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

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.

描述(申请人提供)：虽然大多数创伤复苏方案的偏差是由于处理不同损伤的患者所需的灵活性造成的，但其他偏差是可能导致严重不良患者结果的“错误”。我们的长期目标是为创伤复苏和其他快节奏、高风险的重症监护环境开发计算机化的决策支持，监控已知与不良结果相关的偏差的工作流程，并对这些偏差发出警报，以便采取补救措施防止不良后果。这项提案的总体目标是：(A)制定一种可扩展的方法，以识别创伤复苏过程中的活动；(B)利用流程挖掘，查明创伤复苏工作流程中与不利结果相关的偏差。中心假设是创伤复苏活动可以实时监测和分析工作流程偏差，这些偏差增加了患者预后不良的可能性。这项拟议研究的基本原理是，实时识别不良后果的风险状况将使医疗队能够采取措施，减少或防止医疗差错的影响。核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，用于创建创伤复苏期间发生的活动的事件日志；以及2)识别和描述团队在创伤复苏期间管理重大错误的能力。根据第一个目标，该办法将涉及(1)使用射频识别(RFID)技术和其他方式创建人类活动和物体使用的复苏事件日志，以及(2)将传感器日志与使用人工视频审查(“地面真相”)获得的日志进行比较。对于第二个目标，该方法将涉及使用手动捕获的事件日志中的一致活动序列来开发和完善基于知识的复苏工作流程模型。这个项目意义重大，因为这些方法是开发计算机化决策支持系统的重要早期步骤，该系统可以通过监测和支持重症护理小组的工作来改善结果。拟议的研究具有创新性，因为它代表了对现状的实质性偏离，重点是开发从传感器获取数据的方法，以自动跟踪多个同时进行的活动，并在可变的工作流程中检测与不利结果相关的偏差。这些方法有望成为计算机化系统的基础，用于医疗队的实时决策支持，以改善创伤复苏和其他危重护理过程中的患者结果。