Journal of Medical Internet Research最新文献

{"title":"Remote Patient Monitoring System for Polypathological Older Adults at High Risk for Hospitalization: Retrospective Cohort Study.","authors":"Damien Testa, Israa Salma, Vincent Iborra, Victoire Roussel, Mireille Dutech, Etienne Minvielle, Elise Cabanes","doi":"10.2196/71527","DOIUrl":"https://doi.org/10.2196/71527","url":null,"abstract":"<p><strong>Background: </strong>Health care systems are increasingly facing challenges posed by the aging of populations. In particular, hospitalization, both initial and subsequent, is often observed among older adult patients. However, research suggests that nearly 23% of all hospitalizations could be avoided. In this perspective, remote patient monitoring (RPM) systems are emerging as a promising solution, enabling professionals to detect and manage patient complexities early within home-based care settings.</p><p><strong>Objective: </strong>This study aims to provide additional analyses regarding the impact of the EPOCA RPM system for polypathological older adult patients on the total number of unplanned hospitalization days and admissions, as well as emergency department (ED) visits. In a prior study, we evaluated the impact when the operator of the RPM system is a geriatrician. In this study, we assess the impact when the general practitioner is the operator.</p><p><strong>Methods: </strong>We used a retrospective, before-and-after cohort design. Polypathological older adult patients aged 70 and older, who benefited from the EPOCA RPM system for at least 1 year (between February 2022 and August 2024), were included in the analysis. We compared the outcomes between the previous year (Y-1) and the follow-up year (Y) by the EPOCA RPM system. Statistical analyses were significant at P value <.05.</p><p><strong>Results: </strong>In total, 80 patients were included in the analysis, with an average age of 87. The results showed a significant reduction (P<.001) between Y-1 and Y in the total number of unplanned hospital admissions (by 57%), hospitalization days (by 49%), and ED visits (by 62%). Our findings reflected a significant decrease per patient from 0.99 to 0.42 in hospital admissions, from 0.99 to 0.37 in ED visits, and a reduction of 9.7 hospitalization days per year (P<.001). Additional analyses stratifying by hospitalization history, disability level, and caregiver status showed that the greatest effect of the RPM system was on patients with high risk and severe disability. Finally, there was no observed increase in mortality or transfers to intensive care units.</p><p><strong>Conclusions: </strong>Our findings are consistent with our previous results regarding the potential benefits of the EPOCA RPM system in managing care for polypathological older adult patients, this time with general practitioners as system operators. They also support existing evidence on the promise of RPM in improving care and health outcomes for older adult patients while alleviating hospital burdens by reducing unplanned hospitalizations and ED visits. It is, therefore, essential to incorporate reimbursement policies for these RPM initiatives so as to facilitate their adoption within health care systems and enhance their impact on health outcomes.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71527"},"PeriodicalIF":5.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144637242","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":"Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline.","authors":"HongYi Li, Jun-Fen Fu, Andre Python","doi":"10.2196/71916","DOIUrl":"https://doi.org/10.2196/71916","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitable algorithms to support their work.</p><p><strong>Objective: </strong>We aimed to provide clinicians and other health care practitioners with systematic guidance in selecting an LLM that is relevant and appropriate to their needs and facilitate the integration process of LLMs in health care.</p><p><strong>Methods: </strong>We conducted a literature search of full-text publications in English on clinical applications of LLMs published between January 1, 2022, and March 31, 2025, on PubMed, ScienceDirect, Scopus, and IEEE Xplore. We excluded papers from journals below a set citation threshold, as well as papers that did not focus on LLMs, were not research based, or did not involve clinical applications. We also conducted a literature search on arXiv within the same investigated period and included papers on the clinical applications of innovative multimodal LLMs. This led to a total of 270 studies.</p><p><strong>Results: </strong>We collected 330 LLMs and recorded their application frequency in clinical tasks and frequency of best performance in their context. On the basis of a 5-stage clinical workflow, we found that stages 2, 3, and 4 are key stages in the clinical workflow, involving numerous clinical subtasks and LLMs. However, the diversity of LLMs that may perform optimally in each context remains limited. GPT-3.5 and GPT-4 were the most versatile models in the 5-stage clinical workflow, applied to 52% (29/56) and 71% (40/56) of the clinical subtasks, respectively, and they performed best in 29% (16/56) and 54% (30/56) of the clinical subtasks, respectively. General-purpose LLMs may not perform well in specialized areas as they often require lightweight prompt engineering methods or fine-tuning techniques based on specific datasets to improve model performance. Most LLMs with multimodal abilities are closed-source models and, therefore, lack of transparency, model customization, and fine-tuning for specific clinical tasks and may also pose challenges regarding data protection and privacy, which are common requirements in clinical settings.</p><p><strong>Conclusions: </strong>In this review, we found that LLMs may help clinicians in a variety of clinical tasks. However, we did not find evidence of generalist clinical LLMs successfully applicable to a wide range of clinical tasks. Therefore, their clinical deployment remains challenging. On the basis of this review, we propose an interactive online guideline for clinicians to select suitable LLMs by clinical task. With a clinical perspective and free of unnecessary technical jargon, this guideline may be used as a reference to successfully apply LLMs in clinical settings.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71916"},"PeriodicalIF":5.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612178","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":"Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.","authors":"Zixiang Ye, Zhangyu Lin, Enmin Xie, Chenxi Song, Rui Zhang, Hao-Yu Wang, Shanshan Shi, Lei Feng, Kefei Duo","doi":"10.2196/70943","DOIUrl":"https://doi.org/10.2196/70943","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Given the challenges faced during percutaneous coronary intervention (PCI) for heavily calcified lesions, accurately predicting PCI success is crucial for enhancing patient outcomes and optimizing procedural strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to use machine learning (ML) to identify coronary angiographic vascular characteristics and PCI procedures associated with the immediate procedural success rates of PCI in patients exhibiting moderate to severe coronary artery calcification (MSCAC).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This study included patients who underwent PCI between January 2017 and December 2018 in a cardiovascular hospital, comprising 3271 patients with MSCAC and 17,998 with no or mild coronary artery calcification. Six ML models-k-nearest neighbor, gradient boosting decision tree, Extreme Gradient Boosting (XGBoost), logistic regression, random forest, and support vector machine-were developed and validated, with synthetic minority oversampling technique used to address imbalance data. Model performance was compared using multiple parameters, and the optimal algorithm was selected. Model interpretability was facilitated by Shapley Additive Explanations (SHAP), identifying the top 6 coronary angiographic features with the highest SHAP values. The importance of different PCI procedures was also elucidated via SHAP values. Testing validation was performed in a separate cohort of 1437 patients with MSCAC in 2013. External validation was conducted in a general hospital of 204 patients with MSCAC in 2021. Sensitivity analyses were conducted in patients with acute coronary syndrome and chronic coronary syndrome.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In the development cohort, 7.6% (n=248) of patients with MSCAC experienced PCI failure compared to 4.3% (n=774) of patients with no or mild coronary artery calcification. The XGBoost model demonstrated superior performance, achieving the highest area under the receiver operator characteristic curve (AUC) of 0.984, average precision (AP) of 0.986, F1-score of 0.970, and G-mean of 0.970. Calibration curves indicated reliable predictive accuracy. The key predictive factors identified included lesion length, minimum lumen diameter, thrombolysis in myocardial infarction flow grade, chronic total occlusion, reference vessel diameter, and diffuse lesion (SHAP value 1.65, 1.40, 0.92, 0.60, 0.54, and 0.47, respectively). The use of modified balloons for calcified lesions had a positive effect on PCI success in patients with MSCAC (SHAP value 0.16). Sensitivity analyses showed consistent model performance across subgroups with similar top 5 coronary angiographic variables. The optimized XGBoost model maintained robust predictive performance in the testing cohort, with an AUC of 0.972, AP of 0.962, and F1-score of 0.940, and in the external validation set, with an AUC of 0.810, AP of 0.957, and F1-score of 0.892.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This st","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70943"},"PeriodicalIF":5.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612179","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":"Standardizing Survey Data Collection to Enhance Reproducibility: Development and Comparative Evaluation of the ReproSchema Ecosystem.","authors":"Yibei Chen, Dorota Jarecka, Sanu Ann Abraham, Remi Gau, Evan Ng, Daniel M Low, Isaac Bevers, Alistair Johnson, Anisha Keshavan, Arno Klein, Jon Clucas, Zaliqa Rosli, Steven M Hodge, Janosch Linkersdörfer, Hauke Bartsch, Samir Das, Damien Fair, David Kennedy, Satrajit S Ghosh","doi":"10.2196/63343","DOIUrl":"https://doi.org/10.2196/63343","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Inconsistencies in survey-based (eg, questionnaire) data collection across biomedical, clinical, behavioral, and social sciences pose challenges to research reproducibility. ReproSchema is an ecosystem that standardizes survey design and facilitates reproducible data collection through a schema-centric framework, a library of reusable assessments, and computational tools for validation and conversion. Unlike conventional survey platforms that primarily offer graphical user interface-based survey creation, ReproSchema provides a structured, modular approach for defining and managing survey components, enabling interoperability and adaptability across diverse research settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study examines ReproSchema's role in enhancing research reproducibility and reliability. We introduce its conceptual and practical foundations, compare it against 12 platforms to assess its effectiveness in addressing inconsistencies in data collection, and demonstrate its application through 3 use cases: standardizing required mental health survey common data elements, tracking changes in longitudinal data collection, and creating interactive checklists for neuroimaging research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We describe ReproSchema's core components, including its schema-based design; reusable assessment library with &gt;90 assessments; and tools to validate data, convert survey formats (eg, REDCap [Research Electronic Data Capture] and Fast Healthcare Interoperability Resources), and build protocols. We compared 12 platforms-Center for Expanded Data Annotation and Retrieval, formr, KoboToolbox, Longitudinal Online Research and Imaging System, MindLogger, OpenClinica, Pavlovia, PsyToolkit, Qualtrics, REDCap, SurveyCTO, and SurveyMonkey-against 14 findability, accessibility, interoperability, and reusability (FAIR) principles and assessed their support of 8 survey functionalities (eg, multilingual support and automated scoring). Finally, we applied ReproSchema to 3 use cases-NIMH-Minimal, the Adolescent Brain Cognitive Development and HEALthy Brain and Child Development Studies, and the Committee on Best Practices in Data Analysis and Sharing Checklist-to illustrate ReproSchema's versatility.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;ReproSchema provides a structured framework for standardizing survey-based data collection while ensuring compatibility with existing survey tools. Our comparison results showed that ReproSchema met 14 of 14 FAIR criteria and supported 6 of 8 key survey functionalities: provision of standardized assessments, multilingual support, multimedia integration, data validation, advanced branching logic, and automated scoring. Three use cases illustrating ReproSchema's flexibility include standardizing essential mental health assessments (NIMH-Minimal), systematically tracking changes in longitudinal studies (Adolescent Brain Cognitive Development and HEALthy Brain and Child Development), and c","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e63343"},"PeriodicalIF":5.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612180","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 Psychosocial Interventions Tailored for People in Opioid Use Disorder Treatment: Scoping Review.","authors":"Madison Scialanca, Karen Alexander, Babak Tofighi","doi":"10.2196/69538","DOIUrl":"10.2196/69538","url":null,"abstract":"<p><strong>Background: </strong>A total of 60% of patients with opioid use disorder (OUD) leave treatment early. Psychosocial interventions can enhance treatment retention by addressing behavioral and mental health needs related to early treatment discontinuation, but intervention engagement is key. If well-designed, digital platforms can increase the engagement, reach, and accessibility of psychosocial interventions. Prior reviews of OUD treatment have predominantly focused on outcomes, such as reductions in substance use, without identifying the underlying behavior change principles that drive the effectiveness of interventions.</p><p><strong>Objective: </strong>This scoping review aims to document and describe recent digital psychosocial interventions, including their behavior change strategies, for patients receiving medication for OUD (MOUD).</p><p><strong>Methods: </strong>Predefined search terms were used to search Ovid, CINAHL, and PubMed databases for peer-reviewed literature published in the last 10 years. The database search resulted in 1381 relevant studies, and 16 of them remained after applying the inclusion criteria. Studies were included if they (1) evaluated a digital intervention with behavioral, psychosocial, or counseling components for people in OUD treatment and (2) were published in English in peer-reviewed journals.</p><p><strong>Results: </strong>The 16 studies reviewed comprised 6 randomized controlled trials, 6 pilot studies, 2 qualitative studies, and 2 retrospective cohort studies. Smartphone apps (n=8) were the most prevalent intervention delivery method, with other studies using telemedicine (n=3), virtual reality (n=1), telephone calls (n=1), or text messaging (n=3) to deliver psychosocial interventions in either a synchronous (n=7) or asynchronous (n=9) manner. The digital interventions reviewed predominately delivered cognitive behavioral therapy education through a phone call (n=1), a text message (n=2), a smartphone app (n=7), or tele-counseling (n=1). The predominant behavior change strategies implemented were self-monitoring, feedback and reinforcement, psychoeducation, cue awareness, and providing instruction. One intervention reviewed uses the evidence base of mindfulness-oriented recovery enhancement.</p><p><strong>Conclusions: </strong>Participants in the studies reviewed indicated a preference for digital, flexible, patient-centered psychosocial interventions that emphasized improved patient-provider relationships. While randomized controlled trials comprised a significant portion of the studies, the inclusion of pilot studies and qualitative research highlights the field's ongoing exploration of feasibility and effectiveness. These findings underscore the growing role of digital health solutions in psychosocial care, though further research is needed to optimize engagement, delivery, and long-term outcomes.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e69538"},"PeriodicalIF":5.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612177","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: \"Impact of Virtual Care With Remote Automated Monitoring on the Rate of Acute Hospital Care Post Discharge and Index Length of Hospital Stay: Protocol for the Post Discharge After Surgery Virtual Care With Remote Automated Monitoring Technology 3 (PVC-RAM-3) Trial\".","authors":"Sandra Ofori, Michael H McGillion, Flavia K Borges, Carley Ouellette, Ameen Patel, David Conen, Maura Marcucci, Michael Ke Wang, Lily Jaeyoung Park, Conor Bell, Jennifer Lounsbury, Kanae Nagatani, Vikas Tandon, Trevor J Wilkieson, Ahraaz Wyne, Valerie Harvey, Stephanie Harrison, Rahima Nenshi, Jessica Bogach, John Harlock, Margherita Cadeddu, Shawn Forbes, Shariq Haider, Reza D Mirza, Sunita Narang, Clare J Reade, Daniel M Tushinski, Amit Raut, Samir Raza, Ted Scott, Anthony Adili, Jeremy Petch, P J Devereaux","doi":"10.2196/78893","DOIUrl":"10.2196/78893","url":null,"abstract":"<p><p>[This corrects the article DOI: .].</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e78893"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608591","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":"Factors Affecting Patients' Use of Telehealth Services: Cross-Sectional Survey Study.","authors":"Jiajia Qu","doi":"10.2196/63295","DOIUrl":"https://doi.org/10.2196/63295","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The increased integration of telehealth services into health care systems, especially during the COVID-19 pandemic, transformed patient-provider interactions. Despite numerous benefits that promote health equity and resource allocation, patients' acceptance and use of telehealth have declined post pandemic. To enhance health care delivery and patient satisfaction, we study the factors of this decline from the perspective of patient characteristics that influence the adoption and use of telehealth services.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study examines the direct impact of patient trust, social determinants of health, and health self-efficacy on telehealth usage, the indirect effect of confidence in health information seeking, patient-centered communication, and health literacy barriers on telehealth usage through trust.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This paper uses secondary data from cycle 6 of the Health Information National Trends Survey, a nationally representative dataset collected by the National Cancer Institute. This dataset used a mixed-mode experimental design, with data collected between March and November 2022. The survey included 2 experimental conditions: concurrent (web and paper surveys offered simultaneously) and sequential (web survey offered first, followed by paper). A total of 6252 respondents participated, with a household response rate of 28.1% (6252/22,471). Respondents were randomly assigned to 1 of 3 web-based survey groups to address data quality issues such as speeding and straight lining. We use structural equation modeling to test our research questions, evaluating both direct and indirect pathways influencing telehealth usage. Common method bias is addressed through Harman's single-factor test, and robustness checks ensure the validity and reliability of our results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Out of 5554 participants who had at least 1 doctor visit within the past 12 months, 44.89% used telehealth services in the past year. Trust has an inverted U-shaped relationship with confidence in health information seeking (β=-.031; P=.002); we find trust positively influenced by patient-centered communication (β=.156; P&lt;.001) and negatively affected by health literacy barriers (β=-.063; P&lt;.001). Trust enhances telehealth usage (β=.025; P&lt;.001), with social determinants of health exerting a positive impact (β=.105; P&lt;.001) and health self-efficacy having a negative impact (β=-.019; P=.007).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study finds that trust, social determinants of health, and health self-efficacy directly impact telehealth usage. Additionally, telehealth usage is indirectly influenced by patient characteristics, such as confidence in health information seeking and health literacy barriers, as well as by a patient-centered communication environment. The findings emphasize the need for targeted interventions to improve patient health literacy and engagement, thereby pr","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e63295"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608605","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":"Facilitators and Challenges to Adoption of a Digital Health Tool for Opioid Use Disorder Treatment in Primary Care: Mixed Methods Study.","authors":"Omar Nieto, Allison D Rosen, Mariah M Kalmin, Li Li, Steven J Shoptaw, Steven P Jenkins, Zahra Zarei Ardestani, Bengisu Tulu","doi":"10.2196/69953","DOIUrl":"https://doi.org/10.2196/69953","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The United States is facing an opioid overdose epidemic resulting in an unprecedented number of preventable deaths. The use of medications including buprenorphine and methadone has proven effective for opioid use disorder (OUD), but many patients struggle to stay in treatment. Novel solutions, such as digital health tools, offer one option to help improve clinic management and improve treatment engagement.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;Using a mixed methods approach, we investigated facilitators and barriers to the use of a third-party digital health platform called Opioid Addiction Recovery Support (OARS) to aid OUD treatment engagement and adherence in a primary care setting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Patient and provider use of OARS was observed for 10 months and summarized using descriptive statistics. Differences in use were assessed using Wilcoxon signed rank tests. Additionally, key informant interviews were conducted with providers who prescribe medication for opioid use disorder (MOUD) and their case managers to understand the facilitators and barriers to implementation. Qualitative data were analyzed using a coding reliability thematic analysis approach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Among 205 patients invited to use OARS, the median age was 37 (IQR 31-44) years, 130 (63.4%) identified as men, and 193 (94.1%) identified as non-Hispanic White. Of these 205 patients, 158 (77.1%) used the app at least 1 time. The median number of days the 158 patients viewed test results was 1 (IQR 1-3), progress was 1 (IQR 0-2), and educational content was 0 (IQR 0-1). The 55 patients whose providers had manually entered their results into OARS when the electronic health record (EHR) integration failed viewed test results (P=.002), progress (P&lt;.001), and educational content (P&lt;.001) more days than the 103 patients who could not view their results in OARS. Providers and the lead case manager reported that OARS increased patient-provider communication, allowed patients to better track their overall MOUD treatment, and enhanced providers' ability to identify patients at risk for relapse. They also acknowledged that the lack of integration between OARS with the EHR resulted in administrative burdens, which impacted provider use of the system.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Findings from this study highlight the challenges of successfully implementing OARS with patients who receive MOUD in primary care settings. Our results show a lack of OARS uptake among providers, case managers, and patients, despite positive assessments made by participants. We also show several barriers that impacted provider use, including the lack of integration between OARS and EHR. Future research is needed (1) to determine whether digital health tools like OARS are efficacious in improving OUD outcomes and, if proved efficacious, (2) to identify ways to routinize the use of digital health tools in MOUD treatment, primarily by solving t","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e69953"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608592","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":"AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients.","authors":"Jose Manuel Ruiz Giardin, Óscar Garnica, Nieves Mesa Plaza, Juan Víctor SanMartín López, Ana Farfán Sedano, Elena Madroñal Cerezo, Miguel Ángel Duarte Millán, Aida Izquierdo Martínez, Luis Rivas, Marta Rivilla, Alejandro Morales Ortega, Begoña Frutos Pérez, Cristina De Ancos Aracil, Ruth Calderón, Guillermo Soria Fernandez, Jorge Marrero Francés, David Bernal Bello, Jose Ángel Satué Bartolomé, María Toledano Macías, Sara Piedrabuena García, Marta Guerrero Santillán, Rafael Cristóbal, Belen Mora, Laura Velázquez Ríos, Vanesa García de Viedma, Paula Cuenca Ruiz, Ibone Ayala Larrañaga, Lorena Carpintero, Celia Lara, Alvaro Ricardo Llerena, Virginia García Bermúdez, Gema Delgado Cárdenas, Paloma Pardo Rovira, Elena Tejero Sánchez, Maria Jesús Domínguez García, Carolina Mariño, Cristina Bravo, Ana Ontañon, Mario García, Jose Ignacio Hidalgo Pérez, Santiago Prieto Menchero, Natalia González Pereira, Sonia Gonzalo Pascua, Jorge Tarancón Rey, Luis Antonio Lechuga Suárez","doi":"10.2196/70674","DOIUrl":"10.2196/70674","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;One of the main challenges with COVID-19 has been that although there are known factors associated with a worse prognosis, clinicians have been unable to predict which patients, with similar risk factors, will die or require intensive care unit (ICU) care.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to develop a personalized artificial intelligence model to predict the patient risk of mortality and ICU admission related to SARS-CoV-2 infection during the initial medical evaluation before any kind of treatment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;It is a population-based, observational, retrospective study covering from February 1, 2020, to January 24, 2023, with different circulating SARS-CoV-2 viruses, vaccinated status, and reinfections. It includes patients attended by the reference hospital in Fuenlabrada (Madrid, Spain). The models used the random forest technique, Shapley Additive Explanations method, and processing with Python (version 3.10.0; Python Software Foundation) and scikit-learn (version 1.3.0). The models were applied to different epidemic SARS-CoV-2 infection waves. Data were collected from 11,975 patients (4998 hospitalized and 6737 discharged). Predictive models were built with records from 4758 patients and validated with 6977 patients after evaluation in the emergency department. Variables recorded were age, sex, place of birth, clinical data, laboratory results, vaccination status, and radiologic data at admission.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The best mortality predictor achieved an area under the receiver operating characteristic curve (AUC) of 0.92, sensitivity of 0.89, specificity of 0.82, positive predictive value (PPV) of 0.35, and mean negative predictive value (NPV) of 0.98. The ICU admission predictor had an AUC of 0.89, sensitivity of 0.75, specificity of 0.88, PPV of 0.37, and NPV of 0.98. During validation, the mortality model exhibited good performance for the nonhospitalized group, achieving an AUC of 0.95, sensitivity of 0.88, specificity of 0.98, PPV of 0.21, and NPV of 0.99, predicting the death of 30 of 34 patients who were not hospitalized. For the hospitalized patients, the mortality model achieved an AUC of 0.85, sensitivity of 0.86, specificity of 0.74, PPV of 0.24, and NPV of 0.98. The model for predicting ICU admission had an AUC of 0.82, sensitivity of 1.00, specificity of 0.59, PPV of 0.05, and NPV of 1.00. The models' metrics presented stability along all pandemic waves. Key mortality predictors included age, Charlson value, and tachypnea. The worse prognosis was linked to high values in urea, erythrocyte distribution width, oxygen demand, creatinine, procalcitonin, lactate dehydrogenase, heart failure, D-dimer, oncological and hematological diseases, neutrophil, and heart rate. A better prognosis was linked to higher values of lymphocytes and systolic and diastolic blood pressures. Partial or no vaccination provided less protection than full vaccination.&lt;/p&gt;&lt;","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e70674"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608590","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":"Sleep Quality as a Mediator of Internet Gaming Disorder and Executive Dysfunction in Adolescents: Cross-Sectional Questionnaire Study.","authors":"Michoel L Moshel, Wayne Warburton, Rainer Thomasius, Kerstin Paschke","doi":"10.2196/68571","DOIUrl":"https://doi.org/10.2196/68571","url":null,"abstract":"<p><strong>Background: </strong>Internet gaming disorder (IGD) has been associated with impairments in executive functioning, particularly inattention and impulsivity. Sleep quality has separately been linked to both gaming behavior and cognitive performance, yet its role as a mediating factor in this relationship is underexplored.</p><p><strong>Objective: </strong>This study aimed to determine whether sleep quality mediates the relationship between IGD symptoms and executive dysfunction in adolescents, specifically focusing on the domains of inattention and hyperactivity or impulsivity. A reverse mediation model was also tested to explore the bidirectional nature of these relationships.</p><p><strong>Methods: </strong>A representative sample of 1000 adolescents (539/1000, 53.9% males), aged between 12 and 17 years (mean 14.52, SD 1.64), completed validated self-report measures of IGD symptoms, executive dysfunction, and sleep quality. Structural equation modeling was used to test direct and indirect effects with age and gender included as covariates.</p><p><strong>Results: </strong>Of the sample, 2.4% (24/1000) met criteria for IGD (875/1000, 87.5% males), and 22.6% (226/1000) met criteria for chronic sleep reduction. Among those with IGD, 54.2% (542/1000) also experienced chronic sleep reduction. In model A (IGD → Sleep → Executive Dysfunction), IGD symptoms were associated with poorer sleep quality (a=0.32, 95% CI 0.19-0.44), which in turn were associated with greater executive dysfunction (b=0.05, 95% CI 0.01-0.10). The indirect effect was significant (a×b=0.02, 95% CI 0.01-0.04), and sleep quality was a partial mediator. In the reverse model (model B), executive dysfunction was associated with poorer sleep quality (a=0.15, 95% CI 0.06-0.25), which subsequently was associated with higher IGD symptoms (b=0.11, 95% CI 0.07-0.16); indirect effect a×b=0.02, 95% CI 0.01-0.04. Simple slope analysis showed that IGD symptoms were associated only with executive dysfunction at average or poor levels of sleep quality. At higher levels of sleep quality, this relationship was no longer significant.</p><p><strong>Conclusions: </strong>The results of this study suggest that sleep quality may be an important intermediary mechanism by which IGD might contribute to executive dysfunction and provide a basis for the development and implementation of strategies that target sleep issues in IGD. Prospective longitudinal research is needed to examine the directionality of the relationships between IGD, sleep quality, and executive dysfunction longitudinally.</p>","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e68571"},"PeriodicalIF":5.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144600723","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}