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<p>Safe medication management is a cornerstone of high-quality patient care, yet it remains one of the most complex and challenging components of healthcare delivery. This scoping review investigates analytical approaches for patient safety-focused medication management, with an emphasis on artificial intelligence and decision-theoretic methods. Guided by PRISMA-ScR, we searched PubMed, Web of Science, ACM, INFORMS, IEEE, and CINAHL for English-language studies published through December 2023. We identified 64 unique studies, over half of which were published since 2020, with 27% based in the United States. Across all studies, 49% addressed adverse drug event detection or prediction, 24% focused on medication error detection, and 13% examined medication reconciliation or review. The remainder explored related areas such as polypharmacy management, dosage adjustment, and medication recommendation. Although artificial intelligence methods were widely applied, only 7 studies used decision-theoretic frameworks, and just 5 combined both approaches. Overall, the literature reflects a predominant focus on single-drug event detection but limited research on managing incomplete or inaccurate medication lists across patient encounters. Future work should explore the combination of predictive, prescriptive, and generative analytics by integrating traditional machine learning, generative AI, and decision-theoretic methods to advance a more comprehensive safe medication management.</p>

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Analytical approaches for medication management for patient safety: a scoping review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • Xinyu Yao,
  • Amogh Rao,
  • Rema Padman

摘要

Safe medication management is a cornerstone of high-quality patient care, yet it remains one of the most complex and challenging components of healthcare delivery. This scoping review investigates analytical approaches for patient safety-focused medication management, with an emphasis on artificial intelligence and decision-theoretic methods. Guided by PRISMA-ScR, we searched PubMed, Web of Science, ACM, INFORMS, IEEE, and CINAHL for English-language studies published through December 2023. We identified 64 unique studies, over half of which were published since 2020, with 27% based in the United States. Across all studies, 49% addressed adverse drug event detection or prediction, 24% focused on medication error detection, and 13% examined medication reconciliation or review. The remainder explored related areas such as polypharmacy management, dosage adjustment, and medication recommendation. Although artificial intelligence methods were widely applied, only 7 studies used decision-theoretic frameworks, and just 5 combined both approaches. Overall, the literature reflects a predominant focus on single-drug event detection but limited research on managing incomplete or inaccurate medication lists across patient encounters. Future work should explore the combination of predictive, prescriptive, and generative analytics by integrating traditional machine learning, generative AI, and decision-theoretic methods to advance a more comprehensive safe medication management.