Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study
Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study
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DOI：
10.1016/j.surg.2019.01.002
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发表时间：
2019-05-01
期刊：
影响因子：
3.8
通讯作者：
Bihorac, Azra
中科院分区：
文献类型：
--
作者：
Brennan, Meghan;Puri, Sahil;Bihorac, Azra
背景：主要的术后并发症与增加的费用和死亡率有关。电子健康记录的复杂性淹没了医生利用这些信息进行最佳和及时的术前风险评估的能力。我们假设，在智能决策支持平台中实施的数据驱动的预测性风险算法简化并增强了医生的风险评估。方法：这项前瞻性、非随机的试点研究对一家第四纪学术医学中心的20名医生进行了术前风险评估的可用性和准确性的比较，MySurgeryRisk是一种经过验证的机器学习算法，使用实时、智能决策支持平台的模拟工作流程。结果：MySurgeryRisk算法的受试者操作特征曲线下面积在0.73~0.85之间，明显优于医生初始风险评估的受试者操作特征曲线下面积(受试者操作特征曲线下面积在0.47~0.69之间)。在与算法交互后，医生显著改进了对急性肾损伤和重症监护病房入院超过48小时的风险评估，导致重新分类的净改进分别为12%和16%。医生评价该算法简单易用。结论：利用来自电子健康记录的数据，实施经过验证的MySurgeryRisk计算算法进行实时预测分析，以增强医生的决策是可行的，并为医生所接受。医生作为关键利益相关者及早参与这项技术的设计和实施将对其未来的成功至关重要。(C)2019 Elsevier Inc.保留所有权利。
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. (C) 2019 Elsevier Inc. All rights reserved.