Health Services Research & Development

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Wang JK, Hom J, Balasubramanian S, Schuler A, Shah NH, Goldstein MK, Baiocchi MTM, Chen JH. An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. Journal of Biomedical Informatics. 2018 Oct 1; 86:109-119.
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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?=?1822) were stratified into low-mortality (21.8%, n?=?397) and high-mortality (6.0%, n?=?110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates. Three patient cohorts were assembled: patients seen by low-mortality clinicians, high-mortality clinicians, and an unfiltered crowd of all clinicians (n?=?1046, 1046, and 5230 post-propensity score matching, respectively). Predicted order lists were automatically generated from recommender system algorithms trained on each patient cohort and evaluated against (i) real-world practice patterns reflected in patient cases with better-than-expected mortality outcomes and (ii) reference standards derived from clinical practice guidelines. RESULTS: Across six common admission diagnoses, order lists learned from the crowd demonstrated the greatest alignment with guideline references (AUROC range?=?0.86-0.91), performing on par or better than those learned from low-mortality clinicians (0.79-0.84, P?