Journal of the American Medical Informatics Association最新文献

{"title":"Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden.","authors":"Shreya J Shah, Anna Devon-Sand, Stephen P Ma, Yejin Jeong, Trevor Crowell, Margaret Smith, April S Liang, Clarissa Delahaie, Caroline Hsia, Tait Shanafelt, Michael A Pfeffer, Christopher Sharp, Steven Lin, Patricia Garcia","doi":"10.1093/jamia/ocae295","DOIUrl":"https://doi.org/10.1093/jamia/ocae295","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.</p><p><strong>Materials and methods: </strong>This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings.</p><p><strong>Results: </strong>Paired survey analysis (n = 38) revealed large statistically significant reductions in task load (-24.42, p <.001) and burnout (-1.94, p <.001), and moderate statistically significant improvements in usability scores (+10.9, p <.001). Post-survey responses (n = 46) indicated favorable utility with improved perceptions of efficiency, documentation quality, and ease of use.</p><p><strong>Discussion: </strong>In one of the first pilot implementations of ambient AI scribe technology, improvements in physician task load, burnout, and usability were demonstrated.</p><p><strong>Conclusion: </strong>Ambient AI scribes like DAX Copilot may enhance clinical workflows. Further research is needed to optimize widespread implementation and evaluate long-term impacts.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830526","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":"Establishing best practices in large language model research: an application to repeat prompting.","authors":"Robert J Gallo, Michael Baiocchi, Thomas R Savage, Jonathan H Chen","doi":"10.1093/jamia/ocae294","DOIUrl":"https://doi.org/10.1093/jamia/ocae294","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example.</p><p><strong>Materials and methods: </strong>Using data from a prior study investigating potential model bias in peer review of medical abstracts, we compared methods that ignore correlation in model outputs from repeated prompting with a random effects method that accounts for this correlation.</p><p><strong>Results: </strong>High correlation within groups was found when repeatedly prompting the model, with intraclass correlation coefficient of 0.69. Ignoring the inherent correlation in the data led to over 100-fold inflation of effective sample size. After appropriately accounting for this issue, the authors' results reverse from a small but highly significant finding to no evidence of model bias.</p><p><strong>Discussion: </strong>The establishment of best practices for LLM research is urgently needed, as demonstrated in this case where accounting for repeat prompting in analyses was critical for accurate study conclusions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830529","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 to: Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.","authors":"","doi":"10.1093/jamia/ocae309","DOIUrl":"10.1093/jamia/ocae309","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824595","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":"Empowering the biomedical research community: Innovative SAS deployment on the All of Us Researcher Workbench.","authors":"Izabelle Humes, Cathy Shyr, Moira Dillon, Zhongjie Liu, Jennifer Peterson, Chris St Jeor, Jacqueline Malkes, Hiral Master, Brandy Mapes, Romuladus Azuine, Nakia Mack, Bassent Abdelbary, Joyonna Gamble-George, Emily Goldmann, Stephanie Cook, Fatemeh Choupani, Rubin Baskir, Sydney McMaster, Chris Lunt, Karriem Watson, Minnkyong Lee, Sophie Schwartz, Ruchi Munshi, David Glazer, Eric Banks, Anthony Philippakis, Melissa Basford, Dan Roden, Paul A Harris","doi":"10.1093/jamia/ocae216","DOIUrl":"10.1093/jamia/ocae216","url":null,"abstract":"<p><strong>Objectives: </strong>The All of Us Research Program is a precision medicine initiative aimed at establishing a vast, diverse biomedical database accessible through a cloud-based data analysis platform, the Researcher Workbench (RW). Our goal was to empower the research community by co-designing the implementation of SAS in the RW alongside researchers to enable broader use of All of Us data.</p><p><strong>Materials and methods: </strong>Researchers from various fields and with different SAS experience levels participated in co-designing the SAS implementation through user experience interviews.</p><p><strong>Results: </strong>Feedback and lessons learned from user testing informed the final design of the SAS application.</p><p><strong>Discussion: </strong>The co-design approach is critical for reducing technical barriers, broadening All of Us data use, and enhancing the user experience for data analysis on the RW.</p><p><strong>Conclusion: </strong>Our co-design approach successfully tailored the implementation of the SAS application to researchers' needs. This approach may inform future software implementations on the RW.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2994-3000"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.","authors":"Han Yang, Sicheng Zhou, Zexi Rao, Chen Zhao, Erjia Cui, Chetan Shenoy, Anne H Blaes, Nishitha Paidimukkala, Jinhua Wang, Jue Hou, Rui Zhang","doi":"10.1093/jamia/ocae199","DOIUrl":"10.1093/jamia/ocae199","url":null,"abstract":"<p><strong>Objective: </strong>This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables.</p><p><strong>Materials and methods: </strong>We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period.</p><p><strong>Results: </strong>The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors.</p><p><strong>Discussion: </strong>The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness.</p><p><strong>Conclusion: </strong>This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2800-2810"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"All of whom? Limitations encountered using All of Us Researcher Workbench in a Primary Care residents secondary data analysis research training block.","authors":"Fred Willie Zametkin LaPolla, Marco Barber Grossi, Sharon Chen, Tai Wei Guo, Kathryn Havranek, Olivia Jebb, Minh Thu Nguyen, Sneha Panganamamula, Noah Smith, Shree Sundaresh, Jonathan Yu, Gabrielle Mayer","doi":"10.1093/jamia/ocae162","DOIUrl":"10.1093/jamia/ocae162","url":null,"abstract":"<p><strong>Objectives: </strong>The goal of this case report is to detail experiences and challenges experienced in the training of Primary Care residents in secondary analysis using All of Us Researcher Workbench. At our large, urban safety net hospital, Primary Care/Internal Medicine residents in their third year undergo a research intensive block, the Research Practicum, where they work as a team to conduct secondary data analysis on a dataset with faculty facilitation. In 2023, this research block focused on use of the All of Us Researcher Workbench for secondary data analysis.</p><p><strong>Materials and methods: </strong>Two groups of 5 residents underwent training to access the All of Us Researcher Workbench, and each group explored available data with a faculty facilitator and generated original research questions. Two blocks of residents successfully completed their research blocks and created original presentations on \"social isolation and A1C\" levels and \"medical discrimination and diabetes management.\"</p><p><strong>Results: </strong>Departmental faculty were satisfied with the depth of learning and data exploration. In focus groups, some residents noted that for those without interest in performing research, the activity felt extraneous to their career goals, while others were glad for the opportunity to publish. In both blocks, residents highlighted dissatisfaction with the degree to which the All of Us Researcher Workbench was representative of patients they encounter in a large safety net hospital.</p><p><strong>Discussion: </strong>Using the All of Us Researcher Workbench provided residents with an opportunity to explore novel questions in a massive data source. Many residents however noted that because the population described in the All of Us Researcher Workbench appeared to be more highly educated and less racially diverse than patients they encounter in their practice, research may be hard to generalize in a community health context. Additionally, given that the data required knowledge of 1 of 2 code-based data analysis languages (R or Python) and work within an idiosyncratic coding environment, residents were heavily reliant on a faculty facilitator to assist with analysis.</p><p><strong>Conclusion: </strong>Using the All of Us Researcher Workbench for research training allowed residents to explore novel questions and gain first-hand exposure to opportunities and challenges in secondary data analysis.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3008-3012"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User guide for Social Determinants of Health Survey data in the All of Us Research Program.","authors":"Theresa A Koleck, Caitlin Dreisbach, Chen Zhang, Susan Grayson, Maichou Lor, Zhirui Deng, Alex Conway, Peter D R Higgins, Suzanne Bakken","doi":"10.1093/jamia/ocae214","DOIUrl":"10.1093/jamia/ocae214","url":null,"abstract":"<p><strong>Objectives: </strong>Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions.</p><p><strong>Target audience: </strong>This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey.</p><p><strong>Scope: </strong>We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3032-3041"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Returning value to communities from the All of Us Research Program through innovative approaches for data use, analysis, dissemination, and research capacity building.","authors":"Suzanne Bakken, Elaine Sang, Berry de Brujin","doi":"10.1093/jamia/ocae276","DOIUrl":"10.1093/jamia/ocae276","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"31 12","pages":"2773-2780"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex-based disparities with cost-related medication adherence issues in patients with hypertension, ischemic heart disease, and heart failure.","authors":"Ivann Agapito, Tu Hoang, Michael Sayer, Ali Naqvi, Pranav M Patel, Aya F Ozaki","doi":"10.1093/jamia/ocae203","DOIUrl":"10.1093/jamia/ocae203","url":null,"abstract":"<p><strong>Importance and objective: </strong>Identifying sources of sex-based disparities is the first step in improving clinical outcomes for female patients. Using All of Us data, we examined the association of biological sex with cost-related medication adherence (CRMA) issues in patients with cardiovascular comorbidities.</p><p><strong>Materials and methods: </strong>Retrospective data collection identified the following patients: 18 and older, completing personal medical history surveys, having hypertension (HTN), ischemic heart disease (IHD), or heart failure (HF) with medication use history consistent with these diagnoses. Implementing univariable and adjusted logistic regression, we assessed the influence of biological sex on 7 different patient-reported CRMA outcomes within HTN, IHD, and HF patients.</p><p><strong>Results: </strong>Our study created cohorts of HTN (n = 3891), IHD (n = 5373), and HF (n = 2151) patients having CRMA outcomes data. Within each cohort, females were significantly more likely to report various cost-related medication issues: being unable to afford medications (HTN hazards ratio [HR]: 1.68, confidence interval [CI]: 1.33-2.13; IHD HR: 2.33, CI: 1.72-3.16; HF HR: 1.82, CI: 1.22-2.71), skipping doses (HTN HR: 1.76, CI: 1.30-2.39; IHD HR: 2.37, CI: 1.69-3.64; HF HR: 3.15, CI: 1.87-5.31), taking less medication (HTN HR: 1.86, CI: 1.37-2.45; IHD HR: 2.22, CI: 1.53-3.22; HF HR: 2.99, CI: 1.78-5.02), delaying filling prescriptions (HTN HR: 1.83, CI: 1.43-2.39; IHD HR: 2.02, CI: 1.48-2.77; HF HR: 2.99, CI: 1.79-5.03), and asking for lower cost medications (HTN HR: 1.41, CI: 1.16-1.72; IHD HR: 1.75, CI: 1.37-2.22; HF HR: 1.61, CI: 1.14-2.27).</p><p><strong>Discussion and conclusion: </strong>Our results clearly demonstrate CRMA issues disproportionately affect female patients with cardiovascular comorbidities, which may contribute to the larger sex-based disparities in cardiovascular care. These findings call for targeted interventions and strategies to address these disparities and ensure equitable access to cardiovascular medications and care for all patients.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2924-2931"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Communicating research findings as a return of value to All of Us Research Program participants: insights from staff at Federally Qualified Health Centers.","authors":"Kathryn P Smith, Jenn Holmes, Jennifer Shelley","doi":"10.1093/jamia/ocae207","DOIUrl":"10.1093/jamia/ocae207","url":null,"abstract":"<p><strong>Objectives: </strong>Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools.</p><p><strong>Materials and methods: </strong>Using an online 25-item assessment, we collected information from All of Us staff at 7 Federally Qualified Health Centers.</p><p><strong>Results: </strong>Topics of greatest interest or relevance included income insecurity (83%), diabetes (78%), and mental health (78%). Respondents prioritized in-person outreach in the community (70%) as a preferred setting to share data stories. Familiarity with available dissemination tools varied.</p><p><strong>Discussion: </strong>Responses support prioritizing materials for in-person outreach and training staff how to use dissemination tools.</p><p><strong>Conclusion: </strong>The findings will inform All of Us communication strategy, content, materials, and staff training resources to effectively deliver data stories as return of value to participants.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2962-2967"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}