-638. [CrossRef] [Medline]
    80. de Wit LM, van Uden-Kraan CF, Lissenberg-Witte BI, Melissant HC, Fleuren MA, Cuijpers P, et al. Adoption and implementation of a web-based self-management application "Oncokompas" in routine cancer care: a national pilot study. Support Care Cancer. Aug 18, 2019;27(8):2911-2920. [FREE Full text] [CrossRef] [Medline]
    81. deRiel E, Puttkammer N, Hyppolite N, Diallo J, Wagner S, Honoré JG, et al. Success factors for implementing and sustaining a mature electronic medical record in a low-resource setting: a case study of iSanté in Haiti. Health Policy Plan. Mar 01, 2018;33(2):237-246. [CrossRef] [Medline]
    82. Dharmayat KI, Tran T, Hardy V, Chirambo BG, Thompson MJ, Ide N, et al. Sustainability of 'mHealth' interventions in sub- Saharan Africa: a stakeholder analysis of an electronic community case management project in Malawi. Malawi Med J. Sep 03, 2019;31(3):177-183. [FREE Full text] [CrossRef] [Medline]
    83. Dittrich T. Die neue elektronische „Notfallpatientenakte“. Notf Rett. Nov 13, 2020;24(3):239-241. [CrossRef]
    84. Dixon BE, Tao GY, Wang JE, Tu WZ, Hoover S, Zhang ZY, et al. An integrated surveillance system to examine testing, services, and outcomes for sexually transmitted diseases. Stud Health Technol Inform. 2017;245:361-365. [Medline]
    85. Donati M, Celli A, Ruiu A, Saponara S, Fanucci L. A telemedicine service system exploiting BT/BLE wireless sensors for remote management of chronic patients. Technologies. Jan 18, 2019;7(1):13. [CrossRef]
    86. Draganescu D, Lupuleasa D, Dumitrescu IB, Parvu CE, Ciolan DF. Evidence on e-prescribing systems worldwide. First Romanian results. Farmacia. Apr 2013;61(2):353-360. [FREE Full text]
    87. Dubovitskaya A, Baig F, Xu ZG, Shukla R, Zambani PS, Swaminathan A, et al. ACTION-EHR: patient-centric blockchain-based electronic health record data management for cancer care. J Med Internet Res. Aug 21, 2020;22(8):e13598. [FREE Full text] [CrossRef] [Medline]
    88. Dugonik A, Kalač Pandurovič M, Vok M, Dugonik B. Development and significance of the E-surveillance system for contact allergies in Slovenia. Acta Dermatovenerol Croat. Jul 2017;25(2):91-98. [Medline]
    89. Dyb K, Warth LL. The Norwegian national summary care record: a qualitative analysis of doctors' use of and trust in shared patient information. BMC Health Serv Res. Apr 06, 2018;18(1):252. [FREE Full text] [CrossRef] [Medline]
    90. Estuar MR, Batangan D, Coronel A, Castro FE, Amarra AC, Caliso RA, et al. eHealth TABLET: a developing country perspective in managing the development and deployment of a mobile - cloud electronic medical record for local government units. In: Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management. 2014. Presented at: MDM '14; July 14-18, 2014:313-316; Brisbane, Australia. URL: https://ieeexplore.ieee.org/document/6916935 [CrossRef]
    91. Fanta GB, Pretorius L, Erasmus L. Hospitals' readiness to implement sustainable SmartCare systems in Addis Ababa, Ethiopia. In: Proceedings of the 2019 Portland International Conference on Management of Engineering and Technology. 2019. Presented at: PICMET '19; August 25-29, 2019:1-9; Portland, OR. URL: https://ieeexplore.ieee.org/document/8893824 [CrossRef]
    92. Finkelstein J, Joshi A, Arora M. Home automated telemanagement in hypertension. In: Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems. 2004. Presented at: CBMS '04; June 25, 2004:452-459; Bethesda, MD. URL: https://ieeexplore.ieee.org/document/1311756
    93. Ford DV, Jones KH, Verplancke JP, Lyons RA, John G, Brown G, et al. The SAIL Databank: building a national architecture for e-health research and evaluation. BMC Health Serv Res. Sep 04, 2009;9:157. [FREE Full text] [CrossRef] [Medline]
    94. France FR. eHealth in Belgium, a new "secure" federal network: role of patients, health professions and social security services. Int J Med Inform. Feb 2011;80(2):e12-e16. [CrossRef] [Medline]
    95. Galli R, Keith JC, McKenzie K, Hall GS, Henderson K. TelEmergency: a novel system for delivering emergency care to rural hospitals. Ann Emerg Med. Mar 2008;51(3):275-284. [CrossRef] [Medline]
    96. Ganesan D. Human rights implications of the digital revolution in health care in India. Health Hum Rights. Jun 2022;24(1):5-19. [FREE Full text] [Medline]
    97. Gavrilov G, Vlahu-Gjorgievska E, Trajkovik V. Analysis of introducing e-services: a case study of health insurance fund of Macedonia. J Health Organ Manag. 2016;30(3):354-371. [CrossRef]
    98. Gilbert SS, Bulula N, Yohana E, Thompson J, Beylerian E, Werner L, et al. The impact of an integrated electronic immunization registry and logistics management information system (EIR-eLMIS) on vaccine availability in three regions in Tanzania: a pre-post and time-series analysis. Vaccine. Jan 16, 2020;38(3):562-569. [FREE Full text] [CrossRef] [Medline]
    99. Gios L, Crema Falceri G, Micocci S, Patil L, Testa S, Sforzin S, et al. Use of eHealth platforms and apps to support monitoring and management of home-quarantined patients with COVID-19 in the province of Trento, Italy: app development and implementation. JMIR Form Res. May 31, 2021;5(5):e25713. [FREE Full text] [CrossRef] [Medline]
    100. Gong Y, Zhou JY. What are the demands of telegeriatrics medical services for elderly patients during the COVID-19 pandemic. Front Public Health. Aug 8, 2022;10:935684. [FREE Full text] [CrossRef] [Medline]
    101. Graham ML, Uesugi KH, Niederdeppe J, Gay GK, Olson CM. The theory, development, and implementation of an e-intervention to prevent excessive gestational weight gain: e-Moms Roc. Telemed J E Health. Dec 2014;20(12):1135-1142. [FREE Full text] [CrossRef] [Medline]
    102. Grisot M, Vassilakopoulou P, Aanestad M. The Norwegian eHealth platform: development through cultivation strategiesincremental changes. In: Aanestad M, Grisot M, Hanseth O, Vassilakopoulou P, editors. Information Infrastructures within European Health Care: Working with the Installed Base. Cham, Switzerland. Springer; 2017:193-208.
    103. Guerra GM, Wen CL, Motta RA, Vieira MM, Fistarol IR, de Oliveira JC, et al. Developmentimplementation of the e-care portal health education for hypertension. In: Proceedings of the 11th International Technology, Education and Development Conference. 2017. Presented at: INTED '17; Valencia, Spain:7392-7403; March 6-8, 2017. URL: https://observatorio.fm.usp.br/entities/publication/51369631-b540-4ff4-988c-d975bb4d2e2e [CrossRef]
    104. Gunter TD, Terry NP. The emergence of national electronic health record architectures in the United States and Australia: models, costs, and questions. J Med Internet Res. Mar 14, 2005;7(1):e3. [FREE Full text] [CrossRef] [Medline]
    105. Hägglund M, Scandurra I. Patients' online access to electronic health records: current status and experiences from the implementation in Sweden. Stud Health Technol Inform. 2017;245:723-727. [Medline]
    106. Hamaya R, Fukuda H, Takebayashi M, Mori M, Matsushima R, Nakano K, et al. Effects of an mHealth app (Kencom) with integrated functions for healthy lifestyles on physical activity levels and cardiovascular risk biomarkers: observational study of 12,602 users. J Med Internet Res. Apr 26, 2021;23(4):e21622. [FREE Full text] [CrossRef] [Medline]
    107. Hardardottir GA, Thoroddsen A. National eHealth Implementation: Country Experience. Amsterdam, the Netherlands. Ios Press; 2016.
    108. Harno K, Kauppinen-Mäkelin R, Syrjäläinen J. Managing diabetes care using an integrated regional e-health approach. J Telemed Telecare. Dec 02, 2006;12 Suppl 1(1_suppl):13-15. [CrossRef] [Medline]
    109. Hartvigsen G, Johansen MA, Hasvold P, Bellika JG, Arsand E, Arild E, et al. Challenges in telemedicine and eHealth: lessons learned from 20 years with telemedicine in Tromsø. Stud Health Technol Inform. 2007;129(Pt 1):82-86. [Medline]
    110. Harzheim E, Gonçalves MR, Umpierre RN, da Silva Siqueira AC, Katz N, Agostinho MR, et al. Telehealth in Rio Grande do Sul, Brazil: bridging the gaps. Telemed J E Health. Nov 2016;22(11):938-944. [CrossRef] [Medline]
    111. Hebert E, Ferguson W, McCullough S, Chan M, Drobakha A, Ritter S, et al. mBody health: digitizing disabilities in Sierra Leone. In: Proceedings of the 2016 IEEE Global Humanitarian Technology Conference. 2016. Presented at: GHTC '16; October 13-16, 2016:717-723; Seattle, WA. URL: https://ieeexplore.ieee.org/document/7857357 [CrossRef]
    112. Heider AR, Maloney NA, Satchidanand N, Allen GM, Mueller R, Gangloff S, et al. Developing a communitywide electronic health record disease registry in primary care practices: lessons learned from the Western new york beacon community. EGEMS (Wash DC). 2014;2(3):1089. [FREE Full text] [CrossRef] [Medline]
    113. Herbek S, Eisl HA, Hurch M, Schator A, Sabutsch S, Rauchegger G, et al. The electronic health record in Austria: a strong network between health care and patients. Eur Surg. Jun 2012;44(3):155-163. [CrossRef]
    114. Hernández-Ávila JE, Palacio-Mejía LS, Lara-Esqueda A, Silvestre E, Agudelo-Botero M, Diana ML, et al. Assessing the process of designing and implementing electronic health records in a statewide public health system: the case of Colima, Mexico. J Am Med Inform Assoc. 2013;20(2):238-244. [FREE Full text] [CrossRef] [Medline]
    115. Herrington G, Zardins Y, Hamilton A. A pilot trial of emergency telemedicine in regional Western Australia. J Telemed Telecare. Oct 2013;19(7):430-433. [CrossRef] [Medline]
    116. Hofer F, Schreyögg J, Stargardt T. Effectiveness of a home telemonitoring program for patients with chronic obstructive pulmonary disease in Germany: evidence from the first three years. PLoS One. 2022;17(5):e0267952. [FREE Full text] [CrossRef] [Medline]
    117. Hostenkamp G. The introduction of the electronic medication card: what Germany can learn from Denmark. Gesundh ökon Qual manag. Aug 23, 2016;22(01):54-60. [CrossRef]
    118. Hoth AB, Shafer C, Dillon DB, Mayer R, Walton G, Ohl ME. Iowa TelePrEP: a public-health-partnered telehealth model for human immunodeficiency virus preexposure prophylaxis delivery in a rural state. Sex Transm Dis. Aug 2019;46(8):507-512. [CrossRef] [Medline]
    119. Hsu M, Chu T, Yen J, Chiu W, Yeh G, Chen T, et al. Development and implementation of a national telehealth project for long-term care: a preliminary study. Comput Methods Programs Biomed. Mar 2010;97(3):286-292. [CrossRef] [Medline]
    120. Idachaba FE, Idachaba EM. Robust e-Health communication architecture for rural communities in developing countries. Eng Technol Appl Sci Res. Jun 04, 2012;2(3):237-240. [CrossRef]
    121. Ionescu B, Gadea C, Solomon B, Ionescu D, Stoicu-Tivadar V, Trifan M. A cloud based real-time collaborative platform for eHealth. In: Cornet R, StoicuTivadar L, Horbst A, Calderon CL, Andersen SK, HercigonjaSzekeres M, editors. Digital Healthcare Empowering Europeans. Amsterdam, the Netherlands. Ios Press; 2015:919-923.
    122. Janzek-Hawlat S, Ammenwerth E, Dorda W, Duftschmid G, Hackl W, Hörbst A, et al. The Austrian e-Medikation pilot evaluation: lessons learned from a national medication list. Stud Health Technol Inform. 2013;192:347-351. [Medline]
    123. Jensen TB, Thorseng AA. Building national healthcare infrastructure: the case of the Danish e-Health portal. In: Aanestad M, Grisot M, Hanseth O, Vassilakopoulou P, editors. Information Infrastructures within European Health Care: Working with the Installed Base. Cham, Switzerland. Springer; 2017:209-224.
    124. Jonsson F, Carson DB, Goicolea I, Hurtig AK. Strengthening community health systems through novel eHealth initiatives? Commencing a realist study of the virtual health rooms in rural Northern Sweden. Int J Health Policy Manag. Jan 01, 2022;11(1):39-48. [FREE Full text] [CrossRef] [Medline]
    125. Kassari P, Papaioannou P, Billiris A, Karanikas H, Eleftheriou S, Thireos E, et al. Electronic registry for the management of childhood obesity in Greece. Eur J Clin Invest. Mar 11, 2018;48(3). [CrossRef] [Medline]
    126. Khader Y, Alyahya M, El-Khatib Z, Batieha A, Al-Sheyab N, Shattnawi K. The Jordan Stillbirth and Neonatal Mortality Surveillance (JSANDS) system: evaluation study. J Med Internet Res. Jul 21, 2021;23(7):e29143. [FREE Full text] [CrossRef] [Medline]
    127. Fidel Kinori SG, Carot-Sans G, Cuartero A, Valero-Bover D, Roma Monfa R, Garcia E, et al. A web-based app for emotional management during the COVID-19 pandemic: platform development and retrospective analysis of its use throughout two waves of the outbreak in Spain. JMIR Form Res. Mar 31, 2022;6(3):e27402. [FREE Full text] [CrossRef] [Medline]
    128. Klingwort J, Schnell R. Critical limitations of digital epidemiology: why COVID-19 apps are useless. Surv Res Methods. 2020;14(2):95-100. [CrossRef]
    129. K??hler F, Nettlau H, Schweizer T, Waller T, Anker SD. The research project of the German Federal Ministry of Economics and Technology: 'partnership for the heart' - a new approach in telemedicine. Dis Manag Health Out. 2006;14(Suppl 1):37-41. [CrossRef]
    130. Kose I, Akpinar N, Gurel M, Arslan Y, Ozer H, Yurt N, et al. Turkey's national health information system (NHIS). In: Cunningham P, Cunningham M, editors. Collaboration and the Knowledge Economy: Issues, Applications, Case Studies. Amsterdam, the Netherlands. Ios Press; 2008:1-7.
    131. Kouroubali A, Kondylakis H, Kavlentakis G, Logothetides F, Stathiakis N, Petrakis Y, et al. An eHealth platform for the holistic management of COVID-19. Stud Health Technol Inform. Sep 04, 2020;273:182-188. [CrossRef] [Medline]
    132. Lai L, Sato R, He SH, Ouchi K, Leiter R, de Lima Thomas J, et al. Usage patterns of a web-based palliative care content platform (PalliCOVID) during the COVID-19 pandemic. J Pain Symptom Manage. Oct 2020;60(4):e20-e27. [FREE Full text] [CrossRef] [Medline]
    133. Lämsä E, Timonen J, Ahonen R. Pharmacy customers' experiences with electronic prescriptions: cross-sectional survey on nationwide implementation in Finland. J Med Internet Res. Feb 23, 2018;20(2):e68. [FREE Full text] [CrossRef] [Medline]
    134. Latifi R, Dasho E, Merrell RC, Lopes M, Azevedo V, Bekteshi F, et al. Cabo Verde telemedicine program: initial results of nationwide implementation. Telemed J E Health. Nov 2014;20(11):1027-1034. [CrossRef] [Medline]
    135. Latifi R, Dasho E, Shatri Z, Tilley E, Osmani KL, Doarn CR, et al. Telemedicine as an innovative model for rebuilding medical systems in developing countries through multipartnership collaboration: the case of Albania. Telemed J E Health. Jun 2015;21(6):503-509. [CrossRef] [Medline]
    136. Lazaridis C, DeSantis SM, Jauch EC, Adams RJ. Telestroke in South Carolina. J Stroke Cerebrovasc Dis. Oct 2013;22(7):946-950. [CrossRef] [Medline]
    137. Le Pape MA, Suárez JC, Mhayi A, Haazen D, Özaltin E. Developing an HMIS architecture framework to support a national health care eHealth strategy reform: a case study from Morocco. Health Syst Reform. Jan 02, 2017;3(1):56-67. [CrossRef] [Medline]
    138. Lee MS, Ray KN, Mehrotra A, Giboney P, Yee Jr HF, Barnett ML. Primary care practitioners' perceptions of electronic consult systems: a qualitative analysis. JAMA Intern Med. Jun 01, 2018;178(6):782-789. [FREE Full text] [CrossRef] [Medline]
    139. Lehmann NJ, Karagülle MU, Spielmann F, George B, Zick B, Heuer J. mHealthAtlas - an expert-based multi-sided platform for the evaluation of mHealth applications. In: Proceedings of the 2021 IEEE 9th International Conference on Healthcare Informatics. 2021. Presented at: ICHI '21; August 9-12, 2021:449-450; Victoria, BC. URL: https://ieeexplore.ieee.org/document/9565707 [CrossRef]
    140. Leluțiu-Weinberger C, Manu M, Ionescu F, Dogaru B, Kovacs T, Dorobănțescu C, et al. An mHealth intervention to improve young gay and bisexual men's sexual, behavioral, and mental health in a structurally stigmatizing national context. JMIR Mhealth Uhealth. Nov 14, 2018;6(11):e183. [FREE Full text] [CrossRef] [Medline]
    141. Lewiecki EM, Boyle JF, Arora S, Bouchonville 2nd MF, Chafey DH. Telementoring: a novel approach to reducing the osteoporosis treatment gap. Osteoporos Int. Jan 2017;28(1):407-411. [CrossRef] [Medline]
    142. Li SE, Hossain M, Gilman B, Forrow LV, Lee KM, Brown R. Effects of a nursing home telehealth program on spending and utilization for Medicare residents. Health Serv Res. Oct 2022;57(5):1191-1200. [FREE Full text] [CrossRef] [Medline]
    143. Mahdi S, Michalik-Denny EK, Buckland NJ. An assessment of behavior change techniques in two versions of a dietary mobile application: the change4life food scanner. Front Public Health. Feb 23, 2022;10:803152. [FREE Full text] [CrossRef] [Medline]
    144. Makai P, Perry M, Robben SH, Schers H, Heinen M, Olde Rikkert MG, et al. Which frail older patients use online health communities and why? A mixed methods process evaluation of use of the Health and Welfare portal. J Med Internet Res. Dec 17, 2014;16(12):e278. [FREE Full text] [CrossRef] [Medline]
    145. Mammen JR, Schoonmaker JD, Java J, Halterman J, Berliant MN, Crowley A, et al. Going mobile with primary care: smartphone-telemedicine for asthma management in young urban adults (TEAMS). J Asthma. Jan 2022;59(1):132-144. [CrossRef] [Medline]
    146. Markwick L, McConnochie K, Wood N. Expanding telemedicine to include primary care for the urban adult. J Health Care Poor Underserved. Aug 2015;26(3):771-776. [CrossRef] [Medline]
    147. Mash R, Schouw D, Fischer AE. Evaluating the implementation of the GREAT4Diabetes WhatsApp chatbot to educate people with type 2 diabetes during the COVID-19 pandemic: convergent mixed methods study. JMIR Diabetes. Jun 24, 2022;7(2):e37882. [FREE Full text] [CrossRef] [Medline]
    148. Mathew D. Information technology and public health management of disasters--a model for South Asian countries. Prehosp Disaster Med. Jun 28, 2005;20(1):54-60. [CrossRef] [Medline]
    149. Mochari-Greenberger H, Peters A, Vue L, Pande RL. A nationally scaled telebehavioral health program for chronic pain: characteristics, goals, and psychological outcomes. Telemed J E Health. Aug 2017;23(8):640-648. [CrossRef] [Medline]
    150. Mochari-Greenberger H, Vue L, Luka A, Peters A, Pande RL. A tele-behavioral health intervention to reduce depression, anxiety, and stress and improve diabetes self-management. Telemed J E Health. Aug 2016;22(8):624-630. [CrossRef] [Medline]
    151. Modi D, Dholakia N, Gopalan R, Venkatraman S, Dave K, Shah S, et al. mHealth intervention "ImTeCHO" to improve delivery of maternal, neonatal, and child care services-a cluster-randomized trial in tribal areas of Gujarat, India. PLoS Med. Oct 24, 2019;16(10):e1002939. [FREE Full text] [CrossRef] [Medline]
    152. Moll J, Rexhepi H, Cajander Å, Grünloh C, Huvila I, Hägglund M, et al. Patients' experiences of accessing their electronic health records: national patient survey in Sweden. J Med Internet Res. Nov 01, 2018;20(11):e278. [FREE Full text] [CrossRef] [Medline]
    153. Monamele CG, Messanga Essengue LL, Ripa Njankouo M, Munshili Njifon HL, Tchatchueng J, Tejiokem MC, et al. Evaluation of a mobile health approach to improve the early warning system of influenza surveillance in Cameroon. Influenza Other Respir Viruses. Sep 14, 2020;14(5):491-498. [FREE Full text] [CrossRef] [Medline]
    154. Müller-Wieland D, Ickrath M. The electronic health record for diabetes (eDA) of the German diabetes association. Diabetologe. Mar 17, 2021;17(3):260-264. [CrossRef]
    155. Nalleballe K, Sharma R, Brown A, Joiner R, Kapoor N, Morgan T, et al. Ideal telestroke time targets: telestroke-based treatment times in the United States stroke belt. J Telemed Telecare. Apr 23, 2020;26(3):174-179. [CrossRef] [Medline]
    156. Nascimento BR, Brant LC, Castro AC, Froes LE, Ribeiro AL, Cruz LV, et al. Impact of a large-scale telemedicine network on emergency visits and hospital admissions during the coronavirus disease 2019 pandemic in Brazil: data from the UNIMED-BH system. J Telemed Telecare. Feb 2023;29(2):103-110. [FREE Full text] [CrossRef] [Medline]
    157. Ngoma M, Mushi B, Morse RS, Ngoma T, Mahuna H, Lambden K, et al. mPalliative care link: examination of a mobile solution to palliative care coordination among Tanzanian patients with cancer. JCO Glob Oncol. Aug 2021;7:1306-1315. [FREE Full text] [CrossRef] [Medline]
    158. Nurmansyah MI, Rosidati C, Yustiyani Y, Nasir NM. Measuring the success of PeduliLindungi application use for supporting COVID-19 prevention: a case study among college students in Jakarta, Indonesia. Kesmas Natl Public Health J. Aug 19, 2022;17(sp1):1. [CrossRef]
    159. Oliveira TC, Bayer S, Gonçalves L, Barlow J. Telemedicine in Alentejo. Telemed J E Health. Jan 2014;20(1):90-93. [FREE Full text] [CrossRef] [Medline]
    160. Otu AA, Effa EE, Onwusaka O, Omoyele C, Arakelyan S, Okuzu O, et al. mHealth guideline training for non-communicable diseases in primary care facilities in Nigeria: a mixed methods pilot study. BMJ Open. Aug 26, 2022;12(8):e060304. [FREE Full text] [CrossRef] [Medline]
    161. Pal Bhowmick I, Chutia D, Chouhan A, Nishant N, Raju PL, Narain K, et al. Validation of a mobile health technology platform (FeverTracker) for malaria surveillance in India: development and usability study. JMIR Form Res. Nov 10, 2021;5(11):e28951. [FREE Full text] [CrossRef] [Medline]
    162. Pearce C, Bainbridge M. A personally controlled electronic health record for Australia. J Am Med Inform Assoc. 2014;21(4):707-713. [FREE Full text] [CrossRef] [Medline]
    163. Perrotta D, Bella A, Rizzo C, Paolotti D. Participatory online surveillance as a supplementary tool to sentinel doctors for influenza-like illness surveillance in Italy. PLoS One. 2017;12(1):e0169801. [FREE Full text] [CrossRef] [Medline]
    164. Petersen MJ, Baune DA, Huggins IG, LaMarche DJ. Telemedicine in Utah: the rural Utah telemedicine pilot project. In: Proceedings of Frontiers in Education 26th Annual Conference on Technology-Based Re-Engineering Engineering Education. 1996. Presented at: FIE '96; November 6-9, 1996:986-989; Salt Lake City, UT. URL: https://ieeexplore.ieee.org/document/573152 [CrossRef]
    165. Pollack TM, Nhung VT, Vinh DT, Hao DT, Trang LT, Duc PA, et al. Building HIV healthcare worker capacity through telehealth in Vietnam. BMJ Glob Health. 2020;5(4):e002166. [FREE Full text] [CrossRef] [Medline]
    166. Power K, McCrea Z, White M, Breen A, Dunleavy B, O'Donoghue S, et al. The development of an epilepsy electronic patient portal: facilitating both patient empowerment and remote clinician-patient interaction in a post-COVID-19 world. Epilepsia. Sep 10, 2020;61(9):1894-1905. [FREE Full text] [CrossRef] [Medline]
    167. Prinja S, Gupta A, Bahuguna P, Nimesh R. Cost analysis of implementing mHealth intervention for maternal, newborn and child health care through community health workers: assessment of ReMIND program in Uttar Pradesh, India. BMC Pregnancy Childbirth. Oct 03, 2018;18(1):390. [FREE Full text] [CrossRef] [Medline]
    168. Prinja S, Nimesh R, Gupta A, Bahuguna P, Gupta M, Thakur JS. Impact of m-health application used by community health volunteers on improving utilisation of maternal, new-born and child health care services in a rural area of Uttar Pradesh, India. Trop Med Int Health. Jul 13, 2017;22(7):895-907. [FREE Full text] [CrossRef] [Medline]
    169. Ray G, Altschuler A, Karmali R, Binswanger I, Glanz JM, Clarke CL, et al. Development and implementation of a prescription opioid registry across diverse health systems. JAMIA Open. Jul 2022;5(2):ooac030. [FREE Full text] [CrossRef] [Medline]
    170. Rosewell A, Makita L, Muscatello D, John LN, Bieb S, Hutton R, et al. Health information system strengthening and malaria elimination in Papua New Guinea. Malar J. Jul 05, 2017;16(1):278. [FREE Full text] [CrossRef] [Medline]
    171. Rosewell A, Ropa B, Randall H, Dagina R, Hurim S, Bieb S, et al. Mobile phone-based syndromic surveillance system, Papua New Guinea. Emerg Infect Dis. Nov 2013;19(11):1811-1818. [FREE Full text] [CrossRef] [Medline]
    172. Ruas SS, Assunção AÁ. Teleconsultations by primary care physicians of Belo Horizonte: challenges in the diffusion of innovation. Telemed J E Health. May 2013;19(5):409-414. [CrossRef] [Medline]
    173. Sá-Sousa A, Fonseca JA, Pereira AM, Ferreira A, Arrobas A, Mendes A, et al. The Portuguese severe asthma registry: development, features, and data sharing policies. Biomed Res Int. 2018;2018:1495039. [FREE Full text] [CrossRef] [Medline]
    174. Saeed SA. Tower of babel problem in telehealth: addressing the health information exchange needs of the North Carolina statewide telepsychiatry program (NC-STeP). Psychiatr Q. Jun 14, 2018;89(2):489-495. [CrossRef] [Medline]
    175. Sajwani A, Qureshi K, Shaikh T, Sayani S. eHealth for remote regions: findings from Central Asia health systems strengthening project. Stud Health Technol Inform. 2015;209:128-134. [Medline]
    176. Salzsieder E, Augstein P. The Karlsburg Diabetes Management System: translation from research to eHealth application. J Diabetes Sci Technol. Jan 01, 2011;5(1):13-22. [FREE Full text] [CrossRef] [Medline]
    177. Schaller M, Dornauer V, Hackl WO, Lechleitner G, Überegger M, Ammenwerth E. Implementing national electronic health records in nursing homes in Tyrol: a nursing management perspective. Stud Health Technol Inform. Jun 23, 2020;271:240-247. [CrossRef] [Medline]
    178. Segal E, Alwan L, Pitney C, Taketa C, Indorf A, Held L, et al. Establishing clinical pharmacist telehealth services during the COVID-19 pandemic. Am J Health Syst Pharm. Aug 20, 2020;77(17):1403-1408. [FREE Full text] [CrossRef] [Medline]
    179. Seiwerth S, Danilovic Z. The telepathology and teleradiology network in Croatia. Anal Cell Pathol. 2000;21(3-4):223-228. [FREE Full text] [CrossRef] [Medline]
    180. Sellberg N, Eltes J. The Swedish patient portal and its relation to the national reference architecture and the overall eHealth infrastructure. In: Aanestad M, Grisot M, Hanseth O, Vassilakopoulou P, editors. Information Infrastructures within European Health Care: Working with the Installed Base. Cham, Switzerland. Springer; 2017:225-244.
    181. Sepper R, Ross P, Tiik M. Nationwide health data management system: a novel approach for integrating biomarker measurements with comprehensive health records in large populations studies. J Proteome Res. Jan 07, 2011;10(1):97-100. [CrossRef] [Medline]
    182. Séroussi B, Bouaud J. Use of a nationwide personally controlled electronic health record by healthcare professionals and patients: a case study with the French DMP. In: Cornet R, Randell R, McCowan C, Peek N, Scott PJ, editors. Informatics for Health: Connected Citizen-Led Wellness and Population Health. Amsterdam, the Netherlands. Ios Press; 2017:333-337.
    183. Séroussi B, Bouaud J. The (Re)-relaunching of the DMP, the French shared medical record: new features to improve uptake and use. In: Ugon A, Karlsson D, Klein GO, Moen A, editors. Building Continents of Knowledge in Oceans of Data: The Future of Co-Created Ehealth. Amsterdam, the Netherlands. Ios Press; 2018:256-260.
    184. Shaikh A, AlReshan MS, Asiri Y, Sulaiman A, Alshahrani H. Tele-COVID: a telemedicine SOA-based architectural design for COVID-19 patients. CMC Comput Mater Con. 2021;67(1):549-576. [CrossRef]
    185. Shilpa DM, Naik PR, Shewade HD, Sudarshan H. Assessing the implementation of a mobile App-based electronic health record: a mixed-method study from South India. J Educ Health Promot. 2020;9:102. [FREE Full text] [CrossRef] [Medline]
    186. Shinde S, Shinde R, Shanbhag S, Solanki M, Sable P, Kimbahune S. mHEALTH-PHC - application design for rural health care. In: Proceedings of the 2014 IEEE Canada International Humanitarian Technology Conference. 2014. Presented at: IHTC '14; June 1-4, 2014:1-5; Montreal, QC. URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7147514 [CrossRef]
    187. Siddiqui AA, Raza-us-Samad M, Siddiqui FA, Shaikh ZA. Community health information system focusing Pakistan. In: Proceedings of the 5th International Conference on Information and Communication Technologies. 2013. Presented at: ICICT '13; December 14-15, 2013:1-9; Karachi, Pakistan. URL: https://ieeexplore.ieee.org/document/6732779 [CrossRef]
    188. Silva BS, de Azevedo Guimarães EA, de Oliveira VC, Cavalcante RB, Pinheiro MM, Gontijo TL, et al. National immunization program information system: implementation context assessment. BMC Health Serv Res. Apr 21, 2020;20(1):333. [FREE Full text] [CrossRef] [Medline]
    189. Siminerio L, Ruppert K, Huber K, Toledo FG. Telemedicine for reach, education, access, and treatment (TREAT): linking telemedicine with diabetes self-management education to improve care in rural communities. Diabetes Educ. 2014;40(6):797-805. [CrossRef] [Medline]
    190. Simonyan D, Gagnon MP, Duchesne T, Roos-Weil A. Effects of a telehealth programme using mobile data transmission on primary healthcare utilisation among children in Bamako, Mali. J Telemed Telecare. Sep 2013;19(6):302-306. [CrossRef] [Medline]
    191. Singh Sodha T, Grønbæk A, Bhandari A, Mary B, Sudke A, Smith LT. mHealth learning tool for skilled birth attendants: scaling the Safe Delivery App in India. BMJ Open Qual. Aug 2022;11(Suppl 1):e001928. [FREE Full text] [CrossRef] [Medline]
    192. Singh Y, Jackson D, Bhardwaj S, Titus N, Goga A. National surveillance using mobile systems for health monitoring: complexity, functionality and feasibility. BMC Infect Dis. Sep 16, 2019;19(Suppl 1):786. [FREE Full text] [CrossRef] [Medline]
    193. Skorning M, Bergrath S, Rörtgen D, Brokmann JC, Beckers SK, Protogerakis M, et al. [E-health in emergency medicine - the research project Med-on-@ix]. Anaesthesist. Mar 2009;58(3):285-292. [CrossRef] [Medline]
    194. Smith GE, Elliot AJ, Lake I, Edeghere O, Morbey R, Catchpole M, et al. Public Health England Real-time Syndromic Surveillance Team. Syndromic surveillance: two decades experience of sustainable systems - its people not just data! Epidemiol Infect. Jan 22, 2019;147:e101. [FREE Full text] [CrossRef] [Medline]
    195. Soler D, Baldacchino D, Amato-Gauci A. Pilot of a child health electronic surveillance system (CHESS) in Malta. J Public Health. May 01, 2007;15(3):199-209. [CrossRef]
    196. Spielberg F, Levy V, Lensing S, Chattopadhyay I, Venkatasubramanian L, Acevedo N, et al. Fully integrated e-services for prevention, diagnosis, and treatment of sexually transmitted infections: results of a 4-county study in California. Am J Public Health. Dec 2014;104(12):2313-2320. [CrossRef]
    197. Stanimirovic D, Savic D. Two Years of ePrescription in Slovenia - applications and potentials. Stud Health Technol Inform. 2018;247:261-265. [Medline]
    198. Stocks J, Choi Y, Ibrahim S, Huchko M. Iterative development of a mobile phone app to support community health volunteers during cervical cancer screening in Western Kenya: qualitative study. JMIR Form Res. Feb 24, 2022;6(2):e27501. [FREE Full text] [CrossRef] [Medline]
    199. Ströher A, Honekamp W. [ELGA--the electronic health record in the light of data protection and data security]. Wien Med Wochenschr. Jul 2011;161(13-14):341-346. [CrossRef] [Medline]
    200. Sudhamony S, Nandakumar K, Binu PJ, Issac Niwas SI. Telemedicine and tele-health services for cancer-care delivery in India. IET Commun. 2008;2(2):231. [CrossRef]
    201. Syred J, Holdsworth G, Howroyd C, Spelman K, Baraitser P. Choose to test: self-selected testing for sexually transmitted infections within an online service. Sex Transm Infect. May 2019;95(3):171-174. [FREE Full text] [CrossRef] [Medline]
    202. Talboom-Kamp EP, Holstege MS, Chavannes NH, Kasteleyn MJ. Effects of use of an eHealth platform e-Vita for COPD patients on disease specific quality of life domains. Respir Res. Jul 10, 2019;20(1):146. [FREE Full text] [CrossRef] [Medline]
    203. Thompson S, Mercer MA, Hofstee M, Stover B, Vasconcelos P, Meyanathan S. Connecting mothers to care: effectiveness and scale-up of an mHealth program in Timor-Leste. J Glob Health. Dec 2019;9(2):020428. [FREE Full text] [CrossRef] [Medline]
    204. Titov N, Dear BF, Nielssen O, Wootton B, Kayrouz R, Karin E, et al. User characteristics and outcomes from a national digital mental health service: an observational study of registrants of the Australian MindSpot Clinic. Lancet Digit Health. Nov 2020;2(11):e582-e593. [FREE Full text] [CrossRef] [Medline]
    205. Tsai WH, Wu YS, Cheng CS, Kuo MH, Chang YT, Hu FK, et al. A technology acceptance model for deploying masks to combat the COVID-19 pandemic in Taiwan (My Health Bank): web-based cross-sectional survey study. J Med Internet Res. Apr 21, 2021;23(4):e27069. [FREE Full text] [CrossRef] [Medline]
    206. van der Heijden JP, de Keizer NF, Witkamp L, Spuls PI. Evaluation of a tertiary teledermatology service between peripheral and academic dermatologists in the Netherlands. Telemed J E Health. Apr 2014;20(4):332-337. [CrossRef] [Medline]
    207. Velinov G, Jakimovski B, Lesovski D, Ivanova Panova D, Frtunik D, Kon-Popovska M. EHR system MojTermin: implementation and initial data analysis. Stud Health Technol Inform. 2015;210:872-876. [Medline]
    208. Vilardaga R, Rizo J, Ries RK, Kientz JA, Ziedonis DM, Hernandez K, et al. Formative, multimethod case studies of learn to quit, an acceptance and commitment therapy smoking cessation app designed for people with serious mental illness. Transl Behav Med. Nov 25, 2019;9(6):1076-1086. [FREE Full text] [CrossRef] [Medline]
    209. Vinekar A, Jayadev C, Mangalesh S, Shetty B, Vidyasagar D. Role of tele-medicine in retinopathy of prematurity screening in rural outreach centers in India - a report of 20,214 imaging sessions in the KIDROP program. Semin Fetal Neonatal Med. Oct 2015;20(5):335-345. [CrossRef] [Medline]
    210. Wac K, Bults R, van Beijnum B, Widya I, Jones V, Konstantas D, et al. Mobile patient monitoring: the MobiHealth system. Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1238-1241. [CrossRef] [Medline]
    211. Wang S, Lee SB, Pardue C, Ramsingh D, Waller J, Gross H, et al. Remote evaluation of acute ischemic stroke: reliability of National Institutes of Health Stroke Scale via telestroke. Stroke. Oct 2003;34(10):e188-e191. [CrossRef] [Medline]
    212. Wherton J, Greenhalgh T, Shaw SE. Expanding video consultation services at pace and scale in Scotland during the COVID-19 pandemic: national mixed methods case study. J Med Internet Res. Oct 07, 2021;23(10):e31374. [FREE Full text] [CrossRef] [Medline]
    213. Widmer RJ, Allison TG, Lerman LO, Lerman A. Digital health intervention as an adjunct to cardiac rehabilitation reduces cardiovascular risk factors and rehospitalizations. J Cardiovasc Transl Res. Jul 2015;8(5):283-292. [FREE Full text] [CrossRef] [Medline]
    214. Williams A, Turer C, Smith J, Nievera I, McCulloch L, Wareg N, et al. Adoption of an electronic medical record tool for childhood obesity by primary care providers. Appl Clin Inform. Mar 18, 2020;11(2):210-217. [FREE Full text] [CrossRef] [Medline]
    215. Wolf G, Mendelson D. The my health record system: potential to undermine the paradigm of patient confidentiality? Univ N S W Law J. Jun 2019;42(2):619-651. [CrossRef]
    216. Wootton R, Wu WI, Bonnardot L. Nucleating the development of telemedicine to support healthcare workers in resource-limited settings: a new approach. J Telemed Telecare. Oct 2013;19(7):411-417. [CrossRef] [Medline]
    217. Wrzosek N, Zimmermann A, Balwicki L. Survey on Patients' opinions and preferences concerning the use of the internet patient account in Poland: a preliminary report. Acta Pol Pharm Drug Res. Jun 18, 2022;79(2):289-299. [CrossRef]
    218. Joseph Wu TS, Kagoli M, Kaasbøll JJ, Bjune GA. Integrated disease surveillance and response (IDSR) in Malawi: implementation gaps and challenges for timely alert. PLoS One. Nov 29, 2018;13(11):e0200858. [FREE Full text] [CrossRef] [Medline]
    219. Yang CY, Chen RJ, Chou WL, Lee YJ, Lo YS. An integrated influenza surveillance framework based on national influenza-like illness incidence and multiple hospital electronic medical records for early prediction of influenza epidemics: design and evaluation. J Med Internet Res. Feb 01, 2019;21(2):e12341. [FREE Full text] [CrossRef] [Medline]
    220. Yaqoob T, Mir F, Abbas H, Shahid WB, Shafqat N, Amjad MF. Feasibility analysis for deploying national healthcare information system (NHIS) for Pakistan. In: Proceedings of the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services. 2017. Presented at: Healthcom '17; October 12-15, 2017:1-6; Dalian, China. URL: https://ieeexplore.ieee.org/document/8210836 [CrossRef]
    221. Yeh MJ, Saltman RB. Creating online personal medical accounts: recent experience in two developed countries. Health Policy Technol. Jun 2019;8(2):171-178. [CrossRef]
    222. Zaidi S, Kazi AM, Riaz A, Ali A, Najmi R, Jabeen R, et al. Operability, usefulness, and task-technology fit of an mHealth app for delivering primary health care services by community health workers in underserved areas of Pakistan and Afghanistan: qualitative study. J Med Internet Res. Sep 17, 2020;22(9):e18414. [FREE Full text] [CrossRef] [Medline]
    223. Zaidi S, Shaikh SA, Sayani S, Kazi AM, Khoja A, Hussain SS, et al. Operability, acceptability, and usefulness of a mobile app to track routine immunization performance in rural Pakistan: interview study among vaccinators and key informants. JMIR Mhealth Uhealth. Feb 13, 2020;8(2):e16081. [FREE Full text] [CrossRef] [Medline]
    224. Zanaboni P, Kummervold PE, Sørensen T, Johansen MA. Patient use and experience with online access to electronic health records in Norway: results from an online survey. J Med Internet Res. Feb 07, 2020;22(2):e16144. [FREE Full text] [CrossRef] [Medline]
    225. Zens M, Shajanian Zarneh Y, Dolle J, De Bock F. [Digital public health-leverage for community capacity building in health promotion : current situation, developmental issues and TEAviisari as a model implementation]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. Jun 25, 2020;63(6):729-740. [CrossRef] [Medline]
    226. Zeretzke-Bien C, McCall J, Wylie T, Chowdhury MA, Balakrishnan M, Hendry P, et al. Using an online vaccination registry to confirm tetanus status in children with tetanus-prone wounds. West J Emerg Med. Aug 21, 2020;21(5):1140-1146. [FREE Full text] [CrossRef] [Medline]
    227. Zhang D, Shi L, Ido MS, Green DE, Li Y, Su D, et al. Impact of participation in a telestroke network on clinical outcomes. Circ Cardiovasc Qual Outcomes. Jan 2019;12(1):e005147. [CrossRef] [Medline]
    228. Zhou YY, Garrido T, Chin HL, Wiesenthal AM, Liang LL. Patient access to an electronic health record with secure messaging: impact on primary care utilization. Am J Manag Care. Jul 2007;13(7):418-424. [FREE Full text] [Medline]
    229. Zhou YY, Kanter MH, Wang JJ, Garrido T. Improved quality at Kaiser Permanente through e-mail between physicians and patients. Health Aff (Millwood). Jul 2010;29(7):1370-1375. [CrossRef] [Medline]
    230. GDP (current US$) - high income, low income, upper middle income, lower middle income. The World Bank. 2022. URL: https:/​/data.​worldbank.org/​indicator/​NY.​GDP.​MKTP.​CD?end=2022&locations=XD-XM-XT-XN&name_desc=true&start=2000 [accessed 2024-06-09]
    231. Timpel P, Oswald S, Schwarz PE, Harst L. Mapping the evidence on the effectiveness of telemedicine interventions in diabetes, dyslipidemia, and hypertension: an umbrella review of systematic reviews and meta-analyses. J Med Internet Res. Mar 18, 2020;22(3):e16791. [FREE Full text] [CrossRef] [Medline]
    232. Maaß L, Freye M, Pan CC, Dassow HH, Niess J, Jahnel T. The definitions of health apps and medical apps from the perspective of public health and law: qualitative analysis of an interdisciplinary literature overview. JMIR Mhealth Uhealth. Oct 31, 2022;10(10):e37980. [FREE Full text] [CrossRef] [Medline]
    233. Sood S, Mbarika V, Jugoo S, Dookhy R, Doarn CR, Prakash N, et al. What is telemedicine? A collection of 104 peer-reviewed perspectives and theoretical underpinnings. Telemed J E Health. Oct 2007;13(5):573-590. [CrossRef] [Medline]
    234. SO 13606-1:2019: health informatics — electronic health record communication: part 1: reference model. International Organization for Standardization (ISO). 2019. URL: https://www.iso.org/standard/67868.html [accessed 2024-05-11]
    235. Sheikh M. Digital health information system in Africa’s resource poor countries: current challenges and opportunities. J Health Inform Dev Ctries. 2014;8(1):78-87.
    236. Acilar A. Exploring the aspects of digital divide in a developing country. Inform Sci Inf Technol. 2011;8:231-244. [CrossRef]
    237. Socha-Dietrich K. Empowering the health workforce to make the most of the digital revolution. Organisation for Economic Co-operation and Development. URL: https:/​/www.​oecd-ilibrary.org/​docserver/​37ff0eaa-en.​pdf?expires=1717739288&id=id&accname=guest&checksum=484AA5943C75F20A98800EA9F187ED9B [accessed 2024-04-29]
    238. Brooks S, Donovan P, Rumble C. Developing nations, the digital divide and research databases. Ser Rev. Dec 2005;31(4):270-278. [CrossRef]
    239. State of digital health around the world today. Global Digital Health Monitor (GDHM). URL: https://monitor.digitalhealthmonitor.org/indicators_info [accessed 2024-01-24]
    240. Digital health atlas. World Health Organization (WHO). URL: https://digitalhealthatlas.org/en/-/ [accessed 2024-01-25]
    241. Naszay M, Stockinger A, Jungwirth D, Haluza D. Digital age and the Public eHealth perspective: prevailing health app use among Austrian internet users. Inform Health Soc Care. Dec 2018;43(4):390-400. [CrossRef] [Medline]
    242. Maroju RG, Choudhari SG, Shaikh MK, Borkar SK, Mendhe H. Role of telemedicine and digital technology in public health in India: a narrative review. Cureus. Mar 2023;15(3):e35986. [FREE Full text] [CrossRef] [Medline]
    243. Aqil A, Lippeveld T, Hozumi D. PRISM framework: a paradigm shift for designing, strengthening and evaluating routine health information systems. Health Policy Plan. May 2009;24(3):217-228. [FREE Full text] [CrossRef] [Medline]
    244. Recommendations on digital interventions for health system strengthening. World Health Organization (WHO). 2019. URL: https://www.who.int/publications/i/item/9789241550505 [accessed 2024-04-29]
    245. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 02, 2018;169(7):467-473. [FREE Full text] [CrossRef] [Medline]
    246. Iwelunmor J, Blackstone S, Veira D, Nwaozuru U, Airhihenbuwa C, Munodawafa D, et al. Toward the sustainability of health interventions implemented in sub-Saharan Africa: a systematic review and conceptual framework. Implement Sci. Mar 23, 2016;11:43. [FREE Full text] [CrossRef] [Medline]
    247. Kaboré SS, Ngangue P, Soubeiga D, Barro A, Pilabré AH, Bationo N, et al. Barriers and facilitators for the sustainability of digital health interventions in low and middle-income countries: a systematic review. Front Digit Health. 2022;4:1014375. [FREE Full text] [CrossRef] [Medline]

    ‎
    DiPH: digital public health
    DiPHI: digital public health intervention
    EHR: electronic health record
    ESF: evidence standards framework
    ICT: information and communication technology
    NICE: National Institute for Health and Care Excellence
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
    WHO: World Health Organization

    Edited by T de Azevedo Cardoso; submitted 24.10.23; peer-reviewed by J Köberlein-Neu, P Velmovitsky; comments to author 09.01.24; revised version received 31.01.24; accepted 15.05.24; published 17.07.24.

    Copyright

    ©Laura Maaß, Konstantinos Angoumis, Merle Freye, Chen-Chia Pan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.07.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.