Drug-related errors are a leading cause of preventable patient harm in the clinical setting. We present the first wearable camera system to automatically detect potential errors, prior to medication delivery. We demonstrate that using deep learning algorithms, our system can detect and classify drug labels on syringes and vials in drug preparation events recorded in real-world operating rooms. We created a first-of-its-kind large-scale video dataset from head-mounted cameras comprising 4K footage across 13 anesthesiology providers, 2 hospitals and 17 operating rooms over 55 days. The system was evaluated on 418 drug draw events in routine patient care and a controlled environment and achieved 99.6% sensitivity and 98.8% specificity at detecting vial swap errors. These results suggest that our wearable camera system has the potential to provide a secondary check when a medication is selected for a patient, and a chance to intervene before a potential medical error.

在临床环境中，与药物相关的错误是导致可预防的患者伤害的主要原因。我们推出了第一款可穿戴摄像系统，可在药物递送前自动检测潜在错误。我们证明，使用深度学习算法，我们的系统可以检测和分类真实手术室中记录的药物制备事件中注射器和小瓶上的药物标签。我们从头戴式摄像机创建了一个首创的大规模视频数据集，其中包括 55 天内 13 家麻醉学提供者、2 家医院和 17 个手术室的 4K 镜头。该系统在常规患者护理和受控环境中对 418 例药物抽吸事件进行了评估，在检测小瓶更换错误方面达到了 99.6% 的灵敏度和 98.8% 的特异性。这些结果表明，我们的可穿戴摄像头系统有可能在为患者选择药物时提供二次检查，并有机会在潜在的医疗错误之前进行干预。