Predicting next-day discharge via electronic health record access logs | Zendy

Xinmeng Zhang | Zendy; Chao Yan | Zendy; Bradley Malin | Zendy; Mayur B. Patel | Zendy; You Chen | Zendy

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Predicting next-day discharge via electronic health record access logs

Author(s) -

Xinmeng Zhang,

Chao Yan,

Bradley Malin,

Mayur B. Patel,

You Chen

Publication year - 2021

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocab211

Subject(s) - audit , electronic health record , health records , medical diagnosis , medicine , medical record , demographics , receiver operating characteristic , resource (disambiguation) , computer science , medical emergency , machine learning , health care , computer network , demography , management , pathology , sociology , economics , radiology , economic growth

Hospital 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.

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