ArticleJournal of the American Medical Informatics Association : JAMIA2021
Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.Computers in biology and medicine · 2026Article
- Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis.JAMIA open · 2026Article
- Exploring Task Patterns in EHR Workflows Using Action Sequence Embedding and Graph-Based Analysis.Studies in health technology and informatics · 2025Article
- Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling.JAMIA open · 2025Article
- Novel subtypes of metabolic associated steatotic liver disease linked to clinical outcomes: implications for precision medicine.Journal of translational medicine · 2025Article
- Emerging Domains for Measuring Health Care Delivery With Electronic Health Record Metadata.Journal of medical Internet research · 2025Review
- Reimagining clinical AI: from clickstreams to clinical insights with EHR use metadata.npj health systems · 2025Review
- Association Between Patient Portal Engagement and Weight Loss Outcomes in Patients After Bariatric Surgery: Longitudinal Observational Study Using Electronic Health Records.Journal of medical Internet research · 2024Observational
- Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions.Journal of medical Internet research · 2024Article
- Learning from Non-Routine Events and Teamwork in Intensive Care Units: Challenges and Opportunities.Studies in health technology and informatics · 2024Article
- Enhanced Patient Portal Engagement Associated with Improved Weight Loss Outcomes in Post-Bariatric Surgery Patients.medRxiv : the preprint server for health sciences · 2024Article
- Optimizing Large Language Models for Discharge Prediction: Best Practices in Leveraging Electronic Health Record Audit Logs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity.JAMA network open · 2023Article
- Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Using electronic health record audit log data for research: insights from early efforts.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Team is brain: leveraging EHR audit log data for new insights into acute care processes.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Measuring and Maximizing Undivided Attention in the Context of Electronic Health Records.Applied clinical informatics · 2022Article
- Inferring EHR Utilization Workflows through Audit Logs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics. MATERIALS AND
methodsWe leveraged unsupervised learning approaches to learn tasks from sequences of events in EHR audit logs. We developed metrics characterizing the prevalence of unique events and event repetition and applied them to categorize tasks into 4 complexity profiles. Between these profiles, Mann-Whitney U tests were applied to measure the differences in performance time, event type, and clinician prevalence, or the number of unique clinicians who were observed performing these tasks. In addition, we apply process mining frameworks paired with clinical annotations to support the validity of a sample of our identified tasks. We apply our approaches to learn tasks performed by nurses in the Vanderbilt University Medical Center neonatal intensive care unit.
resultsWe examined EHR audit logs generated by 33 neonatal intensive care unit nurses resulting in 57 234 sessions and 81 tasks. Our results indicated significant differences in performance time for each observed task complexity profile. There were no significant differences in clinician prevalence or in the frequency of viewing and modifying event types between tasks of different complexities. We presented a sample of expert-reviewed, annotated task workflows supporting the interpretation of their clinical meaningfulness.
conclusionsThe use of the audit log provides an opportunity to assist hospitals in further investigating clinician activities to optimize EHR workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.