Analytical approaches for medication reconciliation-related topics: a scoping review
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Objective
This scoping review examines literature related to analytical methods for medication reconciliation in the digital era, particularly using artificial intelligence and operations research approaches, and analyzes their effectiveness in reducing medication errors and improving the accuracy of medication lists during care transitions.
Materials and Methods
Following PRISMA-ScR guidelines, we performed a comprehensive literature search in PubMed, Web of Science, ACM, INFORMS, IEEE, and CINAHL databases for English-language studies until December 2023 that explored artificial intelligence, machine learning, and operations research methods for medication reconciliation.
Results
We identified 64 unique studies that are closely related to our research topic, with 53% published since 2020 and 27% U.S.-based. Only 8% directly addressed the complete medication reconciliation process; the remainder focused on related areas, including adverse drug event detection/prediction and medication error detection. Merely 7 studies used decision-theoretic operations research methods, while most used machine learning models and only 5 studies used a combination of artificial intelligence and operations research methods for general medication reconciliation purposes.
Conclusions
The reviewed literature provides growing evidence of research on adverse event detection for a single drug type but limited work on investigating the holistic incomplete/inaccurate list of prescribed medications for a patient. We also found that most of the literature focused on single methodologies for medication reconciliation. Future studies need to explore how to leverage predictive, prescriptive, and generative analytics, combining both artificial intelligence, including machine learning and generative AI, and operations research approaches to improve medication reconciliation for care transition safety with medication management.
