Mayo Clinic Proceedings. Digital health最新文献

{"title":"Natural Language Processing for Enhanced Clinical Decision Support in Allergy Verification for Medication Prescriptions","authors":"Juan Pablo Botero-Aguirre MS ,&nbsp;Michael Andrés García-Rivera MS","doi":"10.1016/j.mcpdig.2025.100244","DOIUrl":"10.1016/j.mcpdig.2025.100244","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and validate a named entity recognition (NER) model based on BERT-based model trained on Spanish-language corpor, for extracting allergy-related information from unstructured electronic health records.</div></div><div><h3>Patients and Methods</h3><div>The model was fine-tuned using 16,176 manually annotated allergy-related entities from anonimized patient records (hospitalized patients between January 1, 2021, and June 30, 2024). The data set was divided into training (80%) and testing (20%) subsets, and model performance was evaluated using accuracy, recall, and F1 score. The validated model was applied to another data set with 80,917 medication prescriptions from 5859 hospitalized patients with at least one prescribed medication (during August and September 2024) to detect potential prescription errors. Sensitivity, specificity, and Cohen κ were calculated using manual expert review as the gold standard.</div></div><div><h3>Results</h3><div>The model achieved an accuracy of 87.28% and an F1 score of 0.80. It effectively identified medication names (F1=0.91) and adverse reactions (F1=0.85) but struggled with recommendation-related entities (F1=0.29). The model detected prescription errors in 0.96% of cases, with a sensitivity of 75.73% and specificity of 99.98%. The weighted κ score (0.7797) indicated substantial agreement with expert annotations.</div></div><div><h3>Conclusion</h3><div>The BERT-based model trained on Spanish-language corpora–based NER model demonstrated strong performance in identifying nonallergic cases (specificity, 99.98%; negative predictive value, 99.97%) and showed promise for clinical decision support. Despite moderate sensitivity (75.73%), these results highlight the feasibility of using Spanish-language NER models to enhance medication safety.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100244"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571685","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":"An Automated Mobile Cognitive Test for the Identification of Cognitive Impairment: A Cross-sectional Feasibility and Diagnostic Study","authors":"Louis Y. Tee MD, PhD ,&nbsp;Li Feng Tan MBBS ,&nbsp;Santhosh Seetharaman MBBS ,&nbsp;Lian Leng Low MBBS ,&nbsp;Zhi Peng Ong BS ,&nbsp;Munirah Bashil BS ,&nbsp;Hock Hai Teo PhD","doi":"10.1016/j.mcpdig.2025.100252","DOIUrl":"10.1016/j.mcpdig.2025.100252","url":null,"abstract":"<div><h3>Objective</h3><div>To develop Digital Processing Speed Test (DPST), a free, automated, multilingual, artificial intelligence–based cognitive testing application, with the aim to enhance recognition of cognitive impairment in underserved communities by leveraging mobile health to improve cognitive testing’s accessibility.</div></div><div><h3>Patients and Methods</h3><div>In this cross-sectional feasibility and diagnostic study, we determined the test performance of DPST for the identification of mild cognitive impairment (MCI) and dementia, compared with traditional cognitive tests, such as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The study was conducted from January 19, 2021, to November 12, 2023. In total, 476 adult participants were recruited by consecutive sampling at waiting areas of primary and secondary care clinics. The participants completed MMSE and MoCA with trained assessors and then performed DPST independently on a mobile device. The reference standard was a clinical diagnosis of MCI/dementia by a memory specialist blinded to the DPST score.</div></div><div><h3>Results</h3><div>Area under the receiver operating characteristic curve analyses showed that area under the curves were similar for the 3 tests (MMSE, 0.862; MoCA, 0.888; DPST, 0.861). Likewise, sensitivity (DPST, 85.2%; MMSE, 85.2%; MoCA, 90.2%), negative likelihood ratio (DPST, 0.197; MMSE, 0.193; MoCA, 0.129), specificity (DPST, 75.0%; MMSE, 76.5%; MoCA, 76.2%), and positive likelihood ratio (DPST, 3.41; MMSE, 3.62; MoCA, 3.79) were similar.</div></div><div><h3>Conclusion</h3><div>Digital Processing Speed Test, a free, automated, multilingual cognitive test conducted on a mobile device, has similar test performance to MMSE and MoCA. Nonetheless, DPST does not capture the multidomain cognitive deficits that characterize MCI/dementia. Moreover, test-retest reliability and interrater agreement of artificial intelligence–based handwriting recognition needs further confirmation.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756917","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":"Digital Health Rights, Adult Systems: Are We Leaving Adolescents Behind?","authors":"Louisa Peralta PhD ,&nbsp;Cristyn Davies PhD ,&nbsp;Kellie Burns PhD ,&nbsp;Leena Paakkari PhD","doi":"10.1016/j.mcpdig.2025.100254","DOIUrl":"10.1016/j.mcpdig.2025.100254","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813863","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":"United States Food and Drug Administration Regulation of Clinical Software in the Era of Artificial Intelligence and Machine Learning","authors":"Vidhi Singh BS,&nbsp;Susan Cheng MD, MPH,&nbsp;Alan C. Kwan MD, MS,&nbsp;Joseph Ebinger MD, MS","doi":"10.1016/j.mcpdig.2025.100231","DOIUrl":"10.1016/j.mcpdig.2025.100231","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470263","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 Health Care in Hospital-at-Home Models for Patients With Acute Infections: A Scoping Review","authors":"Maria Normand Larsen MD ,&nbsp;Tatjana Sandreva Dreisig MD ,&nbsp;Maja Kjær Rasmussen MSc ,&nbsp;Anders N.Ø. Schultz MD ,&nbsp;Thyge Lynghøj Nielsen MD, PhD ,&nbsp;Thea K. Fischer MD, DMSc","doi":"10.1016/j.mcpdig.2025.100250","DOIUrl":"10.1016/j.mcpdig.2025.100250","url":null,"abstract":"<div><div>Given the imbalance between high care demand and strained hospital capacity, hospital-at-home (HaH) models offer a potential solution by providing hospital-level care in patients’ homes. This scoping review maps the literature on hospital-led virtual health care within HaH models for acute infections, focusing on intervention characteristics and evaluation designs. Following Johanna Briggs Institute guidelines and PRISMA-ScR, we included studies on virtual and hybrid HaH models using telemedicine for remote monitoring and interventions. The literature searches were performed from October 3, 2022 to October 22, 2022, and updated on July 11, 2024 and identified 15,062 potentially relevant records. From these, 79 studies met the inclusion criteria, highlighting the diversity of HaH models and their evaluations. Hybrid models provided broader treatment options, but many studies lacked detailed intervention descriptions, complicating implementation and meta-analyses. Most studies evaluated patient outcomes, with limited attention to health care staff and relatives. Nearly 45,000 participants were assessed, but only 254 participated in randomized controlled trials, indicating a need for more high-level evidence. Relevant gaps remain, including model heterogeneity and inconsistent reporting.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748793","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":"Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.","authors":"Isaiah Z Yao, Min Dong, William Y K Hwang","doi":"10.1016/j.mcpdig.2025.100253","DOIUrl":"10.1016/j.mcpdig.2025.100253","url":null,"abstract":"<p><p>Deep learning (DL) has revolutionized cancer detection accuracy, speed, and accessibility. Leveraging sophisticated algorithms, DL has demonstrated transformative potential across diverse applications, including imaging-based diagnostics and genomic analysis, ultimately leading to better detection, improved patient treatment outcomes, and decreased overall mortality rates. Despite its promise, integrating DL into clinical practice presents substantial challenges, including limitations in data quality and standardization, as well as ethical and regulatory concerns, and the need for model interpretability and transparency. This review provides a comprehensive analysis of recent research (2018-2024) retrieved from PubMed and IEEE Xplore databases, encompassing 1304 studies from PubMed and 115 from IEEE, to highlight the current applications, opportunities, and challenges of DL in oncology. Additionally, this paper explores emerging solutions, including federated learning, explainable artificial intelligence, and synthetic data generation, to address these barriers. The review also emphasizes the importance of interdisciplinary collaboration, the integration of next-generation artificial intelligence techniques, and the adoption of multimodal data approaches to improve diagnostic precision and support personalized cancer treatment. By systematically analyzing key developments and challenges, this review aims to guide future research and DL technologies in oncology, promoting equitable and impactful advancements in cancer care.</p>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 3","pages":"100253"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877120","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":"A Systematic Review of Natural Language Processing Techniques for Early Detection of Cognitive Impairment","authors":"Ravi Shankar PhD ,&nbsp;Anjali Bundele MPH ,&nbsp;Amartya Mukhopadhyay FRCP","doi":"10.1016/j.mcpdig.2025.100205","DOIUrl":"10.1016/j.mcpdig.2025.100205","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically evaluate the effectiveness and methodologic approaches of natural language processing (NLP) techniques for early detection of cognitive decline through speech and language analysis.</div></div><div><h3>Methods</h3><div>We conducted a comprehensive search of 8 databases from inception through August 31, 2024, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies were included if they used NLP techniques to analyze speech or language data for detecting cognitive impairment and reported diagnostic accuracy metrics. Two independent reviewers (R.S. and A.B.) screened articles and extracted data on study characteristics, NLP methods, and outcomes.</div></div><div><h3>Results</h3><div>Of 23,562 records identified, 51 studies met inclusion criteria, involving 17,340 participants (mean age, 72.4 years). Combined linguistic and acoustic approaches achieved the highest diagnostic accuracy (average 87%; area under the curve [AUC], 0.89) compared with linguistic-only (83%; AUC, 0.85) or acoustic-only approaches (80%; AUC, 0.82). Lexical diversity, syntactic complexity, and semantic coherence were consistently strong predictors across cognitive conditions. Picture description tasks were most common (n=21), followed by spontaneous speech (n=15) and story recall (n=8). Crosslinguistic applicability was found across 8 languages, although language-specific adaptations were necessary. Longitudinal studies (n=9) reported potential for early detection but were limited by smaller sample sizes (average n=159) compared with cross-sectional studies (n=42; average n=274).</div></div><div><h3>Conclusion</h3><div>Natural language processing techniques show promising diagnostic accuracy for detecting cognitive impairment across multiple languages and clinical contexts. Although combined linguistic-acoustic approaches appear most effective, methodologic heterogeneity and small sample sizes in existing studies suggest the need for larger, standardized investigations to establish clinical utility.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697822","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":"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":"2025-06-01","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":"Comparison of Participant and Site Perceptions of Decentralized Clinical Trials in the USA","authors":"Roland Barge PhD ,&nbsp;Patrick Floody MBA","doi":"10.1016/j.mcpdig.2025.100201","DOIUrl":"10.1016/j.mcpdig.2025.100201","url":null,"abstract":"<div><h3>Objective</h3><div>To define potential participant and site perceptions of decentralized clinical trials (DCTs).</div></div><div><h3>Participants and Methods</h3><div>Two qualitative surveys were conducted between January 2022 and August 2022 to assess current awareness of, and perceptions about, DCTs. The first survey received 141 responses from staff at our clinical trial sites; the second survey received 481 responses from US-based healthy individuals or those living with an illness.</div></div><div><h3>Results</h3><div>There was a difference in perceptions and willingness between participants and sites toward DCTs. Participants expressed more comfort with hybrid and fully remote trials than did the sites. Site staff were more concerned and less trusting than participants of DCTs; participants’ main concerns were regarding practicality and medical safety, whereas the focus for sites was on burden, trust, and security. Both sites and participants expressed confidence in fully remote clinical study activities when they have appropriate support; sites were less tolerant of fully remote clinical study activities if professional support was not provided. Overall, sites were more willing to manage the use of DCT-related technologies than were participants. It is highly likely that participants’ willingness to manage DCT technologies relates to the perceived burden of use (ie, willingness decreases as burden or impact on daily life increases). Sponsors, contract research organizations, and DCT vendors generally had positive views on DCTs. However, different stakeholders had different concerns.</div></div><div><h3>Conclusion</h3><div>These results highlight the need for collaborative research and development of DCTs, as well as a clear DCT framework and regulatory guidance.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 2","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696654","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":"2025-06-01","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}