The potential for leveraging machine learning to filter medication alerts. (5th January 2022)

• Record Type: Journal Article
• Authors: Liu, Siru; Kawamoto, Kensaku; Del Fiol, Guilherme; Weir, Charlene; Malone, Daniel C; Reese, Thomas J; Morgan, Keaton; ElHalta, David; Abdelrahman, Samir
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 5(2022)
• Page Start: 891
• Page End: 899
• Publication Date: 2022-01-05
• Subjects: clinical decision support -- machine learning -- alert fatigue; 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/ocab292
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view. Materials and Methods: We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion with physicians and pharmacists. We developed models while maintaining a sensitivity of 0.99. Two null hypotheses were developed: H1—there is no difference in precision among prediction models; and H2—the removal of any feature category does not change precision. Results: A total of 3, 481, 634 medication alerts with 751 features were evaluated. With sensitivity fixed at 0.99, LightGBM achieved the highest precision of 0.192 and less than 0.01 for the pre-defined maximal false-negative rate by subject-matter experts (H1) ( P < 0.001). This model could reduce alert volume by 54.1%. We removed different combinations of features (H2) and found that not all features significantly contributed to precision. Removing medication order features (eg, dosage) most significantly decreased precision (−0.147, P = 0.001). Conclusions: Machine learning potentially enables the intelligent filtering of medication alerts.