Automatic Workflow Capture & Analysis for Improving Trauma Resuscitation Outcomes

自动工作流程捕获

• 批准号: 8761390
• 负责人: RANDALL S. BURD
• 金额: $ 42.58万
• 依托单位: CHILDREN'S RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8761390
• 关键词: Cessation of life; Clinical; Commit; Computer Assisted; Consensus Sequence; Critical Care; Data; Decision Support Systems; Development; Evaluation; Event; General Hospitals; Goals; Health; Human; Injury; Intervention; Lead; Life; Literature; Manuals; Measures; Medical; Medical Errors; Methods; Mining; Minor; Mission; Modality; Modeling; Monitor; Movement; Outcome; Patients; Phase; Predisposition; Process; Protocol Compliance; Protocols documentation; Public Health; Real-Time Systems; Recovery; Research; Resources; Resuscitation; Risk; Safety; Solutions; Technology; Testing; Time; Trauma; Variant; Work; adverse outcome; base; computerized; flexibility; high risk; improved; injured; innovation; knowledge base; multidisciplinary; patient safety; prevent; radiofrequency; response; sensor; success; Cessation of life; Clinical; Commit; Computer Assisted; Consensus Sequence; Critical Care; Data; Decision Support Systems; Development; Evaluation; Event; General Hospitals; Goals; Health; Human; Injury; Intervention; Lead; Life; Literature; Manuals; Measures; Medical; Medical Errors; Methods; Mining; Minor; Mission; Modality; Modeling; Monitor; Movement; Outcome; Patients; Phase; Predisposition; Process; Protocol Compliance; Protocols documentation; Public Health; Real-Time Systems; Recovery; Research; Resources; Resuscitation; Risk; Safety; Solutions; Technology; Testing; Time; Trauma; Variant; Work; adverse outcome; base; computerized; flexibility; high risk; improved; injured; innovation; knowledge base; multidisciplinary; patient safety; prevent; radiofrequency; response; sensor; success

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.

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结果并为这些偏差提供警报，从而采取补救措施以防止不良结果。该提案的总体目标是实现这一长期目标的下一步，是：（a）开发一种可扩展的方法来识别创伤复苏期间的活动； （b）确定使用过程挖掘的创伤复苏工作流程中与不良结果相关的偏差。中心假设是，可以实时监测和分析创伤复苏活动的工作流偏差，从而增加患者不良预后的可能性。拟议的研究的理由是，对不良后果的风险状况的实时识别将使医疗团队能够采取减少或防止医疗错误影响的措施。中心假设将通过追求两个具体目标来检验：1）开发一种可扩展的自动方法来创建创伤复苏期间发生的活动的事件日志； 2）确定并表征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类运动和对象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评论获得的日志进行比较（“地面真相”）。为了第二个目标，该方法将涉及使用手动捕获的事件日志中的活动序列的基于知识的复苏工作流程模型的开发和完善。该项目很重要，因为这些方法是朝着开发计算机决策支持系统发展的重要一步，可以通过监视和支持重症监护团队的工作来改善结果。拟议的研究具有创新性，因为它代表了与现状的实质性不同，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，并检测可变工作流中与不良结果相关的偏差。这些方法有望为计算机系统的基础，以对医疗团队进行实时决策支持，以改善创伤复苏期间患者结果和其他重症监护过程。