An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes

Jason K. Wang, Jason Hom, Santhosh Balasubramanian, Alejandro Schuler, Nigam H. Shah, Mary K. Goldstein, Michael T. M. Baiocchi, Jonathan H. Chen

Abstract: Highlights • Patterns from clinical order entry data can yield relevant decision support content. • Automatic patterns outperform manually-authored order sets by multiple metrics. • Deviation in observed from expected patient outcomes can stratify clinicians. • Patterns from all clinicians prove more robust than those from “preferred” clinicians. Abstract Objective Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. Materials and methods Inpatient electronic health records from 2010 to 2013 were extracted from a tertiary academic hospital. Clinicians (n