An approach to automatic process deviation detection in a time-critical clinical process.
An approach to automatic process deviation detection in a time-critical clinical process.
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DOI：
10.1016/j.jbi.2018.07.022
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发表时间：
2018-09
影响因子：
4.5
通讯作者：
Burd RS
中科院分区：
文献类型：
--
作者：
Yang S;Sarcevic A;Farneth RA;Chen S;Ahmed OZ;Marsic I;Burd RS
先前的研究表明，在复杂的医疗过程中，微小的错误和偏离推荐指南的情况会累积起来，增加重大错误得不到纠正的可能性，从而导致不良后果。实时自动和准确地检测过程偏差可以帮助医疗团队更好地预防或减轻错误的影响，并改善患者的治疗结果。我们的目标是开发一种自动检测创伤复苏中的错误和过程偏差的方法。通过视频回顾，我们对两年内（2014-2016年）在一级创伤中心收集的95例儿科创伤复苏的活动痕迹进行了编码。使用基于阶段的一致性检查算法来检测真假偏差（警报），将24个随机选择的活动轨迹与创伤复苏工作流的知识驱动模型进行比较。对假警报的分析确定了三种类型的原因：(1)模型差距或模型（“想象的工作”）与实际实践（“完成的工作”）之间的差异，(2)活动跟踪编码中的错误，以及(3)算法限制。我们修复了系统，以消除模型差距，减少编码错误，并解决算法限制。修复后的系统首先用另外20条轨迹进行评估，然后应用于95条轨迹的整个数据集。在培训期间，我们在24个活动跟踪中检测到573个过程偏差，这些活动跟踪包括1,099个活动。在这些偏差中，只有27%代表真实偏差，其余73%是假警报。初始偏差检测准确率仅为66.6%，f1得分为0.42。修复后的系统在系统验证期间检测准确率提高到95.2% （0.85 f1分），在测试期间检测准确率提高到98.5% （0.96 f1分）。在将修复后的偏差检测系统部署到所有95个作业轨迹后，我们在5,659个作业中检测到1,060个工艺偏差（每次复苏11.2个偏差）。在这些轨迹中的5659个活动中，4893个符合修复后的知识驱动工作流模型，294个为遗漏错误，538个为委托错误，228个为调度错误。我们的方法自动偏差检测提供了一种方法来识别重复，省略和无序的活动，可以包括在复杂的医疗过程的决策支持系统的设计。我们的研究结果表明，评估检测到的偏差对于修复最能代表“已完成工作”的知识驱动模型的重要性。
Prior research has shown that minor errors and deviations from recommended guidelines in complex medical processes can accumulate to increase the likelihood that a major error will go uncorrected and lead to an adverse outcome. Real-time automatic and accurate detection of process deviations may help medical teams better prevent or mitigate the effect of errors and improve patient outcomes. Our goal was to develop an approach for automatic detection of errors and process deviations in trauma resuscitation. Using video review, we coded activity traces of 95 pediatric trauma resuscitations collected in a Level 1 trauma center over two years (2014–2016). Twenty-four randomly selected activity traces were compared with a knowledge-driven model of trauma resuscitation workflow using a phase-based conformance checking algorithm for detecting true and false deviations (alarms). An analysis of false alarms identified three types of causes: (1) model gaps or discrepancies between the model (“work as imagined”) and actual practice (“work as done”), (2) errors in activity traces coding, and (3) algorithm limitations. We repaired the system to remove model gaps, reduce coding errors, and address algorithm limitations. The repaired system was first evaluated with another 20 traces and then applied to the entire dataset of 95 traces. During the training, we detected 573 process deviations in 24 activity traces that include 1,099 activities. Among these deviations, only 27% represented true deviations and the remaining 73% were false alarms. This initial deviation detection accuracy was only 66.6%, with a F1-score of 0.42. Detection accuracy of the repaired system increased to 95.2% (0.85 F1-score) during system validation and to 98.5% (0.96 F1-score) during testing. After deploying the repaired deviation detection system to all 95 activity traces, we detected 1,060 process deviations in 5,659 activities (11.2 deviations per resuscitation). Among the 5,659 activities in these traces, 4,893 fit the repaired knowledge-driven workflow model, 294 were errors of omission, 538 were errors of commission, and 228 were scheduling errors. Our approach to automatic deviation detection provides a method for identifying repeated, omitted and out-of-sequence activities that can be included in the design of decision support systems for complex medical processes. Our findings show the importance of assessing detected deviations for repairing a knowledge-driven model that best represents “work as done.”
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