Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders. (6th May 2021)

• Record Type: Journal Article
• Authors: Hogue, Sophie-Camille; Chen, Flora; Brassard, Geneviève; Lebel, Denis; Bussières, Jean-François; Durand, Audrey; Thibault, Maxime
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 8(2021)
• Page Start: 1712
• Page End: 1718
• Publication Date: 2021-05-06
• Subjects: machine learning -- clinical pharmacy information systems -- decision support systems -- clinical -- medical order entry systems -- hospital pharmaceutical services; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocab071
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objectives: The study sought to assess the clinical performance of a machine learning model aiming to identify unusual medication orders. Materials and Methods: This prospective study was conducted at CHU Sainte-Justine, Canada, from April to August 2020. An unsupervised machine learning model based on GANomaly and 2 baselines were trained to learn medication order patterns from 10 years of data. Clinical pharmacists dichotomously (typical or atypical) labeled orders and pharmacological profiles (patients' medication lists). Confusion matrices, areas under the precision-recall curve (AUPRs), and F1 scores were calculated. Results: A total of 12 471 medication orders and 1356 profiles were labeled by 25 pharmacists. Medication order predictions showed a precision of 35%, recall (sensitivity) of 26%, and specificity of 97% as compared with pharmacist labels, with an AUPR of 0.25 and an F1 score of 0.30. Profile predictions showed a precision of 49%, recall of 75%, and specificity of 82%, with an AUPR of 0.60, and an F1 score of 0.59. The model performed better than the baselines. According to the pharmacists, the model was a useful screening tool, and 9 of 15 participants preferred predictions by medication, rather than by profile. Discussion: Predictions for profiles had higher F1 scores and recall compared with medication order predictions. Although the performance was much better for profile predictions, pharmacists generally preferred medication order predictions. Conclusions: Based on the AUPR, this model showed better performance for the identification of atypical pharmacological profiles than for medication orders. Pharmacists considered the model a useful screening tool. Improving these predictions should be prioritized in future research to maximize clinical impact.