Intention-aware Recommender System for Improving Trauma Resuscitation Outcomes

用于改善创伤复苏结果的意图感知推荐系统

基本信息

批准号：
10386911
负责人：
RANDALL S. BURD
金额：
$ 64.79万
依托单位：
CHILDREN'S RESEARCH INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10386911
关键词：
Achievement Address Adherence Adopted Adoption Area Awareness Caring Cessation of life Communication Complex Computer Vision Systems Critical Care Critical Illness Data Data Sources Decision Making Decision Support Systems Development Devices Failure Funding Goals Health Professional Hemorrhage Human Human Activities Information Dissemination Injury Intention Leadership Literature Machine Learning Manuals Medical Medical Errors Methods Mission Monitor Morbidity - disease rate Multiple Trauma Outcome Output Patient Monitoring Patient-Focused Outcomes Patients Performance Phase Process Progress Reports Protocol Compliance Protocols documentation Provider Public Health Recommendation Research Resuscitation Risk Safety Stream System Technology Testing Time Training Trauma United States National Library of Medicine Variant Work adverse outcome base cognitive load computerized design digital disability experience high risk improved improved outcome innovation instrument learning strategy member mortality risk multidisciplinary novel novel strategies preference prevent preventable death radio frequency sensor severe injury simulation success

项目摘要

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.

项目摘要重伤患者的医疗错误风险比其他住院在初始复苏阶段，患者几乎有一半与错误有关的可预防死亡。虽然协议，仿真和领导培训在这种情况下提高了团队绩效，多达12个协议即使有经验丰富的团队，也观察到每次复苏的偏差。考虑到下降的结果可能是由于性能差距而产生的，需要建立新颖的方法来应用实时的方法在关键护理环境中的决策支持。长期目标是实施创伤的决策支持复苏和其他快节奏的高风险重症监护设置，可改善性能，减少错误，并防止不利结果。这种续约的总体目标是垂直提高什么通过设计，实施和测试意图意识到在第一个资金期间实现推荐系统（1）使用传感器数据识别和跟踪当前目标，患者的输出（2）的监视和从数字设备捕获的数据，提出了支持遵守目标的建议 - 基于协议，（3）在墙壁显示器上实时显示这些建议。中心假设是否与意图（“意图”或“当前”目标）保持一致的决策支持将增强协议合规性，导致与创伤复苏有关的改善结果。这种续签的理由是支持与团队意图保持一致的协议合规性的建议更有可能是通过减少分心并与较低的认知负荷相关的采用。在初步数据的指导下中心假设将通过追求两个具体目的来检验：1）设计和实施自动实现预测和监视创伤复苏的评估和治疗目标的时间方法；和 2）生成并显示推荐的活动计划，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用于使用数据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，机器学习策略将可以实施和测试，以产生对团队意愿有反应的建议。提议研究之所以创新，是因为它专注于将目标整合为投入的实时方法的开发提出满足最新和相关信息需求的建议。提议研究很重要，因为预计它将改善对严重受伤和其他重症患病的护理通过促进及时且适当地实现关键评估和治疗目标来促进患者仍然处于医疗错误的高风险设置。这项研究的结果预计将有通过解决复杂决策之间的不匹配，对结果的重要积极影响以及人类易受错误护理环境中的错误的脆弱性。