A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events
A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events
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DOI：
10.1016/j.prro.2019.10.017
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发表时间：
2020-05-01
影响因子：
3.3
通讯作者：
Ford, Eric C.
中科院分区：
文献类型：
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作者：
Hartvigson, Pehr E.;Gensheimer, Michael F.;Ford, Eric C.
目的：放射肿瘤学中的错误检测在很大程度上依赖于自愿报告，许多不良事件和未遂事件可能未被发现。触发工具使用患者图表中的现有数据来识别其他未解释的事件，并已成功应用于其他医学领域。我们开发了一种自动化的放射肿瘤学特定的触发工具，并验证了它对近错过的数据从高容量的事件学习系统（ILS）。方法和材料：20触发器来自电子放射肿瘤学信息系统。在大约3.5年的时间里，系统的数据被随机分为训练集和测试集。使用正则化逻辑回归模型估计训练集中每个疗程的高级别（3-4级）未遂事件的概率。将预测模型应用于测试集。25个标记的治疗过程与ILS条目的记录进行了审查，探讨触发器和近misses之间的关联，和25个标记的课程没有ILS条目进行了审查，以检测未报告的近misses.Results：3159治疗过程分析，357有一个等级3至4 ILS条目; 2210课程组成的训练集，和测试集有949课程。训练集和测试集的曲线下面积分别为0.650和0.652。在20个触发因素中，9个在单变量分析中达到统计学显著性。在试验组中，25个治疗疗程中有50%的治疗疗程具有最高的预测可能性，即ILS进入的高等级未遂事件与触发因素和未遂事件之间有直接关系。审查的25个治疗过程中，预测的可能性最高的高级别的近距离失误没有ILS条目发现2未报告的近距离失误events.Conclusions：放射肿瘤学特定的自动触发工具进行适度，并确定额外的治疗过程与近距离失误事件。放射肿瘤学触发工具值得进一步探索。(C)2019年美国放射肿瘤学会。爱思唯尔公司出版All rights reserved.
Purpose: Error detection in radiation oncology relies heavily on voluntary reporting, and many adverse events and near misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise-unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology-specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS).Methods and Materials: Twenty triggers were derived from an electronic radiation oncology information system. Data from the systems over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near misses.Results: Of the 3159 treatment courses analyzed, 357 had a grade 3 to 4 ILS entry; 2210 courses composed the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, 9 reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near miss with an ILS entry had a direct relationship between the triggers and the near miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near miss without an ILS entry found 2 unreported near-miss events.Conclusions: The radiation oncology-specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration. (C) 2019 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.