Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Predicting next-day discharge via electronic health record access logs

Predicting next-day discharge via electronic health record access logs 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Predicting next-day discharge via electronic health record access logs

Download PDF
11 pages

Loading next page...
 
/lp/oxford-university-press/predicting-next-day-discharge-via-electronic-health-record-access-logs-kd10lUphQL

References (55)

Publisher
Oxford University Press
Copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1093/jamia/ocab211
Publisher site
See Article on Publisher Site

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.

Journal

Journal of the American Medical Informatics Association – Oxford University Press

Published: Sep 30, 2021

Keywords: hospital capacity management; daily discharge prediction; EHR audit logs; user-EHR interactions

There are no references for this article.