Association model data learned from clinicians stratified by patient mortality outcomes at a Tertiary Academic Center

In this data article, we learn clinical order patterns from inpatient electronic health record (EHR) data at a tertiary academic center from three different cohorts of providers: (1) Clinicians with lower-than-expected patient mortality rates, (2) clinicians with higher-than-expected patient mortality rates, and (3) an unfiltered population of clinicians. We extract and make public these order patterns learned from each clinician cohort associated with six common admission diagnoses (e.g. pneumonia, chest pain, etc.). We also share a reusable reference standard or benchmark for evaluating automatically-learned clinical order patterns for each admission diagnosis, based on a manual review of clinical practice literature. The data shared in this article can support further study, evaluation, and translation of data-driven CDS systems. Further interpretation and discussion of this data can be found in Wang et al. (2018).