Journal of the American Medical Informatics Association最新文献

{"title":"Patterns of willingness to share health data with key stakeholders in US consumers: a latent class analysis.","authors":"Ashwini Nagappan, Xi Zhu","doi":"10.1093/jamia/ocaf014","DOIUrl":"10.1093/jamia/ocaf014","url":null,"abstract":"<p><strong>Objective: </strong>To identify distinct patterns in consumer willingness to share health data with various stakeholders and analyze characteristics across consumer groups.</p><p><strong>Materials and methods: </strong>Data from the Rock Health Digital Health Consumer Adoption Survey from 2018, 2019, 2020, and 2022 were analyzed. This study comprised a Census-matched representative sample of U.S. adults. Latent class analysis (LCA) identified groups of respondents with similar data-sharing attitudes. Groups were compared by sociodemographics, health status, and digital health utilization.</p><p><strong>Results: </strong>We identified three distinct LCA groups: (1) Wary (36.8%), (2) Discerning (47.9%), and (3) Permissive (15.3%). The Wary subgroup exhibited reluctance to share health data with any stakeholder, with predicted probabilities of willingness to share ranging from 0.07 for pharmaceutical companies to 0.34 for doctors/clinicians. The Permissive group showed a high willingness, with predicted probabilities greater than 0.75 for most stakeholders except technology companies and government organizations. The Discerning group was selective, willing to share with healthcare-related entities and family (predicted probabilities >0.62), but reluctant to share with other stakeholders (predicted probabilities <0.29). Individual characteristics were associated with LCA group membership.</p><p><strong>Discussion: </strong>Findings highlight a persistent trust in traditional healthcare providers. However, the varying willingness to share with non-traditional stakeholders suggests that while some consumers are open to sharing, others remain hesitant and selective. Data privacy policies and practices need to recognize and respond to multifaceted and stakeholder-specific attitudes.</p><p><strong>Conclusion: </strong>LCA reveals significant heterogeneity in health data-sharing attitudes among U.S. consumers, providing insights to inform the development of data privacy policies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"702-711"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054082","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":"An examination of ambulatory care code specificity utilization in ICD-10-CM compared to ICD-9-CM: implications for ICD-11 implementation.","authors":"Susan H Fenton, Cassandra Ciminello, Vickie M Mays, Mary H Stanfill, Valerie Watzlaf","doi":"10.1093/jamia/ocaf003","DOIUrl":"10.1093/jamia/ocaf003","url":null,"abstract":"<p><strong>Objective: </strong>The ICD-10-CM classification system contains more specificity than its predecessor ICD-9-CM. A stated reason for transitioning to ICD-10-CM was to increase the availability of detailed data. This study aims to determine whether the increased specificity contained in ICD-10-CM is utilized in the ambulatory care setting and inform an evidence-based approach to evaluate ICD-11 content for implementation planning in the United States.</p><p><strong>Materials and methods: </strong>Diagnosis codes and text descriptions were extracted from a 25% random sample of the IQVIA Ambulatory EMR-US database for 2014 (ICD-9-CM, n = 14 327 155) and 2019 (ICD-10-CM, n = 13 062 900). Code utilization data was analyzed for the total and unique number of codes. Frequencies and tests of significance determined the percentage of available codes utilized and the unspecified code rates for both code sets in each year.</p><p><strong>Results: </strong>Only 44.6% of available ICD-10-CM codes were used compared to 91.5% of available ICD-9-CM codes. Of the total codes used, 14.5% ICD-9-CM codes were unspecified, while 33.3% ICD-10-CM codes were unspecified.</p><p><strong>Discussion: </strong>Even though greater detail is available, a 108.5% increase in using unspecified codes with ICD-10-CM was found. The utilization data analyzed in this study does not support a rationale for the large increase in the number of codes in ICD-10-CM. New technologies and methods are likely needed to fully utilize detailed classification systems.</p><p><strong>Conclusion: </strong>These results help evaluate the content needed in the United States national ICD standard. This analysis of codes in the current ICD standard is important for ICD-11 evaluation, implementation, and use.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"675-681"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973003","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":"Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation.","authors":"Ying Li, Surabhi Datta, Majid Rastegar-Mojarad, Kyeryoung Lee, Hunki Paek, Julie Glasgow, Chris Liston, Long He, Xiaoyan Wang, Yingxin Xu","doi":"10.1093/jamia/ocaf030","DOIUrl":"10.1093/jamia/ocaf030","url":null,"abstract":"<p><strong>Objectives: </strong>We developed and validated a large language model (LLM)-assisted system for conducting systematic literature reviews in health technology assessment (HTA) submissions.</p><p><strong>Materials and methods: </strong>We developed a five-module system using abstracts acquired from PubMed: (1) literature search query setup; (2) study protocol setup using population, intervention/comparison, outcome, and study type (PICOs) criteria; (3) LLM-assisted abstract screening; (4) LLM-assisted data extraction; and (5) data summarization. The system incorporates a human-in-the-loop design, allowing real-time PICOs criteria adjustment. This is achieved by collecting information on disagreements between the LLM and human reviewers regarding inclusion/exclusion decisions and their rationales, enabling informed PICOs refinement. We generated four evaluation sets including relapsed and refractory multiple myeloma (RRMM) and advanced melanoma to evaluate the LLM's performance in three key areas: (1) recommending inclusion/exclusion decisions during abstract screening, (2) providing valid rationales for abstract exclusion, and (3) extracting relevant information from included abstracts.</p><p><strong>Results: </strong>The system demonstrated relatively high performance across all evaluation sets. For abstract screening, it achieved an average sensitivity of 90%, F1 score of 82, accuracy of 89%, and Cohen's κ of 0.71, indicating substantial agreement between human reviewers and LLM-based results. In identifying specific exclusion rationales, the system attained accuracies of 97% and 84%, and F1 scores of 98 and 89 for RRMM and advanced melanoma, respectively. For data extraction, the system achieved an F1 score of 93.</p><p><strong>Discussion: </strong>Results showed high sensitivity, Cohen's κ, and PABAK for abstract screening, and high F1 scores for data extraction. This human-in-the-loop AI-assisted SLR system demonstrates the potential of GPT-4's in context learning capabilities by eliminating the need for manually annotated training data. In addition, this LLM-based system offers subject matter experts greater control through prompt adjustment and real-time feedback, enabling iterative refinement of PICOs criteria based on performance metrics.</p><p><strong>Conclusion: </strong>The system demonstrates potential to streamline systematic literature reviews, potentially reducing time, cost, and human errors while enhancing evidence generation for HTA submissions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"616-625"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12005633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558513","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":"AI-Techniques Loss-Based Algorithm for Severity Classification (ATLAS): a novel approach for continuous quantification of exertional symptoms during incremental exercise testing.","authors":"Abed A Hijleh, Sophia Wang, Danilo C Berton, Igor Neder-Serafini, Sandra Vincent, Matthew James, Nicolle Domnik, Devin Phillips, Luiz E Nery, Denis E O'Donnell, J Alberto Neder","doi":"10.1093/jamia/ocaf051","DOIUrl":"https://doi.org/10.1093/jamia/ocaf051","url":null,"abstract":"<p><strong>Objective: </strong>Heightened muscular effort and breathlessness (dyspnea) are disabling sensory experiences. We sought to improve the current approach of assessing these symptoms only at the maximal effort to new paradigms based on their continuous quantification throughout cardiopulmonary exercise testing (CPET).</p><p><strong>Materials and methods: </strong>After establishing sex- and age-adjusted reference centiles (0-10 Borg scale), we developed a novel algorithm (AI-Techniques Loss-Based Algorithm for Severity Classification [ATLAS]) based on reciprocal exponential loss for CPET data from patients with chronic obstructive lung disease of varied severity.</p><p><strong>Results: </strong>Categories of dyspnea intensity by ATLAS-but not dyspnea at peak exercise-correctly discriminated patients in progressively higher resting and exercise impairment (P < .05).</p><p><strong>Discussion: </strong>This new AI-techniques approach will be translated to the care of disabled patients to uncover the seeds and consequences of their activity-related symptoms.</p><p><strong>Conclusions: </strong>We used innovative informatics research to change paradigms in displaying, quantifying, and analyzing effort-related symptoms in patient populations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732704","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":"Incorporating area-level social drivers of health in predictive algorithms using electronic health record data.","authors":"Agata Foryciarz, Nicole Gladish, David H Rehkopf, Sherri Rose","doi":"10.1093/jamia/ocaf009","DOIUrl":"10.1093/jamia/ocaf009","url":null,"abstract":"<p><strong>Objectives: </strong>The inclusion of social drivers of health (SDOH) into predictive algorithms of health outcomes has potential for improving algorithm interpretation, performance, generalizability, and transportability. However, there are limitations in the availability, understanding, and quality of SDOH variables, as well as a lack of guidance on how to incorporate them into algorithms when appropriate to do so. As such, few published algorithms include SDOH, and there is substantial methodological variability among those that do. We argue that practitioners should consider the use of social indices and factors-a class of area-level measurements-given their accessibility, transparency, and quality.</p><p><strong>Results: </strong>We illustrate the process of using such indices in predictive algorithms, which includes the selection of appropriate indices for the outcome, measurement time, and geographic level, in a demonstrative example with the Kidney Failure Risk Equation.</p><p><strong>Discussion: </strong>Identifying settings where incorporating SDOH may be beneficial and incorporating them rigorously can help validate algorithms and assess generalizability.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"595-601"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015033","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":"The effect of a combined mHealth and community health worker intervention on HIV self-management.","authors":"Fabiana Cristina Dos Santos, D Scott Batey, Emma S Kay, Haomiao Jia, Olivia R Wood, Joseph A Abua, Susan A Olender, Rebecca Schnall","doi":"10.1093/jamia/ocae322","DOIUrl":"10.1093/jamia/ocae322","url":null,"abstract":"<p><strong>Objective: </strong>To identify demographic, social, and clinical factors associated with HIV self-management and evaluate whether the CHAMPS intervention is associated with changes in an individual's HIV self-management.</p><p><strong>Method: </strong>This study was a secondary data analysis from a randomized controlled trial evaluating the effects of the CHAMPS, a mHealth intervention with community health worker sessions, on HIV self-management in New York City (NYC) and Birmingham. Group comparisons and linear regression analyses identified demographic, social, and clinical factors associated with HIV self-management. We calculated interactions between groups (CHAMPS intervention and standard of care) over time (6 and 12 months) following the baseline observation, indicating a difference in the outcome scores from baseline to each time across groups.</p><p><strong>Results: </strong>Our findings indicate that missing medical appointments, uncertainty about accessing care, and lack of adherence to antiretroviral therapy are associated with lower HIV self-management. For the NYC site, the CHAMPS showed a statistically significant positive effect on daily HIV self-management (estimate = 0.149, SE = 0.069, 95% CI [0.018 to 0.289]). However, no significant effects were observed for social support or the chronic nature of HIV self-management. At the Birmingham site, the CHAMPS did not yield statistically significant effects on HIV self-management outcomes.</p><p><strong>Discussion: </strong>Our study suggests that CHAMPS intervention enhances daily self-management activities for people with HIV at the NYC site, indicating a promising improvement in routine HIV care.</p><p><strong>Conclusion: </strong>Further research is necessary to explore how various factors influence HIV self-management over time across different regions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"510-517"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973005","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":"Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.","authors":"Xiayuan Huang, Jatin Arora, Abdullah Mesut Erzurumluoglu, Stephen A Stanhope, Daniel Lam, Hongyu Zhao, Zhihao Ding, Zuoheng Wang, Johann de Jong","doi":"10.1093/jamia/ocae297","DOIUrl":"10.1093/jamia/ocae297","url":null,"abstract":"<p><strong>Background: </strong>Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.</p><p><strong>Methods: </strong>To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.</p><p><strong>Results: </strong>Taking disease risk prediction as a use case, we demonstrate that explicitly modeling family relations significantly improves predictions across the disease spectrum. We then show how ALIGATEHR's attention mechanism, which links patients' disease risk to their relatives' clinical profiles, successfully captures genetic aspects of diseases using longitudinal EHR diagnosis data. Finally, we use ALIGATEHR to successfully distinguish the 2 main inflammatory bowel disease subtypes with highly shared risk factors and symptoms (Crohn's disease and ulcerative colitis).</p><p><strong>Conclusion: </strong>Overall, our results highlight that family relations should not be overlooked in EHR research and illustrate ALIGATEHR's great potential for enhancing patient representation learning for predictive and interpretable modeling of EHRs.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"435-446"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900000","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":"Objective study validity diagnostics: a framework requiring pre-specified, empirical verification to increase trust in the reliability of real-world evidence.","authors":"Mitchell M Conover, Patrick B Ryan, Yong Chen, Marc A Suchard, George Hripcsak, Martijn J Schuemie","doi":"10.1093/jamia/ocae317","DOIUrl":"10.1093/jamia/ocae317","url":null,"abstract":"<p><strong>Objective: </strong>Propose a framework to empirically evaluate and report validity of findings from observational studies using pre-specified objective diagnostics, increasing trust in real-world evidence (RWE).</p><p><strong>Materials and methods: </strong>The framework employs objective diagnostic measures to assess the appropriateness of study designs, analytic assumptions, and threats to validity in generating reliable evidence addressing causal questions. Diagnostic evaluations should be interpreted before the unblinding of study results or, alternatively, only unblind results from analyses that pass pre-specified thresholds. We provide a conceptual overview of objective diagnostic measures and demonstrate their impact on the validity of RWE from a large-scale comparative new-user study of various antihypertensive medications. We evaluated expected absolute systematic error (EASE) before and after applying diagnostic thresholds, using a large set of negative control outcomes.</p><p><strong>Results: </strong>Applying objective diagnostics reduces bias and improves evidence reliability in observational studies. Among 11 716 analyses (EASE = 0.38), 13.9% met pre-specified diagnostic thresholds which reduced EASE to zero. Objective diagnostics provide a comprehensive and empirical set of tests that increase confidence when passed and raise doubts when failed.</p><p><strong>Discussion: </strong>The increasing use of real-world data presents a scientific opportunity; however, the complexity of the evidence generation process poses challenges for understanding study validity and trusting RWE. Deploying objective diagnostics is crucial to reducing bias and improving reliability in RWE generation. Under ideal conditions, multiple study designs pass diagnostics and generate consistent results, deepening understanding of causal relationships. Open-source, standardized programs can facilitate implementation of diagnostic analyses.</p><p><strong>Conclusion: </strong>Objective diagnostics are a valuable addition to the RWE generation process.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"518-525"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957972","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":"Descriptive epidemiology demonstrating the All of Us database as a versatile resource for the rare and undiagnosed disease community.","authors":"Drenen J Magee, Sierra Kicker, Aeisha Thomas","doi":"10.1093/jamia/ocae241","DOIUrl":"10.1093/jamia/ocae241","url":null,"abstract":"<p><strong>Objective: </strong>We aim to demonstrate the versatility of the All of Us database as an important source of rare and undiagnosed disease (RUD) data, because of its large size and range of data types.</p><p><strong>Materials and methods: </strong>We searched the public data browser, electronic health record (EHR), and several surveys to investigate the prevalence, mental health, healthcare access, and other data of select RUDs.</p><p><strong>Results: </strong>Several RUDs have participants in All of Us [eg, 75 of 100 rare infectious diseases (RIDs)]. We generated health-related data for undiagnosed, sickle cell disease (SCD), cystic fibrosis (CF), and infectious (2 diseases) and chronic (4 diseases) disease pools.</p><p><strong>Conclusion: </strong>Our results highlight the potential value of All of Us with both data breadth and depth to help identify possible solutions for shared and disease-specific biomedical and other problems such as healthcare access, thus enhancing diagnosis, treatment, prevention, and support for the RUD community.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"579-585"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883641","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":"An electronic health record metadata-mining approach to identifying patient-level interprofessional clinician teams in the intensive care unit.","authors":"Olga Yakusheva, Lara Khadr, Kathryn A Lee, Hannah C Ratliff, Deanna J Marriott, Deena Kelly Costa","doi":"10.1093/jamia/ocae275","DOIUrl":"10.1093/jamia/ocae275","url":null,"abstract":"<p><strong>Objectives: </strong>Advances in health informatics rapidly expanded use of big-data analytics and electronic health records (EHR) by clinical researchers seeking to optimize interprofessional ICU team care. This study developed and validated a program for extracting interprofessional teams assigned to each patient each shift from EHR event logs.</p><p><strong>Materials and methods: </strong>A retrospective analysis of EHR event logs for mechanically-ventilated patients 18 and older from 5 ICUs in an academic medical center during 1/1/2018-12/31/2019. We defined interprofessional teams as all medical providers (physicians, physician assistants, and nurse practitioners), registered nurses, and respiratory therapists assigned to each patient each shift. We created an EHR event logs-mining program that extracts clinicians who interact with each patient's medical record each shift. The algorithm was validated using the Message Understanding Conference (MUC-6) method against manual chart review of a random sample of 200 patient-shifts from each ICU by two independent reviewers.</p><p><strong>Results: </strong>Our sample included 4559 ICU encounters and 72 846 patient-shifts. Our program extracted 3288 medical providers, 2702 registered nurses, and 219 respiratory therapists linked to these encounters. Eighty-three percent of patient-shift teams included medical providers, 99.3% included registered nurses, and 74.1% included respiratory therapists; 63.4% of shift-level teams included clinicians from all three professions. The program demonstrated 95.9% precision, 96.2% recall, and high face validity.</p><p><strong>Discussion: </strong>Our EHR event logs-mining program has high precision, recall, and validity for identifying patient-levelshift interprofessional teams in ICUs.</p><p><strong>Conclusions: </strong>Algorithmic and artificial intelligence approaches have a strong potential for informing research to optimize patient team assignments and improve ICU care and outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"426-434"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839957","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}