Can algorithms identify unusual medication orders or profiles more accurately than humans? Not necessarily. From a report: A study coauthored by researchers at the Universite Laval and CHU Sainte-Justine in Montreal found that one model physicians used to screen patients performed poorly on some orders. The study offers a reminder that unvetted AI and machine learning may negatively impact outcomes in medicine. Pharmacists review lists of active medications — i.e., pharmacological profiles — for inpatients under their care. This process aims to identify medications that could be abused, but most medication orders don’t show drug-related problems. Publications from over a decade ago illustrate technology’s potential to help pharmacists streamline workflows by taking on tasks like reviewing orders. But while more recent research has investigated AI’s potential in pharmacology, few studies have demonstrated its efficacy. The coauthors of this latest work looked at a model deployed in a tertiary-care mother-and-child academic hospital between April 2020 and August 2020. The model was trained on a dataset of 2,846,502 medication orders from 2005 to 2018. These had been extracted from a pharmacy database and preprocessed into 1,063,173 profiles. Prior to data collection, the model was retrained every month with 10 years of the most recent data from the database in order to minimize drift, which occurs when a model loses its predictive power.
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