Automatic Workflow Capture & Analysis for Improving Trauma Resuscitation Outcomes

描述（申请人提供）：虽然大多数创伤复苏方案的偏差是由于管理创伤患者所需的灵活性而产生的变化，但其他偏差是可能导致严重不良患者结果的“错误”。我们的长期目标是开发创伤复苏和其他快节奏，高风险重症监护环境，用于监控已知与不良结局相关的偏差的工作流程，并对这些偏差提供警报，允许采取补救措施以防止不良结局。该提案的总体目标是实现这一长期目标的下一步，即：（a）开发一种可扩展的方法，用于识别创伤复苏期间的活动;（B）使用流程挖掘识别与创伤复苏工作流程中不良结果相关的偏差。中心假设是，创伤复苏活动可以被监测和分析在真实的时间的工作流程的偏差，增加了不良的患者结果的可能性。这项研究的基本原理是，实时识别不良后果的风险状况将使医疗团队能够采取措施减少或预防医疗差错的影响。中心假设将通过追求两个特定目标进行测试：1）开发一种可扩展的自动方法，用于创建创伤复苏期间发生的活动的事件日志; 2）识别和描述团队在创伤复苏期间管理重大错误的能力。在第一个目标下，该方法将涉及（i）使用射频识别（RFID）技术和其他模式来创建人体运动和物体使用的复苏事件日志，以及（ii）将传感器日志与使用手动视频审查获得的日志进行比较（“地面实况”）。对于第二个目标，该方法将涉及基于知识的复苏工作流程模型的开发和完善，使用来自手动捕获的事件日志的活动的共识序列。这个项目是重要的，因为这些方法是一个重要的早期步骤，对计算机化的决策支持系统，可以通过监测和支持重症监护团队的工作，以改善结果的发展。拟议的研究是创新的，因为它代表了从现状的实质性偏离，重点是开发从传感器获取数据的方法，以自动跟踪多个并发活动，并检测与可变工作流程中的不良结果相关的偏差。这些方法有望成为计算机化系统的基础，用于医疗团队的实时决策支持，改善创伤复苏和其他重症监护过程中的患者结局。

DESCRIPTION (provided by applicant): Although most deviations from trauma resuscitation protocols are variations that result from the flexibility needed for managing patients with differet injuries, other deviations are "errors" that can contribute to significant adverse patient outcomes Our long-term goal is to develop computerized decision support for trauma resuscitation and other fast-paced, high-risk critical care settings that monitors workflow for deviations that are known to be associated with adverse outcomes and provides alerts to these deviations, allowing remedial actions to be taken to prevent adverse outcomes. The overall objectives for this proposal, which are the next steps in the attainment of this long-term goal, are to: (a) develop a scalable approach for recognizing activities during trauma resuscitation; and (b) identify deviations associated with adverse outcomes within the workflow of trauma resuscitation using process mining. The central hypothesis is that trauma resuscitation activities can be monitored and analyzed in real time for workflow deviations that increase the likelihood of adverse patient outcomes. The rationale for the proposed research is that real-time identification of risk conditions for adverse outcomes will allow medical teams to take measures for reducing or preventing the impact of medical errors. The central hypothesis will be tested by pursuing two specific aims: 1) develop a scalable and automatic approach for creating an event log of activities occurring during trauma resuscitation; and 2) identify and characterize the team's ability to manage major errors during trauma resuscitation. Under the first aim, the approach will involve (i) the use of radiofrequency identification (RFID) technology and other modalities to create resuscitation event logs of human movement and object use and (ii) comparisons of sensor logs with logs obtained using manual video review ("ground truth"). For the second aim, the approach will involve the development and refinement of knowledge-based resuscitation workflow models using consensus sequences of activities from manually captured event logs. This project is significant because these methods are an essential early step toward the development of computerized decision support systems that can improve outcomes by monitoring and supporting the work of critical care teams. The proposed research is innovative because it represents a substantive departure from the status quo, focusing on developing methods for obtaining data from sensors to automatically track multiple, concurrent activities and for detecting deviations associated with adverse outcomes within a variable workflow. These methods are expected to form a basis for computerized systems for real-time decision support of medical teams that improve patient outcome during trauma resuscitation and other critical care processes.