JMIR Publications

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  Digital Health Strategic Planning

  Apr 1, 2026

  Channel Allocation and Equity in Preventive Campaigns for Older Adults: Agent-Based Modeling Study

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  Authors List:

  • Jihye Lee  
  • Juyoung Park  
  • Yuna Kim  
  • Duk-Jo Kong  

  Abstract:

  Background: Preventive campaigns for older adults must decide how to allocate limited resources across media channels. However, these channel allocation and budget decisions rarely use explicit criteria for distributional equity or structured strategic planning tools. Consequently, health systems may optimize average uptake while leaving large gaps across socioeconomic groups and media use profiles. Objective: This study aimed to develop and apply a data-driven agent-based model as a strategic planning tool for preventive campaigns targeting older adults, comparing channel allocation, personalization, and loss framing options under explicit budget and equity guardrails. Methods: We built an agent-based model calibrated to national survey data from South Korea on influenza vaccination and routine health screening among older adults (vaccination, N=2405; screening, N=2400). Fifteen prespecified campaign scenarios varied channel allocation across television, digital, and print media; budget intensity; 2 equity-focused personalization strategies; and graded loss framing. Primary outcomes were final adoption and time to adoption. Equity outcomes included the minimum class-level adoption and 90‐10 gap across latent classes. Each scenario was simulated over 12 monthly steps with 100 Monte Carlo replications. We conducted sensitivity analyses varying link functions and key social reinforcement parameters. Results: Personalization improved uptake and equity relative to the integrated baseline. In the vaccination model (N=2405), adoption increased from 91.2% (n=2193) to 93.3% (n=2244) and 94.6% (n=2275). Minimum class-level adoption increased from 86.8% to 90.3% and 90.9%. The 90‐10 gap narrowed from 5.7 to 4.5 and 4.7 percentage points. In the screening model (N=2400), adoption increased from 83.8% (n=2011) to 88.2% (n=2117) and 89.5% (n=2148). Minimum class-level adoption increased from 77.6% to 83.2% and 85.3%. The 90‐10 gap narrowed from 9.2 to 7.4 and 6.2 percentage points. Television-only strategies achieved high adoption but had less favorable equity profiles than personalization. High-budget strategies achieved high adoption but required higher total exposure. Stronger loss framing produced small, monotonic gains in adoption and shortened the time to adoption without worsening equity in the tested range. Scenario rankings were stable in sensitivity analyses. Conclusions: This agent-based modeling study illustrates how ex ante planning can improve preventive campaign design by comparing channel allocation and personalization options under explicit equity and budget criteria. For campaigns targeting older adults, equity-focused reweighting and class-tailored television-digital portfolios improved or preserved mean adoption while strengthening distributional equity under fixed budgets. In contrast, undifferentiated channel diversification without personalization offered a less favorable efficiency-equity trade-off. These findings support integrating explicit equity guardrails into early-stage channel allocation and prioritizing targeted personalization over simple channel diversification. Future work should validate these patterns in other populations and health systems and link simulated diffusion trajectories with observed exposure and engagement in real-world campaigns. It should also extend guardrail-based planning tools to organizational settings and multiyear decision contexts.

  • J Med Internet Res 2026;28(1):e88429

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  Telehealth and Telemonitoring

  Apr 1, 2026

  Digital Primary Health in Rwanda: Qualitative Study of User Experiences and Implementation Lessons From Babyl’s Telemedicine Platform

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  Authors List:

  • Sabine Musange Furere  
  • Gashaija Absolomon  
  • Nathalie Umutoni  
  • Stella Matutina Umuhoza  
  • Felix K Rubuga  
  • Yvonne Delphine Nsaba-Uwera  
  • Patrick Singa Muhoza  
  • Regis Hitimana  
  • Jeanine Condo  
  • James Humuza  

  Abstract:

  Background: Digital health innovations address health care accessibility challenges in low- and middle-income countries. Babyl, Rwanda’s largest telemedicine platform, reached 450 of 510 health facilities and enrolled 2 million patients before halting in September 2023 for system redesign. Limited research has explored implementation experiences and user perspectives that influenced its sustainability. Objective: This study aims to explore user experiences and implementation lessons from Babyl’s digital health platform, examining drivers that supported or hindered adoption and scale-up. This qualitative study uniquely examines the lived experiences of diverse stakeholders, active users, lapsed users, nonusers, health care providers, and Babyl agents to understand implementation challenges that contributed to the platform’s halt. Methods: A qualitative, cross-sectional study used 20 focus group discussions (FGDs) and 32 key informant interviews (KIIs) across 12 health centers in ten districts with diverse utilization rates, geographic locations, and Babyl agent availability. FGDs captured collective community perspectives while KIIs provided in-depth individual experiences, enabling data triangulation. FGDs included active users, lapsed users, registered nonusers, and eligible nonregistrants. KIIs involved health center heads, health care providers, and Babyl agents. Data were analyzed using thematic analysis following Braun and Clarke’s framework. Data saturation was achieved when no new themes emerged from the last 3 FGDs and 5 KIIs. All transcripts were validated through member checking with a subset of participants, and intercoder reliability was established with a Cohen kappa of 0.82 across 2 independent coders. Results: Five themes emerged: (1) knowledge and perceptions of digital health, (2) enablers and barriers to utilization, (3) experience and satisfaction, (4) benefits, and (5) improvement suggestions. Participants held positive perceptions of digital health for improving access and reducing wait times. Key enablers included qualified providers, convenience, privacy, and Babyl agents. Major barriers included negative perceptions of remote care quality, service delays, limited digital literacy, device access challenges, and inadequate health facility integration. Users reported high satisfaction with consultations but experienced process confusion. Patient and provider perspectives diverged: patients emphasized convenience, while providers expressed concerns about diagnostic limitations without physical examination. Digital literacy and smartphone access were pronounced barriers among rural and older participants. Recommendations included community mobilization, universal agent deployment, expanded coverage, and sustainable financing. Conclusions: Multiple implementation challenges at individual, community, health system, and policy levels contributed to Babyl’s discontinuation. Critical lessons include the importance of genuine health system integration, sustainable financing, stakeholder engagement, and gradual scaling. Findings provide insights for Rwanda’s health sector digitalization and other African nations investing in telemedicine platforms.

  • J Med Internet Res 2026;28(1):e84832

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  News and Perspectives

  Mar 31, 2026

  Our AI-Powered Discoveries Are Trapped in a Predigital System

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  Authors List:

  • Boon-How Chew

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  • J Med Internet Res 2026;28(1):e96018

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  Policy and Policy Proposals

  Mar 31, 2026

  Association Between Telemedicine Adoption and Physician Job Satisfaction: Cross-Sectional Study

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  Authors List:

  • Xiaohui Zhai  
  • Zhongliang Zhou  
  • Zhichao Wang  
  • Guanping Liu  
  • Peter C Coyte  

  Abstract:

  Background: Telemedicine has expanded rapidly in recent years, with particularly pronounced growth following the COVID-19 pandemic. By improving access to care and offering greater flexibility in service delivery, it has become an important component of health care. Although the benefits of telemedicine for patients are well documented, its effects on physician job satisfaction remain insufficiently understood. Given the importance of job satisfaction for workforce stability, physician well-being, and quality of care, further examination of how telemedicine affects physician job satisfaction is warranted. Objective: This study aims to examine the association between telemedicine adoption and physician job satisfaction and to assess whether the physician-patient relationship mediates this association. Methods: A cross-sectional survey was conducted among health care professionals in Xi’an, China. Data were collected between November 7 and December 8, 2023, via an online questionnaire administered using the REDCap (Research Electronic Data Capture; Vanderbilt University) platform. A total of 12,052 physicians were included in the analysis. Physician job satisfaction was measured using a validated 6-point Likert scale. Telemedicine adoption was assessed through self-report. A partial proportional odds model was used to examine the association between telemedicine adoption and job satisfaction, adjusting for a comprehensive set of potential confounders. Additionally, the Karlson-Holm-Breen (KHB) decomposition method was used to explore the mediating role of physician-patient relationship quality in this association. Results: Among 12,052 surveyed physicians, 1642 (13.62%) reported adopting telemedicine, whereas 10,410 (86.38%) did not. After adjusting for demographic characteristics, work-related factors, psychological factors, and physician-patient relationship, telemedicine adoption was significantly associated with higher job satisfaction (odds ratio [OR] 1.17, 95% CI 1.05‐1.30). Findings were robust across multiple sensitivity analyses. Subgroup analyses indicated that the association did not vary across physician subgroups, and no significant interaction effects were observed. Mediation analysis revealed a total effect of telemedicine on job satisfaction of 0.33 (95% CI 0.17‐0.50), with an indirect effect of 0.10 (95% CI 0.07‐0.13) through improved physician-patient relationships, accounting for 30.30% of the total effect. Conclusions: These findings suggest that telemedicine adoption is positively associated with physician job satisfaction, partially mediated by the physician-patient relationship. Policies should promote telemedicine adoption while prioritizing platform designs that support effective physician-patient interactions to enhance provider well-being and care outcomes.

  • J Med Internet Res 2026;28(1):e82285

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  News and Perspectives

  Mar 31, 2026

  Emerging Risks of AI-to-AI Interactions in Health Care: Lessons From Moltbook

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  • Tejas S Athni

  Abstract:

  • J Med Internet Res 2026;28(1):e96199

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  News and Perspectives

  Mar 31, 2026

  Further Promise and Potential for Precision Medicine in Oncology

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  • Shalini Kathuria Narang

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  • J Med Internet Res 2026;28(1):e95657

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  Web-based and Mobile Health...

  Mar 31, 2026

  Designing a Carbohydrate Counting App for Young Adults With Type 1 Diabetes: Usability Testing Interview Study

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  Authors List:

  • Asmaa Housni  
  • Aidan Shulkin  
  • Alexandra Katz  
  • Giuliana Giannini  
  • Amélie Roy-Fleming  
  • Meranda Nakhla  
  • Courtney South  
  • Anne-Sophie Brazeau  

  Abstract:

  Background: Carbohydrate counting (CC) assists people with type 1 diabetes (T1D) adjust mealtime insulin doses; however, it is often burdensome. Mobile apps can simplify this process by automating carbohydrate estimation and insulin calculations, yet no comprehensive solution currently combines photo-based carbohydrate recognition with an integrated bolus calculator. Objective: This study aimed to identify user-informed design principles from usability testing interviews to optimize a novel app supporting young adults with T1D in CC and insulin dosing. Methods: We conducted 4 iterative rounds of usability testing interviews, each with 3 to 5 participants, using a think-aloud protocol to evaluate how easily and effectively users interacted with the app and to identify areas for improvement. Interviews were analyzed qualitatively to derive main design principles, and findings from each round informed the refinement of the app prior to subsequent testing. Results: A total of 18 participants completed the usability testing (median age of 23, IQR 19-24 y and diabetes duration of 9, IQR 6-12 y; n=12, 66.7% young women). Thematic analysis highlighted that a person-centered design that prioritizes the lived experiences of youth with T1D was essential to position the app as a self-management support system, beyond a clinical tool. Personalization was central, including customizable treatment profiles, tailored dashboard metrics, esthetic preferences, and artificial intelligence–driven recommendations based on personal trends. Early usability barriers revealed the need for intuitive navigation, streamlined multistep processes, and clear guidance for data entry and interpretation. Participants valued culturally inclusive content and familiar terminology to enhance accessibility and engagement. Users perceived strong potential for the app to centralize diabetes management tasks, integrate contextual factors (eg, exercise, diet, and timing of insulin) with glucose data, generate sharable reports to facilitate patient-practitioner communication, and strengthen self-efficacy through personalized trend analysis. Concerns about over-reliance on automation underscored the necessity of transparent data verification and user override options to maintain trust in insulin dosing decisions. Conclusions: Iterative usability testing highlighted the importance of balancing automation with user control, personalization, and contextual understanding of personal trends, as key design principles to enhance engagement and the apps’ relevance as a self-management tool. Incorporating these features into a CC and insulin-dosing app could improve self-efficacy in youth living with T1D.

  • J Med Internet Res 2026;28(1):e86024

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  Viewpoints and Perspectives

  Mar 31, 2026

  From Knowledge Graphs to Digital Twins: Perspectives on Modeling Patient Outcomes for Health Care Quality Assessment

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  Authors List:

  • Anna-Katharina Nitschke  
  • Juan G Diaz Ochoa  
  • Simone Neumaier  
  • Markus Knott  

  Abstract:

  Medical applications of mathematical modeling, including machine learning models, knowledge graphs, and health digital twins, primarily involve the prediction of patient outcomes. This expert perspective examines how mathematical modeling can contribute to health care quality management. Definitions of procedures, patient outcomes, and quality metrics are provided with a quantitative focus. The emphasis is subsequently placed on 3 categories of patient-centered quality of care, namely, patient safety, procedure accuracy, and procedure efficacy, for which a conceptual and mathematical description is provided. Different levels of modeling tasks essential for managing patient-centered quality of care are identified. This article facilitates a deeper understanding of the topic by assigning relevant publications to these 3 quality categories. Focus is placed on the applicability of graph-based methods, including knowledge graphs and health digital twins, to improve quality management in health care. We have presented a clinical scenario and provided information on methodological limitations, future research directions, and practical implications.

  • J Med Internet Res 2026;28(1):e81946

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  Digital Health Reviews

  Mar 30, 2026

  Artificial Intelligence Tools for Automating Evidence Synthesis: Scoping Review

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  Authors List:

  • Sashika Harasgama  
  • Helen Pearce  
  • Cameron Appel  
  • Liam Loftus  
  • Helena Painter  
  • Isla Kuhn  
  • Justine Karpusheff  
  • Aji Ceesay  
  • John Ford  

  Abstract:

  Background: Rapidly and accurately synthesizing large volumes of evidence is a time- and resource-intensive process. Once published, reviews often risk becoming outdated, limiting their usefulness for decision makers. Recent advancements in artificial intelligence (AI) have enabled researchers to automate stages of the evidence synthesis process, from literature searching and screening to data extraction and analysis. As previous reviews on this topic have been published, a significant number of tools have been further developed and evaluated. Furthermore, as generative AI increasingly automates evidence synthesis, understanding how it is studied and applied is crucial, given both its benefits and risks. Objective: This review aimed to map the current landscape of evaluated AI tools used to automate evidence synthesis. Methods: Following the Joanna Briggs Institute methodology for scoping reviews, we searched Ovid MEDLINE, Ovid Embase, Scopus, and Web of Science in February 2025 and conducted a gray literature search in April 2025. We included articles published in any language from January 2021 onward. Two reviewers independently screened citations using Rayyan, and data were extracted based on study design and key AI-related technical features. Results: We identified 7841 unique citations through database searches and 19 records through gray literature searching. A total of 222 articles were included in the review. We identified 65 AI tools and 25 open-source models or machine learning (ML) algorithms that automate parts of or the whole evidence synthesis pathway. A total of 54.1% (n=120) of the studies were published in 2024, reflecting a trend toward researching general-purpose large language models (LLMs) for evidence synthesis automation. The most popular tool studied was generative pretrained transformer models, including its conversational interface ChatGPT (n=70, 31.5%). Moreover, 31.1% (n=69) studied tools automated by traditional ML algorithms. No studies compared traditional ML tools to LLM-based tools. In addition, 61.7% (n=137) and 26.1% (n=58) studied AI-assisted automation of title and abstract screening and data extraction, respectively, the 2 most intensive stages and, therefore, amenable to automation. Technical performance outcomes were the most frequently reported, with only 4.1% (n=9) of studies reporting time- or workload-specific outcomes. Few studies pragmatically evaluated AI tools in real-world evidence synthesis settings. Conclusions: This review comprehensively captures the broad, evolving suite of AI automation tools available to support evidence synthesis, leveraged by increasingly complex AI approaches that range from traditional ML to LLMs. The notable shift toward studying general-purpose generative AI tools reflects how these technologies are actively transforming evidence synthesis practice. The lack of studies in our review comparing different AI approaches for specific automation stages or evaluating their effectiveness pragmatically represents a significant research gap. Optimal tool selection will likely depend on the review topic and methodology and researcher priorities. While they offer potential for reducing workload, ongoing evaluation to mitigate AI bias and to ensure the integrity of reviews is essential for safeguarding evidence-based decision-making.

  • J Med Internet Res 2026;28(1):e81597

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  Web-based and Mobile Health...

  Mar 30, 2026

  Digital Intervention (MiVacunaLA 2.0) to Promote COVID-19 Vaccine Acceptance Among Hispanic Children: Community-Based Randomized Controlled Trial

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  Authors List:

  • Luisa R Blanco  
  • Alexandra Klomhaus  
  • Rebecca Dudovitz  
  • Denise Marquez  
  • Ray Lopez-Chang  
  • Evelyn Aleman  
  • Bas Weerman  
  • Michael Moldoff  
  • Keith Norris  
  • Yelba Castellon-Lopez  

  Abstract:

  Background: Early in the children’s COVID-19 rollout in the United States, racial and ethnic vaccination rate disparities were evident. Based on COVID-19 communication literature and qualitative interviews with Hispanic parents, we developed a mobile phone–delivered digital intervention to address factors associated with low vaccine confidence. Objective: We conducted a community-based randomized controlled trial of a digital intervention called MiVacunaLA/MyShotLA to increase COVID-19 vaccine uptake among Hispanic children. The fully automated digital intervention was designed in collaboration with community organizations and linguistically and culturally tailored to meet the informational needs of Hispanic caregivers. The intervention focused on families with unvaccinated children 5 to 11 years old but was offered to families with any unvaccinated children 17 years or younger. Methods: Participants were recruited with community organization partners and trained parent ambassadors via an open online screener. The 4-week intervention consisted of 3 SMS text messages with culturally and linguistically tailored educational information weekly. Intervention materials were delivered digitally through a closed online platform. Study team members were blinded. We used a difference-in-difference model with an intention-to-treat approach. The primary outcome was self-reported COVID-19 vaccine uptake among household children collected via online questionnaires. Secondary outcomes included COVID-19 vaccine knowledge, vaccine trust, and measures of participant engagement. We conducted a sensitivity analysis using the treatment-on-the-treated approach. Results: In total, 254 participants completed the baseline survey (128 control and 126 intervention). The average participant age was 34 (SD 6.3) years with an average of 1.7 (SD 0.8) minors in the household, and among households, 62.2% (n=158) reported having children aged 5 to 11 years old. Most participants (n=207, 81.5%) reported English as their primary language. We found a statistically significant difference of 13.3% (95% CI 0.3%-26.4%; =.04) points in self-reported vaccine uptake between intervention and control groups among caregivers of Hispanic children aged 5 to 11 years old. We also found a statistically significant point difference of 14.3% (95% CI 0%‐23.7%; =.003) between intervention and control groups in trust of governmental approval processes for the children’s COVID-19 vaccine. Most participants reported that the weekly digital videos and educational information were “very” (892/1031, 86.5%) or “extremely” (888/1019, 87.1%) useful. Conclusions: MiVacunaLA demonstrates that a culturally tailored, community-based, mobile phone–delivered vaccine educational intervention can increase COVID-19 vaccine uptake among Hispanic children and improve caregivers’ trust in governmental vaccine processes. MiVacunaLA is innovative in its integration of community-informed design with a fully automated, mobile phone–centric format and builds on prior literature by prospectively evaluating a culturally tailored, SMS text messaging–linked web curriculum in a community setting. Findings provide evidence that scalable, low-cost, digital strategies can measurably improve trust and uptake in a population facing persistent vaccination gaps. Real-world implications include the portability and adaptability of this approach across diverse communities and settings to support timely, community-engaged vaccination efforts for broader applicability and scalability in public health. Trial Registration: ClinicalTrials.gov NCT05234372; https://clinicaltrials.gov/study/NCT05234372

  • J Med Internet Res 2026;28(1):e78103

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  Digital Health Reviews

  Mar 30, 2026

  Effectiveness of Mobile Health for Improving Medication Adherence in Patients With Cancer: Systematic Review and Meta-Analysis of Randomized Controlled Trials

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  Authors List:

  • Wei Chao Liu  
  • Li Ping Wang  
  • Qian Yu Zhang  
  • Jing Han Yang  

  Abstract:

  Background: Medication adherence among patients with cancer is generally low. Mobile health (mHealth) has gradually been applied to improve this situation, but systematic evidence of its effectiveness remains lacking. Objective: We aimed to evaluate the effect of mHealth on improving medication adherence among patients with cancer. Methods: This systematic review included randomized controlled trials (RCTs) evaluating the impact of mHealth on medication adherence among patients with cancer. Systematic searches were conducted in PubMed, Web of Science, CINAHL, Cochrane Library, Embase, Sinomed, CNKI, Cqvip, and ClinicalTrials.gov from inception to December 31, 2025. Two researchers independently performed literature screening, data extraction, and risk of bias assessment. Effects were pooled using a random-effects model (Hartung-Knapp-Sidik-Jonkman), and standardized mean differences (SMDs) and odds ratios (ORs) with 95% CIs have been reported. Evidence quality was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework. Results: A total of 17 RCTs (1309 participants) from 8 countries published between 2016 and 2025 were included. mHealth interventions included mobile apps, websites, and text messaging services. The meta-analysis revealed that compared with controls, mHealth interventions significantly improved medication adherence rates (OR 3.47, 95% CI 1.92-6.26; =.002), medication adherence scores (SMD 1.01, 95% CI 0.51-1.52; =.001), self-efficacy (SMD 0.90, 95% CI 0.29-1.51; =.01), and service satisfaction while reducing symptom burden (SMD −0.38, 95% CI −0.61 to −0.14; =.008). However, mHealth had no significant effect on health literacy (SMD 0.51, 95% CI –1.50 to 2.52; =.29). Subgroup analysis revealed that interventions lasting <3 months outperformed those lasting ≥3 months in improving adherence scores (SMD 1.37, 95% CI 0.78-1.96 vs SMD 0.49, 95% CI −0.39 to 1.37; ²=5.98; =.01). Regarding intervention format, text messaging services demonstrated superior efficacy compared with mobile apps and websites (SMD 1.53, 95% CI −5.49 to 8.55 vs SMD 1.01, 95% CI 0.42-1.61 and SMD 0.11, 95% CI −0.34 to 0.56, respectively; ²=10.28; =.006). Across cancer types, mHealth most significantly improved adherence scores in patients with breast cancer (SMD 1.29, 95% CI −5.25 to 7.83), outperforming the findings in patients with leukemia and other cancer types (SMD 0.28, 95% CI −0.87 to 1.42 and SMD 1.09, 95% CI 0.10-2.08, respectively; ²=8.86; =.01). Conclusions: Our findings confirm that mHealth plays a positive role in improving medication adherence, enhancing patient self-efficacy, increasing patient satisfaction with services, and alleviating symptom burden. However, these findings should be interpreted with caution owing to substantial heterogeneity, a moderate risk of bias, and a low certainty of evidence. Future research should enhance methodological quality by conducting multicenter, large-sample, high-quality RCTs and should explore the long-term effects and cost-effectiveness of mHealth across diverse health care settings and patient populations to clarify its role and value within comprehensive cancer care management systems. Trial Registration: PROSPERO CRD420251162181; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251162181

  • J Med Internet Res 2026;28(1):e85949

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  Personal Health Records,...

  Mar 30, 2026

  Use, Utility, and User Experience of Cloud-Based Medical Imaging in Pulmonary Nodule Care in China: Mixed Methods Study

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  Authors List:

  • Ziguo Chen  
  • Junhan Wu  
  • Yunying Chen  
  • Weitao Zhuang  
  • Jiaxuan Huang  
  • Weifeng Zhong  
  • Zijie Li  
  • Shuhuan Xie  
  • Chaofan Liu  
  • Guojie Lu  
  • Guibin Qiao  

  Abstract:

  Background: The detection of pulmonary nodules (PNs) has increased with the use of low-dose computed tomography screening. Effective management requires timely longitudinal surveillance and reliable comparison with prior examinations, yet access to previous imaging across institutions is often fragmented, leading to delays and potentially unnecessary repeat scans and costs. Cloud-based medical imaging (CMI) solutions offer a potential means of improving access and facilitating cross-institutional data exchange. However, the adoption and utility of CMI in PN care, especially in China, remain underexplored. Objective: This study aims to evaluate the possession, use, and impact of CMI on health care utilization, patient knowledge, and financial burden, as well as to identify usability and interoperability barriers through qualitative investigation. Methods: A mixed methods cross-sectional study was conducted from October 2022 to May 2024. The study involved 701 patients with PNs who completed structured surveys, and 20 participants (10 patients and 10 physicians) were interviewed. CMI use was defined as self-reported ability to view radiological images on a mobile device. We compared CMI users and nonusers and estimated adjusted odds ratios using multivariable logistic regression, then applied 1:1 propensity score matching to examine associations between CMI use and health care utilization, costs, and patient perceptions, and qualitative interviews were analyzed for usability themes. Results: The study found that 611 (87.2%) out of 701 patients had obtained CMI, with 404 (57.6%) out of 701 patients actively using it. In multivariable analysis, older age was independently associated with lower CMI use (odds ratios 0.985, 95% CI 0.972‐0.999). After 1:1 propensity score matching, CMI users accessed more internet hospitals, consulted more physicians, and reported lower health care costs compared to nonusers. Users also demonstrated higher disease knowledge. Qualitative data identified key barriers, including poor system usability, limited retention time for images, and weak interoperability. CMI was perceived as beneficial for patient convenience and clinical efficiency, though concerns over image quality and system fragmentation were prevalent. Conclusions: While CMI is widely available, its usage remains suboptimal. Increased use is associated with enhanced health care engagement and reduced costs, suggesting that improving system usability and ensuring consistent access to imaging may help realize potential benefits of CMI. Future improvements should focus on ensuring long-term access, better retention protocols, and overcoming interoperability issues.

  • J Med Internet Res 2026;28(1):e86745

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