International Journal of Medical Informatics最新文献

{"title":"A personalized and complex mHealth intervention for the universal prevention of Perinatal mental Disorders in routine maternal Care: Design and development of e-Perinatal app","authors":"Company-Córdoba Rosalba ,&nbsp;Caffieri Alessia ,&nbsp;Barquero-Jimenez Carlos ,&nbsp;Cruz-Cabrera Roberto ,&nbsp;De-Juan-Iglesias Paula ,&nbsp;Gil-Cosano José J. ,&nbsp;Goossens Lennert ,&nbsp;Nieto-Casado Francisco J. ,&nbsp;Ureña-Lorenzo Amalia ,&nbsp;Gómez-Gómez Irene ,&nbsp;Motrico Emma","doi":"10.1016/j.ijmedinf.2026.106290","DOIUrl":"10.1016/j.ijmedinf.2026.106290","url":null,"abstract":"<div><h3>Background</h3><div>Perinatal Mental Disorders (PMDs) are common during pregnancy and the first postpartum year, with negative consequences for women, their partners, and infants, as well as broader societal costs. While numerous interventions have been developed to prevent PMDs, there remains a need for a universal, personalized, and cost-effective solution integrated into routine maternal care. The <em>e-Perinatal</em> study aimed to address this gap. This paper describes the design of the <em>e-Perinatal</em> intervention, delivered via a dedicated mobile health app.</div></div><div><h3>Methods</h3><div>Guided by the Medical Research Council framework, the <em>e-Perinatal</em> app integrates Self-Determination Theory, Normalization Process Theory, and Patient and Public Involvement perspectives. Existing evidence was reviewed, and stakeholders participated in the co-development of digital micro-interventions (DMs). A clinical rule-based algorithm was implemented to generate personalized recommendations across four pathways (1) weekly content delivery, (2) user preferences, (3) individual risk profile, and (4) PMD monitoring.</div></div><div><h3>Results</h3><div>The <em>e-Perinatal</em> app includes: 1) DMs focused on psychological, physical activity, and healthy lifestyle domains; 2) a personalized recommendation engine; 3) a social support section; 4) mental health monitoring; 5) an ‘SOS’ button for assistance; and 6) an appointment reminder tool. In total, 332 evidence-based DMs were developed for women and their partners and delivered in text, audio, and video formats. A clinical rule-based algorithm tailors recommendations according to user characteristics and perinatal stage, employing adaptive content filtering to optimize personalization.</div></div><div><h3>Conclusion</h3><div>the <em>e-Perinatal</em> app is a personalized mHealth intervention to<!--> <!-->prevent PMDs within routine maternal care. The intervention combines evidence-based strategies, personalized recommendations, and adaptive digital content to prevent PMDs. Future research will assess effectiveness, implementation, and real-world impact of <em>e-Perinatal</em> intervention for PMD prevention.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106290"},"PeriodicalIF":4.1,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating illness in a virtual world: the role of immersive technology across chronic care continuum – A scoping review","authors":"Ramakrishna Dantu ,&nbsp;Mohammad Murad ,&nbsp;Kirti Sharma ,&nbsp;Kirti Dutta ,&nbsp;Laura Cravens-Ray","doi":"10.1016/j.ijmedinf.2026.106311","DOIUrl":"10.1016/j.ijmedinf.2026.106311","url":null,"abstract":"<div><div>Immersive technologies offer promising capabilities for chronic disease management, but their implementation and specific applications across the chronic care continuum remain limited. This study examines how immersive technologies are being utilized across various chronic disease contexts through a scoping review. Using a comprehensive mapping of literature published between 1995 and 2024, we identified 2,012 relevant articles from major databases using WHO and CDC-defined chronic disease keywords and finally focused on 127 studies for detailed manual review.</div><div>Our approach combined text analytics (BERTopic modelling) with manual synthesis. This methodology revealed eight key themes where immersive technologies are being applied: medical procedures, training and education for healthcare professionals, substance use disorder therapy, cognitive rehabilitation, physical rehabilitation, exergaming and biofeedback, navigation and spatial therapy, and pain, stress, and anxiety management. These themes reflect the growing use of immersive technologies to support diverse activities in chronic care settings.</div><div>The findings highlight the breadth of immersive technology applications across multiple points in chronic care. Our study introduces a thematic framework for understanding immersive applications in healthcare and identifies research directions and opportunities for future investigation. Future research should explore long-term integration into clinical workflows, as well as inclusivity and adoption across diverse populations.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106311"},"PeriodicalIF":4.1,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient engagement and performance expectancy towards epilepsy digital health interventions: systematic literature review and meta-analysis","authors":"Tolesa Fanta Jilcha ,&nbsp;Peter Richard Christopher Leeson ,&nbsp;Khin Than Win","doi":"10.1016/j.ijmedinf.2026.106306","DOIUrl":"10.1016/j.ijmedinf.2026.106306","url":null,"abstract":"<div><h3>Background</h3><div>Digital Health is currently showing promising results in reducing patient and caregiver suffering that arise from misconceptions.</div></div><div><h3>Objective</h3><div>To synthesize existing evidence on Perceived Usefulness, interest in use and willingness to use towards Epilepsy Digital Health Interventions.</div></div><div><h3>Method</h3><div>Databases were searched for studies reporting on the outcomes of interest by using a comprehensive search strategy. Studies published in English from January 2015 to September 2025 were included. The Newcastle-Ottawa Quality Assessment Scale was employed to evaluate the quality of included studies. Stata version 19 was used to compute a pooled proportion using a random-effects model. Heterogeneity was assessed using the Cochrane chi-square and the index of heterogeneity test. Sensitivity tests and subgroup analyses were performed. Publication bias was examined by funnel plots and Egger’s test.</div></div><div><h3>Result</h3><div>Overall, 6041 studies were found from databases. After a step-by-step screening, 23 studies were included in this review. The total number of participants was 6703 with a sample size ranges from 12 to 1168. The pooled proportions of Perceived Usefulness, interest to use, and willingness to use Digital Health were 0.66 (0.58, 0.75), 0.69 (0.50, 0.88), and 0.75 (0.66, 0.83), respectively. In this review, Sensitivity tests indicated that none of the included studies exerted extreme influence on the pooled prevalence; and Funnel plots and Egger’s test (p ≤ 0.772) showed no evidence of publication bias.</div></div><div><h3>Conclusion</h3><div>In this review<strong>,</strong> 66% of respondents perceive Digital Health as useful; 69% were interested in using Digital Health, and 75% were willing to engage with Digital Health. Most of the studies were from high-income countries, with no studies found from developing countries. This review emphasizes the importance of focusing on the user’s perceptions, their interest and willingness to use Digital Health Interventions. It also stresses the need for further studies in low-income countries.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106306"},"PeriodicalIF":4.1,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging performance and uncertainty: Cautionary notes on machine learning and large language models in TBI prognostication","authors":"Hasan Nawaz Tahir ,&nbsp;Anfal Khan ,&nbsp;Muhammad Yousaf ,&nbsp;Shahnila Javed ,&nbsp;Mursala Tahir ,&nbsp;Yousaf Ali","doi":"10.1016/j.ijmedinf.2026.106315","DOIUrl":"10.1016/j.ijmedinf.2026.106315","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106315"},"PeriodicalIF":4.1,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart insurance analytics: A novel ensemble feature selection approach to unlock health insurance coverage predictions in Sierra Leone","authors":"David B. Olawade ,&nbsp;Augustus Osborne ,&nbsp;Afeez A. Soladoye ,&nbsp;Olaitan E. Oluwadare ,&nbsp;Emmanuel O. Awogbindin ,&nbsp;Ojima Z. Wada","doi":"10.1016/j.ijmedinf.2026.106313","DOIUrl":"10.1016/j.ijmedinf.2026.106313","url":null,"abstract":"<div><h3>Background</h3><div>Predicting health insurance uptake remains a critical challenge for policymakers and insurance providers seeking to optimise coverage strategies and resource allocation. In Sierra Leone, health insurance uptake remains extremely low, and understanding determinants is vital for universal health coverage goals.</div></div><div><h3>Objective</h3><div>To develop and evaluate an innovative ensemble feature selection methodology for health insurance uptake prediction, establishing new performance benchmarks through systematic comparison of multiple machine learning algorithms using comprehensive validation strategies.</div></div><div><h3>Methods</h3><div>This study employed supervised machine learning to predict health insurance uptake among 15,574 women using data from the 2019 Sierra Leone Demographic and Health Survey (SLDHS). We implemented an ensemble feature selection approach that requires consensus across Adaptive Ant Colony Optimisation, Recursive Feature Elimination, and Backwards Elimination techniques. Seven algorithms were systematically compared: Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Random Forest, Gradient Boosting, XGBoost, and LightGBM. SMOTE addressed class imbalance, whilst validation employed nested 5-fold cross-validation, 10-fold cross-validation, and hold-out testing to prevent information leakage.</div></div><div><h3>Results</h3><div>Random Forest achieved exceptional performance with 0.9973 accuracy, 0.9973 precision, 0.9973 recall, 0.9973 F1-score, and perfect 1.0000 ROC AUC on hold-out testing. XGBoost delivered comparable results with 0.9914 across all metrics and 0.9998 ROC AUC. Backward Feature Elimination consistently yielded superior results across ensemble methods. However, the near-perfect performance warrants cautious interpretation and requires external validation to confirm generalizability.</div></div><div><h3>Conclusions</h3><div>This research establishes new performance benchmarks for health insurance prediction, significantly exceeding existing literature, which has direct implications for health insurance policy and practice in Sierra Leone. The innovative ensemble feature selection methodology provides a robust framework for enhancing prediction accuracy across healthcare applications, offering immediate practical value for stakeholders. Future work should prioritize external validation, explainability analysis, and temporal stability assessment to ensure practical deployment readiness.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106313"},"PeriodicalIF":4.1,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of three-year mortality in elderly inpatients with coronary artery disease combined with heart failure","authors":"Shihui Fu ,&nbsp;Zilei Zhao ,&nbsp;Xuhui Liu ,&nbsp;Jianfeng Guo ,&nbsp;Kai Wang ,&nbsp;Yali Zhao ,&nbsp;Shan Nan ,&nbsp;Qianshuo Liu ,&nbsp;Yan Nie ,&nbsp;Jinwen Tian","doi":"10.1016/j.ijmedinf.2026.106307","DOIUrl":"10.1016/j.ijmedinf.2026.106307","url":null,"abstract":"<div><h3>Objective</h3><div>Accurate prediction of survival outcome is essential for early intervention and treatment optimization. This study aimed to develop a model utilizing machine learning techniques for predicting three-year mortality in elderly inpatients with coronary artery disease (CAD) combined with heart failure (HF).</div></div><div><h3>Methods</h3><div>This study enrolled 987 elderly inpatients with CAD. This cohort was randomly divided into the training and validation datasets in a 7:3 ratio. Five machine learning methods, including Logistic Regression, Random Forest, Support Vector Machine, eXtreme Gradient Boosting, and Gradient Boosting Decision Trees, were implemented to construct predictive models.</div></div><div><h3>Results</h3><div>Overall, the median age of this cohort was 85 [81,89] years. Three-year mortality in elderly inpatients with CAD combined with HF was 56.46%. The least absolute shrinkage and selection operator method and five-fold cross-validation identified that ten features were significantly associated with three-year mortality. Logistic Regression showed better performance than other models in the Brier Score, Area Under The Curve, Accuracy, Precision, Recall, and F1 Score of 0.1105, 0.9014, 0.8764, 0.9167, 0.8627, and 0.8889, respectively. The Shapley Additive exPlanations method revealed that age, interventricular septum thickness, gamma gap, serum creatinine, N-terminal pro-B-type natriuretic peptide (NT.proBNP), and neutrophil-to-lymphocyte ratio were identified as risk factors, and mean systolic blood pressure, hemoglobin, albumin, and sodium were protective factors. Age, albumin, and NT.proBNP were three features most associated with three-year mortality. The network application could be available at <span><span>https://cad-hf-predict.tracebook.org.cn</span><svg><path></path></svg></span>.</div></div><div><h3>Conclusion</h3><div>Logistic Regression exhibits excellent predictive performance for predicting three-year mortality in elderly inpatients with CAD combined with HF.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106307"},"PeriodicalIF":4.1,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rural and urban patient perceptions of electronic health record data use in research and clinical care: A cross-sectional survey research study","authors":"Melissa L. Harry ,&nbsp;Morgan L. Brenholdt ,&nbsp;Anthony W. Olson ,&nbsp;Claudia C. Ramjattan ,&nbsp;Megan A. Schwalbe","doi":"10.1016/j.ijmedinf.2026.106310","DOIUrl":"10.1016/j.ijmedinf.2026.106310","url":null,"abstract":"<div><h3>Purpose</h3><div>Understand patient perceptions of sharing and use of electronic health record (EHR) data in research and clinical care, including whether differences exist between rural and urban patients.</div></div><div><h3>Methods</h3><div>We adapted survey items from existing surveys and developed some new items, making revisions following cognitive interviews with eight patients in a rural-serving health system. We then invited 18,251 health system patients to take the electronic survey (7/11/2023–10/13/2023, 10/31/2023–01/02/2024). Analyses included bivariate statistics and multivariable ordered and binary logistic regression examining associations between participant responses and rurality using respondent zip code and associated 2020 Rural-Urban Commuting Area code (rural: 4–10; urban: 1–3). We analyzed open-ended survey questions with qualitative content analysis.</div></div><div><h3>Findings</h3><div>Of 1,929 participants who started the survey (10.6% response rate), 1,912 completed questions beyond demographics and were included in the analytical sample. Most respondents were female (66.9%), White (93.4%), employed for wages (45.1%) or retired (37.2%), had at least some college (88.3%), and lived in urban locales (55.0%). Rural respondents had significantly lower medical mistrust levels than urban. Comfort with sharing data for research was high amongst respondents, particularly when de-identified. Some differences were seen between rural and urban respondents in adjusted models, foremost being rural respondents having higher adjusted odds (aOR = 1.43, 95% CI = 1.16–1.77, <em>p</em> = 0.001) of being more comfortable sharing data if their zip code was removed. Rural respondents had significantly higher odds of being comfortable with some demographic data being in the EHR and accessible to health system providers and researchers compared to urban respondents.</div></div><div><h3>Conclusions</h3><div>Respondents generally supported sharing health data for research and care purposes. Although zip code is frequently used to demarcate rurality in U.S.-based research, rural respondents may be more comfortable sharing data when zip code is removed. Opportunities to assuage concerns regarding data use are also described.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106310"},"PeriodicalIF":4.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to comment on “medication-based mortality prediction in COPD using machine learning and conventional statistical methods”","authors":"Ana Paula Bruno Pena-Gralle ,&nbsp;Amélie Forget ,&nbsp;Yohann Moanahere Chiu ,&nbsp;Marc-André Legault ,&nbsp;Marie-France Beauchesne ,&nbsp;Lucie Blais","doi":"10.1016/j.ijmedinf.2026.106304","DOIUrl":"10.1016/j.ijmedinf.2026.106304","url":null,"abstract":"","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106304"},"PeriodicalIF":4.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and temporal validation of machine learning models for predicting clinically relevant medication reconciliation discrepancies at the emergency department: A single-center retrospective study","authors":"Greet Van De Sijpe ,&nbsp;Tuur Schrooten ,&nbsp;Sabrina De Winter ,&nbsp;Lorenz Van der Linden ,&nbsp;Peter Vanbrabant ,&nbsp;Annabel Dompas ,&nbsp;Bo Bertels ,&nbsp;Maarten De Vos ,&nbsp;Isabel Spriet","doi":"10.1016/j.ijmedinf.2026.106309","DOIUrl":"10.1016/j.ijmedinf.2026.106309","url":null,"abstract":"<div><h3>Objective</h3><div>Medication discrepancies at hospital admission are common and can cause preventable patient harm. Predictive models can help prioritize medication reconciliation for high-risk patients. This study aimed to develop and validate machine learning (ML) models for predicting clinically relevant medication reconciliation discrepancies in emergency department (ED) patients, and to compare their performance with logistic regression.</div></div><div><h3>Methods</h3><div>We conducted a single-center, retrospective study at UZ Leuven. The dataset included patients admitted to the ED between 2017 and 2019 (development set) and 2021–2022 (temporal validation set). The outcome variable was the presence of at least one clinically relevant medication discrepancy, defined by expert panel adjudication. Variables were extracted from the electronic health record, with care to avoid data leakage. Three models – logistic regression, random forest, and eXtreme Gradient Boosting – were developed using tailored variable selection strategies, and validated temporally. Model performance was assessed via discrimination, calibration, and classification metrics. Clinical utility was assessed using decision curve analysis.</div></div><div><h3>Results</h3><div>The development and validation cohorts included 817 and 349 patients, respectively. LR and RF models demonstrated moderate discrimination on temporal validation (AUROC 0.67–0.68). The XGBoost model showed lower discrimination (AUROC 0.63). Calibration was comparable across models. Decision curve analysis showed only small differences in net benefit between models across clinically relevant threshold probabilities.</div></div><div><h3>Conclusion</h3><div>ML models provided no clear improvement over logistic regression, which achieved similar predictive performance and greater interpretability. These findings highlight both the potential and the limitations of ML for supporting targeted medication reconciliation in ED workflows. Future research should explore the added value of richer data sources, such as unstructured clinical narratives.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106309"},"PeriodicalIF":4.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146108314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of online medication consultation from home-based patients on a tertiary hospital WeChat platform: a cross-sectional study","authors":"Chen Wang ,&nbsp;Liu-Cheng Li ,&nbsp;Jie Ying ,&nbsp;Qin Chen ,&nbsp;Kai-Li Mao ,&nbsp;Lian-Di Kan","doi":"10.1016/j.ijmedinf.2026.106308","DOIUrl":"10.1016/j.ijmedinf.2026.106308","url":null,"abstract":"<div><div><strong>Background</strong>: Amidst the rapid digital transformation of healthcare, internet hospitals have catalyzed substantial growth in pharmacist-led online medication consultation services. Nevertheless, there remains a notable paucity of empirical analysis and evaluation regarding pharmacists’ provision of online pharmaceutical care.</div><div><strong>Objective</strong>: The present study was to analyze the characteristics of online medication consultation from home-based patients on a tertiary hospital WeChat platform.</div><div><strong>Methods</strong>: A retrospective analysis was performed on 5,746 consultation records from April 2022 to March 2025. Consultation categories and frequencies, response rates, and patient demographic characteristics (gender distribution and age profiles) were systematically analyzed to elucidate specific patient needs within the online consultation paradigm. Statistical modeling was employed to examine associations between consultation efficacy and variables including patient gender, consultation year, and temporal patterns. Furthermore, frequently inquired medications were quantified to discern prevailing consultation trends.</div><div><strong>Results</strong>: A total of 5,746 consultations were analyzed. Physical examination result inquiries were most frequent but least response rate (16.30 %). Medication timing and administration methods ranked as the second and third most frequent consultation categories, respectively, with higher response rates of 74.38 % and 68.94 %. The patient population was predominantly female (<em>p</em> = 0.017) with a median age of 30 years. Among the three annual periods, April 2023-March 2024 yielded the highest consultation volume but lowest response rate. Across four daily time intervals, consultation volume peaked during afternoon hours and was lowest in the late-night period, with comparable response rates among periods. Antibiotics and gastrointestinal medications represented the most frequent consultation topics.</div><div><strong>Conclusion:</strong>Online consultation provides patients with convenient access to professional guidance on medication administration, timing and selection. However, the user demographic is predominantly younger, necessitating strategies to enhance accessibility for elderly populations. Increased pharmacist staffing during afternoon hours is warranted to accommodate peak consultation volumes, particularly specialists in antimicrobial therapy and <em>Helicobacter pylori</em> regimens. These findings inform targeted quality improvements at dispensing windows, emphasizing proactive counseling on high-frequency consultation topics identified through online interactions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"210 ","pages":"Article 106308"},"PeriodicalIF":4.1,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}