Abstract
Technology assistance of pharmacist verification tasks through the use of machine intelligence has the potential to detect dangerous and costly pharmacy dispensing errors. National Drug Codes (NDC) are unique numeric identifiers of prescription drug products for the United States Food and Drug Administration. The physical form of the medication, often tablets and capsules, captures the unique features of the NDC product to help ensure patients receive the same medication product inside their prescription bottle as is found on the label from a pharmacy. We report and evaluate using an automated check to predict the shape, color, and NDC for images showing a pile of pills inside a prescription bottle. In a test set containing 65,274 images of 345 NDC classes, overall macro-average precision was 98.5%. Patterns of incorrect NDC predictions based on similar colors, shapes, and imprints of pills were identified and recommendations to improve the model are provided.
Highlights
Medication errors occur when pharmacy staff count out and give their patient the incorrect medication inside a prescription bottle labeled for a different medication[1,2,3]
These images are used by a pharmacist to verify that the medication inside the prescription bottle is the exact same medication product found on the prescription label for the patient
The ResNet-18 has experienced success in computer vision for many classification tasks because it solved the gradient vanishing problem and handled the increasing depth of neural networks[20,21]. It consists of an 18-layer residual neural network model that was pre-trained on the ImageNet dataset[19] and fine-tuned the parameters using our prescription medication images dataset
Summary
Medication errors occur when pharmacy staff count out and give their patient the incorrect medication inside a prescription bottle labeled for a different medication[1,2,3]. To assist humans in the verification process, MI models could perform a pill classification task using images taken of medication filled inside a prescription bottle.
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