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Multiple criteria decision engineering to support management in military healthcare and logistics operations.

机译：多准则决策工程可支持军事医疗保健和后勤行动中的管理。

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摘要

The U.S. Department of Defense Military Health System (MHS) is a unique health system in that it recruits and trains its own medical staff, has a generally physically fit patient population, and is a closed, single-payer system. The unique mission of the MHS comes with its own set of healthcare and logistics challenges above and beyond those of a civilian US-based health system. At the surface, the MHS is charged with delivering quality healthcare to a diverse population. At the core, however, that charge includes maintaining peacetime healthcare delivery capacity while ensuring the deployment readiness of the active force, and deploying, establishing and running forward deployed healthcare facilities to provide the necessary health services support for combat, stability, peacekeeping, and humanitarian assistance operations. Further complicating the delivery of quality care is the transient nature of healthcare providers either due to deployments or routine personnel moves between hospitals, clinics, and field units.;As a result of these challenges, this doctoral dissertation employs methods of multiple criteria decision engineering to assist strategic decision-making and to support the complex planning and management of military healthcare resources, personnel, logistics, and financial incentives.;Multi-criteria and stochastic optimization models that leverage mixed-integer programming, Monte Carlo simulation, discrete event simulation, text mining, clustering analysis, regression modeling and econometrics are developed to provide critical insights for military decision-makers. The multiple criteria decision engineering methods in this dissertation are applied to several real-world decision problems within military healthcare and logistics operations to illustrate the impact and relevance of the results.;First, we proffer the Multi-Objective Auto-Optimization Model (MAOM) -- a resource allocation-based optimization model that adjusts resources (system inputs) automatically -- which provides decision-makers with a decision-support tool for re-allocating resources in large health systems that are centrally controlled and funded, such as the MHS. The necessity to efficiently balance and re-allocate system resources among hospitals in a hospital network is paramount, especially as health systems experience increasing demand and costs for health services.;Second, we proffer the Objective Force Model (OFM), a deterministic, mixed-integer linear weighted goal programming model to optimize workforce planning for the U.S. Army Medical Department (AMEDD) Personnel Proponency Directorate (APPD). We also develop two stochastic variants of the linear OFM, which incorporate probabilistic components associated with uncertain officer continuation rates. We employ a discrete event simulation model to verify and validate the results.;Third, we develop a multiple criteria decision analysis (MCDA) framework to optimize the military humanitarian assistance/disaster relief (HA/DR) aerial delivery supply chain network under uncertainty. The model uses stochastic, mixed-integer, weighted goal programming to optimize network design, logistics costs, staging locations, procurement amounts, and inventory levels. The MCDA framework enables decision-makers to explore the trade-offs between military HA/DR aerial delivery supply chain efficiency and responsiveness, while optimizing across a wide range of real-world, probabilistic scenarios to account for the inherent uncertainty in the location of global humanitarian disasters, as well as the amount of demand to be met.;Fourth, we propose the Fuzzy Multi-Objective Auto-Optimization Model (FMAOM), an optimization model with fuzzy constraints that can be used for automatic resource re-allocation with respect to different levels of risk preferences. The efficient use of resources in health systems is crucial mostly due to the increasing demand and limited funding.;Fifth, we measure the effect of a monetary incentive model on hospital efficiency and outcomes. The Army component of the MHS implemented a pay-for-performance financial incentive program in 2007 in an effort to stimulate patient quality, access, and satisfaction improvements. Using a retrospective, quasi-experimental design, the empirical analysis incorporates data envelopment analysis (DEA) with time windows and difference-in-differences estimation. Hospitals are evaluated in the U.S. Army, Air Force, and Navy during the period of 2001--2012. The results indicate a statistically significant reduction in efficiency for the hospitals that received financial incentives. The health policy implications of this study are applicable in light of the national healthcare debate and may assist healthcare policy-makers in determining the efficacy and associated trade-offs of pay-for-performance financing models.;Last, we introduce the Stochastic Multi-Objective Auto-Optimization Model (SMAOM) for resource allocation decision-making under uncertainty in the MHS. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The usefulness of the proposed model is illustrated by a computational experiment in which a traditional DEA model is compared to the proposed SMAOM for 128 hospitals in the three services (Air Force, Navy, Army) in the MHS using hospital-level data from 2009 - 2013. (Abstract shortened by ProQuest.).
机译：美国国防部军事卫生系统（MHS）是一种独特的卫生系统，它可以招募和培训自己的医务人员，总体上身体适合患者群体，并且是封闭的单付费系统。 MHS的独特使命是其自身的医疗保健和物流挑战，这超出了美国民用卫生系统的挑战。从表面上看，MHS负责为多样化的人群提供优质的医疗保健。但是，该费用的核心包括维持和平时期的医疗保健提供能力，同时确保现役部队的部署就绪，以及部署，建立和运行已部署的医疗保健设施，以为战斗，稳定，维持和​​平和人道主义提供必要的医疗服务支持协助行动。由于部署或常规人员在医院，诊所和现场单位之间的调动，医疗保健提供者的瞬态特性使提供优质医疗服务的工作进一步复杂化。由于这些挑战，本博士论文采用多准则决策工程方法协助战略决策并支持军事医疗资源，人员，后勤和财务激励的复杂计划和管理;利用混合整数规划，蒙特卡洛模拟，离散事件模拟，文本的多准则和随机优化模型挖掘，聚类分析，回归建模和计量经济学的发展为军事决策者提供了重要的见解。本文将多准则决策工程方法应用于军事医疗和后勤行动中的几个现实决策问题，以说明结果的影响和相关性。首先，我们提出了多目标自动优化模型（MAOM） -基于资源分配的优化模型，可自动调整资源（系统输入）–为决策者提供决策支持工具，用于在集中控制和资助的大型卫生系统（例如MHS）中重新分配资源。在医院网络中医院之间有效平衡和重新分配系统资源的必要性至关重要，尤其是当卫生系统对卫生服务的需求和成本不断增加时。第二，我们提供了确定性的，混合的客观力模型（OFM） -整数线性加权目标规划模型，用于优化美国陆军医疗部（AMEDD）人事倾向理事会（APPD）的劳动力计划。我们还开发了线性OFM的两个随机变体，其中包含与不确定人员续任率相关的概率成分。我们采用离散事件模拟模型来验证和验证结果；第三，我们开发了多标准决策分析（MCDA）框架，以优化不确定性下的军事人道主义援助/救灾（HA / DR）空中交付供应链网络。该模型使用随机，混合整数，加权目标规划来优化网络设计，物流成本，暂存地点，采购数量和库存水平。 MCDA框架使决策者能够探索军用HA / DR空中交付供应链效率与响应能力之间的权衡，同时在各种现实世界的概率场景中进行优化，以解决全球定位所固有的不确定性第四，我们提出了模糊多目标自动优化模型（FMAOM），这是一种具有模糊约束的优化模型，可用于相对于资源的自动重新分配不同程度的风险偏好。卫生系统中资源的有效利用至关重要，这主要是由于需求增加和资金有限。第五，我们测量了货币激励模型对医院效率和结果的影响。 MHS的陆军部门在2007年实施了按绩效付费的财政激励计划，以刺激患者的质量，准入和满意度的提高。使用回顾性的准实验设计，经验分析将数据包络分析（DEA）与时间窗和差异差估计结合在一起。在2001--2012年期间，美国陆军，空军和海军对医院进行了评估。结果表明，获得财务奖励的医院的效率在统计上有显着下降。这项研究对健康政策的影响可以根据全国医疗保健辩论而适用，并且可以帮助医疗保健决策者确定绩效工资模式的有效性和相关的权衡取舍。，我们介绍了随机多目标自动优化模型（SMAOM），用于在MHS中存在不确定性的情况下进行资源分配决策。该模型可以自动确定医院级别在哪里重新分配系统输入资源，以优化整体系统性能，同时考虑模型参数的不确定性。通过计算实验说明了该提议模型的实用性，该实验通过使用2009年的医院级数据，将传统的DEA模型与MHS的三个部门（空军，海军，陆军）的128家医院提议的SMAOM进行了比较- 2013。（摘要由ProQuest缩短）。

著录项

  • 作者

    Bastian, Nathaniel D.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Industrial engineering.;Operations research.;Systems science.;Military studies.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 198 p.
  • 总页数 198
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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