International Journal of Medical Informatics最新文献

{"title":"Integrating semantic retrieval and chain-of-thought reasoning in small language models for SNOMED CT normalization","authors":"Pilar López-Úbeda ,&nbsp;Teodoro Martín-Noguerol ,&nbsp;Antonio Luna","doi":"10.1016/j.ijmedinf.2026.106340","DOIUrl":"10.1016/j.ijmedinf.2026.106340","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Breast lesion biopsy assessment generates a high volume of pathology reports, posing a significant workload for pathologists. Standardized coding systems such as SNOMED CT Morphological codes enable consistent documentation, facilitate accurate data sharing, support clinical decision-making, and allow automated quality control. This study aims to evaluate systems that assist pathologists in normalizing and classifying free-text pathology reports to SNOMED CT Morphological codes, providing a short list of candidate codes for selection.</div></div><div><h3>Methods</h3><div>We used 2,718 breast biopsy pathology reports from over 20 hospitals, reported by nine expert pathologists in total. A normalization pipeline combining Small Language Models (SLMs) with semantic retrieval was evaluated to map free-text reports to SNOMED CT Morphological codes. Three strategies were evaluated: zero-shot prompting, Chain-of-Thought (CoT) with retrieval-augmented generation (RAG), and RAG combined with CoT, each generating a short list of candidate codes for pathologist selection. The strategies were assessed using ranking-oriented metrics adapted to the multi-label setting, including Hit@K, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (nDCG@K), and Recall@K, which measure both the presence and ranking of correct codes within the top-K predictions. Additionally, out-of-vocabulary (OOV) metrics were reported.</div></div><div><h3>Results</h3><div>The RAG + CoT strategy achieved the highest performance, with Hit@5 scores of 70.97% for LLaMA and 72.11% for Gemma and demonstrated a strong concentration of correct codes at Rank 1. CoT + RAG improved over zero-shot prompting but tended to place correct codes at lower ranks.</div></div><div><h3>Conclusion</h3><div>Integrating SLMs with RAG and CoT provides an effective tool to support pathologists in coding breast biopsy pathology reports. By offering a short, curated list of SNOMED CT Morphological codes, the system enhances clinical workflow, improves data quality, and supports both prospective and retrospective analyses.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106340"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146183336","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":"Case-based reasoning for clinical trial recruitment tools in oncology: When you need patients to find patients","authors":"Lou-Anne Guillotel ,&nbsp;Thierry Lesimple ,&nbsp;Oussama Zekri ,&nbsp;Marc Cuggia ,&nbsp;Boris Campillo-Gimenez","doi":"10.1016/j.ijmedinf.2026.106301","DOIUrl":"10.1016/j.ijmedinf.2026.106301","url":null,"abstract":"<div><h3>Background</h3><div>Patient recruitment for clinical trials remains a major challenge, with 86% of trials failing to meet enrollment targets on time. In over 77% of cases, recruitment difficulties stem from matching problems between trials and patients. Case-Based Reasoning (CBR) offers a distinct patient-to-patient approach by determining eligibility through comparison with previously enrolled patients, yet this methodology remains underexplored in contemporary oncology trial matching despite its potential advantages.</div></div><div><h3>Objective</h3><div>To compare the performance of two CBR approaches—random forest (RF) and target patient similarity (TPS)—in predicting patient eligibility for recent oncology clinical trials using real-world electronic health record data.</div></div><div><h3>Methods</h3><div>We selected three breast cancer clinical trials (2019–2022) from our institutional registry. Patient data were extracted from our clinical data warehouse, including structured data (laboratory results, diagnosis codes, procedures, treatments) and unstructured clinical narratives processed using natural language processing. For each trial, we trained RF classifiers and TPS models using repeated hold-out validation (25 splits, 70/30 train-test). Performance was evaluated using discriminative metrics (AUC, positive precision, recall, F1-score) and ranking metrics (P@5, P@10, MAP, MRR, NDCG@5, NDCG@10). We analyzed model performance across varying numbers of eligible patients in training datasets (2 to 70% of the total number of eligible patients).</div></div><div><h3>Results</h3><div>Both approaches demonstrated strong discriminative performance across three trials, with average AUCs of 84.1 % for RF and 76.4 % for TPS, driven primarily by high recall (82.3 % and 77.7 %, respectively). However, positive precision remained low (13.3 % and 9.9 %), reflecting high false-positive rates due to class imbalance. RF showed superior ranking performance, particularly for the trial with the largest eligible cohort (n = 542; P@5 = 78.6 %, MRR = 88.0 %), compared to TPS (P@5 = 47.9 %, MRR = 69.2 %). Both approaches reached performance plateaus with only around 10 eligible patients in training datasets. Variable importance analysis revealed that treatment-related features, diagnostic codes, and procedures were consistently the most important predictors, with relevant patterns identified even with minimal training data.</div></div><div><h3>Conclusions</h3><div>CBR approaches can effectively support patient pre-screening for oncology clinical trials, with RF demonstrating moderately superior performance over TPS. Both methods show robust discriminative performance with small training datasets, though ranking performance varies substantially across trials. Our findings suggest that CBR approaches may benefit from integration with query-based or prompt-based methods during early recruitment phases when training data is scarce.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106301"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137631","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":"Digital literacy training within interventions to support older adults with cardiovascular disease in using technologies: a systematic review","authors":"Kathy L. Rush ,&nbsp;Cherisse L. Seaton ,&nbsp;Rowan Ross ,&nbsp;Taylor Robertson ,&nbsp;Angeliki-Iliana Louloudi ,&nbsp;Peter Loewen ,&nbsp;Kristen R. Haase ,&nbsp;Jennifer Jakobi ,&nbsp;Robert Janke","doi":"10.1016/j.ijmedinf.2026.106312","DOIUrl":"10.1016/j.ijmedinf.2026.106312","url":null,"abstract":"<div><h3>Background</h3><div>Advancement in digitalization in the health sector have created numerous opportunities for cardiovascular disease (CVD) self-management but also challenges, especially for older adults with lower digital health literacy. Reviews have examined impacts of digital health technology interventions on health outcomes without examining the role of training provided. The aim of this review is to synthesize evidence about the impacts of digital literacy training (DLT) and its characteristics as a component of digital interventions related to cardiovascular health on patient reported outcome and experience measures among older adults with CVD.</div></div><div><h3>Methods</h3><div>In accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a search of MEDLINE, EMBASE, CINAHL, and PsycINFO databases for articles published between inception to March 31, 2025 was conducted. Empirical studies reporting digital health technology training with adults (<em>M</em> age 60 + years) with CVD were eligible for inclusion. Articles included were quality-rated using the Mixed Methods Appraisal Tool. Data were extracted according to the DLT and health technologies alongside patient-reported outcome (i.e technology- and health-related) and experience measures.</div></div><div><h3>Results</h3><div>Of the 56 included studies (totaling 7698 participants), DLT varied considerably, with 51 describing in-person training. Two studies (totaling 519 participants) examined the role of training with positive impacts on technology- and health-related outcomes. In many of the remaining studies, positive technology-related outcomes were evident but could not be linked back to DLT separate from the overall intervention. In studies (n = 10) where training was evaluated, feedback from patients largely affirmed the training was needed.</div></div><div><h3>Discussion</h3><div>The collective evidence suggests DLT overall is useful and needed in digital interventions for older adults with CVD. More work is needed to elucidate the distinct role of DLT characteristics and to determine for whom and under what conditions DLT impacts health and technology-related outcomes.</div></div><div><h3>Registration</h3><div>The protocol for this review was registered Aug 12, 2024 in Open Science Framework (OSF) (See: https://osf.io/unhd9)</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106312"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133684","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 of a Validation and Inspection Tool for Armband-based Lifelog data (VITAL) to facilitate the clinical use of wearable health data: A prototype and usability evaluation","authors":"Eunyoung Im ,&nbsp;Sunghoon Kang ,&nbsp;Hyeoneui Kim","doi":"10.1016/j.ijmedinf.2026.106322","DOIUrl":"10.1016/j.ijmedinf.2026.106322","url":null,"abstract":"<div><h3>Introduction</h3><div>The growing use of wearable devices has expanded the availability of health-related data. However, despite their potential to support clinical decision making, excessive data volume, inefficient processing systems, limited interoperability, and concerns regarding data quality continue to hinder their practical use in clinical settings. To advance the clinical utilization of wearable health data, we developed the Validation and Inspection Tool for Armband-based Lifelog data (VITAL), a pipeline for data integration, visualization, and quality management, and evaluated its usability.</div></div><div><h3>Methods</h3><div>The development of VITAL followed a structured process comprising requirement gathering, system implementation, and usability evaluation. System requirements were identified through interviews with four clinicians. Wearable health data were collected from Samsung, Apple, Fitbit, and Xiaomi devices and integrated into a standardized dataframe at 10-minute intervals. The prototype focused on three core domains: physical activity, biometrics, and sleep. Data quality was operationalized using quantifiable metrics for completeness, recency, and plausibility to enable systematic evaluation.</div></div><div><h3>Results</h3><div>VITAL provides interactive visualization and integrated data quality management functions. Usability testing was conducted through individual interviews with seven clinicians, who completed task-based evaluations and a Unified Theory of Acceptance and Use of Technology (UTAUT) survey. All participants successfully completed the assigned tasks with minimal errors. The UTAUT results indicated favorable user acceptance, with mean scores of 4.20 for performance expectancy, 3.96 for effort expectancy, and 4.14 for intention to use. Interviews further highlighted strengths in visualization and usability, while also suggesting interface simplification and the inclusion of additional clinical data types, such as electrocardiograms and dietary information.</div></div><div><h3>Conclusion</h3><div>VITAL demonstrated the feasibility of harmonizing, visualizing, and evaluating wearable health data for clinical use. These findings suggest that the tool is practical and valuable for supporting clinical workflows, warranting further large-scale studies to validate its effectiveness in real-world settings.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106322"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191618","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 of an interpretable machine learning model for predicting 4-year chronic kidney disease risk in elderly hypertensive patients","authors":"Panji Wang ,&nbsp;Yuan Meng ,&nbsp;Zhaowei Sun ,&nbsp;Jiaju Li ,&nbsp;Hailong Tao","doi":"10.1016/j.ijmedinf.2026.106320","DOIUrl":"10.1016/j.ijmedinf.2026.106320","url":null,"abstract":"<div><h3>Introduction</h3><div>Age and hypertension are key drivers of renal impairment, predisposing older hypertensive adults to faster kidney function decline and higher mortality. We aim to develop an interpretable machinelearning model to predict 4-year chronic kidney disease (CKD) risk in this population.</div></div><div><h3>Methods</h3><div>Our study incorporated 4,142 hypertensive patients from the Health and Retirement Study (HRS) 2010 and 2012 cohorts for model development and internal validation, with additional temporal validation performed within the HRS 2006 and 2008 cohorts. External validation was conducted using three distinct subcohorts derived from the China Health and Retirement Longitudinal Study (CHARLS) database. Feature selection was implemented through an integrated LASSO-Boruta algorithm, followed by model construction using eight machine learning approaches. Discriminative performance was rigorously evaluated through multiple metrics, including receiver operating characteristic (ROC) curve analysis, accuracy, sensitivity, specificity, and Brier score. The optimal model underwent interpretability analysis via SHapley Additive exPlanations (SHAP) to elucidate decision-making mechanisms and was subsequently deployed as a web-based clinical prediction tool.</div></div><div><h3>Results</h3><div>Using a combined LASSO–Boruta strategy, we identified nine routinely available predictors for model development. In the training set, SVM achieved the highest AUC (0.735), closely followed by XGBoost (0.734); notably, in the temporal validation cohort, XGBoost was the only model with an AUC &gt; 0.700 (0.702). Overall performance metrics derived from confusion matrices, together with Brier scores, suggested that XGBoost provided a favorable balance between sensitivity and specificity while maintaining acceptable probabilistic calibration. Calibration curves further suggested that XGBoost showed relatively stable agreement between predicted and observed risks across datasets, supporting its selection for subsequent SHAP-based interpretation and web deployment; SHAP identified age as the leading contributor to CKD risk.</div></div><div><h3>Conclusions</h3><div>We developed an interpretable model using routine clinical indicators to predict 4-year CKD risk in elderly hypertensive adults, with applicability across Asian and Caucasian populations.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106320"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121129","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":"Clinical-radiological machine learning model for non-invasive diagnosis and stratification of peripheral artery disease: a multicenter study","authors":"Bowen Hou ,&nbsp;Jinhan Qiao ,&nbsp;Zheng Ran ,&nbsp;Yitong Li ,&nbsp;Zhongyichen Huang ,&nbsp;Xiaolong Luo ,&nbsp;Xiaoming Li","doi":"10.1016/j.ijmedinf.2026.106338","DOIUrl":"10.1016/j.ijmedinf.2026.106338","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Peripheral artery disease (PAD) is an atherosclerotic disorder prevalent in the elderly that leads to peripheral function decline and body composition changes. Current diagnostic approaches lack sensitivity for early PAD detection and staging. This study aimed to develop and validate machine learning (ML) models of clinical and CT-based radiological features to improve PAD diagnosis and severity stratification.</div></div><div><h3>Methods</h3><div>A retrospective multicenter study was conducted using data from two institutions. Clinical and radiological features (including volumetric body composition and muscle texture parameters extracted from calf and thigh segments) were analyzed. Participants were randomly divided into training (70%) and test (30%) sets, stratified by PAD status. Models with different ML algorithms were developed and compared. Model interpretability was assessed with Shapley additive explanation (SHAP) analysis, and performance was evaluated through receiver operating characteristic analysis, Hosmer-Lemeshow testing, Brier score and calibration curves.</div></div><div><h3>Results</h3><div>This study comprised 342 participants, divided into training (n = 176), test set (n = 76) from Institute 1, external validation (n = 90) from Institute 2. Three models were developed: clinical model (CM), radiological model (RM), and combined clinical-radiological model (CRM). The calf-based CRM using random forest algorithm achieved area under the curves of 0.871 (training), 0.870 (test), and 0.828 (validation), demonstrating good calibration (<em>p</em> ≥ 0.05) and the low Brier score. SHAP analysis visually interpreted feature contributions toward PAD diagnosis and staging.</div></div><div><h3>Conclusions</h3><div>The CRM model effectively integrated calf-derived radiological and clinical features into a noninvasive, interpretable tool for PAD diagnosis and severity stratification, demonstrating strong clinical applicability.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106338"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133629","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":"Harmonizing patient-reported outcome measures for nasal complaints using traditional and machine learning methods","authors":"Miljan Jović ,&nbsp;Esther Hof ,&nbsp;Maryam Amir Haeri ,&nbsp;Jasper J. Hoorweg ,&nbsp;Stéphanie M. van den Berg","doi":"10.1016/j.ijmedinf.2026.106319","DOIUrl":"10.1016/j.ijmedinf.2026.106319","url":null,"abstract":"<div><h3>Background</h3><div>Nasal obstruction measurement instruments are widely used in the field of nasal surgery. There are various scales that measure nasal obstruction and they differ regarding the number of items, their wording, and the type of response options. In order to pool the data and analyze it together, it is necessary to harmonize it so that we can compare participants’ nasal obstruction scores irrespective of instrument they filled in. Data harmonization is still not used in the field of nasal obstruction assessment.</div></div><div><h3>The Aim</h3><div>The aim of this study was to find the best harmonization method in terms of predicting the scores on a target instrument based on the scores from another instrument as precise as possible in the case of four different nasal complaints instruments. A method was sought to find a transformation of scores on the NOSE, Utrecht-Q and SCHNOS that makes them equivalent to ENFAS scores.</div></div><div><h3>Methods</h3><div>A total of 1324 unique patients completed all four measurement instruments. We tried linear equating, Item Response Theory (IRT), and the following machine learning methods: linear regression, random forest regression, support vector machine regression, and neural network. We used the root-mean-square error (RMSE) of differences between predicted and observed scores to evaluate the quality of harmonization in 5-fold cross-validation.</div></div><div><h3>Results</h3><div>The ML methods gave overall the best results (the lowest RMSEs) and outperformed IRT (which is considered as a common choice for data harmonization in psychometrics).</div></div><div><h3>Conclusion</h3><div>The ML methods led to the best quality of the results, confirming their strong potential for data harmonization. This study shows that next to linear equating and IRT that are commonly used for data harmonization, we can also use ML methods for the same purpose and, by doing so, to even increase the quality of the harmonization in certain use cases.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"211 ","pages":"Article 106319"},"PeriodicalIF":4.1,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133655","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":"2026-05-01","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":"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":"2026-05-01","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":"2026-05-01","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}