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.

通过使用机器智能为药剂师验证任务提供技术支持，有可能检测出危险且代价高昂的药房配药错误。国家药品代码 (NDC) 是美国食品和药物管理局处方药产品的唯一数字标识符。药物的物理形式，通常是片剂和胶囊，体现了 NDC 产品的独特特性，以帮助确保患者在处方瓶内收到与药房标签上相同的药物产品。我们使用自动检查进行报告和评估，以预测显示处方瓶内一堆药丸的图像的形状、颜色和 NDC。在包含 345 个 NDC 类别的 65,274 张图像的测试集中，总体宏观平均精度为 98.5%。