Accessibility settings

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

Systematic Review Protocols (778) Cost-Effectiveness and Economics (527) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (2468) Innovations and Technology in Cancer Care (720) Cancer Survivorship (465) Formative Evaluation of Digital Health Interventions (5513)

Published on in Vol 15 (2026)

This is a member publication of University College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/101773, first published .
Two women sharing a phone screen, one with cancer, showing support and hope.

Economic Evaluations of Digital Health Interventions for Cancer Survivorship Care: Protocol for a Systematic Review

Economic Evaluations of Digital Health Interventions for Cancer Survivorship Care: Protocol for a Systematic Review

Authors of this article:

Jiaqing Liu1 Author Orcid Image ;   Ran Li2 Author Orcid Image ;   Xiyi Wang1 Author Orcid Image ;   Yun Hu1 Author Orcid Image ;   Hassan Haghparast-Bidgoli2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Protocol

    1Shanghai Jiao Tong University, Shanghai, Shanghai, China

    2University College London, London, England, United Kingdom

    Corresponding Author:

    Ran Li, PhD

    University College London

    Gower Street

    London, England, WC1E 6BT

    United Kingdom

    Phone: 44 20 7679 2000

    Email: ran.li.18@ucl.ac.uk


    Background: With the increasing number of survivors of cancer, there is a corresponding rise in global health care expenditure. Digital health interventions have the potential to improve cancer survivorship by overcoming mobility and geographic barriers and, more critically, by leveraging AI to deliver dynamic, just-in-time supportive interventions and real-time risk stratification based on continuous monitoring of patient-generated health data. Although the clinical benefits of digital health in cancer survivorship care are widely recognized, their economic value remains unclear. This review aims to systematically collate and synthesize the available economic evidence on digital health interventions for cancer survivorship.

    Objective: This systematic review aims to review and synthesize the available economic evidence on digital health interventions designed for cancer survivorship care to inform policy and resource allocation.

    Methods: We will systematically review the full and partial economic evaluations of digital health for cancer survivorship. The search will cover the following databases: PubMed, Embase, Web of Science, and Scopus. It will also be supplemented by gray literature identified through Google Scholar. Studies will be included if they target cancer survivorship and report economic outcomes of digital health interventions. Data extraction will be structured by the World Bank’s framework for economic evaluation of digital health interventions, allowing a thematic synthesis of costs, outcomes, and perspectives. The reporting quality and transparency of each included study will then be critically appraised using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. A narrative synthesis will then be conducted to group and compare the findings by intervention type, cancer type, evaluation perspective, and reported cost-effectiveness.

    Results: This systematic review was registered with PROSPERO on January 19, 2026 (CRD420251251151). The database searches identified 5091 records. After the removal of 896 (17.6%) duplicates in Covidence, 4195 records were screened by title and abstract. Following title and abstract screening, 3831 (91.3%) records were excluded, 364 (8.7%) records were retained for full-text review, and 46 studies were included in the review. As of November 2026, data extraction has been completed, while quality assessment and data synthesis are ongoing. The review is expected to be completed in January 2027.

    Conclusions: This will be the first systematic review to comprehensively synthesize the economic evidence of digital health interventions for survivors of cancer. The findings will provide an evidence base to guide health care policy, investment decisions, and the implementation of sustainable and cost-effective digital survivorship care models.

    Trial Registration: PROSPERO CRD420251251151; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251251151

    International Registered Report Identifier (IRRID): DERR1-10.2196/101773

    JMIR Res Protoc 2026;15:e101773

    doi:10.2196/101773

    Keywords

    economic evaluation; digital health; cancer survivorship; artificial intelligence; AI 


    Background

    Globally, the economic cost of cancer is projected to reach INT $25.2 trillion (in international dollars at constant 2017 prices) between 2020 and 2050 [1], driven not only by the increasing cost of health care but also by the needs of a rapidly expanding population of survivors of cancer worldwide [2]. China bears nearly a quarter of the world’s new cancer diagnoses but has demonstrated substantial improvements in survival outcomes [3]. The overall 5-year survival rate for all cancers combined has increased significantly, rising from 30.9% (2003 to 2005) to a recent estimate of 43.7% (2019 to 2021) [4,5]. Similarly, in the United States, the number of survivors is projected to exceed 22 million by 2035 [6].

    Cancer survivorship is not merely a period but a “complex state of being” that encompasses the physical, psychological, social, and economic challenges experienced by survivors and their families [7]. The success in oncology, however, has exposed a critical gap in the care continuum: the transition into long-term survivorship. At this stage, survivors’ needs fundamentally shift—from a focus on acute treatment toward the ongoing management of complex physical symptoms, psychosocial symptoms, and financial burden. This evolution in survivors’ needs highlights a significant challenge in delivering appropriate, sustainable, and cost-effective survivorship care [8]. The traditional cancer care model, focused on physician-led, phased outpatient care, is unable to provide survivors with the sustained and multidimensional health support they need during the survivorship stage [9,10]. Unaddressed needs, including psychosocial distress and inadequate symptom self-management, lead to increased health care use [11], reduced productivity [12], and lower quality of life [13], imposing a heavy burden on the health care system and society. In response, digital health—a broad category of technologies, including eHealth , mobile health, telemedicine, and AI—has emerged as a promising solution [14]. It uses technology and data to improve health care delivery [15], with the potential to improve clinical outcomes and optimize resource utilization.

    Despite their growing implementation and demonstrated clinical benefits, the economic implications of digital health interventions in cancer survivorship care remain inadequately understood. The adoption of such technologies often requires substantial upfront investment, including infrastructure development, software licensing, hardware procurement, and workforce training, complicating economic assessment [16,17]. Consequently, a synthesis of existing economic evidence is essential to guide policy and investment decisions. To date, no systematic review has comprehensively synthesized the economic evidence relating to digital health interventions for cancer survivorship care. This protocol outlines the first systematic review designed to identify, synthesize, and appraise the economic evidence for digital health interventions in cancer survivorship, guided by the World Bank’s framework for economic evaluation for digital health interventions [18]. This review aims to fill a critical research gap, providing the evidence base needed to translate promising digital tools from pilot projects into scalable and sustainable survivorship care models.

    Review Question

    This review focuses on the following question: What is the economic value of digital health interventions in supporting cancer survivorship care compared with alternative interventions?

    Review Objectives

    The objectives of this review are as follows: (1) to identify key economic and contextual parameters to inform future economic modeling and policy decision-making in cancer survivorship care; (2) to explore variations in cost-effectiveness results by intervention type, cancer type, and health care levels; and (3) to examine contextual and implementation factors influencing economic outcomes and clinical findings.


    Study Design

    This systematic review protocol has been registered in PROSPERO (CRD420251251151) and will be reported using the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) checklist (Multimedia Appendix 1) [19]. This review will be informed by the World Bank’s framework for economic evaluation of digital health interventions [18], which consists of five steps: (1) determine the context, (2) determine the intervention type, (3) establish the level of complexity, (4) set the analytic principles, and (5) represent the value proposition. The systematic review will include both full and partial economic evaluation studies of digital health interventions in cancer survivorship. Full economic evaluations systematically compare both the costs and outcomes of 2 or more interventions, including cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis, cost-minimization analysis, and cost-consequence analysis. Partial economic evaluations provide the cost or cost and outcome of an intervention but do not compare them with an alternative intervention [20].

    Search Strategy

    The literature search will be conducted in 5 online databases: PubMed, Web of Science, Embase, and Scopus, as well as Google Scholar for gray literature. In addition, the reference list of the final selected studies will be consulted for further evidence. The key terms used include the following 4 search themes: digital health, survivorship, cancer, and economic evaluation. The complete retrieval strategy is provided in Multimedia Appendix 2.

    Eligibility Criteria

    Web-based survivorship care plan tools had begun to be implemented in real-world settings during 2007 to 2008 [21]. As these tools were in the early stages of implementation, studies published before this period were considered less likely to provide economic evaluation evidence on digital health interventions for cancer survivorship care. Therefore, 2008 was selected as the starting year for the literature search. Eligible study designs include trials and observational studies, with interventions required to be in the digital health category. Reviews, editorials, commentaries, conference abstracts, or protocols will be excluded. Digital health interventions may include web-based programs, mobile apps, SMS-based interventions, telemedicine, and video consultations. Studies focusing exclusively on nondigital interventions or lacking economic outcomes will be excluded. The detailed criteria for inclusion in this study are shown in Table 1.

    Table 1. Study inclusion criteria.
    PICOSaInclusion criteria
    PopulationPatients diagnosed with any type of cancer, regardless of the stage of disease progression
    InterventionAny tool based on digital health (eg, online programs, mobile health apps, SMS-based support, telehealth, and video consultations), as well as AI-enabled tools (eg, chatbots, predictive algorithms, and personalized feedback systems)
    ComparatorAny comparator, including usual care, nondigital interventions, another digital health intervention, or no comparator for partial economic evaluations
    OutcomeEconomic outcomes, including intervention costs, health care costs, resource use, cost savings, incremental cost-effectiveness ratios, cost per quality-adjusted life year, benefit-cost ratio, and return on investment
    Study designPrimary research studies, including trials and observational studies
    OtherEnglish-language studies and peer-reviewed studies

    aPICOS: population, intervention, comparator, outcome, and study design.

    Selection of Studies

    All records retrieved from the databases will be imported into EndNote 21 (Clarivate) for reference management, and the review workflow will be managed using Covidence (Veritas Health Innovation). Duplicates will be removed prior to screening. We will then conduct the first round of screening based on the title and abstract. At the full-text stage, studies will be assessed against the predefined inclusion and exclusion criteria, with consideration of the intervention type, study population, and reported outcomes. Two reviewers will independently carry out the title and abstract screening and full-text screening. Any discrepancies between the 2 reviewers will be resolved through discussion with a third reviewer. The study selection procedure will be shown in a PRISMA-P flowchart.

    Data Extraction

    We will extract the following general and economic evaluation specific characteristics from the selected studies: author and publication year, geographical setting, study design, target population, type and description of digital intervention, type of economic evaluation, perspective, time horizon, and economic outcomes. Reported sensitivity analyses, key findings, and study limitations will also be extracted. Data extraction will be conducted by 1 reviewer and verified by a second reviewer.

    Quality of Reporting and Risk of Bias Assessment

    The reporting quality of the included studies will be assessed independently by 2 reviewers using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist [22]. This tool provides a framework to assess the completeness of reporting in economic evaluations. Discrepancies will be resolved by a third reviewer. The results of the quality assessment will be summarized in tabular and graphical formats to identify patterns across included studies.

    We will conduct a risk of bias assessment to appraise the internal validity of included studies and support the credibility of the synthesized findings. Assessment tools will be selected according to the study design. For randomized controlled trials, we will use the Cochrane Risk of Bias 2 tool, examining domains such as randomization, allocation concealment, blinding, incomplete outcome data, and selective reporting. For observational studies, we will apply the Newcastle-Ottawa Scale to evaluate the selection of study groups, comparability between groups, and outcomes.

    Data Synthesis and Analysis

    Results will be reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines [23]. Given the anticipated heterogeneity in digital health interventions, study populations, health care settings, and economic evaluation methods, a structured narrative synthesis will be conducted rather than a formal meta-analysis. The synthesis will be informed by the principles outlined in chapter 20 (“Economic Evidence”) of the Cochrane Handbook for Systematic Reviews of Interventions [24].

    The characteristics and economic findings of the included studies will first be summarized in structured evidence tables. Studies will then be grouped into four primary digital health intervention categories: (1) mobile health, (2) eHealth, (3) telemedicine, and (4) AI. For interventions involving multiple digital health technologies, classification will be based on the primary intervention component described in the original study, while additional technological components will also be recorded. Where no primary component can be clearly identified, the intervention will be classified as multicomponent. Within these categories, economic findings will be synthesized separately by evaluation type (eg, cost-effectiveness analysis, cost-utility analysis, and partial economic evaluation) and economic perspective (eg, health care payer and societal).

    All cost data will be converted to international dollars and presented in 2026 value with adjustments for inflation. The review will report the economic outcomes regarding cost-effectiveness, including incremental costs, health outcomes, incremental cost-effectiveness ratios (ICERs), benefit-cost ratio, and the specific willingness-to-pay thresholds against which the study was evaluated. Findings from partial economic evaluations will be presented separately, and cost-effectiveness will not be inferred from cost data alone.

    When economic findings differ across studies, their comparability will first be assessed in terms of interventions, comparators, outcome measures, and economic evaluation methods. Subsequently, potential reasons for inconsistent results will be explored by examining differences in study populations, intervention characteristics, health care settings, economic evaluation methods, analytic perspectives, time horizons, and cost components. Reported sensitivity analyses, reporting quality assessed using CHEERS, and risk of bias assessed using appropriate tools will also be considered. Findings will be presented in structured summary matrices to illustrate patterns across studies. Where inconsistencies cannot be adequately explained, findings will be reported separately.

    Ethical Considerations

    This protocol does not require ethics approval. The results of the systematic review will be published in peer-reviewed journals and presented at national and international conferences.

    Patient and Public Involvement

    No patients or members of the public were involved in the design, conduct, reporting, or dissemination plans of this research, as this is a protocol for a systematic review of existing literature.


    This systematic review was registered with PROSPERO on January 19, 2026 (CRD420251251151). The review started on December 15, 2025, and is expected to be completed by January 2027. The database searches identified 5091 records. After the removal of 896 (17.6%) duplicates using Covidence, 4195 records remained for title and abstract screening. Following title and abstract screening, 3831 records were excluded, 364 (8.7%) records were advanced to full-text eligibility assessment, and 46 studies were included in the review (Figure 1). As of November 2026, data extraction has been completed, while quality assessment and data synthesis are ongoing.

    The completed review is expected to synthesize economic evidence on digital health interventions for cancer survivorship care, including costs, cost-effectiveness, budget-related outcomes, and other cost-related outcomes. The findings are expected to identify methodological trends, contextual factors, and evidence gaps to inform future economic modeling, policy evaluation, and implementation of digital survivorship care.

    ‎
    Figure 1. PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) selection flowchart.

    Anticipated Findings

    This systematic review will contribute to the evidence base at the intersection of cancer survivorship, digital health, and health economics. While prior research has largely focused on the clinical effectiveness of digital interventions for discrete survivorship outcomes, the economic value of these technologies across the survivorship continuum remains insufficiently synthesized [25-27]. By systematically evaluating and integrating available economic evidence, this review will address a critical gap concerning the cost-effectiveness and value for money of digital health interventions in survivorship care. The findings will provide evidence to inform health care policy, resource allocation, and implementation decisions. In addition, stakeholder engagement may support contextual interpretation of results and facilitate translation into clinical practice and sustainable care models.

    From the perspectives of health care payers and health care institutions, this review is intended to inform decision-making regarding resource allocation and reimbursement for digital health services [28]. For payers, it will synthesize evidence on ICERs and budget impact, directly informing coverage and reimbursement policies by identifying which options offer the best value for money within established willingness-to-pay thresholds [29]. Concurrently, for health care institutions such as hospitals and clinics, this review will clarify how digital health interventions can be economically efficient and support a more rational allocation of health care resources [30,31]. The evidence will highlight operational efficiencies (eg, reducing unnecessary in-person visits or staff workload) and opportunities to alleviate pressure on offline services and optimize clinical capacity, thereby informing providers’ decisions about service planning [32].

    From the patient’s perspective, this review will integrate evidence on how digital interventions impact their overall well-being. While this may include alleviating “financial toxicity” by reducing direct out-of-pocket expenses and nonmedical supplies, the perspective extends to more fundamental health outcomes. By enabling self-management and remote monitoring, digital interventions can facilitate the early detection of adverse events or disease recurrence. This may translate into specific clinical benefits, such as improved overall survival [33]; reduced hospital readmissions [34]; and, ultimately, improved health-related quality of life [35]. Therefore, our synthesis will seek evidence on outcomes such as quality-adjusted life years gained, which outline these crucial health benefits and represent the ultimate value from the patient’s perspective.

    Finally, the societal perspective offers the most comprehensive assessment of an intervention’s value by accounting for all costs and consequences. The total economic burden of cancer includes substantial indirect costs, such as productivity losses for both survivors and their caregivers [36]. A synthesis of studies of the societal perspective is expected to demonstrate that even interventions with higher upfront costs may generate significant net savings for society by enabling survivors and caregivers to return to work sooner. This macro perspective is essential for developing sustainable public health strategies to manage the growing population of survivors of cancer.

    Strengths and Limitations

    This systematic review protocol has several notable advantages. First, this will be the first systematic review to comprehensively identify and synthesize the economic evidence for digital health interventions specifically among survivors of cancer. Second, reporting quality (CHEERS) and risk of bias (Risk of Bias 2 and Newcastle-Ottawa Scale) will be assessed independently. This multidimensional approach will allow us to evaluate both the methodological quality of the included studies and the completeness of their reporting.

    However, the review may face limitations related to heterogeneity. We expect variations in the digital interventions (eg, SMS vs complex apps), populations (different cancer types and stages), and health care settings (diverse national systems and cost structures). This heterogeneity may preclude a formal meta-analysis and will require a structured narrative synthesis instead. Second, the potential for publication bias is a concern, as studies demonstrating the cost-effectiveness of digital interventions may be more likely to be published than those with negative outcomes. To mitigate this risk, efforts will be made to identify relevant gray literature during the search process. Finally, the rapid evolution of digital health technologies may limit the applicability of older economic evaluations to current health care settings, as technological capabilities, implementation costs, and health care delivery models change over time.

    Conclusions

    This systematic review will address an important evidence gap by evaluating the economic value of digital health interventions for cancer survivorship care. The results will help clarify whether, how, and under what conditions these interventions may offer value for money across different cancer populations, intervention types, and health care contexts. The findings will inform future research, economic modeling, and policy decisions regarding the sustainable implementation of digital survivorship care.

    Funding

    This work is funded by the University College London-Shanghai Jiao Tong University Strategic Partner Funds, the Shanghai Jiao Tong University-University College London Seed Fund Program 2025/26, the Shanghai Science and Technology Foundation Soft Science Research Project (grant 25692114800), and the Shanghai Municipal Philosophy and Social Science Planning Office.

    Generative AI Use

    The authors declare that generative AI was not used in the preparation of this manuscript.

    Authors' Contributions

    XW, YH, RL, and HH-B contributed to the conception and design of the study protocol. JL drafted the initial manuscript. RL, XW, YH, and HH-B critically reviewed the manuscript for important intellectual content. All authors read and approved the final manuscript for publication. RL is the guarantor of the review.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

    DOCX File , 30 KB
    Multimedia Appendix 2

    Search strategy.

    DOCX File , 20 KB
    1. Chen S, Cao Z, Prettner K, Kuhn M, Yang J, Jiao L, et al. Estimates and projections of the global economic cost of 29 cancers in 204 countries and territories from 2020 to 2050. JAMA Oncol. Apr 01, 2023;9(4):465-472. [FREE Full text] [CrossRef] [Medline]
    2. Guy GP, Ekwueme DU, Yabroff KR, Dowling EC, Li C, Rodriguez JL, et al. Economic burden of cancer survivorship among adults in the United States. J Clin Oncol. Oct 20, 2013;31(30):3749-3757. [FREE Full text] [CrossRef] [Medline]
    3. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263. [FREE Full text] [CrossRef] [Medline]
    4. Zeng H, Chen W, Zheng R, Zhang S, Ji JS, Zou X, et al. Changing cancer survival in China during 2003-15: a pooled analysis of 17 population-based cancer registries. Lancet Glob Health. May 2018;6(5):e555-e567. [FREE Full text] [CrossRef] [Medline]
    5. Zeng H, Zheng R, Sun K, Zhou M, Wang S, Li L, et al. Cancer survival statistics in China 2019-2021: a multicenter, population-based study. J Natl Cancer Cent. Jun 22, 2024;4(3):203-213. [FREE Full text] [CrossRef] [Medline]
    6. Wagle NS, Nogueira L, Devasia TP, Mariotto AB, Yabroff KR, Islami F, et al. Cancer treatment and survivorship statistics, 2025. CA Cancer J Clin. 2025;75(4):308-340. [FREE Full text] [CrossRef] [Medline]
    7. Mollica MA, Doose M, Reed C, Tonorezos E. Defining concepts in cancer survivorship. Cancer. Aug 15, 2025;131(16):e70039. [FREE Full text] [CrossRef] [Medline]
    8. Institute of Medicine and National Research Council. Hewitt M, Greenfield S, Stovall E, editors. From Cancer Patient to Cancer Survivor: Lost in Transition. Washington, DC. The National Academies Press; 2006.
    9. Jefford M, Howell D, Li Q, Lisy K, Maher J, Alfano CM, et al. Improved models of care for cancer survivors. Lancet. Apr 16, 2022;399(10334):1551-1560. [FREE Full text] [CrossRef] [Medline]
    10. Pan LC, Wu XR, Lu Y, Zhang HQ, Zhou YL, Liu X, et al. Artificial intelligence empowered digital health technologies in cancer survivorship care: a scoping review. Asia Pac J Oncol Nurs. Aug 23, 2022;9(12):100127. [FREE Full text] [CrossRef] [Medline]
    11. Abdelhadi OA, Pollock BH, Joseph JG, Keegan TH. Psychological distress and associated additional medical expenditures in adolescent and young adult cancer survivors. Cancer. Apr 01, 2022;128(7):1523-1531. [FREE Full text] [CrossRef] [Medline]
    12. Mehnert A. Employment and work-related issues in cancer survivors. Crit Rev Oncol Hematol. Feb 2011;77(2):109-130. [CrossRef] [Medline]
    13. Zafar SY, Peppercorn JM, Schrag D, Taylor DH, Goetzinger AM, Zhong X, et al. The financial toxicity of cancer treatment: a pilot study assessing out-of-pocket expenses and the insured cancer patient's experience. Oncologist. 2013;18(4):381-390. [FREE Full text] [CrossRef] [Medline]
    14. Labrique A, Agarwal S, Tamrat T, Mehl G. WHO digital health guidelines: a milestone for global health. NPJ Digit Med. Sep 18, 2020;3(1):120. [FREE Full text] [CrossRef] [Medline]
    15. Kim DW, Eala M, Lee G, Lam MB, Martin N, Nakfoor B, et al. Chapter 91 - Digital health. In: Translational Radiation Oncology. Cambridge, MA. Academic Press; 2023:551-556.
    16. Marwaha JS, Landman AB, Brat GA, Dunn T, Gordon WJ. Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation. NPJ Digit Med. Jan 27, 2022;5(1):13. [FREE Full text] [CrossRef] [Medline]
    17. Gomes M, Murray E, Raftery J. Economic evaluation of digital health interventions: methodological issues and recommendations for practice. Pharmacoeconomics. Apr 2022;40(4):367-378. [FREE Full text] [CrossRef] [Medline]
    18. Wilkinson T, Wang M, Friedman J, Görgens M. A framework for the economic evaluation of digital health interventions. World Bank. 2023. URL: https://openknowledge.worldbank.org/handle/10986/39713 [accessed 2026-11-02]
    19. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) 2015 statement. Syst Rev. Jan 01, 2015;4(1):1. [FREE Full text] [CrossRef] [Medline]
    20. Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic Evaluation of Health Care Programmes. 4th Edition. Oxford, UK. Oxford University Press; 2015.
    21. Hill-Kayser CE, Vachani C, Hampshire MK, Jacobs LA, Metz JM. An internet tool for creation of cancer survivorship care plans for survivors and health care providers: design, implementation, use and user satisfaction. J Med Internet Res. Sep 04, 2009;11(3):e39. [FREE Full text] [CrossRef] [Medline]
    22. Husereau D, Drummond M, Augustovski F, de Bekker-Grob E, Briggs AH, Carswell C, et al. Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations. Int J Technol Assess Health Care. Jan 11, 2022;38(1):e13. [CrossRef]
    23. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. Mar 29, 2021;10(1):89. [FREE Full text] [CrossRef] [Medline]
    24. Aluko P, Graybill E, Craig D, Henderson C, Drummond M, Wilson EC, et al. Chapter 20: Economic evidence. In: Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions Version 6.5. 11 River Street, Hoboken, NJ, USA. John Wiley and Sons; 2024.
    25. Liu X, Ning L, Fan W, Jia C, Ge L. Electronic health interventions and cervical cancer screening: systematic review and meta-analysis. J Med Internet Res. Oct 31, 2024;26:e58066. [FREE Full text] [CrossRef] [Medline]
    26. Nazari AM, Sarmadi S, Ghazanfari MJ, Gholami M, Emami Zeydi A, Zare-Kaseb A. The effectiveness of play therapy on depression and anxiety in hospitalized children with cancer: a systematic review. Support Care Cancer. Jan 10, 2025;33(2):88. [CrossRef] [Medline]
    27. Wang T, Tang C, Jiang X, Guo Y, Zhu S, Xu Q. Effectiveness of web-based mindfulness-based interventions for patients with cancer: systematic review and meta-analyses. J Med Internet Res. Jun 25, 2024;26:e47704. [FREE Full text] [CrossRef] [Medline]
    28. Mathias R, McCulloch P, Chalkidou A, Gilbert S. How can regulation and reimbursement better accommodate flexible suites of digital health technologies? NPJ Digit Med. Jul 02, 2024;7(1):170. [FREE Full text] [CrossRef] [Medline]
    29. Iino H, Hashiguchi M, Hori S. Estimating the range of incremental cost-effectiveness thresholds for healthcare based on willingness to pay and GDP per capita: a systematic review. PLoS One. Apr 14, 2022;17(4):e0266934. [FREE Full text] [CrossRef] [Medline]
    30. Porter ME. What is value in health care? N Engl J Med. Dec 23, 2010;363(26):2477-2481. [CrossRef] [Medline]
    31. Ekeland AG, Bowes A, Flottorp S. Effectiveness of telemedicine: a systematic review of reviews. Int J Med Inform. Nov 2010;79(11):736-771. [CrossRef] [Medline]
    32. Hollander JE, Carr BG. Virtually perfect? Telemedicine for Covid-19. N Engl J Med. Apr 30, 2020;382(18):1679-1681. [CrossRef] [Medline]
    33. Denis F, Lethrosne C, Pourel N, Molinier O, Pointreau Y, Domont J, et al. Randomized trial comparing a web-mediated follow-up with routine surveillance in lung cancer patients. J Natl Cancer Inst. Sep 01, 2017;109(9):djx029. [FREE Full text] [CrossRef] [Medline]
    34. Wilson PM, Ramar P, Philpot LM, Soleimani J, Ebbert JO, Storlie CB, et al. Effect of an artificial intelligence decision support tool on palliative care referral in hospitalized patients: a randomized clinical trial. J Pain Symptom Manage. Jul 2023;66(1):24-32. [FREE Full text] [CrossRef] [Medline]
    35. Basch E, Schrag D, Henson S, Jansen J, Ginos B, Stover AM, et al. Effect of electronic symptom monitoring on patient-reported outcomes among patients with metastatic cancer: a randomized clinical trial. JAMA. Jun 28, 2022;327(24):2413-2422. [CrossRef] [Medline]
    36. Wranik WD, Muir A, Hu M. Costs of productivity loss due to occupational cancer in Canada: estimation using claims data from Workers' Compensation Boards. Health Econ Rev. Dec 2017;7(1):9. [FREE Full text] [CrossRef] [Medline]

    ‎
    CHEERS: Consolidated Health Economic Evaluation Reporting Standards
    ICER: incremental cost-effectiveness ratio
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    PRISMA-P: Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols

    Edited by A Stone; submitted 21.May.2026; peer-reviewed by O Ayilara; comments to author 12.Oct.2026; revised version received 26.Oct.2026; accepted 28.Oct.2026; published 13.Nov.2026.

    Copyright

    ©Jiaqing Liu, Ran Li, Xiyi Wang, Yun Hu, Hassan Haghparast-Bidgoli. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 13.Nov.2026.

    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 JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.