DIGITAL HEALTH最新文献

{"title":"Assessing the accuracy and readability of large language models in answering Turkish-language patient-generated queries on radiological contrast agents.","authors":"Abdullah Enes Ataş","doi":"10.1177/20552076261491151","DOIUrl":"10.1177/20552076261491151","url":null,"abstract":"<p><strong>Objective: </strong>To assess the clinical accuracy, structural readability, and Ensuring Quality Information for Patients (EQIP-36) quality of Large Language Model (LLM) responses to real patient queries regarding radiological contrast agents and to define their safe boundaries in practice.</p><p><strong>Methods: </strong>The 30 most popular Turkish patient questions reflecting fears and risk perceptions about contrast agents were identified via Google Trends and AlsoAsked.com. These queries were presented to ChatGPT-5.4, Gemini 3, and Claude 4.6 Sonnet using a zero-shot learning approach. Responses were evaluated by experts for clinical accuracy against the 2024/2025 American College of Radiology (ACR) Manual on Contrast Media. Information quality and readability were measured using the expanded EQIP-36 scale and the Ateşman and Bezirci-Yılmaz indices.</p><p><strong>Results: </strong>Claude 4.6 Sonnet demonstrated the highest clinical accuracy and high compliance with guidelines (>90%). Gemini 3 exhibited a more conservative stance, occasionally resulting in over-triage by exaggerating risks. While all models scored exceptionally well in the Content and Structure dimensions of the EQIP-36, generating readable texts, they universally failed in the Identification dimension by omitting author names, update dates, or bibliographic sources. Readability indices indicated that comprehending the texts required an average of 10.5 to 11.6 years of formal education.</p><p><strong>Conclusion: </strong>LLMs generate highly readable and subjectively reassuring Turkish-language texts in response to patient queries regarding radiological contrast agents. However, due to domain-specific structural gaps regarding quantitative thresholds and lack of citations, they should be positioned as hybrid communication tools subject to mandatory review by specialist physicians rather than standalone medical advisors.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261491151"},"PeriodicalIF":3.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13583281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public attention to cardiac arrest in China: A Baidu Index analysis of online search trends and hotspot events.","authors":"Kang Zheng, Jingjing Zhao, Shuai Xue, Yang Liang, Hua Zhang, Chen Liu, Qingbian Ma, Lanfang Du","doi":"10.1177/20552076261490734","DOIUrl":"10.1177/20552076261490734","url":null,"abstract":"<p><strong>Objective: </strong>To analyze trends in public attention to cardiac arrest-related information in mainland China using the Baidu Index.</p><p><strong>Methods: </strong>This study retrieved Baidu Index data for the search keywords \"cardiac arrest,\" \"myocardial infarction,\" \"cerebral infarction,\" \"cardiopulmonary resuscitation (CPR),\" and \"automated external defibrillator (AED)\" from January 1, 2019, to December 31, 2025. Temporal trends and differences in Baidu Index values among these keywords were analyzed. For the three cardiac arrest-related keywords (cardiac arrest, CPR, AED), the top 10 high-search events were identified. Event duration and corresponding real-world triggers (hotspot events) were recorded and analyzed.</p><p><strong>Results: </strong>Median (IQR) Baidu Index values were significantly higher for myocardial infarction [4384 (1382) vs 1612 (451), Z=-43.260, P<0.001] and cerebral infarction [6923 (2733) vs 1612 (451), Z=-43.529, P<0.001] than for cardiac arrest. Median (IQR) Baidu Index value for CPR was higher than for cardiac arrest [2790 (1279) vs 1612 (451), Z=-42.101, P<0.001], whereas the value for AED was lower [1368 (529) vs 1612 (451), Z=-12.878, P<0.001]. All keywords showed a declining trend from 2023 onward. Among 23 identified high-search events, 21.74% involved public figures and 69.56% were emergency cases. Public figure events had a longer duration than emergency cases [11.0 (15.0) days vs 3.5 (4.0) days, U = 15.000, Z=-2.083, P = 0.040]. Three events produced simultaneous extreme search peaks across all three cardiac arrest-related keywords, all involved public figures.</p><p><strong>Conclusion: </strong>Public attention to cardiac arrest remained lower than to myocardial infarction and cerebral infarction, while attention to AED was increasing annually. The use of search engines for health information-seeking was declining. Hotspot events, particularly those involving public figures, can significantly elevate public interest in cardiac arrest and related first-aid knowledge.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261490734"},"PeriodicalIF":3.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13583277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital health technologies in frontotemporal dementia: A scoping review.","authors":"Liset de Boer, Olaiya A Aro, Harro Seelaar, Lize C Jiskoot, Jackie M Poos","doi":"10.1177/20552076261483445","DOIUrl":"10.1177/20552076261483445","url":null,"abstract":"<p><strong>Objective: </strong>Digital health technologies are increasingly used to assess cognition and behavior in neurodegenerative diseases. While such tools have shown promise in Alzheimer's disease, including the preclinical stages, their use in frontotemporal dementia (FTD) remains underexplored. This scoping review provides an overview of the current literature on digital health technologies in FTD.</p><p><strong>Methods: </strong>We conducted a literature search according to the PRISMA-ScR guidelines and included 62 studies investigating digital health technologies in individuals with FTD.</p><p><strong>Results: </strong>The identified technologies included eye tracking paradigms, smartphone- and tablet-based cognitive assessments, and passive monitoring approaches such as wearable sensors and mobile applications. Most studies reported that these technologies could distinguish individuals with FTD from healthy controls or other dementia syndromes. However, many studies were experimental or feasibility-focused, and systematic evaluation of psychometric properties remains limited.</p><p><strong>Conclusion: </strong>In order to move the field forward, we argue that an in-depth investigation of the relationships between digital health technologies, classical pen-and-paper assessments of cognitive and behavioral functioning, and other biomarkers is necessary to establish clinical validity. Other important aspects to address in future research include the use of diverse cohorts and studying longitudinal effects. In our view, these next steps are crucial to advance digital health technologies toward implementation in clinical practice and clinical trials for FTD.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261483445"},"PeriodicalIF":3.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13583275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Confucian values and continuance intention in leaderboard-based health gamification.","authors":"Zhuo Li, Teng Wang, Qingcui Song","doi":"10.1177/20552076261490673","DOIUrl":"10.1177/20552076261490673","url":null,"abstract":"<p><strong>Objective: </strong>Although gamification has been shown to enhance user motivation and engagement, its effects may vary across cultural contexts. This study examines how Confucian values shape users' continuance intention in leaderboard-based health gamification through positive and negative psychological pathways.</p><p><strong>Methods: </strong>We conducted an online survey of 300 WeChat Run users and analyzed the data using partial least squares structural equation modeling (PLS-SEM). Drawing on the Confucian values perspective and the dedication-constraint model, we conceptualized self-cultivation as an enabling Confucian value and interpersonal harmony as a constraining Confucian value.</p><p><strong>Results: </strong>Competition affordance positively affected continuance intention (β = 0.135, p < 0.01), whereas emotional exhaustion exerted a stronger negative effect on continuance intention (β = -0.436, p < 0.001). Self-cultivation positively influenced competition affordance (β = 0.303, p < 0.001), while interpersonal harmony positively affected emotional exhaustion (β = 0.132, p < 0.05). Furthermore, competition affordance mediated the positive relationship between self-cultivation and continuance intention (β = 0.040, p < 0.01), whereas emotional exhaustion mediated the negative relationship between interpersonal harmony and continuance intention (β = -0.055, p < 0.05).</p><p><strong>Conclusions: </strong>Leaderboard-based health gamification activates both an enabling pathway, in which self-cultivation strengthens continuance intention through competition affordance, and a constraining pathway, in which interpersonal harmony increases emotional exhaustion and reduces continuance intention. Moving beyond the broad individualism-collectivism framework, this study shows how specific Confucian values shape heterogeneous user responses and suggests that designers should encourage constructive competition while reducing harmony-related psychological strain in East Asia.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261490673"},"PeriodicalIF":3.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13583276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing and validating an interpretable machine learning model for frailty risk prediction in patients with chronic diseases.","authors":"Yangyan Fan, Lihong Hou, Jie Zheng, Qifan Yang, Bin Liu, Dai Zhang, Zhiping Yang, Daiming Fan","doi":"10.1177/20552076261491104","DOIUrl":"10.1177/20552076261491104","url":null,"abstract":"<p><strong>Objectives: </strong>Chronic diseases and frailty are both prevalent among middle-aged and older adults. Individuals with chronic disorders are at a higher risk of developing frailty. This study aims to develop and validate a machine learning model for predicting frailty risk among middle-aged and older adults with chronic diseases, and to assess their generalizability across Chinese and American populations.</p><p><strong>Method: </strong>Data from the China Health and Retirement Longitudinal Study (CHARLS) were utilized and split into a training set and an internal validation set at a ratio of 7:3. A subset from the National Health and Nutrition Examination Survey (NHANES) served as the external validation set. Seven machine learning algorithms were employed to predict frailty risk, with feature selection performed using LASSO regression. The SHapley Additive explanation (SHAP) analysis was applied to enhance model interpretability.</p><p><strong>Results: </strong>Among the 8,535 CHARLS participants, frailty was identified in 23.7% of patients. The Gradient Boosting Decision Tree (GBDT) model demonstrated the best decision-making performance compared to other models, with an AUC of 0.815 (95% CI: 0.797, 0.833) in the internal validation set, accuracy of 0.735 (95% CI: 0.681, 0.752), sensitivity of 0.751 (95% CI: 0.722, 0.844), and specificity of 0.730 (95% CI: 0.633, 0.6752). In the external validation set, the GBDT model achieved an AUC of 0.748 (95% CI: 0.721, 0.775). SHAP revealed the five most important predictors influencing frailty risk: sleep duration, education level, cognitive impairment, age, and visual impairment.</p><p><strong>Conclusion: </strong>The GBDT model developed effectively predicts the risk of frailty in middle-aged and older adult patients and may serve as a practical tool for the early identification of high-risk populations. In the future, the generalizability of this model requires validation across more diverse cultural contexts to enhance its broader applicability.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261491104"},"PeriodicalIF":3.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13583297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating telemedicine into dental workflows for medically complex patients: An exploratory pilot feasibility and care coordination study.","authors":"Shawn Adibi, Mahshid Mohajeri, Julian Nathaniel Holland, Elizabeth Scott Hamilton, Lorena Millan, Bonita A Wynkoop, Chelsea J Guy-Frank, Mary C Farach-Carson","doi":"10.1177/20552076261490257","DOIUrl":"10.1177/20552076261490257","url":null,"abstract":"<p><strong>Objective: </strong>This pilot study evaluated the operational feasibility, workflow integration, and care coordination of an on-site telemedicine clearance pathway (the Medically Integrated Oral Health Program) for medically complex dental patients presenting with hypertension and/or diabetes mellitus.</p><p><strong>Methods: </strong>A non-randomized, two-arm interventional (quasi-experimental) pilot study was conducted with a pragmatic sample of 20 adult dental patients (11 Telemedicine group, 9 Conventional referral group). Allocation was participant selected. Evaluated feasibility metrics included protocol adherence, missed appointments, and data completeness across a 12-month period. Descriptive clinical and laboratory parameters, including Oral Health Risk Assessment Value Index (OHRAVI) scores, body mass index (BMI), blood pressure (BP), and HbA1c, were tracked longitudinally at approximately three-month intervals.</p><p><strong>Results: </strong>The in-office telemedicine protocol was successfully executed at baseline. However, substantial patient attrition was observed over time, with retention dropping to 54.5% in the Telemedicine group and 44.4% in the Conventional group by the first follow-up. Descriptive observations indicated stable median OHRAVI scores and relative stability in median BMI within the telemedicine cohort, whereas the conventional cohort exhibited slight increases in weight metrics. Blood pressure data demonstrated high variability across both groups. Follow-up HbA1c data were heavily limited by missing data and sparse reporting, which precluded statistical or causal interpretation. Total appointment non-adherence was comparable between groups.</p><p><strong>Conclusion: </strong>The on-site telemedicine clearance pathway was operationally feasible at initial presentation. However, severe attrition and incomplete longitudinal data prevented reliable evaluation of comparative clinical effectiveness or sustained care coordination. These findings primarily identify implementation and data-collection challenges that should inform a future adequately powered study.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261490257"},"PeriodicalIF":3.9,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13571017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148964031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to design digital health interventions with artificial intelligence: A scoping review.","authors":"Shuimei Liu, L Raymond Guo","doi":"10.1177/20552076261490346","DOIUrl":"10.1177/20552076261490346","url":null,"abstract":"<p><strong>Objectives: </strong>Digital health interventions often struggle to scale due to rigid, resource-intensive design methods. A shift is underway toward evaluating AI-as-Agent, a dynamic partner in the design process, rather than a static clinical product. This systematic scoping review maps the landscape of generative digital health design and its impact on healthcare transformation.</p><p><strong>Methods: </strong>Following PRISMA-ScR guidelines, we searched PubMed, IEEE Xplore, and ACM Digital Library (2020-2025) for studies utilizing AI tools to actively support the design, development, or evaluation of digital health artifacts with human-in-the-loop validation.</p><p><strong>Results: </strong>Twenty-one studies met inclusion criteria. We identified distinct design roles for AI: Creator, Facilitator, Co-Designer, Tester, and Evaluator. Results indicate AI shows potential to mitigate critical design risks: synthetic data enables pre-clinical risk abatement; generative co-design fosters epistemic agency and patient inclusivity; and automated workflow analysis reduces clinician cognitive load. Based on these findings, we propose the AGENT Framework (Algorithmic Simulation, Generative Co-Design, Embedded Guardrails, Next-Best-Action, Tracked Evolution).</p><p><strong>Conclusions: </strong>AI is evolving into a collaborative design partner capable of enhancing the equity, scalability, and safety of digital health interventions. The AGENT framework offers a structured roadmap for stakeholders to integrate AI into the innovation lifecycle.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261490346"},"PeriodicalIF":3.9,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13571019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148964057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Willingness to participate in telehealth visits among US adults: Findings from the 2024 health information national trends survey.","authors":"Navreet K Singh, Nancy Buderer, Aisha T Langford","doi":"10.1177/20552076261491086","DOIUrl":"10.1177/20552076261491086","url":null,"abstract":"<p><strong>Objective: </strong>Telehealth visits offer flexibility and convenience, and remain an important care delivery option. Understanding patient willingness to engage in telehealth is critical for equitable expansion of these services.</p><p><strong>Methods: </strong>Secondary analysis of the 2024 Health Information National Trends Survey (HINTS) 7 completed by US adults. Participants were asked, \"How willing are you to do a telehealth visit in the future if one is offered to you?\" We compared \"very willing\" vs. all other levels of willingness. Univariable and multivariable logistic regression models assessed the association between telehealth visit willingness and various patient characteristics. After excluding participants with missing data for any study variable, 5,759 participants were included in analyses. Odds ratios and 95% confidence intervals are reported.</p><p><strong>Results: </strong>In this sample, 39% were very willing to do a telehealth visit. Higher odds of being very willing to do a telehealth visit were found in those who were: 35-49 years old (vs. 18-34), very confident in filling out medical forms (vs. not), had anxiety or depression (vs. not), or had a prior telehealth visit. Participants who were 75 years or older (vs. 18-34), who agreed that learning new technology is frustrating (vs. disagreed), or who had diabetes (vs. not) had lower odds of being very willing to do a telehealth visit.</p><p><strong>Conclusion: </strong>Although most participants were not \"very willing\" to do a telehealth visit, certain subgroups, including those with anxiety or depression, reported greater willingness. Efforts to improve platform usability and provide digital support may help increase participation among those less willing.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261491086"},"PeriodicalIF":3.9,"publicationDate":"2026-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13571015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148964038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient use of AI chatbots: The overlooked frontline of health care.","authors":"Matthew Hutnyan","doi":"10.1177/20552076261490748","DOIUrl":"10.1177/20552076261490748","url":null,"abstract":"<p><p>Development of tailored AI health care applications has become a major research focus in recent years, but relatively little research has addressed current patient use of general-purpose AI chatbots for health-related tasks. Today, many people use AI chatbots to seek information and advice, make diagnoses, and inform or even receive intervention. The appeal is clear, but the benefits and risks are less so. While we build AI tools for the future of medicine, we must also gather empirical evidence regarding use and impact of existing tools.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261490748"},"PeriodicalIF":3.9,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13570025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilization of district health information system 2 among health professionals in resource-limited settings: A systematic review.","authors":"Sefefe Birhanu Tizie, Ayenew Sisay Gebeyew, Aynadis Worku Shimie, Muluken Belachew Mengistie, Abraham Keffale Mengistu, Tilahun Bitew, Menberu Gete, Andualem Fentahun Senishaw, Ashagrie Anteneh, Bayou Tilahun Assaye, Gizaw Hailiye Teferi","doi":"10.1177/20552076261486950","DOIUrl":"10.1177/20552076261486950","url":null,"abstract":"<p><strong>Background: </strong>District Health Information system 2 (DHIS2) has become a core digital platform for routine health information management. Although health systems use DHIS2 to strengthen reporting and support evidence-informed decision-making, the extent of its utilization varies across settings. This systematic review synthesized evidence on DHIS2 utilization and its determinants among health professionals in resource-limited settings.</p><p><strong>Methods: </strong>We conducted a systematic review in accordance with the PRISMA 2020 guidelines. We searched PubMed, Scopus, Web of Science, African Journals Online (AJOL), Google Scholar, institutional repositories, and government databases. Two reviewers independently screened articles and extracted data, and any differences were resolved through discussion with a third reviewer. Methodological quality was assessed using the Newcastle-Ottawa Scale, and a narrative synthesis was used to summarize study characteristics, patterns of system use, and determinants of DHIS2 utilization.</p><p><strong>Results: </strong>Sixteen studies involving 5,195 Health professionals were included. DHIS2 utilization varied widely, ranging from routine data entry and reporting to advanced applications such as planning, monitoring, and decision-making. Higher utilization was associated with supportive leadership, regular supervision, clearly defined roles, functional monitoring teams, digital skills, confidence, and perceived usefulness. Common barriers included inadequate infrastructure, unreliable electricity supply, and limited internet connectivity.</p><p><strong>Conclusion: </strong>DHIS2 utilization is shaped by interactions among health professionals' competencies, organizational support, and technological infrastructure. Addressing infrastructure gaps and strengthening capacity development may improve effective use of routine health information systems. Future research should use standardized measures to assess DHIS2 utilization and its impact on health system performance. PROSPERO registration: CRD4420251273313.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":"12 ","pages":"20552076261486950"},"PeriodicalIF":3.9,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148957354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}