Screening for Medication Errors and Adverse Events Using Outlier Detection Screening Algorithms in an Inpatient Setting.

Abstract

To evaluate the potential of a novel system using outlier detection screening algorithms and to identify medication related risks in an inpatient setting. In the first phase of the study, we evaluated the transferability of models refined at another medical center using a different electronic medical record system (EMR) on 3 years of historical data (2017-2019), extracted from the local EMR system. Following the retrospective analysis, the system's models were fine-tuned to the specific local practice patterns. In the second, prospective phase of the study, the system was fully integrated in the local EMR and after a short run-in period was activated live. All alerts generated by the system, in both phases, were analyzed by a clinical team of physicians and pharmacists for accuracy and clinical relevance. In the retrospective phase of the study, 226,804 medical orders were analyzed, generating a total of 2731 alerts (1.2% of medical orders). Of the alerts analyzed, 69% were clinically relevant alerts and 31% were false alerts. In the prospective phase of the study, 399 alerts were generated by the system (1.6% of medical orders). The vast majority of the alerts (72%) were considered clinically relevant, and 41% of the alerts caused a change in prescriber behavior (i.e. cancel/modify the medical order). In an inpatient setting of a 600 bed computerized decision support system (CDSS) -naïve medical center, the system generated accurate and clinically valid alerts with low alert burden enabling physicians to improve daily medical practice.