I-Corps: Medication Adherence System

这个I-Corps项目更广泛的影响/商业潜力是提高护士和护理人员以及需要管理/验证药物（注射和药丸）的任何人的能力，并防止潜在的人为错误。这是非常必要的，因为已知给药错误对患者安全有害，损害患者对医疗保健系统的信心，增加患者的医疗保健成本，并对患者的生活质量产生不利影响。这将通过促进患者，家庭和医疗保健提供者之间的信任来帮助社会。此外，该项目的更广泛影响超出了医疗保健提供者，护士，护理人员，疗养院护士和普通公众的类似人员，因为任何人都可能服用错误的药物。此外，该工具还将提供有关防止错误次数的新数据，以培训医务人员以及所有公众了解此类错误的可能性。这个I-Corps项目是基于开发用于增强现实设备的人工智能增强药物管理软件（包括智能手机和增强现实眼镜），以实现在护理点对药物进行移动的、准确和自动的验证。通过这种增强现实应用程序，患者身份识别和药物信息检索是第一步，其中通过面部或条形码识别验证患者身份，并从电子健康记录系统中检索药物信息。该系统通过条形码扫描或神经网络验证药物名称和时间，定制设计的深度学习框架识别注射剂量/体积。该系统还为注射路线提供提示和提醒，并自动将图像和验证信息保存到记录系统中以供记录。为了提高系统对不利环境的鲁棒性，如不完美的视角，环境光/背景，以及物体的低分辨率，还从简单的人类直觉中汲取灵感，结合了多阶段图像处理程序。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is to enhance the capabilities of nurses and caregivers as well as anyone who needs to administer/verify the medications (injection and pill) and prevent potential human errors. It is very essential as medication administration errors are known to be detrimental to patient safety, compromise patients’ confidence in the healthcare system, increase patient’s healthcare costs, and adversely affect the patient’s quality of life. This will help society by promoting trust between patients, families, and healthcare providers. Moreover, the broader impact of this project goes beyond the healthcare providers, nurses, caregivers, nursing home nurses, and similar ones to the general public as taking wrong medication could occur to anyone. Furthermore, the tool would provide new data on the number of times mistakes were prevented for training of the medical staff as well as all general public on the possibility of such errors.This I-Corps project is based on the development of an artificial intelligence-enhanced medication administration software for Augmented Reality devices (including smartphone and Augmented Reality glasses) to enable mobile, accurate and automatic verification of medication at the point of care. Via this Augmented Reality application, patient identification and medication information retrieval are the first steps, where patient identification is verified through face or barcode recognition, and medication information is retrieved from the electronic health record system. The system verifies medication name and time through barcode scanning or neural network, and a custom-designed deep learning framework recognizes injection dose/volume. This system also provides heads-up instructions and reminders for injection routes, and automatically saves images and verified information into the records system for documentation purposes. To improve the system’s robustness to the adverse environment, such as imperfect viewpoint, ambient light/background, and low resolution of the object, a multi-stage image processing procedure is also incorporated by drawing inspiration from simple human intuitions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.