SCH: EXP: Continuous Patient Monitoring in the Emergency Department: A Wearable Device Technology Enabled Dynamic Decision Support Model

SCH：EXP：急诊科的连续患者监护：可穿戴设备技术支持的动态决策支持模型

• 批准号: 1602379
• 负责人: David Claudio
• 金额: $ 33.14万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1602379&HistoricalAwards=false
• 关键词: SCH; EXP; Continuous; Patient; Monitoring

The objective of the proposed exploratory project is to develop a dynamic mathematical decision support (DS) model for patient prioritization in the Emergency Department (ED). This model will integrate the use of wearable devices that collect patients' vital signs in real time and decision theory to account for patients' changes while they wait. The research will focus on the design and application of integrated systems for decision making by merging the mathematical models with the Emergency Severity (ESI) Index and wearable devices. The objective of the proposed exploratory project is to develop a dynamic mathematical decision support (DS) model for patient prioritization in the Emergency Department (ED). The coupling of technology and decision theory can help reduce the level of uncertainty that is inherent to the decision making process of triage nurses. The mathematical models will combine Multi-attribute Utility Theory (MAUT) with fuzzy logic and decision trees to predict patients' criticality and priority using the Emergency Severity Index (ESI) as baseline. In addition, the MAUT model will be enhanced to incorporate attribute changes over time. Wearable devices will be used to collect patients' vital signs in real time; this information will be integrated into mathematical decision models to assist nurses in data gathering, analysis, sorting and presentation. Nurses will be presented with organized information regarding the status of the patients in a meaningful manner that would allow them to notice and respond to any significant changes in patients' status. This research will aid the triage process, especially in extremely busy emergency rooms where re-assessment is difficult if not impossible, putting patients at risk for undetected deterioration. Project Webpage:http://www.montana.edu/dclaudio/SCHEXP.html

提出的探索性项目的目标是开发一个动态数学决策支持（DS）模型，为急诊科（ED）的患者优先排序。该模型将结合实时收集患者生命体征的可穿戴设备和决策理论的使用，以解释患者在等待过程中的变化。通过将数学模型与应急严重性指数（ESI）和可穿戴设备相结合，研究将重点放在决策集成系统的设计和应用上。提出的探索性项目的目标是开发一个动态数学决策支持（DS）模型，为急诊科（ED）的患者优先排序。技术和决策理论的耦合可以帮助减少不确定性的水平，这是固有的决策过程的分诊护士。数学模型将多属性效用理论（MAUT）与模糊逻辑和决策树相结合，以急诊严重程度指数（ESI）为基准，预测患者的危急程度和优先级。此外，MAUT模型将得到增强，以包含随时间变化的属性更改。可穿戴设备将用于实时采集患者生命体征；这些信息将被整合到数学决策模型中，以帮助护士收集、分析、分类和展示数据。护士将以一种有意义的方式获得有关患者状态的有组织的信息，使他们能够注意到患者状态的任何重大变化并作出反应。这项研究将有助于分诊过程，特别是在极其繁忙的急诊室，重新评估即使不是不可能，也很困难，使患者面临未被发现的恶化风险。项目页面:http://www.montana.edu/dclaudio/SCHEXP.html。