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An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics

Archives of Public Health volume 83, Article number: 129 (2025) Cite this article

Abstract

Background

The COVID- 19 pandemic has significantly impacted global health, underscoring the crucial role of digital health solutions. The World Health Organization's Classification of Digital Interventions, Services, and Applications in Health (CDISAH) provides a framework for categorizing these technologies. This study aims to analyze the adoption and trends of digital health interventions during the COVID- 19 pandemic, mapping them to the CDISAH framework to identify the most and least utilized interventions and technologies.

Methods

This overview-of-reviews study was conducted from 1 st January 2020 to 30 th December 2023, focusing on systematic reviews and meta-analyses retrieved from the Cochrane Database of Systematic Reviews, PubMed, Scopus, Web of Science, IEEE Xplore, and ProQuest. Additionally, gray literature was identified through searches on the Google Scholar platform and reviewing the citations and reference lists of the included studies. The findings were qualitatively mapped to the CDISAH framework.

Results

A total of 64 review articles were analyzed. A content analysis of the included studies identified 292 codes related to healthcare providers, 257 codes related to data services, 88 codes related to individuals, and 43 codes related to health management and support personnel. The results revealed that the most frequent interventions were associated with telemedicine and data management subcategories, while gaps were identified in areas such as individual-based data reporting during the pandemic, highlighting the need for individuals to take a more active role in managing their own health in preparation for future crises.

Conclusions

This study identifies both the strengths and weaknesses of the current digital health landscape. It emphasizes the transformative impact of digital health technologies during the COVID- 19 pandemic and provides a roadmap for future improvements in digital health interventions. By providing a comprehensive overview of digital health during this period, the study underscores the importance of implementing robust digital health strategies within the healthcare system to address existing gaps, leverage strengths, and enhance preparedness and resilience in future public health crises.

Peer Review reports

Text box 1. Contributions to the literature

• Telemedicine has been the most utilized digital health intervention during pandemics.

• A shift towards crowd-sourced data over organizational data is essential for improving future pandemic responses.

• Digital interventions focused on data services saw widespread use, while those supporting health personnel were underutilized.

• The pandemic may signal a shift towards an AI-driven era, with data services at the core of health interventions.

Introduction

The 1918 influenza pandemic affected 27 percent of the world's population, causing an estimated 50 million fatalities. Similarly, the novel coronavirus- 2019 (COVID- 19) emerged in late 2019, spreading globally, and by August 2022, it had affected six percent of the world's population, causing over six million deaths [1]. A key difference between the two pandemics was the significant role digital solutions played in the fight against COVID- 19 [2]. The growth of digital health accelerated during the COVID- 19 pandemic, with ventures in this sector experiencing an unprecedented rise. Funding increased by 72% from the previous record set in 2018, reaching an all-time high of $26.5 billion [3].

Definitions of digital health in various studies typically refer to specific applications and technologies in this field [4,5,6]. However, digital health can be defined as the convergence of these technologies with health, healthcare, living, and society to provide quality care [7]. The COVID- 19 pandemic accelerated its shift from a luxury to a necessity due to social distancing measures [8]. Factors such as global internet accessibility, six-billion smartphone users, the influence of social networks in healthcare, and the use of clinical information systems facilitated this growh [7, 9]. The enduring positive impact of digital health interventions (DHIs) during COVID- 19 highlights the importance of leveraging the experiences and lessons learned [10].

The World Health Organization (WHO) introduced the Digital Health Intervention Classification (DHIC) in 2018, updated in 2023 as the Classification of Digital Interventions, Services, and Applications in Health (CDISAH). This classification is aligned with the International Standards Organization (ISO) System Category. and categorizes DHIs into four user groups: persons, healthcare providers, health management and support personnel, and data services. Each group's functionalities describe the capabilities of digital technologies aimed at achieving user-specific objectives, further organized into overarching groups [11, 12].

During the COVID- 19 pandemic, an avalanche of systematic reviews was published [13]. These reviews primarily focused on specific services and applications, such as telemedicine, mobile health, and artificial intelligence, during pandemics [7, 14]. Several of these reviews categorized functionalities using classifications other than CDISAH [6, 15]. The lack of a uniform classification basis for interventions could hinder the integration and comparison of study results. Alternatively, when CDISAH was utilized for categorizing interventions in studies, the classification process often remained restricted to high-level categories or target groups [16]. These articles offer substantial value for healthcare providers and policymakers, facilitating decision-making and evidence-based healthcare. Nevertheless, identifying and interpreting evidence from multiple systematic reviews, which are sometimes repetitive, confusing, or contradictory, is challenging. An overview of reviews can help address this challenge [17].

Given the explosion of digital health innovations during during the COVID- 19 era, providing an overview of these technologies and interventions can indeed be helpful in gaining insight into the frequency and trends of interventions in this field throughout the epidemic period. This comprehensive analysis can also serve as a valuable tool for identifying shortcomings in current approaches. Anticipating these shortcomings and planning accordingly will be essential for developing effective health strategies for future epidemics, as well as identifying the necessary points for the current focus of researchers and experts in the field of digital health. As a result, the present study aims to identify the trends of interventions and technologies used during the COVID- 19 period to ascertain the most and least used interventions during this timeframe.

Material and methods

Study design

This overview of reviews study was conducted to identify systematic reviews (SRs) and/or meta-analyses published during the COVID- 19 pandemic, specifically addressing digital health interventions across various healthcare domains. The reporting guidelines employed in this overview were based on the fundamental principles outlined in the Preferred Reporting Items for Overviews of Reviews (PRIOR) statement from the EQUATOR Network [18, 19].

Search strategy

A comprehensive search for records was carried out from 1 st January 2020 to 30 th December 2023. The search was conducted in the English-language electronic databases, including the Cochrane Database of Systematic Reviews, PubMed, Scopus, Web of Science, IEEE Xplore, and ProQuest. Grey literature was identified by using the Google Scholar search engine and reviewing the citations and reference lists of the included studies. The detailed search strategy is provided in Supplementary Table S1.

The databases, focused on the title and abstract, searched using terms related to three key concepts: “COVID- 19,” “digital health applications and services,” and"systematic review."Despite a great deal of scholarly work dedicated to the subject of digital health applications, a significant lack of common subgroup terminology persists. Consequently, to fulfill the aims of this overview, an additional collection of search terms was acquired from relevant systematic reviews and ISO-supplied key terms and concepts in digital health systems, as mentioned in the system category of the DHIC first edition [12, 20].

Eligibility criteria

The inclusion and exclusion criteria are shown in Table 1.

Table 1 Inclusion and exclusion criteria for systematic reviews on digital health interventions during COVID- 19 pandemic (2020–2023)
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Study screening and selection

After removing duplicate records using EndNote 8, two reviewers (ZJ and NK) independently assessed a subset of references according to pre-established eligibility criteria. They conducted a comparison of the extraction results and obtained a significant kappa agreement of 0.78, as shown in Supplementary Table S2. Then, the two reviewers (ZJ and NK) conducted title/abstract and full-text screening. A third reviewer (FT), if necessary, resolved any disagreements through discussion and consensus.

Data extraction

The general characteristics of the included studies were systematically extracted by one reviewer (NK) using a predefined data extraction form. A second reviewer (ET) then checked the extracted data for accuracy and completeness. The extracted information included the following details: author name, year of publication, objective, participants, interventions, primary and secondary outcomes, searched databases, study design of included studies, qualitative assessment tool and its overall results, risk of bias assessment tool and its overall results, and study limitations.

Relevant findings from the included studies were extracted, and qualitative framework analyses were performed. The full texts of the included studies were imported into MAXQDA (version 18.2.5). Two reviewers (FT and ET) independently read the papers thoroughly. The findings from all included papers were coded and mapped to version two of CDISAH [21]. In cases where information from reviews was insufficient, primary studies were consulted. Any disagreement was resolved through discussions in multiple meetings.

Data synthesis

The purpose of this overview was to present and describe the existing body of evidence from systematic reviews. To achieve this, data synthesis was conducted to summarize and visualize the large amount of data extracted. Initially, one reviewer (FT) analyzed the extracted codes for each subcategory, highlighting key points and patterns. Similar codes were grouped and integrated to capture their essence in MS Word. Subsequently, two reviewers (FS and NS) checked the synthesized data for accuracy.

Critical appraisal assessments of included reviews

Two reviewers (ZJ and ET) independently assessed the methodological quality and risk of bias of each systematic review and/or meta-analysis using two assessment tools: A Measurement Tool to Assess Reviews (AMSTAR) 2 rating scale and the Risk of Bias in Systematic Reviews (ROBIS). The reviewers resolved their disagreements through discussion and consensus, with the assistance of a third reviewer (FT) when necessary. Based on AMSTAR 2 guideline outlined by Beverley et al. (2017), studies were categorized into four quality levels:

  • High-quality reviews: These exhibit no or minimal weaknesses in critical domains

  • Moderate-quality reviews: These contain more than one non-critical weakness but no critical flaws

  • Low-quality reviews: These include at least one critical flaw, which may affect the reliability of their conclusions

  • Critically low-quality reviews: These have multiple critical flaws, indicating a substantial risk of bias [22].

The ROBIS tool evaluates the risk of bias in systematic reviews across Phase 2 and Phase 3. Phase 2 has four domains: study eligibility criteria, identification and selection of studies, data collection and study appraisal, and synthesis and findings. The results for each domain and Phase 3 were categorized as follows:

  • Low risk: Indicates that the review has been conducted with minimal bias, ensuring reliability and validity

  • High risk: Suggests potential bias due to methodological shortcomings, which may compromise the trustworthiness of the findings.

  • Unclear risk: Assigned when there is insufficient information to determine the level of bias [23].

Results

Search results and description of evidence

A total of 64 studies were included in this overview [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87]. Fig. 1 shows the selection process for the included review studies. Approximately 30% of these included studies examined DHI during COVID- 19, focusing on the type of technology without limiting the scope to specific healthcare areas such as the use of AI during the COVID- 19 pandemic. Additionally, 39% of studies focused specifically on DHIs for COVID- 19, while 31% addressed DHIs in specific areas such as support for the elderly (n = 4), rehabilitation (n = 2), mental health (n = 2), dermatology (n = 2), pharmacology (n = 1), and the management of special diseases like dementia (n = 1), diabetes (n = 1), and cardiovascular disease (n = 1) during COVID- 19. The main characteristics of the included studies have been provided in Supplementary Table S3.

Fig. 1
figure 1

PRIOR flow diagram of study selection process for systematic reviews on digital health interventions during COVID- 19 (2020–2023)

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Methodological quality and risk of bias of reviews

According to the specified criteria of AMSTAR 2, the majority of the included studies (n = 54) were rated as"critically low" [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76, 87]. Other studies were classified as"low quality"(n = 6) [81,82,83,84,85,86],"moderate quality"(n = 3) [77,78,79], and"high quality"(n = 1) [80]. Common methodological deficiencies were identified in items 1, 2, 7, 9, 10, 13, and 14. Further details are provided in Fig. 2 and Supplementary Table S4.

Fig. 2
figure 2

Quality assessment of systematic reviews on digital health interventions during the COVID- 19 pandemic

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The included studies were also evaluated for bias using the ROBIS tool [5]. According to the criteria in Phases 2 and 3 of the ROBIS tool, approximately half of the included studies were deemed to have a"low risk"(n = 33), 39% had an"unclear risk,"and 9% had a"high risk."Additional details are presented in Fig. 3 and Supplementary Table S5.

Fig. 3
figure 3

Risk of bias assessment of included systematic reviews on digital health interventions during the COVID- 19 pandemic

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Description of mapping with CDISAH

After a comprehensive review of the included studies full texts using MAXQDA software, a total of 680 codes were extracted, elucidating the utilization of technologies for delivering healthcare interventions during the COVID- 19 pandemic. The corresponding number of codes related to each group is presented in Fig. 4. These codes were then analyzed, and the compiled results are demonstrated in Table 2. The Supplementary Table S6 also shows the items for which there was uncertainty during the analysis between two subgroups, as they were closely related to both groups. In the end, we chose one group, or in some cases, we could not select a subgroup and instead assigned it solely to its target group. This may indicate a limitation in the CDISAH framework's ability to classify interventions effectively.

Fig. 4
figure 4

Distribution of extracted codes for digital health interventions by target group using the classification of digital interventions, services, and applications in health

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Table 2 Mapping digital health application and functionalities during the COVID- 19 pandemic (2020–2023) using the classification of digital interventions, services, and applications in health
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It is important to note that the number of codes attributed to each category and subcategory may exceed the number of references listed for each subcategory, as the included studies were systematic reviews, where multiple interventions might be discussed within a single reference. Consequently, the findings indicate that the majority of DHIs during the COVID- 19 period primarily focused on healthcare providers (292), followed by data services (257), persons (88), and health management and support personnel (43). Further detailed information regarding each specific target group is elaborated upon in the subsequent sections.

Persons

Shows that during the COVID- 19 period, there were no interventions related to person-based reporting (1.5), and categories like person-centered financial transactions (1.7), consent management (1.8), and person-to-person communication (1.3) were infrequent.. The first two interventions involved managerial aspects such as billing and obtaining consent, while the third involved peer group discussions among patients (1.3). However, transmitting targeted health information (1.1.2) was the most common intervention for this group.

Within the overarching groups 1.1 and 1.2, healthcare facilities play a pivotal role in delivering health information, diagnoses, and reminders to patients using various technologies. These included television, radio, or digital billboards to raise community awareness about COVID- 19 (1.2.1) or telephone contacts to support special patients in their self-management of diseases (1.1.2). Alerts were also employed, either broadly, like robots sending warnings in crowds (1.2.2), or targeted, such as follow-up reminders (1.1.3).In groups 1.4 and 1.6, patients actively sought information online or through helplines (1.6.1), engaged in self-monitoring using wearable devices (1.4.2), or used mobile apps to remotely input data into their medical records (1.4.1). These independent actions empower patients to make informed health decisions.

Healthcare providers

The DHIs designed for healthcare providers stood out as the most frequent interventions among other target groups during the COVID- 19 pandemic, showing a broad spectrum of technologies aimed at improving patient care, optimizing workflows, and enhancing communication within the healthcare ecosystem. Among these interventions, telemedicine (2.4) emerged as a central focus, offering a wide range of services. In terms of code frequency, it ranked second only to data management among all interventions.

Moreover, decision support systems (2.3) and scheduling and actively planning for healthcare providers (2.7) emerged as two other prominent areas, with frequencies of 31 and 24, respectively. Conversely, certain types of interventions, such as referral coordination (2.6), identification and registration of persons (2.1), and laboratory and diagnostic imaging management (2.10), were less common during the COVID- 19 pandemic. This suggests that these areas have potential for further development and investment. Of particular note, healthcare provider financial transactions (2.11) within the healthcare provider group lacked specific coding, indicating a potential gap in digital health. Some items related to telemedicine could not be further categorized based on functionalities indicated in Supplementary Table S6.

Health management and support personnel

The qualitative analysis of DHIs for health management and support personnel reveals them as the least frequent target group, with only 47 codes identified. The overarching groups of civil registration and vital statistics (3.4) and facility management (3.7) within this target group lack related codes, suggesting potential areas for further investigation. The majority of interventions in this group are concentrated in supply chain management (3.2), with a particular focus on managing inventory and distributing health commodities (3.2.1). Digital COVID- 19 vaccination certificates and digital commercial carts are unique technologies tailored to the specific needs of this target group. Additionally, technologies such as artificial intelligence (AI), drones, dashboards, and web-based applications witness widespread adoption, aligning with trends observed across other target groups. Three items related to this target group could not be further categorized based on functionalities which are indicated in Supplementary Table S6.

Data service

Despite the healthcare provider target group receiving the majority of interventions during the COVID- 19 period, data management, as an overarching category within the data service target group, received the most attention with 216 intervention codes. The COVID- 19 period employed a diverse array of digital technologies to acquire, store, aggregate, analyze, visualize, and generate information, highlighting the increased importance of data management. In this target group, geospatial information management (4.3) ranked second in terms of intervention codes, with AI playing a significant role in facilitating such interventions. Nevertheless, data coding (4.2) within the data service group did not yield any extracted codes, indicating a need for further discussion regarding the significance of this group and analyzing potential interventions for future pandemics.

Discussion

This overview synthesizes evidence from review studies focusing on DHIs, applications, and services during the COVID- 19 pandemic, mapped against version 2 of the CDISAH. By integrating studies from diverse healthcare domains with varying aims, technologies, and organizational contexts, this work provides a comprehensive picture of DHIs during the pandemic. The broad scope of this synthesis enables the identification of both underrepresented innovations and dominant trends. For instance, it highlights technologies with limited adoption, such as 3D printing for medical equipment, electronic personal protective equipment (ePPE), and robotics used for remote surgery, tele-physiotherapy, or as physician assistants—technologies that were not explicitly addressed in the CDISAH framework. At the same time, it captures highly prevalent trends, such as the widespread adoption of telemedicine and the analysis of large-scale health data for predicting COVID- 19 cases, peak infection periods, transmission pathways, disease outcomes, drug discovery, and other critical applications.

The findings within the “person” target group can be discussed in alignment with Brabham's continuum of user participation in healthcare activities and crowdsourcing processes [88]. In line with this continuum, the CDISAH subcategories of “targeted and untargeted communication to persons” reflect a “traditional top-down hierarchical process” where the locus of control is with organizations and they proactively disseminate information to people. Conversely, “on-demand communication with persons,” “person-based reporting,” and “person-to-person communication” subcategories signify a “bottom-up grassroots process,” where individuals play a key role. Additionally, “person-centered consent management,” “person-centered financial transactions,” and “personal health tracking” represent a “shared top-down and bottom-up process” where control is distributed between organizations and the online community.

The study findings reveal that over half of the extracted codes within the “person” target group are attributed to the traditional top-down hierarchical process, while bottom-up grassroots processes account for only 7% of the codes, with"person-based reporting"having no associated codes. This absence is significant given that crowd-sourced data is a crucial information source during crises [89]. This finding is consistent with Xiong et al.'s study on DHIs in primary care for non-communicable disease management, which also found a lack of codes for"person based reporting"and “client financial transactions” [90]. Moreover, the “shared top-down and bottom-up process” comprises 42% of the total, with"personal health tracking"being particularly prominent.

These findings highlight the shift from traditional top-down approaches to crowd-based services, which has been facilitated by greater accessibility to the internet and mobile phones, as well as advancements in high-tech technologies like zero-effort technologies. As these innovations continue to evolve, patients will increasingly play a more active role in managing their health, a development that is especially critical during pandemics [89]. To effectively support this transition, there is a need for growth in areas such as patient empowerment, patient engagement, and consumer health informatics.

In the “healthcare providers” target group, telemedicine interventions dominated, representing 67% of codes within this group and 30% of overall codes. This finding was anticipated, as telemedicine has been reported as the most prevalent published topic in health information technology from 2000 to 2019, with numerous studies documenting a global increase in its use during the pandemic compared to 2019 rates [91,92,93].

During this overview, certain interventions involving robots assisting healthcare providers in conducting procedures, particularly surgical procedures, and delivering patient care by mimicking human movements from a safe distance were identified. These telerobotic systems align with the concept of telemedicine [94] but do not fit within the subcategories of CDISAH. Our findings suggest a potential need to revise the telemedicine subcategories to more comprehensively encompass the interventions.

Moreover, some experts encountered challenges in assigning correct subcategories to some interventions due to overlapping subcategories. For instance, the subcategory “2.4.4 consultations for case management between healthcare providers” closely overlaps with “2.5.5 peer group for healthcare providers.” While the CDHAS introduction acknowledges that an intervention may have multiple functions and could fit into several subcategories [11, 12], it can be argued that the principles of clarity and mutual exclusivity are critical for an effective classification system. Clarity ensures that there is no confusion regarding the placement of any data item, and mutual exclusivity guarantees that each item fits distinctly into one category [95]. Therefore, it may be beneficial to reconsider and refine certain subcategories within the CDISAH to enhance the system's effectiveness.

“ Health management and support personnel” emerged as the least frequent target group, accounting for only 7% of all extracted codes. Consistent with this finding, the study by Xiong et al. also reported an absence of DHIs tailored for this target group [90]. The most prevalent intervention within this target group was “supply chain management,” while the “Civil Registration and Vital Statistics” and “facility management” subcategories lacked codes. This absence may stem from the foundational and long-term nature of these interventions, resulting in comparatively less emphasis during the rapid transition to digital health amid the COVID- 19 pandemic. Nevertheless, these findings underscore a gap and a potential vulnerability in DHIs deployment during crises. Future studies and interventions should prioritize addressing the needs of this target group. Additionally, policies and strategies aimed at addressing these areas should be developed to mitigate risks in future crises.

Furthermore, within the scope of the present overview, two interventions were identified that considered DHI for healthcare personnel, yet couldn’t be fit into the existing subcategories. These interventions were three-dimensional printing for the rapid production of crucial medical supplies and electronic personal protective equipment to prevent infection. This suggests the need for a revision of CDISAH to comprehensively encompass such interventions.

This study reveals that the broadest category of DHIs was centered around “Data services” target group, constituting 36% of all codes. Within this group, there were no intervention related to data coding and data management accounted for 89% of the codes. This emphasizes the importance of data-oriented approaches during pandemics, as accurate and timely data are critical for crisis management [89].

Overall, this study contributes to the identification of existing strengths and weaknesses in digital health within the healthcare system. During the COVID- 19 pandemic, technologies such as big data analytics, AI and machine learning, blockchain, and m-health have demonstrated their potential in addressing healthcare challenges through data-driven approaches. Multiple studies confirm the accelerated adoption of novel health information technologies amid the COVID- 19 pandemic [96]. This transformative period, characterized by a “digital revolution” driven specifically by the integration of AI and advanced technologies, signifies a shift beyond the information era [97]. As it is claimed [97], this time may be the brink of entering the AI era, in which interventions increasingly revolve around data services, empowering individuals to take more prominent roles in managing their healthcare. Since the evolutions can run backward, future studies should not only focus on strengthening identified weak points but also on sustaining the momentum of these current advantages, especially telemedicine and AI, to ensure continued progress in digital health.

The resulting holistic overview of digital health advancements in this study offers valuable insights that can be leveraged by healthcare organizations to refine future trend analyses, inform strategic decision-making, and implement evidence-based policies. This synthesis not only supports the optimization of digital health integration for future public health emergencies but also provides a foundation for refining existing frameworks, such as CDISAH, to better account for evolving technologies and their functionalities in healthcare. Furthermore, mapping these interventions to the CDISAH framework facilitates a structured analysis of their impact, revealing critical gaps in areas such as person-centered data reporting and healthcare workforce support.

Like any overview of review studies, this research is subject to certain biases that warrant discussion. Selection bias is a primary limitation, as the inclusion of only English-language studies may have influenced the observed trends and findings. To mitigate this, a broad range of databases and gray literature sources were used to ensure diverse global representation. The inclusion of studies from various countries further supports the generalizability of the findings and helps reduce the potential impact of language-related bias.

Additionally, there is a risk of synthesis bias, as the experts involved in reviewing and classifying the studies were primarily from a single country. This could introduce contextual or cultural biases in the interpretation and allocation of functionalities, suggesting that the findings of this study may vary if conducted in different countries due to differences in healthcare infrastructure, digital health policies, and socioeconomic conditions. To address this, three specialists with extensive expertise in digital health were involved in the classification process, enhancing the robustness and generalizability of the findings. Furthermore, the use of the CDISAH, an internationally recognized framework, was intentional. Unlike ad hoc or context-specific classification systems, CDISAH’s clarity and specificity can reduce the likelihood of subjective interpretations, ensuring that individuals with diverse backgrounds can consistently assign codes to interventions. This approach minimizes errors and enhances the reliability of the findings, making them more applicable across different healthcare settings and countries.

Moreover, due to the high volume of publication during the COVID- 19 pandemic this study was limited to systematic reviews, which may lead to overlap among the included primary studies. However, considering the research objective of mapping interventions and the thorough process of checking and synthesizing the extracted codes, this overlap is unlikely to significantly affect the overall results.

Despite these efforts, variations in healthcare policies and digital health governance across countries may still influence the applicability of our findings. To enhance the validity and applicability of future research, we recommend incorporating meta-analyses where possible to provide quantitative validation and conducting subgroup analyses to evaluate the impact of digital interventions across specific populations and contexts. Additionally, longitudinal studies tracking the sustained adoption and effectiveness of digital health solutions beyond the pandemic would strengthen the evidence base for long-term digital health strategies. Furthermore, engaging with policymakers and healthcare providers from diverse regions to co-design and validate digital health frameworks, such as CDISAH, could ensure their relevance and adaptability to local contexts. These steps would not only improve the generalizability of findings but also support the development of more inclusive, context-sensitive, and effective digital health strategies for future public health crises.

Conclusions

This study provides a comprehensive overview of DHIs employed during the COVID- 19 pandemic, mapped to the CDISAH. By analyzing trends and utilization patterns, it highlights both the strengths and limitations of current digital health capabilities within the healthcare system. Furthermore, this study provides a roadmap and emphasizes the critical need for integrated digital health strategies to strengthen healthcare systems'preparedness and resilience against future public health crises. The insights gained from this review also lay the groundwork for further exploration of CDISAH classification framework, with potential future applications in refining and evaluating its structure to better align with emerging digital health trends.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

CDISAH:

Classification of Digital Interventions, Services, and Applications in Health

DHIs:

Digital health interventions

WHO:

World Health Organization

ISO:

International Standards Organization

DHIC:

Digital Health Intervention Classification

PRIOR:

Preferred Reporting Items for Overviews of Reviews

AMSTAR:

A Measurement Tool to Assess Reviews

ROBIS:

Risk of Bias in Systematic Reviews

AI:

Artificial intelligence

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Acknowledgements

We thank Iran University of Medical Sciences for their support in conducting this research. This article is based on a research project approved by the Research Ethics Committee of Iran University of Medical Sciences (IUMS), under the ethical code IR.IUMS.REC.1402.073.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences (IUMS), Tehran, Iran

    Foziye Tahmasbi, Somayeh Nasiri & Farahnaz Sadoughi

  2. Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences (IUMS), Tehran, Iran

    Esmaeel Toni

  3. Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

    Zohreh Javanmard

  4. Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-Sur-Alzette, Luxembourg

    Niloofar Kheradbin

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  1. Foziye Tahmasbi
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Contributions

F.S. and F.T. conceptualized this review. F.S., F.T., and E.T. were responsible for overseeing library resources and supervising the retrieval of resources from databases. E.T. and N.S. conducted the relevant searches. F.S., F.T., and E.T. were responsible for the methodology, validation and supervision. F.T., E.T., Z.J., and N.K. were involved in data extraction and analysis. F.T., E.T., and Z.J. prepared the initial draft of the paper, and F.S. and S.N. contributed to the development and refinement of subsequent drafts. All authors read and approved the final manuscript.

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Correspondence to Farahnaz Sadoughi.

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Tahmasbi, F., Toni, E., Javanmard, Z. et al. An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics. Arch Public Health 83, 129 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13690-025-01590-8

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Archives of Public Health

ISSN: 2049-3258