ArticleJournal of the American Medical Informatics Association : JAMIA2021
Predicting next-day discharge via electronic health record access logs.
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 22 papers.
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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
22 citing papers in PubMed.
- The Trial Enrollment Diversity Dashboard for Acute Leukemia Clinical Research: Intervention Development and Cohort Analysis.JCO oncology practice · 2025Trial
- A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.Journal of medical Internet research · 2026Article
- Earlier medically ready for discharge date entry associated with lower unnecessary bed days.BMC health services research · 2026Article
- Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help.Journal of biomedical informatics · 2026Article
- Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.Ophthalmology science · 2026Article
- Evaluating the Machine Learning Models in Predicting Intensive Care Unit Discharge for Neurosurgical Patients Undergoing Craniotomy: A Big Data Analysis.Neurocritical care · 2025Article
- Large language models are less effective at clinical prediction tasks than locally trained machine learning models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Reimagining clinical AI: from clickstreams to clinical insights with EHR use metadata.npj health systems · 2025Review
- Unlocking inpatient workload insights with electronic health record event logs.Journal of hospital medicine · 2025Article
- 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
- Not the Models You Are Looking For: Traditional ML Outperforms LLMs in Clinical Prediction Tasks.medRxiv : the preprint server for health sciences · 2024Article
- Predicting individual patient and hospital-level discharge using machine learning.Communications medicine · 2024Article
- A machine learning approach using conditional normalizing flow to address extreme class imbalance problems in personal health records.BioData mining · 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
- Comparative Analysis of Fusion Strategies for Imaging and Non-imaging Data - Use-case of Hospital Discharge Prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Predicting Postoperative Hospital Stays Using Nursing Narratives and the Reverse Time Attention (RETAIN) Model: Retrospective Cohort Study.JMIR medical informatics · 2023Article
- Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity.JAMA network open · 2023Article
- Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- 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
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveHospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users' granular interactions with patients' records by communicating various semantics and has been neglected in outcome predictions. MATERIALS AND
methodsThis study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction.
resultsThe data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919-0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860-0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features.
conclusionEHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.
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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.