Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model
Authors: Sophie-Camille Hogue, Flora Chen, Geneviève Brassard, Denis Lebel, Jean-François Bussières, Audrey Durand, Maxime Thibault
Abstract: The objective of this work was to assess the clinical performance of an unsupervised machine learning model aimed at identifying unusual medication orders and pharmacological profiles. We conducted a prospective study between April 2020 and August 2020 where 25 clinical pharmacists dichotomously (typical or atypical) rated 12,471 medication orders and 1,356 pharmacological profiles. Based on AUPR, performance was poor for orders, but satisfactory for profiles. Pharmacists considered the model a useful screening tool.
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