Mayo Clinic Proceedings. Digital health最新文献

{"title":"Optimizing Digital Management of Research and Collaboration With Academic Information Manager","authors":"Peyman Nejat MD ,&nbsp;Vitali Fedosov MD, PhD ,&nbsp;Chady Meroueh MD ,&nbsp;Hugo Botha MB, ChB ,&nbsp;Svetlana Herasevich MD, MS ,&nbsp;Ing Tiong MS, MA ,&nbsp;David Martin MD, PhD ,&nbsp;Brian W. Pickering MD, MS ,&nbsp;Vitaly Herasevich MD, PhD","doi":"10.1016/j.mcpdig.2025.100222","DOIUrl":"10.1016/j.mcpdig.2025.100222","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the efficacy, efficiency, and usability of the current iteration of the fully automatic Academic Information Manager (AIM) within the Department of Anesthesiology and Perioperative Medicine.</div></div><div><h3>Participants and Methods</h3><div>AIM was designed, developed, and deployed to address the growing need for digital information management in academic research. In a randomized, unblinded crossover study from April 1, 2020 to August 1, 2020, 15 participants completed 8 tasks using both AIM and conventional information retrieval methods. We assessed task completion time (efficiency), task completion status and accuracy (efficacy), subjective mental workload using the National Aeronautics and Space Administration Task Load Index (NASA-TLX), and system usability using System Usability Scale questionnaire, with and without AIM.</div></div><div><h3>Results</h3><div>Using AIM resulted in a significant time saving, with significantly higher task completion (99% vs 57%) and accuracy (99% vs 59%) compared with conventional methods. The NASA-TLX scores with AIM showed a statistically significant decrease in mental demand, temporal demand, effort, and frustration, along with an increase in performance, compared with those without AIM. The System Usability Scale score for AIM was above the 90th percentile.</div></div><div><h3>Conclusion</h3><div>Using AIM, we observed a significant increase in efficacy and efficiency, along with a decreased mental workload, as measured by NASA-TLX, and improved usability scores. Implementing AIM will help new investigators quickly and intuitively identify ongoing research at our institution. It will also enable them to broadcast their research interests to find potential collaborators.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912144","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":"InfoKids+: A Validation Study of a Pediatric Acuity Risk Stratification Algorithm","authors":"Carl A. Starvaggi MD ,&nbsp;Sophie Affentranger MMed ,&nbsp;Noelie Lengeler MMed ,&nbsp;Johan N. Siebert MD ,&nbsp;Annick Galetto-Lacour MD ,&nbsp;Rainer Tan PhD ,&nbsp;Manon Jaboyedoff MD ,&nbsp;Claudia E. Kuehni MD ,&nbsp;Mary-Anne Hartley PhD ,&nbsp;Kristina Keitel PhD","doi":"10.1016/j.mcpdig.2025.100220","DOIUrl":"10.1016/j.mcpdig.2025.100220","url":null,"abstract":"<div><h3>Objective</h3><div>To prospectively validate InfoKids+, a pediatric acuity electronic risk stratification algorithm (eRSA), against a nurse-based triage standard (nbTS).</div></div><div><h3>Participants and Methods</h3><div>We conducted a prospective validation study in a Swiss university hospital pediatric emergency department to assess the performance of a pediatric acuity eRSA, InfoKids+, on the basis of a well-established parental guidance application, InfoKids. Participants completed the eRSA once seated in a consultation booth. We compared the acuity levels from InfoKids+ (urgent, &lt;4 hours; nonurgent, &lt;24 hours; and no emergency, ≥24 hours) against an nbTS. The primary outcome was the level of agreement and rate of alignment between InfoKids+ and the reference standard.</div></div><div><h3>Results</h3><div>We included 1990 participants from June 3, 2020, through January 31, 2022. InfoKids+ showed a slight level of agreement with the nbTS (κ_lw=0.08; 95% CI, 0.06-0.10). InfoKids+ triaged 1762 (89%) cases as urgent (&lt;4 hours), 106 (5%) as nonurgent (≤24 hours), and 122 (6%) as no emergency (≥24 hours), compared with 810 (41%), 843 (42%), and 337 (17%) triages by the nbTS, respectively (<em>P</em>&lt;.001). InfoKids+ acuity level aligned with the reference standard in 888 (45%) cases, whereas it overreferred and underreferred in 999 (50%) and 103 (5%) cases, respectively (<em>P</em>&lt;.001).</div></div><div><h3>Conclusion</h3><div>In summary, our study uncovered notable discrepancies between the InfoKids+ algorithmic triage and conventional nurse-based triage. Our results highlight the critical need for rigorous validation of such tools for accuracy and safety before public release to ensure these tools are beneficial and do not inadvertently cause harm or misallocation of resources.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100220"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927369","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":"Gender Disparities in Artificial Intelligence–Generated Images of Hospital Leadership in the United States","authors":"Mia Gisselbaek MD ,&nbsp;Joana Berger-Estilita MD, PhD ,&nbsp;Laurens Minsart MD ,&nbsp;Ekin Köselerli MD ,&nbsp;Arnout Devos PhD ,&nbsp;Francisco Maio Matos PhD ,&nbsp;Odmara L. Barreto Chang MD, PhD ,&nbsp;Peter Dieckmann PhD ,&nbsp;Melanie Suppan MD ,&nbsp;Sarah Saxena MD, PhD","doi":"10.1016/j.mcpdig.2025.100218","DOIUrl":"10.1016/j.mcpdig.2025.100218","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate demographic representation in artificial intelligence (AI)–generated images of hospital leadership roles and compare them with real-world data from US hospitals.</div></div><div><h3>Patients and Methods</h3><div>This cross-sectional study, conducted from October 1, 2024 to October 31, 2024, analyzed images generated by 3 AI text-to-image models: Midjourney 6.0, OpenAI ChatGPT DALL-E 3, and Google Gemini Imagen 3. Standardized prompts were used to create 1200 images representing 4 key leadership roles: chief executive officers, chief medical officers, chief nursing officers, and chief financial officers. Real-world demographic data from 4397 US hospitals showed that chief executive officers were 73.2% men; chief financial officers, 65.2% men; chief medical officers, 85.7% men; and chief nursing officers, 9.4% men (overall: 60.1% men). The primary outcome was gender representation, with secondary outcomes including race/ethnicity and age. Two independent reviewers assessed images, with interrater reliability evaluated using Cohen κ.</div></div><div><h3>Results</h3><div>Interrater agreement was high for gender (κ=0.998) and moderate for race/ethnicity (κ=0.670) and age (κ=0.605). DALL-E overrepresented men (86.5%) and White individuals (94.5%). Midjourney showed improved gender balance (69.5% men) but overrepresented White individuals (75.0%). Imagen achieved near gender parity (50.3% men) but remained predominantly White (51.5%). Statistically significant differences were observed across models and between models and real-world demographics.</div></div><div><h3>Conclusion</h3><div>Artificial intelligence text-to-image models reflect and amplify systemic biases, overrepresenting men and White leaders, while underrepresenting diversity. Ethical AI practices, including diverse training data sets and fairness-aware algorithms, are essential to ensure equitable representation in health care leadership.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100218"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143936608","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":"Erratum to “Impact of Ambient Artificial Intelligence Documentation on Cognitive Load”","authors":"","doi":"10.1016/j.mcpdig.2025.100219","DOIUrl":"10.1016/j.mcpdig.2025.100219","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100219"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848529","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":"Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease","authors":"Yashbir Singh ME, PhD ,&nbsp;Quincy A. Hathaway MD, PhD ,&nbsp;Karthik Dinakar PhD ,&nbsp;Leslee J. Shaw PhD ,&nbsp;Bradley Erickson MD, PhD ,&nbsp;Francisco Lopez-Jimenez MD, MBA, MSc ,&nbsp;Deepak L. Bhatt MD, MPH, MBA","doi":"10.1016/j.mcpdig.2025.100217","DOIUrl":"10.1016/j.mcpdig.2025.100217","url":null,"abstract":"<div><div>This article aimed to explore topological uncertainty in medical imaging, particularly in assessing coronary artery calcification using artificial intelligence (AI). Topological uncertainty refers to ambiguities in spatial and structural characteristics of medical features, which can impact the interpretation of coronary plaques. The article discusses the challenges of integrating AI with topological considerations and the need for specialized methodologies beyond traditional performance metrics. It highlights advancements in quantifying topological uncertainty, including the use of persistent homology and topological data analysis techniques. The importance of standardization in methodologies and ethical considerations in AI deployment are emphasized. It also outlines various types of uncertainty in topological frameworks for coronary plaques, categorizing them as quantifiable and controllable or quantifiable and not controllable. Future directions include developing AI algorithms that incorporate topological insights, establishing standardized protocols, and exploring ethical implications to revolutionize cardiovascular care through personalized treatment plans guided by sophisticated topological analysis. Recognizing and quantifying topological uncertainty in medical imaging as AI emerges is critical. Exploring topological uncertainty in coronary artery disease will revolutionize cardiovascular care, promising enhanced precision and personalization in diagnostics and treatment for millions affected by cardiovascular diseases.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848528","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":"Impact of Digital Interventions in Occupational Health Care: A Systematic Review","authors":"Mirjam M. Jern-Matintupa MD, MPH ,&nbsp;Anita M. Riipinen MD, PhD ,&nbsp;Merja K. Laine MD, PhD","doi":"10.1016/j.mcpdig.2025.100216","DOIUrl":"10.1016/j.mcpdig.2025.100216","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the existing body of evidence and impact of digital interventions on occupational health care.</div></div><div><h3>Methods</h3><div>The search strategy and review process were conducted in accordance with the PRISMA guidelines. The search was carried out during a period from January 1, 2013 to June 5, 2023, using the SCOPUS and Ovid Medline databases. After the identification of the relevant records, screening was conducted in 3 stages, following specific predetermined inclusion and exclusion criteria. A data-extraction model was created on the basis of the aim of the review. The quality of the selected studies was evaluated using the Effective Public Health Practice framework. Owing to the heterogeneity of the outcome measures, we used narrative synthesis to summarize the findings.</div></div><div><h3>Results</h3><div>We identified 382 records in SCOPUS and 441 in Ovid Medline. We selected 54 studies to be included in the evidence synthesis. The health targets of the interventions varied widely, but we identified 2 main focus areas: sedentary behavior (n=17, 32%) and mental health (n=14, 26%). Even when the studies had the same health target, the outcomes and chosen measures varied widely. Given the considerable effect of the primary outcome, mental health appears to be a good target for digital interventions. Online training and computer software could be especially effective.</div></div><div><h3>Conclusion</h3><div>The potential positive impact of digital interventions on mental health, especially online training, should be leveraged by health care professionals and providers. In order to provide more specific recommendations for health care professionals, occupational health care researchers should strive for consensus on outcome measures.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100216"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to Leveraging the Metaverse for Enhanced Longevity as a Component of Health 4.0 [Mayo Clinic Proceedings: Digital Health. 2024;2:139-151]","authors":"","doi":"10.1016/j.mcpdig.2025.100215","DOIUrl":"10.1016/j.mcpdig.2025.100215","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selecting Wearable Devices to Measure Cardiovascular Functions in Community-Dwelling Adults: Application of a Practical Guide for Device Selection","authors":"Jessica K. Lu MEng ,&nbsp;Weilan Wang PhD ,&nbsp;Jorming Goh PhD ,&nbsp;Andrea B. Maier MD, PhD","doi":"10.1016/j.mcpdig.2025.100202","DOIUrl":"10.1016/j.mcpdig.2025.100202","url":null,"abstract":"<div><div>Continuous monitoring of cardiovascular functions can provide crucial insights into the health status and lifestyle behaviors of an individual. Wearable devices offer a convenient and cost-effective solution for collecting cardiovascular measurements outside clinical settings. However, the abundance of available devices poses challenges for researchers, health care professionals, and device users in selecting the most suitable one. This article illustrates the application of a practical guide for selecting wearable devices for the continuous monitoring of cardiovascular functions in community-dwelling adults who are generally healthy or have minimal, well-managed chronic conditions. An initial systematic review of the literature revealed 216 devices, each of which were assessed on the basis of 5 core criteria from the guide: (1) continuous monitoring capability, (2) device availability and suitability, (3) technical performance (accuracy and precision), (4) feasibility of use, and (5) cost evaluation. From the 216 devices, there were 136 devices capable of continuous monitoring. After the exclusion of unavailable and unsuitable devices, 53 devices underwent validation assessment of accuracy and precision. Although COSMIN criteria were applied to evaluate technical performance, a lack of validation for certain devices limits a comprehensive evaluation. After selection of valid devices, the feasibility and cost of 20 devices were examined. Wearable devices, such as the Apple Watch Series 9, Fitbit Charge 6, Garmin vívosmart 5, and Oura Ring Gen3, emerged as suitable devices to measure cardiovascular function in community-dwelling adults. The systematic process for device selection could also be applied to select wearable devices for the measurement of other physiologic variables and lifestyle behaviors.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The PERFORM Study: Artificial Intelligence Versus Human Residents in Cross-Sectional Obstetrics-Gynecology Scenarios Across Languages and Time Constraints","authors":"Canio Martinelli MD ,&nbsp;Antonio Giordano MD ,&nbsp;Vincenzo Carnevale PhD ,&nbsp;Sharon Raffaella Burk PhD ,&nbsp;Lavinia Porto MD ,&nbsp;Giuseppe Vizzielli MD ,&nbsp;Alfredo Ercoli MD","doi":"10.1016/j.mcpdig.2025.100206","DOIUrl":"10.1016/j.mcpdig.2025.100206","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically evaluate the performance of artificial intelligence (AI) large language models (LLMs) compared with obstetrics-gynecology residents in clinical decision-making, examining diagnostic accuracy and error patterns across linguistic domains, time constraints, and experience levels.</div></div><div><h3>Patients and Methods</h3><div>In this cross-sectional study, we evaluated 8 AI LLMs and 24 obstetrics-gynecology residents (Years 1-5) using 60 standardized clinical scenarios. Most AI LLMs and all residents were assessed in May 2024, whereas chat GPT-01-preview, chat-GPT4o, and Claude Sonnet 3.5 were evaluated in November 2024. The assessment framework incorporated English and Italian scenarios under both timed and untimed conditions, along with systematic error pattern analysis. The primary outcome was diagnostic accuracy; secondary end points included AI system stratification, resident progression, language impact, time pressure effects, and integration potential.</div></div><div><h3>Results</h3><div>The AI LLMs reported superior overall accuracy (73.75%; 95% confidence interval [CI], 69.64%-77.49%) compared with residents (65.35%; 95% CI, 62.85%-67.76%; <em>P</em>&lt;.001). High-performing AI systems (ChatGPT-01-preview, GPT4o, and Claude Sonnet 3.5) achieved consistently high cross-linguistic accuracy (88.33%) with minimal language impact (6.67%±0.00%). Resident performance declined significantly under time constraints (from 73.2% to 56.5% adjusted accuracy; Cohen’s d=1.009; <em>P</em>&lt;.001), whereas AI systems reported lesser deterioration. Error pattern analysis indicated a moderate correlation between AI and human reasoning (r=0.666; <em>P</em>&lt;.001). Residents exhibited systematic progression from year 1 (44.7%) to year 5 (87.1%). Integration analysis found variable benefits across training levels, with maximum enhancement in early-career residents (+29.7%; <em>P</em>&lt;.001).</div></div><div><h3>Conclusion</h3><div>High-performing AI LLMs reported strong diagnostic accuracy and resilience under linguistic and temporal pressures. These findings suggest that AI-enhanced decision-making may offer particular benefits in obstetrics and gynecology training programs, especially for junior residents, by improving diagnostic consistency and potentially reducing cognitive load in time-sensitive clinical settings.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Experience With an Optical Character Recognition Search Application for Review of Outside Medical Records”","authors":"","doi":"10.1016/j.mcpdig.2025.100208","DOIUrl":"10.1016/j.mcpdig.2025.100208","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100208"},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}