Hybrid Method Incorporating a Rule-Based Approach and Deep Learning for Prescription Error Prediction.

Journal: Drug safety
Published Date:

Abstract

INTRODUCTION: Recently, automated detection has been a new approach to address the risks posed by prescribing errors. This study focused on prescription errors and utilized real medical data to supplement the Drug Utilization Review (DUR)-based rules, the current prescription error detection method. We developed a new hybrid method through artificial intelligence for prescription error prediction by utilizing actual detection accuracy improvement to reduce 'warning fatigue' for doctors and improve medical care quality.

Authors

  • Seunghee Lee
    Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea.
  • Jeongwon Shin
    Infinigru, Seoul, Republic of Korea.
  • Hyeon Seong Kim
    Infinigru, Seoul, Republic of Korea.
  • Min Je Lee
    Infinigru, Seoul, Republic of Korea.
  • Jung Min Yoon
    Department of Pediatrics, Konyang University Hospital, Daejeon, Republic of Korea.
  • Sohee Lee
  • Yongsuk Kim
    Department of Medical Artificial Intelligence, Konyang University, Daejeon, Republic of Korea.
  • Jong-Yeup Kim
    Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea. jykim@kyuh.ac.kr.
  • Suehyun Lee
    Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea. shleemedi@kyuh.ac.kr.