Collaborative Research: Optimizing Trauma Care Network Design

合作研究：优化创伤护理网络设计

• 批准号: 1761022
• 负责人: Nan Kong
• 金额: $ 24.96万
• 依托单位: Purdue 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: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761022&HistoricalAwards=false
• 关键词: Collaborative; Research; Optimizing; Trauma; Care

This award advances the national health by supporting research that will improve trauma care network design. Trauma is a leading cause of death in younger populations. Trauma care networks include trauma centers, staffed and equipped round the clock to treat the most severely injured cases, community hospitals, equipped to stabilize and transport patients to appropriate trauma centers, and air/ground transport services. The trauma care network should ensure that severely injured patients receive the right care in the right amount of time, but maldistribution of trauma care facilities can result in undertreatment of severely injured patients as well as expensive overtreatment of less-severely injured patients. This project will facilitate coordinated network design between state agencies and hospital networks, two key stakeholders in the trauma care system. It will enable trauma policy makers to quantitatively benchmark trauma care regionally and nationally in terms of triage errors and costs. The research team will collaborate with state officials, hospitals, and emergency care providers to validate the network optimization methods. The award will support graduate student research and provide experiential learning opportunities for undergraduate and graduate students in operations research related to healthcare policy and management. Research findings will be widely distributed to both the academic and practitioner communities. Innovative optimization methods to coordinate the bilevel decision making process for trauma center location are developed in this project. The research frames the trauma network design problem as a bilevel biobjective optimization problem to capture the hierarchical interaction between the upper level (regional authority) decisions involving subsidy allocation to improve social wellbeing and minimize public spending, and lower level (hospital networks) decisions involving upgrade or downgrade to maximize revenue. Novel techniques for cross-decomposition to bound the combinatorial lower level problem and adaptive scalarization to approximate the entire Pareto set of nonlinear nonconvex multiobjective bilevel programs will enable efficient problem solution. Robustness will be evaluated in response to spatial variations in on-scene decisions, and temporal deviations in projected trauma demand and transport resource availability. New surrogate modeling-based algorithms will help solve the resultant maximal parameter subspace identification problem. Data from two states will be used to validate the approach.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.

该奖项通过支持改善创伤护理网络设计的研究来促进国民健康。 创伤是年轻人死亡的主要原因。 创伤护理网络包括创伤中心，配备人员和设备，全天候治疗最严重的受伤病例，社区医院，配备稳定和运送病人到适当的创伤中心，以及空中/地面运输服务。 创伤护理网络应确保严重受伤的患者在适当的时间内得到适当的护理，但创伤护理设施的分布不均可能导致严重受伤的患者治疗不足，以及对伤势较轻的患者进行昂贵的过度治疗。 该项目将促进国家机构和医院网络之间的协调网络设计，这是创伤护理系统中的两个关键利益相关者。它将使创伤政策制定者能够在分诊错误和成本方面对区域和国家的创伤护理进行定量基准。 研究小组将与州政府官员、医院和急救服务提供者合作，验证网络优化方法。 该奖项将支持研究生研究，并为本科生和研究生提供与医疗保健政策和管理相关的运营研究的体验式学习机会。 研究结果将广泛分发给学术界和从业人员。创新的优化方法，以协调两层决策过程中创伤中心的位置在这个项目中开发。 该研究框架的创伤网络设计问题作为一个双层双目标优化问题，以捕捉上层（地区当局）的决策，涉及补贴分配，以改善社会福利和最大限度地减少公共支出，和较低的水平（医院网络）的决策，涉及升级或降级，以最大限度地提高收入之间的层次互动。交叉分解约束的组合低层次问题和自适应标量化近似的非线性非凸多目标双层规划的整个Pareto集的新技术将使有效的问题解决方案。鲁棒性将进行评估，以应对现场决策的空间变化，以及预计创伤需求和运输资源可用性的时间偏差。新的代理模型为基础的算法将有助于解决由此产生的最大参数子空间识别问题。来自两个州的数据将用于验证该方法。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

Walrasian Equilibrium-Based Pricing Mechanism for Health-Data Crowdsensing Under Information Asymmetry

• DOI: 10.1109/tcss.2022.3171566
• 发表时间: 2023-06
• 期刊: IEEE Transactions on Computational Social Systems
• 影响因子: 5
• 作者: Xinxin Guo;N. Kong;Haiyan Wang

Locating trauma centers considering patient safety

• DOI: 10.1007/s10729-021-09576-y
• 发表时间: 2022-01
• 期刊: Health Care Management Science
• 影响因子: 3.6
• 作者: Sagar Hirpara;M. Vaishnav;Pratik J. Parikh;Nan Kong;Priti Parikh

Sustainable multi-commodity capacitated facility location problem with complementarity demand functions

具有互补需求函数的可持续多商品能力设施选址问题

• DOI: 10.1016/j.tre.2020.102165
• 发表时间: 2021
• 期刊: Transportation Research Part E
• 作者: Weiwei Liu;Nan Kong;Mingzheng Wang;Lingling Zhang
• 通讯作者: Lingling Zhang

Incorporating real-time citizen responder information to augment EMS logistics operations: A simulation study

纳入实时公民响应者信息以增强 EMS 物流运营：模拟研究

• DOI: 10.1016/j.cie.2022.108399
• 发表时间: 2022
• 期刊: Computers & Industrial Engineering
• 影响因子: 7.9
• 作者: Paz, Juan Camilo;Kong, Nan;Lee, Seokcheon
• 通讯作者: Lee, Seokcheon

Optimal Two-Tier Outpatient Care Network Redesign With a Real-World Case Study of Shanghai

以上海实际案例研究优化两级门诊网络重新设计

• DOI: 10.1109/tase.2022.3198484
• 发表时间: 2022-09-07
• 期刊: IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
• 影响因子: 5.6
• 作者: Deng，Yewen;Li，Na;Xie，Xiaoqing
• 通讯作者: Xie，Xiaoqing