Building and Implementing a predictive decision support system based on a proactive full capacity protocol to mitigate emergency overcrowding problem

建立和实施基于主动全容量协议的预测决策支持系统，以缓解紧急过度拥挤问题

• 批准号: 10810217
• 负责人: Abdulaziz Ahmed
• 金额: $ 12.77万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2028-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10810217
• 关键词: Building; Implementing; predictive; decision; support

Building and implementing a predictive decision support system based on a proactive full capacity protocol to mitigate emergency overcrowding problem Project Summary Emergency departments (EDs) face a major problem of overcrowding that poses a significant patient safety risk and leads to poor healthcare service quality and high mortality rates. ED overcrowding is a patient flow problem, which can be solved by improving patient flow from arrival to admission or discharge. According to the American College of Emergency Physicians, a full capacity protocol (FCP) is a key approach for improving patient flow and consequently mitigating ED overcrowding. FCP has different levels that are triggered by different criteria, which are based on patient flow measures (PFMs). The current practice of FCP uses real-time (i.e., reactive) information to decide FCP criteria. However, when it comes to implementing FCP interventions, using real-time information is not efficient because in many cases FCP levels are activated too late when ED is already overcrowded. This project improves the reactive FCP to make it proactive through using Artificial Intelligence and predictive analytics. The PFMs will be predicted using deep learning models and then integrated with reactive FCP. A decision support system will be developed to implement the proposed proactive FCP. The overall objective of this project is to develop a framework to mitigate ED overcrowding. There are four aims (Aims 1& 2 under R21; Aims 3& 4 under R33): Aim 1: Develop deep learning models to predict different PFM values and incorporate them in a proactive FCP. Many PFM values represent the patient flow from arrival to admission. We will build multiple deep learning models to predict PFM values (e.g., numbers of boarding). Then, we will update the reactive FCP to include the predicted PFM values. Aim 2: Develop a DES model to evaluate the effectiveness of proactive FCP. Before running the proactive FCP in production, we will compare reactive and proactive FCPs on the outcomes they generate such as average length of stay (LOS), waiting time and staff satisfaction. Aim 3: Design, evaluate, and implement a decision support system (DSS) based on the proactive FCP. We will design user-centric DSS to aid clinicians and the PFCT in implementing the proactive FCP. We will use the proactive FCP criteria as input for the DSS to automate key parts of the proactive FCP interventions. Aim 4: Expand and generalize the DSS by standardizing data input and output interfaces. We will create a FHIR-based application programming interface (API) to allow site-specific configuration, model training, evaluation, and streamlining of implementation processes. Successful completion of this project delivers a state-of-the-art interoperable DSS for the implementation of a proactive FCP based on early, accurate predictions of PFM values to allow proper planning and execution of patient flow processes, thereby mitigating ED overcrowding. Our multi- disciplinary team is well positioned to successfully execute all aims.

构建和实施基于主动式全容量协议的预测性决策支持系统， 缓解紧急过度拥挤问题 项目摘要 急诊科（ED）面临着过度拥挤的主要问题，这造成了重大的患者安全风险，并导致 医疗服务质量差和死亡率高。艾德过度拥挤是一个病人流动问题，可以解决 通过改善从到达到入院或出院的患者流量。根据美国应急学院的数据， 医生们，全容量方案（FCP）是改善患者流量从而缓解艾德的关键方法 过度拥挤FCP有不同的级别，由不同的标准触发，这些标准基于患者流量测量 （PFMs）。FCP的当前实践使用实时（即，反应性）信息来决定FCP标准。但在 在实施FCP干预措施时，使用实时信息效率不高，因为在许多情况下，FCP水平 当艾德已经人满为患时，激活得太晚。该项目通过以下方式改进了被动式FCP，使其具有主动性 使用人工智能和预测分析。将使用深度学习模型预测PFMs，然后 与反应性FCP结合。将开发一个决策支持系统，以实施拟议的主动FCP。的 该项目的总体目标是制定一个框架，以缓解艾德过度拥挤的情况。有四个目标（目标1和2 目标1：开发深度学习模型以预测不同的PFM值， 将其纳入前瞻性FCP。许多PFM值表示从到达到入院的患者流量。我们将 构建多个深度学习模型来预测PFM值（例如，登机人数）。然后，我们将更新无功 FCP包括预测的PFM值。目标2：开发一个DES模型来评估主动FCP的有效性。 在生产中运行主动FCP之前，我们将比较被动和主动FCP的结果， 平均住院时间（LOS），等待时间和员工满意度等。目标3：设计、评估和实施 基于前摄FCP的决策支持系统（DSS）。我们将设计以用户为中心的DSS，以帮助临床医生和 PFCT在实施积极的FCP方面的作用。我们将使用主动FCP标准作为DSS的输入，以自动化关键部件 积极的FCP干预措施。目标4：通过标准化数据输入和输出来扩展和推广DSS 接口。我们将创建一个基于FHIR的应用程序编程接口（API），以允许特定于站点的配置， 示范培训、评估和简化执行过程。该项目的成功完成提供了 一个最先进的可互操作的决策支持系统，用于实施基于PFM早期准确预测的主动FCP 值，以允许正确规划和执行患者流程，从而缓解艾德过度拥挤。我们的多- 纪律团队有能力成功实现所有目标。