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- Title
Hybrid Method Incorporating a Rule-Based Approach and Deep Learning for Prescription Error Prediction.
- Authors
Lee, Seunghee; Shin, Jeongwon; Kim, Hyeon Seong; Lee, Min Je; Yoon, Jung Min; Lee, Sohee; Kim, Yongsuk; Kim, Jong-Yeup; Lee, Suehyun
- 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.
- Subjects
DEEP learning; MEDICATION errors; DRUG utilization; PEDIATRICS; PEDIATRICIANS
- Publication
Drug Safety, 2022, Vol 45, Issue 1, p27
- ISSN
0114-5916
- Publication type
Article
- DOI
10.1007/s40264-021-01123-6