Mayo Clinic Proceedings. Digital health最新文献

{"title":"A Scoping Review of Large Language Models in Personal Sleep Wellness","authors":"Hamid Mansoor PhD","doi":"10.1016/j.mcpdig.2025.100301","DOIUrl":"10.1016/j.mcpdig.2025.100301","url":null,"abstract":"<div><div>As sleep health becomes increasingly central to personal well-being, individuals are turning to digital tools for education, tracking, and behavior change support. Large language models like ChatGPT and Gemini have recently emerged as promising components of these tools, capable of generating personalized, conversational, and context-aware sleep guidance. This scoping survey synthesizes findings from 21 papers that explore the use of large language models in nonclinical, everyday user-focused applications for sleep health. We organize the literature into 4 core use cases: educational question answering, condition-specific support (eg, obstructive sleep apnea), personalized recommendations and coaching, and cognitive behavioral therapy–based self-help systems. We analyze the diverse data sources involved—including wearable sensor data, self-reported metrics, and synthetic benchmarks—as well as model architectures, fine-tuning techniques, and personalization strategies. Finally, we examine evaluation frameworks ranging from expert review to pilot user studies and LLM-based scoring. The review highlights current capabilities, methodological challenges, and open opportunities for advancing trustworthy, personalized sleep support using generative artificial intelligence, while emphasizing that much of the evidence remains preliminary, often short-term, expert-rated, or proxy-based, which limits external validity and generalizability.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100301"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic Patient Education in the Digital Era: Opportunities and Challenges in Diabetes Care","authors":"Jorge C. Correia MD, MSc ,&nbsp;Katarzyna Wac PhD ,&nbsp;Catherine Joly BSc ,&nbsp;Jean-Philippe Assal MD ,&nbsp;Surabhi Joshi MA ,&nbsp;Cosette Fakih El Khoury PhD ,&nbsp;Zoltan Pataky MD","doi":"10.1016/j.mcpdig.2025.100297","DOIUrl":"10.1016/j.mcpdig.2025.100297","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100297"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual Hospital, Real Health Care","authors":"Tejaswini Manavi PhD ,&nbsp;Derek O’Keeffe MEng, MBA, MD, PhD","doi":"10.1016/j.mcpdig.2025.100303","DOIUrl":"10.1016/j.mcpdig.2025.100303","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100303"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Quality and Safety of Ambient Digital Scribe Platforms Using Simulated Ambulatory Encounters","authors":"Taylor N. Anderson MD ,&nbsp;Vishnu Mohan MD ,&nbsp;David A. Dorr MD ,&nbsp;Raj M. Ratwani PhD ,&nbsp;Joshua M. Biro PhD ,&nbsp;Jeffrey A. Gold MD","doi":"10.1016/j.mcpdig.2025.100292","DOIUrl":"10.1016/j.mcpdig.2025.100292","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate and compare the quality and safety of ambient digital scribe (ADS) platforms using simulated ambulatory encounters.</div></div><div><h3>Methods</h3><div>Five ADS platforms were evaluated using audio recordings of fourteen simulated clinical encounters. Audio recordings were played on a laptop computer and captured by ADS platforms on a mobile phone. Generated transcripts were compared to professional transcriptions. Clinical notes were graded using rubrics of key elements for each case. Note errors were classified as omission, commission, or partially correct. Potential clinical harm was assessed using the agency for healthcare research and quality harm scale. Note quality was assessed using the 9-item Physician Documentation Quality Instrument (range 9-45). Statistical comparisons included Friedman and χ² tests with a correction for multiple comparisons.</div></div><div><h3>Results</h3><div>Transcripts generated by platforms A through D contained an average of 13.9 (95% CI, 6.0-17.5) errors, with 19.5% of the transcript errors transmitted to the clinical note (95% CI, 6.6%-28.8%). For clinical notes, mean percent error across platforms was 26.3% (95% CI, 17.0%-31.0%) with a significantly higher proportion of errors in notes generated by platform E (<em>P</em>&lt;.0053 for all comparisons). Of correctly reported elements, only 35.8%±11.3% were consistently correct across all platforms. An average of 3.0 (95% CI, 0-4, range 0-21) errors per case had potential for moderate-to-severe harm. The mean physician documentation quality instrument–9 score was 36±4, with significant variation between platforms.</div></div><div><h3>Conclusion</h3><div>Clinical notes generated by ADS platforms using simulated encounters reports important inter-platform and intra-platform variability in accuracy and quality. These findings indicate a need for standardized, objective evaluation and reporting.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100292"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inline Electronic Medical Record Workplace Violence (WPV) Documentation and Utilization of Automation Increases Capture of WPV Reporting Amongst Emergency Department Staff Within a Large Health System","authors":"Sarayna S. McGuire MD MS ,&nbsp;Matthew R. Neville MS ,&nbsp;Michael Elligan II MBA, PMP ,&nbsp;Heather A. Heaton MD ,&nbsp;Kristine M. Thompson MD ,&nbsp;James E. Colletti MD ,&nbsp;Abi Jireh G. Paraon MSN RN NI-BC ,&nbsp;David J. Julson ,&nbsp;Aidan F. Mullan MA ,&nbsp;Casey M. Clements MD PhD","doi":"10.1016/j.mcpdig.2025.100288","DOIUrl":"10.1016/j.mcpdig.2025.100288","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing LLM Performance for Radiology Report Mining Through Prompt Engineering","authors":"Mrinal K. Dhar PhD ,&nbsp;Muhammed Khalifa MD ,&nbsp;Elif G. Bozkurt MD ,&nbsp;Samuel C. Buchl BA ,&nbsp;Adriana V. Gregory PhD ,&nbsp;Timothy L. Kline PhD","doi":"10.1016/j.mcpdig.2025.100290","DOIUrl":"10.1016/j.mcpdig.2025.100290","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100290"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predict and Prepare: AI-Driven Insights for Clinical Resource Planning","authors":"Lindsey Asher DHA,&nbsp;Greg Davis MHA, FACHE,&nbsp;Brendan M. Carr MD,&nbsp;James E. Colletti MD,&nbsp;Laura E. Walker MD,&nbsp;Derick D. Jones MD","doi":"10.1016/j.mcpdig.2025.100286","DOIUrl":"10.1016/j.mcpdig.2025.100286","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100286"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing Retention in a Large-Scale Decentralized Clinical Trial: Learnings From the COVID-RED Trial","authors":"Laura C. Zwiers MPhil ,&nbsp;Duco Veen PhD ,&nbsp;Marianna Mitratza PhD ,&nbsp;Timo B. Brakenhoff PhD ,&nbsp;Brianna M. Goodale PhD ,&nbsp;Paul Klaver MSc ,&nbsp;Kay Y. Hage MSc ,&nbsp;Marcel van Willigen PhD ,&nbsp;George S. Downward PhD ,&nbsp;Peter Lugtig PhD ,&nbsp;Leendert van Maanen PhD ,&nbsp;Stefan Van der Stigchel PhD ,&nbsp;Peter van der Heijden PhD ,&nbsp;Maureen Cronin PhD ,&nbsp;Diederick E. Grobbee PhD ,&nbsp;COVID-RED Consortium","doi":"10.1016/j.mcpdig.2025.100264","DOIUrl":"10.1016/j.mcpdig.2025.100264","url":null,"abstract":"<div><h3>Objective</h3><div>To present retention strategies implemented in the coronavirus disease 2019 (COVID-19) rapid early detection trial, a decentralized trial investigating the use of a wearable device for severe acute respiratory syndrome coronavirus 2 detection, and to provide insights into study retention and investigate determinants of discontinuation.</div></div><div><h3>Patients and Methods</h3><div>The COVID-2019 rapid early detection trial collected data from 17,825 participants from February 22, 2021 to November 18, 2021. Participants wore a wearable device overnight and synchronized it with a mobile application on waking. Retention strategies included common and personalized activities. Multivariable logistic regression was used to identify participants at high risk of discontinuation after 6 months in the trial. Results were combined with insights from behavioral theory to target participants with additional telephone calls.</div></div><div><h3>Results</h3><div>Total of 14,326 (80.4%) participants remained in the trial after 6 months and 12,208 (68.5%) until the end of the trial. Multivariable logistic regression identified age, employment situation, living situation, and COVID-19 vaccination status as predictors of discontinuation. Subgroups at high risk of discontinuation were identified, and behavioral assessments indicated that the subgroup of vaccinated pensioners would receive additional telephone calls. Their dropout rate was 11.4% after telephone calls.</div></div><div><h3>Conclusion</h3><div>This study describes how innovative and targeted data-driven retention strategies can be applied in a large decentralized clinical trial and presents the implemented retention strategies and discontinuation rates. Results can serve as a starting point for designing retention strategies in future decentralized trials.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100264"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction Notices","authors":"","doi":"10.1016/j.mcpdig.2025.100305","DOIUrl":"10.1016/j.mcpdig.2025.100305","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100305"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Chest X-Ray Opportunistic Screening Model for Coronary Artery Calcium Deposition: A Multi-Objective Model With Multimodal Data Fusion","authors":"Jiwoong Jeong MS ,&nbsp;Chieh-Ju Chao MD ,&nbsp;Reza Arsanjani MD ,&nbsp;Chadi Ayoub MBBS, PhD ,&nbsp;Steven J. Lester MD ,&nbsp;Milagros Pereyra MD ,&nbsp;Ebram F. Said MD ,&nbsp;Michael Roarke BS ,&nbsp;Cecilia Tagle-Cornell MS ,&nbsp;Laura M. Koepke MSN ,&nbsp;Yi-Lin Tsai MD ,&nbsp;Chen Jung-Hsuan MD ,&nbsp;Chun-Chin Chang MD ,&nbsp;Juan M. Farina MD ,&nbsp;Hari Trivedi MD ,&nbsp;Bhavik N. Patel MD, MBA ,&nbsp;Imon Banerjee PhD","doi":"10.1016/j.mcpdig.2025.100300","DOIUrl":"10.1016/j.mcpdig.2025.100300","url":null,"abstract":"<div><h3>Objective</h3><div>To create an opportunistic screening model to predict coronary calcium burden and associated cardiovascular risk using only commonly available frontal chest x-rays (CXR) and patient demographics.</div></div><div><h3>Patients and Methods</h3><div>We proposed a novel multitask learning framework and trained a model using 2121 patients with paired gated computed tomography scans and CXR images internally (Mayo Clinic) from January 1, 2012, to December 31, 2022, with coronary artery calcification (CAC) scores (0, 1-99, and 100+) as ground truths. Results from the internal training were validated on multiple external datasets (Emory University Healthcare and Taipei Veterans General Hospital—from January 1, 2012, to December 31, 2022) with significant racial and ethnic differences.</div></div><div><h3>Results</h3><div>Classification performance between 0, 1-99, and 100+ CAC scores performed moderately on both the internal test and external datasets, reaching average f1-scores of 0.71±0.04 for Mayo, 0.65±0.02 for Emory University Healthcare, and 0.70±0.06 for Taipei Veterans General Hospital. For the clinically relevant risk identification, the performance of our model on the internal and 2 external datasets reached area under the receiver operating curves of 0.86±0.02, 0.77±0.03, and 0.82±0.03 for 0 versus 400+, respectively. For 0 versus 100+, we achieved area under the receiver operating curve of 0.83±0.03, 0.71±0.02, and 0.78±0.01, respectively. Prospective evaluation across 3 Mayo Clinic sites is on par with the external validations and reports only minimal temporal drift.</div></div><div><h3>Conclusion</h3><div>Open-source fusion artificial intelligence-CXR model performed better than existing state-of-the-art models for predicting CAC scores only on internal cohort, with robust performance on external datasets. This proposed model may be useful as a robust, first-pass opportunistic screening method for cardiovascular risk from regular CXR.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}