Intention-aware Recommender System for Improving Trauma Resuscitation Outcomes

项目总结 重伤患者死于医疗差错的风险是其他住院患者的四倍。 患者中，近一半可预防的死亡与最初复苏阶段的错误有关。虽然 在这种情况下，协议、模拟和领导力培训可提高团队绩效，多达12个协议 每次复苏都会出现偏差，即使是经验丰富的团队也是如此。考虑到不良后果 可能由性能差距引起，因此迫切需要建立新的方法来应用实时 重症监护环境中的决策支持。长期目标是实施创伤决策支持 复苏和其他快节奏、高风险的重症监护环境，提高性能，减少错误， 并防止不良后果的发生。这次更新的总体目标是纵向推进 在第一个资助期内通过设计、实施和测试意图感知实现 推荐系统，(1)使用传感器数据、患者的输出识别和跟踪当前目标 监视器和从数字设备捕获的数据，(2)得出支持遵守目标的建议- 基于协议，以及(3)在墙上显示器上实时显示这些建议。中心假说 决策支持是否与意图(“预期的”或“当前的”目标)保持一致将增强协议 依从性，从而改善与创伤复苏相关的结果。这次续签的理由是 支持符合团队意图的协议遵从性的建议更有可能是 采用的方式是不那么分心，并与较低的认知负荷有关。在初步数据的指引下， 中心假设将通过追求两个具体目标来检验：1)设计和实现一个自动化的真实- 预测和监测创伤复苏评估和治疗目标的时间方法；以及 2)生成并展示建议的活动计划，以支持创伤期间当前的目标追求 复苏。对于第一个目标，将应用机器学习方法来使用数据来识别目标 从传感器和其他数字数据源获得。在第二个目标下，机器学习策略将 进行实施和测试，以生成响应团队意图的建议。建议数 研究是创新的，因为它专注于开发将目标整合为输入的实时方法 提出满足最新和最相关信息需求的建议。建议数 这项研究意义重大，因为它有望改善对重伤和其他危重病人的护理。 通过促进及时和适当地实现关键评估和治疗目标 仍处于医疗差错高危状态的设置。这一研究连续体的结果预计将具有 通过解决复杂决策之间的不匹配，对结果产生重要的积极影响 以及人类在重症监护环境中对错误的脆弱性。

PROJECT SUMMARY Critically injured patients have a four-fold higher risk of death from medical errors than other hospitalized patients, with nearly half of preventable deaths related to errors during the initial resuscitation phase. Although protocols, simulation, and leadership training improve team performance in this setting, as many as 12 protocol deviations per resuscitation have been observed, even with experienced teams. Given adverse outcomes that can result from performance gaps, there is a critical need to establish novel approaches for applying real-time decision support in critical-care settings. The long-term goal is to implement decision support for trauma resuscitation and other fast-paced, high-risk critical care settings that improves performance, reduces errors, and prevents adverse outcomes. The overall objective for this renewal is to vertically advance what was achieved during the first funding period by designing, implementing and testing an intention-aware recommender system that (1) recognizes and tracks current goals using sensor data, the output from patient monitors, and data captured from digital devices, (2) derives recommendations that support adherence to goal- based protocols, and (3) displays these recommendations in real time on wall displays. The central hypothesis is that decision support aligning with intentions (“intended” or “current” goals) will enhance protocol compliance, leading to improved outcomes related to trauma resuscitation. The rationale for this renewal is that recommendations supporting protocol compliance that are aligned with team intentions are more likely to be adopted by being less distracting and associated with lower cognitive load. Guided by preliminary data, the central hypothesis will be tested by pursuing two specific aims: 1) design and implement an automated real- time approach for predicting and monitoring the assessment and treatment goals of trauma resuscitation; and 2) generate and display a recommended plan of activities that supports current goal pursuit during trauma resuscitation. For the first Aim, machine learning approaches will be applied for recognizing goals using data obtained from sensors and other digital data sources. Under the second Aim, a machine learning strategy will be implemented and tested that generates recommendations responsive to team intentions. The proposed research is innovative because it focuses on development of real-time methods that integrate goals as an input for making recommendations that meet the most current and relevant information needs. The proposed research is significant because it is expected to improve the care of severely injured and other critically ill patients by promoting timely and appropriate achievement of critical assessment and treatment goals in settings that remain at high-risk for medical errors. The results of this research continuum are expected to have an important positive impact on the outcome by addressing the mismatch between complex decision-making and human vulnerability to error that remain in critical care settings.