Background: Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan's local databases (TLD) to address this issue. However, the international transferability of this model is unclear.
Objective: This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model.
Methods: The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance.
Results: The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model.
Conclusions: Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.
Keywords: clinical decision support; electronic health records; machine learning; medication alert systems; patient safety.
©Yen Po Harvey Chin, Wenyu Song, Chia En Lien, Chang Ho Yoon, Wei-Chen Wang, Jennifer Liu, Phung Anh Nguyen, Yi Ting Feng, Li Zhou, Yu Chuan Jack Li, David Westfall Bates. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 27.01.2021.