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

Seunghee Lee, Jeongwon Shin, Hyeon Seong Kim, Min Je Lee, Jung Min Yoon, Sohee Lee, Yongsuk Kim, Jong-Yeup Kim () and Suehyun Lee ()
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Seunghee Lee: Konyang University Hospital
Jeongwon Shin: Infinigru
Hyeon Seong Kim: Infinigru
Min Je Lee: Infinigru
Jung Min Yoon: Konyang University Hospital
Sohee Lee: Konyang University
Yongsuk Kim: Konyang University
Jong-Yeup Kim: Konyang University Hospital
Suehyun Lee: Konyang University Hospital

Drug Safety, 2022, vol. 45, issue 1, No 3, 27-35

Abstract: 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.

Date: 2022
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DOI: 10.1007/s40264-021-01123-6

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