Journal of Medical Internet Research最新文献

{"title":"Investigating Clinicians' Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey.","authors":"Rui Zheng, Xiao Jiang, Li Shen, Tianrui He, Mengting Ji, Xingyi Li, Guangjun Yu","doi":"10.2196/62732","DOIUrl":"https://doi.org/10.2196/62732","url":null,"abstract":"<p><strong>Background: </strong>An intelligence-enabled clinical decision support system (CDSS) is a computerized system that integrates medical knowledge, patient data, and clinical guidelines to assist health care providers make clinical decisions. Research studies have shown that CDSS utilization rates have not met expectations. Clinicians' intentions and their attitudes determine the use and promotion of CDSS in clinical practice.</p><p><strong>Objective: </strong>The aim of this study was to enhance the successful utilization of CDSS by analyzing the pivotal factors that influence clinicians' intentions to adopt it and by putting forward targeted management recommendations.</p><p><strong>Methods: </strong>This study proposed a research model grounded in the task-technology fit model and the technology acceptance model, which was then tested through a cross-sectional survey. The measurement instrument comprised demographic characteristics, multi-item scales, and an open-ended query regarding areas where clinicians perceived the system required improvement. We leveraged structural equation modeling to assess the direct and indirect effects of \"task-technology fit\" and \"perceived ease of use\" on clinicians' intentions to use the CDSS when mediated by \"performance expectation\" and \"perceived risk.\" We collated and analyzed the responses to the open-ended question.</p><p><strong>Results: </strong>We collected a total of 247 questionnaires. The model explained 65.8% of the variance in use intention. Performance expectations (β=0.228; P<.001) and perceived risk (β=-0.579; P<.001) were both significant predictors of use intention. Task-technology fit (β=-0.281; P<.001) and perceived ease of use (β=-0.377; P<.001) negatively affected perceived risk. Perceived risk (β=-0.308; P<.001) negatively affected performance expectations. Task-technology fit positively affected perceived ease of use (β=0.692; P<.001) and performance expectations (β=0.508; P<.001). Task characteristics (β=0.168; P<.001) and technology characteristics (β=0.749; P<.001) positively affected task-technology fit. Contrary to expectations, perceived ease of use (β=0.108; P=.07) did not have a significant impact on use intention. From the open-ended question, 3 main themes emerged regarding clinicians' perceived deficiencies in CDSS: system security risks, personalized interaction, seamless integration.</p><p><strong>Conclusions: </strong>Perceived risk and performance expectations were direct determinants of clinicians' adoption of CDSS, significantly influenced by task-technology fit and perceived ease of use. In the future, increasing transparency within CDSS and fostering trust between clinicians and technology should be prioritized. Furthermore, focusing on personalized interactions and ensuring seamless integration into clinical workflows are crucial steps moving forward.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e62732"},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803492","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 Benefits and Risks of Antiamyloid Antibodies in Sporadic Alzheimer Disease: Systematic Review and Network Meta-Analysis With a Web Application.","authors":"Danko Jeremic, Juan D Navarro-Lopez, Lydia Jimenez-Diaz","doi":"10.2196/68454","DOIUrl":"https://doi.org/10.2196/68454","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Despite the increasing approval of antiamyloid antibodies for Alzheimer disease (AD), their clinical relevance and risk-benefit profile remain uncertain. The heterogeneity of AD and the limited availability of long-term clinical data make it difficult to establish a clear rationale for selecting one treatment over another.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this work was to assess and compare the efficacy and safety of antiamyloid antibodies through an interactive online meta-analytic approach by performing conventional pair-wise meta-analyses and frequentist and Bayesian network meta-analyses of phase II and III clinical trial results. To achieve this, we developed AlzMeta.app 2.0, a freely accessible web application that enables researchers and clinicians to evaluate the relative and absolute risks and benefits of these therapies in real time, incorporating different prior choices and assumptions of baseline risks of disease progression and adverse events.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We adhered to PRISMA-NMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for reporting of systematic reviews with network meta-analysis) and GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) guidelines for reporting and rating the certainty of evidence. Clinical trial reports (until September 30, 2024) were retrieved from PubMed, Google Scholar, and clinical trial databases (including ClinicalTrials.gov). Studies with &lt;20 sporadic AD patients and a modified Jadad score &lt;3 were excluded. Risk of bias was assessed with the RoB-2 tool. Relative risks and benefits have been expressed as risk ratios and standardized mean differences, with confidence, credible, and prediction intervals calculated for all outcomes. For significant results, the intervention effects were ranked in frequentist and Bayesian frameworks, and their clinical relevance was determined by the absolute risk per 1000 people and number needed to treat (NNT) for a wide range of control responses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among 7 treatments tested in 21,236 patients (26 studies with low risk of bias or with some concerns), donanemab was the best-ranked treatment on cognitive and functional measures, and it was almost 2 times more effective than aducanumab and lecanemab and significantly more beneficial than other treatments on the global (cognitive and functional) Clinical Dementia Rating Scale-Sum of Boxes (NNT=10, 95% CI 8-16). Special caution is required regarding cerebral edema and microbleeding due to the clinically relevant risks of edema for donanemab (NNT=8, 95% CI 5-16), aducanumab (NNT=10, 95% CI 6-17), and lecanemab (NNT=14, 95% CI 7-31), which may outweigh the benefits.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Our results showed that donanemab is more effective and has a safety profile similar to aducanumab and lecanemab, highlighting the need for treatment options with improved safety. Pot","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e68454"},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803383","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":"Correction: Stress Reduction in Perioperative Care: Feasibility Randomized Controlled Trial.","authors":"Haridimos Kondylakis, Irene Alice Chicchi Giglioli, Dimitrios Katehakis, Hatice Aldemir, Paul Zikas, George Papagiannakis, Santiago Hors-Fraile, Pedro L González-Sanz, Konstantinos Apostolakis, Constantine Stephanidis, Francisco J Núñez-Benjumea, Rosa M Baños-Rivera, Luis Fernandez-Luque, Angelina Kouroubali","doi":"10.2196/73608","DOIUrl":"https://doi.org/10.2196/73608","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/54049.].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73608"},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803477","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":"Psychological eHealth Interventions for Patients With Cardiovascular Diseases: Systematic Review and Meta-Analysis.","authors":"Jing Jing Su, Rose Lin, Ladislav Batalik, Arkers Kwan Ching Wong, Sherry L Grace","doi":"10.2196/57368","DOIUrl":"https://doi.org/10.2196/57368","url":null,"abstract":"<p><strong>Background: </strong>Psychological distress is recognized as an independent risk factor for cardiovascular diseases (CVDs), contributing to increased morbidity and mortality. While eHealth is increasingly used to deliver psychological interventions, their effectiveness for patients with CVDs remains unclear.</p><p><strong>Objective: </strong>This meta-analysis aimed to evaluate the effects of eHealth psychological interventions for patients with CVDs.</p><p><strong>Methods: </strong>Eligible studies were retrieved from 5 databases (Embase, Medline, PubMed, CINAHL, and Cochrane Library), covering the period from database inception to December 2024. Randomized controlled trials (RCTs) investigating the effect of evidence-based psychological eHealth interventions to improve psychosocial well-being and cardiovascular outcomes for people with CVDs were included. The Cochrane Risk of Bias tool (version 2) was used to judge the methodological quality of reviewed studies. RevMan (version 5.3) was used for meta-analysis.</p><p><strong>Results: </strong>A total of 12 RCTs, comprising 2319 participants from 10 countries, were included in the review. The results demonstrated significant alleviation of depressive symptoms for patients receiving psychological eHealth intervention compared to controls (number of paper included in that particular analysis, n=7; standardized mean difference=-0.30, 95% CI -0.47 to -0.14; I²=57%; P<.001). More specifically, in 6 trials where internet-based cognitive behavioral therapy was delivered, a significant alleviation of depressive symptoms was achieved (standardized mean difference=-0.39, 95% CI -0.56 to -0.21; I²=53%; P<.001). There was no significant change in anxiety or quality of life. Synthesis without meta-analysis regarding stress, adverse events, and cardiovascular events showed inconclusive findings.</p><p><strong>Conclusions: </strong>Psychological eHealth interventions, particularly internet-based cognitive behavioral therapy, can significantly reduce depressive symptoms among patients with CVDs. A multidisciplinary approach is crucial for comprehensively improving psychological and cardiovascular outcomes. Future studies should explore integrating persuasive design features into eHealth and involving mental health professionals for intervention delivery.</p><p><strong>Trial registration: </strong>PROSPERO CRD42023452276; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023452276.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e57368"},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803501","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":"Correction: Mortality Risk Prediction in Patients With Antimelanoma Differentiation-Associated, Gene 5 Antibody-Positive, Dermatomyositis-Associated Interstitial Lung Disease: Algorithm Development and Validation.","authors":"Hui Li, Ruyi Zou, Hongxia Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Yin Liu, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai","doi":"10.2196/73983","DOIUrl":"https://doi.org/10.2196/73983","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.2196/62836.].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73983"},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803471","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":"Urologists' Estimation of Online Support Group Utilization Behavior of Their Patients With Newly Diagnosed Nonmetastatic Prostate Cancer in Germany: Predefined Secondary Analysis of a Randomized Controlled Trial.","authors":"Philipp Karschuck, Christer Groeben, Rainer Koch, Tanja Krones, Andreas Neisius, Sven von Ahn, Christian Peter Klopf, Steffen Weikert, Michael Siebels, Nicolas Haseke, Christian Weissflog, Martin Baunacke, Christian Thomas, Peter Liske, Georgi Tosev, Thomas Benusch, Martin Schostak, Joachim Stein, Philipp Spiegelhalder, Andreas Ihrig, Johannes Huber","doi":"10.2196/56092","DOIUrl":"https://doi.org/10.2196/56092","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Due to its high incidence, prostate cancer (PC) imposes a burden on Western societies. Individualized treatment decision for nonmetastatic PC (eg, surgery, radiation, focal therapy, active surveillance, watchful waiting) is challenging. The range of options might make affected persons seek peer-to-peer counseling. Besides traditional face-to-face support groups (F2FGs), online support groups (OSGs) became important, especially during COVID-19.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aims to investigate utilization behavior and physician advice concerning F2FGs and OSGs for patients with newly diagnosed PC. We hypothesized greater importance of OSGs to support treatment decisions. We assumed that this form of peer-to-peer support is underestimated by the treating physicians. We also considered the effects of the COVID-19 pandemic.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This was a secondary analysis of data from a randomized controlled trial comparing an online decision aid versus a printed brochure for patients with nonmetastatic PC. We investigated 687 patients from 116 urological practices throughout Germany before primary treatment. Of these, 308 were included before and 379 during the COVID-19 pandemic. At the 1-year follow-up visit, patients filled an online questionnaire about their use of traditional or online self-help, including consultation behaviors or attitudes concerning initial treatment decisions. We measured secondary outcomes with validated questionnaires such as Distress Thermometer and the Patient Health Questionnaire-4 items to assess distress, anxiety, and depression. Physicians were asked in a paper-based questionnaire whether patients had accessed peer-to-peer support. Group comparisons were made using chi-square or McNemar tests for nominal variables and 2-sided t tests for ordinally scaled data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Before COVID-19, 2.3% (7/308) of the patients attended an F2FG versus none thereafter. The frequency of OSG use did not change significantly: OSGs were used by 24.7% (76/308) and 23.5% (89/308) of the patients before and during COVID-19, respectively. OSG users had higher levels of anxiety and depression; 38% (46/121) reported OSG as helpful for decision-making. Although 4% (19/477) of OSG nonusers regretted treatment decisions, only 0.7% (1/153) of OSG users did (P=.03). More users than nonusers reported that OSGs were mentioned by physicians (P&lt;.001). Patients and physicians agreed that F2FGs and OSGs were not mentioned in conversations or visited by patients. For 86% (6/7) of the patients, the physician was not aware of F2FG attendance. Physicians underestimated OSG usage by 2.6% (18/687) versus 24% (165/687) of actual use (P&lt;.001).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Physicians are more aware of F2FGs than OSGs. Before COVID-19, F2FGs played a minor role. One out of 4 patients used OSGs. One-third considered them helpful for treatment decision-making. OSG use rarely ","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e56092"},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803513","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":"Effectiveness of Virtual Reality-Complemented Pulmonary Rehabilitation on Lung Function, Exercise Capacity, Dyspnea, and Health Status in Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.","authors":"Yuyin Chen, Yuanyuan Zhang, Xiuhong Long, Huiqiong Tu, Jibing Chen","doi":"10.2196/64742","DOIUrl":"https://doi.org/10.2196/64742","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition characterized by persistent airflow obstruction. Pulmonary rehabilitation (PR) is a cornerstone of COPD management but remains underutilized due to barriers such as low motivation and accessibility issues. Virtual reality (VR)-complemented PR offers a novel approach to overcoming these barriers by enhancing patient engagement and rehabilitation outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This review aims to evaluate the effect of VR-complemented PR compared with comparators on lung function, exercise capacity, dyspnea, health status, and oxygenation in patients with COPD. Additionally, the study aimed to identify which comparator type (active exercise vs nonactive exercise control group) and intervention duration would result in the greatest improvements in rehabilitation outcomes. The study also assessed patient-reported experience measures, including acceptability and engagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A comprehensive search of 11 international and Chinese databases identified randomized controlled trials (RCTs) published up to November 2024. Data were analyzed using RevMan 5.4, with pooled effect sizes reported as mean differences (MDs) and 95% CIs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 16 RCTs involving 1052 participants were included. VR-complemented PR significantly improved lung function (forced expiratory volume in 1 second [FEV1] [L], MD 0.25, P&lt;.001; FEV1/forced vital capacity [FVC], MD 6.12, P&lt;.001; FVC, MD 0.28, P&lt;.001) compared with comparators. Exercise capacity, assessed by the 6MWD, significantly improved (MD 23.49, P&lt;.001) compared with comparators; however, it did not reach the minimally clinically important difference of 26 m, indicating limited clinical significance despite statistical significance. VR-complemented PR also significantly reduced dyspnea measured by the modified British Medical Research Council scale (MD -0.28, P&lt;.001), improved health status measured by the COPD Assessment Test (MD -2.95, P&lt;.001), and enhanced oxygenation status measured by SpO2 (MD 1.35, P=.04) compared with comparators. Subgroup analyses revealed that VR-complemented PR had a significantly greater effect on FEV1 (L) (MD 0.32, P=.005) and 6MWD (MD 40.93, P&lt;.001) compared with the nonactive exercise control group. Additionally, VR-complemented PR showed a greater improvement in FEV1/FVC (MD 6.15, P&lt;.001) compared with the active exercise control group. Intervention duration influenced outcomes, with 5-12-week programs showing the greatest improvement in 6MWD (MD 38.96, P&lt;.001). VR-complemented PR was well-accepted, with higher adherence and engagement rates than comparators.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;VR-complemented PR significantly improves lung function, exercise capacity, dyspnea, health status, and oxygenation in patients with COPD compared with comparators, while enhancing adherence and engagement. Sub","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64742"},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143795684","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":"An explainable machine-learning model for predicting persistent sepsis associated acute kidney injury: development, validation, and comparison with CCL14.","authors":"Wei Jiang, Yaosheng Zhang, Jiayi Weng, Lin Song, Siqi Liu, Xianghui Li, Shiqi Xu, Keran Shi, Luanluan Li, Chuanqing Zhang, Jing Wang, Quan Yuan, Yongwei Zhang, Jun Shao, Jiangquan Yu, Ruiqiang Zheng","doi":"10.2196/62932","DOIUrl":"https://doi.org/10.2196/62932","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Persistent sepsis-associated acute kidney injury (SA-AKI) portends worse clinical outcomes and remains a therapeutic challenge for clinicians. Early identification and prediction of persistent SA-AKI is crucial.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;The aim of this study was to develop and validate an interpretable machine learning (ML) model that predicts persistent SA-AKI, and to compare its diagnostic performance with CCL14 in a prospective cohort.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Four retrospective cohorts and one prospective cohort were used for model derivation and validation. The derivation cohort utilized the MIMIC-IV database, randomly split into 80% for model construction and 20% for internal validation. External validation is conducted using subsets of the MIMIC-III dataset, the e-ICU dataset, and retrospective cohorts from the ICU of a Northern Jiangsu people's hospital. Prospective data from the same ICU were used for validation and compared with urinary CCL14 biomarker measurements. AKI was defined based on serum creatinine and urine output, using the kidney disease: Improving Global Outcomes (KDIGO) criteria. Routine clinical data within the first 24 hours of ICU admission were collected, and eight ML algorithms were utilized to construct the prediction model. Multiple evaluation metrics, including the area under the receiver operating characteristic curve (AUC), were employed to compare predictive performance. Feature importance was ranked using SHAP, and the final model was explained accordingly. In addition, the model is developed into a web-based application using the Streamlit framework to facilitate its clinical application.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In this study, a total of 46,097 sepsis patients from multiple cohorts were enrolled for analysis. Among the 17,928 sepsis patients in the derivation cohort, 8,081 cases (45.1%) developed into persistent SA-AKI. Among eight ML models, the Gradient Boosting Machine (GBM) model demonstrated superior discriminative ability. Following feature importance ranking, a final interpretable GBM model comprising twelve features (AKI stage, Δcreatinine, urine output, furosemide dose, BMI, SOFA score, KRT, mechanical ventilation, lactate, Bun, PT and age) was established. The final model accurately predicted the occurrence of persistent SA-AKI in both internal (AUC = 0.870) and external validation cohorts (MIMIC-III subset: AUC = 0.891, e-ICU dataset: AUC = 0.932, North Jiangsu people's Hospital retrospective cohort: AUC = 0.983). In the prospective cohort, the GBM model outperformed urinary CCL14 in predicting persistent SA-AKI (GBM AUC = 0.852 vs. CCL14 AUC = 0.821). Additionally, the model has been transformed into an online clinical tool to facilitate its application in clinical settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The interpretable GBM model has been shown to successfully and accurately predict the occurrence of persistent SA-AKI, demonstrating go","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":" ","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811627","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":"Virtual Patients Using Large Language Models: Scalable, Contextualized Simulation of Clinician-Patient Dialogue With Feedback.","authors":"David A Cook, Joshua Overgaard, V Shane Pankratz, Guilherme Del Fiol, Chris A Aakre","doi":"10.2196/68486","DOIUrl":"10.2196/68486","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We aimed to show that VPs powered by large language models (LLMs) can generate authentic dialogues, accurately represent patient preferences, and provide personalized feedback on clinical performance. We also explored using LLMs to rate the quality of dialogues and feedback.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted an intrinsic evaluation study rating 60 VP-clinician conversations. We used carefully engineered prompts to direct OpenAI's generative pretrained transformer (GPT) to emulate a patient and provide feedback. Using 2 outpatient medicine topics (chronic cough diagnosis and diabetes management), each with permutations representing different patient preferences, we created 60 conversations (dialogues plus feedback): 48 with a human clinician and 12 \"self-chat\" dialogues with GPT role-playing both the VP and clinician. Primary outcomes were dialogue authenticity and feedback quality, rated using novel instruments for which we conducted a validation study collecting evidence of content, internal structure (reproducibility), relations with other variables, and response process. Each conversation was rated by 3 physicians and by GPT. Secondary outcomes included user experience, bias, patient preferences represented in the dialogues, and conversation features that influenced authenticity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The average cost per conversation was US $0.51 for GPT-4.0-Turbo and US $0.02 for GPT-3.5-Turbo. Mean (SD) conversation ratings, maximum 6, were overall dialogue authenticity 4.7 (0.7), overall user experience 4.9 (0.7), and average feedback quality 4.7 (0.6). For dialogues created using GPT-4.0-Turbo, physician ratings of patient preferences aligned with intended preferences in 20 to 47 of 48 dialogues (42%-98%). Subgroup comparisons revealed higher ratings for dialogues using GPT-4.0-Turbo versus GPT-3.5-Turbo and for human-generated versus self-chat dialogues. Feedback ratings were similar for human-generated versus GPT-generated ratings, whereas authenticity ratings were lower. We did not perceive bias in any conversation. Dialogue features that detracted from authenticity included that GPT was verbose or used atypical vocabulary (93/180, 51.7% of conversations), was overly agreeable (n=56, 31%), repeated the question as part of the response (n=47, 26%), was easily convinced by clinician suggestions (n=35, 19%), or was not disaffected by poor clinician performance (n=32, 18%). For feedback, detractors included excessively positive feedback (n=42, 23%), failure to mention important weaknesses or strengths (n=41, 23%), or factual inaccuracies (n=39, 22%). Regarding validation of dialogue and feedback scores, items were meticulously developed (content evidence), and we confirmed expected relations with other variables (hi","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":" ","pages":"e68486"},"PeriodicalIF":5.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143033352","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":"Expression of Concern: Evaluating the Clinical Efficacy of an Exergame-Based Training Program for Enhancing Physical and Cognitive Functions in Older Adults With Mild Cognitive Impairment and Dementia Residing in Rural Long-Term Care Facilities: Randomized Controlled Trial.","authors":"","doi":"10.2196/75355","DOIUrl":"10.2196/75355","url":null,"abstract":"","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e75355"},"PeriodicalIF":5.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788526","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}