Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders.

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.

Are pharmacists from the province of Quebec ready to integrate pharmacogenetics into their practice.

Aim: The pharmacists are identified as one of the best positioned health professionals to lead intercollaborative efforts in tailoring medication based on pharmacogenetic information. As pharmacotherapy specialists, they can take on a prominent role in ordering and interpreting pharmacogenetic test results and then guiding optimal drug selection and dose based on those results. Participants & methods: To assess the readiness of pharmacists and trainees in the province of Quebec to assume this role, we surveyed their knowledge in (pharmaco)genetics, their confidence in their ability to use pharmacogenetics and their attitude toward the integration of this tool in clinical practice. Results: A total of 99 pharmacists (community: 67.7%, hospital: 24.2% and other: 8.1%) and 36 students volunteered in a self-administered online survey. About 50% of the questions on the participants' knowledge are answered correctly, with a stepwise increase of right answers with hours of education in (pharmaco)genetics (51.2, 63.8 and 76.7% for <5, 5-25 and >25 h respectively; p < 0.0001). While the majority of participants believe that pharmacogenetics will gain more room in their future practice (80.7%), the overall rate of confidence in their ability to use pharmacogenetics information is low (22%) and 90.3% desire more training. Conclusion: The limited experience of pharmacists in pharmacogenetics appears to be a barrier for its integration in clinical practice.