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GOALI/Collaborative Research: Improving Patient Flow in Hospitals

GOALI/合作研究：改善医院的患者流动

基本信息

批准号：
1763100
负责人：
Ohad Perry
金额：
$ 29.62万
依托单位：
Northwestern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763100&HistoricalAwards=false
关键词：
GOALI Collaborative Research Improving Patient

项目摘要

This Grant Opportunity for Academic Liaison with Industry (GOALI) Program award will advance the national health by reducing congestion and improving patient flow within hospital units and between hospitals. By developing models that predict flow dynamics and address sources of congestion, this project will improve patient safety and satisfaction, and at the same time reduce staffing and equipment costs to the hospital. Reduced congestion will also improve occupancy prediction, thus reducing ambulance diversions within the hospital network. This research employs novel modeling techniques that are informed by a close collaboration between the PIs and the GOALI partner, Northwestern Memorial Hospital (NMH). Models will be developed that are easy to calibrate and sufficiently general, so that their analyses and insights are applicable in a broad range of clinical settings within the hospital. The award provides support for graduate students to engage in research that will be of direct benefit to healthcare operations. The PIs will incorporate the results of their research in courses on queueing and service engineering. The outcomes of the research will be disseminated to both the operations research and the healthcare practitioner communities.This award supports research on multi-server queueing models, tailored specifically to hospital operations, that address key features of patient-flow dynamics. These features include bursty departures due to periodic discharge windows and non-stationary arrival processes. Because of the complexity of the dynamics, the research develops appropriate approximations for queueing phenomena based on fluid approximations during peak loads and infinite server approximations during lightly loaded periods. These methods, in conjunction with high-fidelity simulations, will be used to characterize long-run behavior of the patient-flow process, such as the maximum throughput of hospital units, and their (periodic) time-dependent equilibria behavior under different operational policies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该授予学术与工业联络机会 (GOALI) 计划奖项将通过减少拥堵并改善医院单位内和医院之间的患者流动来促进国民健康。通过开发预测人流动态并解决拥堵来源的模型，该项目将提高患者的安全和满意度，同时减少医院的人员和设备成本。拥堵的减少还将改善占用率预测，从而减少医院网络内的救护车改道。这项研究采用了新颖的建模技术，这些技术是 PI 与 GOALI 合作伙伴西北纪念医院 (NMH) 之间的密切合作所提供的。将开发易于校准且足够通用的模型，以便其分析和见解适用于医院内广泛的临床环境。该奖项为研究生从事对医疗保健运营有直接好处的研究提供支持。 PI 将把他们的研究成果纳入排队和服务工程课程中。研究结果将传播给运筹学和医疗保健从业者社区。该奖项支持专门针对医院运营定制的多服务器排队模型的研究，该模型解决了患者流动态的关键特征。这些特征包括由于周期性放电窗口和非平稳到达过程导致的突发出发。由于动力学的复杂性，该研究基于峰值负载期间的流体近似和轻负载期间的无限服务器近似，为排队现象开发了适当的近似。这些方法与高保真模拟相结合，将用于描述患者流程的长期行为，例如医院单位的最大吞吐量，以及不同运营政策下的（周期性）时间依赖性平衡行为。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。