比较临床判断与 MySurgeryRisk 算法在术前风险评估中的应用：一项初步可用性研究。
Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study.
机构信息
Precision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Anesthesiology, University of Florida College of Medicine, Gainesville.
Department of Computer and Information Science and Engineering, University of Florida Herbert Wertheim College of Engineering, Gainesville.
出版信息
Surgery. 2019 May;165(5):1035-1045. doi: 10.1016/j.surg.2019.01.002. Epub 2019 Feb 18.
BACKGROUND
Major postoperative complications are associated with increased cost and mortality. The complexity of electronic health records overwhelms physicians' abilities to use the information for optimal and timely preoperative risk assessment. We hypothesized that data-driven, predictive-risk algorithms implemented in an intelligent decision-support platform simplify and augment physicians' risk assessments.
METHODS
This prospective, nonrandomized pilot study of 20 physicians at a quaternary academic medical center compared the usability and accuracy of preoperative risk assessment between physicians and MySurgeryRisk, a validated, machine-learning algorithm, using a simulated workflow for the real-time, intelligent decision-support platform. We used area under the receiver operating characteristic curve to compare the accuracy of physicians' risk assessment for six postoperative complications before and after interaction with the algorithm for 150 clinical cases.
RESULTS
The area under the receiver operating characteristic curve of the MySurgeryRisk algorithm ranged between 0.73 and 0.85 and was significantly better than physicians' initial risk assessments (area under the receiver operating characteristic curve between 0.47 and 0.69) for all postoperative complications except cardiovascular. After interaction with the algorithm, the physicians significantly improved their risk assessment for acute kidney injury and for an intensive care unit admission greater than 48 hours, resulting in a net improvement of reclassification of 12% and 16%, respectively. Physicians rated the algorithm as easy to use and useful.
CONCLUSION
Implementation of a validated, MySurgeryRisk computational algorithm for real-time predictive analytics with data derived from the electronic health records to augment physicians' decision-making is feasible and accepted by physicians. Early involvement of physicians as key stakeholders in both design and implementation of this technology will be crucial for its future success.
背景
重大术后并发症与增加的成本和死亡率相关。电子健康记录的复杂性使医生无法利用这些信息进行最佳和及时的术前风险评估。我们假设，在智能决策支持平台中实施的数据驱动、预测风险算法，可以简化并增强医生的风险评估。
方法
这是一项前瞻性、非随机的试点研究，在一家四级学术医疗中心的 20 名医生中进行，使用实时智能决策支持平台的模拟工作流程，比较了医生和 MySurgeryRisk（一种经过验证的机器学习算法）在术前风险评估中的可用性和准确性。我们使用受试者工作特征曲线下面积来比较医生在与算法交互之前和之后对 150 个临床病例的 6 种术后并发症的风险评估的准确性。
结果
MySurgeryRisk 算法的受试者工作特征曲线下面积在 0.73 到 0.85 之间，并且明显优于医生的初始风险评估（受试者工作特征曲线下面积在 0.47 到 0.69 之间），除了心血管并发症之外。与算法交互后，医生显著提高了他们对急性肾损伤和 ICU 入住时间超过 48 小时的风险评估，分别导致重新分类的净改善为 12%和 16%。医生认为该算法易于使用且有用。
结论
实施经过验证的 MySurgeryRisk 计算算法，用于实时预测分析，并利用电子健康记录中的数据来增强医生的决策，是可行的，并且得到了医生的认可。早期让医生作为关键利益相关者参与这项技术的设计和实施，对于其未来的成功至关重要。
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