Abstract
BackgroundIssuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them.MethodsWe collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs.ResultsOur results showed that the total training time for the front-side model and back-side model was 5 h 34 min and 7 h 42 min, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%).ConclusionsIn conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.
Highlights
Issuing of correct prescriptions is a foundation of patient safety
Issuing of correct prescriptions is the mainstay of patient safety
In this study, two deep learning models were employed for training, and the identification results were compared: the front-side model and the back-side model of blisterpackaged drugs
Summary
Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Issuing of correct prescriptions is the mainstay of patient safety. Medication errors are the most important problem that influences safety in health care [1]. A good policy to prevent LASA is to change drug names and their packaging [3]. Researchers used chart reviews and mathematical methods to identify problematic pairs of drug names, and constructed an automated detection system to detect and prevent LASA errors [4]. Major problems remain in drug identification: many drugs look alike; drugs are relatively small in size; and a large number of drugs need to be identified. Existing identification solutions still have their limitations [5,6,7]
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