Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model
This work addresses medication safety by comparing AI predictions with pharmacist evaluations, but it is incremental as it assesses an existing model without major innovations.
The study evaluated an unsupervised machine learning model for identifying unusual medication orders and pharmacological profiles, finding poor performance for orders but satisfactory results for profiles based on AUPR, with pharmacists viewing it as a useful screening tool.
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.