Seunghee Lee1, Jeongwon Shin2, Hyeon Seong Kim2, Min Je Lee2, Jung Min Yoon3, Sohee Lee4, Yongsuk Kim5, Jong-Yeup Kim6,7, Suehyun Lee8,9. 1. Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea. 2. Infinigru, Seoul, Republic of Korea. 3. Department of Pediatrics, Konyang University Hospital, Daejeon, Republic of Korea. 4. Department of Biomedical Engineering, Konyang University, Daejeon, Republic of Korea. 5. Department of Medical Artificial Intelligence, Konyang University, Daejeon, Republic of Korea. 6. Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea. jykim@kyuh.ac.kr. 7. Departments of Biomedical Informatics, College of Medicine, Konyang University, 158 Gwanjeodong-ro, Seo-gu, Daejeon, 35365, Republic of Korea. jykim@kyuh.ac.kr. 8. Health Care Data Science Center, Konyang University Hospital, Daejeon, Republic of Korea. shleemedi@kyuh.ac.kr. 9. Departments of Biomedical Informatics, College of Medicine, Konyang University, 158 Gwanjeodong-ro, Seo-gu, Daejeon, 35365, Republic of Korea. shleemedi@kyuh.ac.kr.
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. OBJECT: This study was conducted in the Department of Pediatrics, targeting children sensitive to drugs to develop a prescription error detection system. Based on the DUR prescription history, 15,281 patient-level observations of children from Konyang University Hospital (KYUH)'s common data model (CDM) and DUR were collected and analyzed retrospectively. METHOD: Among the CDM data, inspection information was interlocked with DUR and reflected as standard information for model development; this included outpatient prescriptions from January 1 to December 31, 2018. Through consultation with pediatric clinicians, rule definitions and model development were conducted for 35 drugs, with 137,802 normal and 1609 prescription errors. RESULTS: We developed a novel hybrid method of error detection in the form of an advanced rule-based deep neural network (ARDNN), which showed the expected performance (precision: 72.86, recall: 81.01, F1 score: 76.72) and reduced alarm pop-up alert fatigue to below 10%. We also created an ARDNN-based comprehensive dashboard that allows doctors to monitor prescription errors with alarm pop-ups when prescribing medications. CONCLUSION: These results can advance the existing rule-based model by developing a prescription error detection model using deep learning. This method can improve overall medical efficiency and service quality by reducing doctors' fatigue.
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. OBJECT: This study was conducted in the Department of Pediatrics, targeting children sensitive to drugs to develop a prescription error detection system. Based on the DUR prescription history, 15,281 patient-level observations of children from Konyang University Hospital (KYUH)'s common data model (CDM) and DUR were collected and analyzed retrospectively. METHOD: Among the CDM data, inspection information was interlocked with DUR and reflected as standard information for model development; this included outpatient prescriptions from January 1 to December 31, 2018. Through consultation with pediatric clinicians, rule definitions and model development were conducted for 35 drugs, with 137,802 normal and 1609 prescription errors. RESULTS: We developed a novel hybrid method of error detection in the form of an advanced rule-based deep neural network (ARDNN), which showed the expected performance (precision: 72.86, recall: 81.01, F1 score: 76.72) and reduced alarm pop-up alert fatigue to below 10%. We also created an ARDNN-based comprehensive dashboard that allows doctors to monitor prescription errors with alarm pop-ups when prescribing medications. CONCLUSION: These results can advance the existing rule-based model by developing a prescription error detection model using deep learning. This method can improve overall medical efficiency and service quality by reducing doctors' fatigue.
Authors: D G Wilson; R G McArtney; R G Newcombe; R J McArtney; J Gracie; C R Kirk; A G Stuart Journal: Eur J Pediatr Date: 1998-09 Impact factor: 3.183