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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

Nathaniel D Bastian1, Tahir Ekin2, Hyojung Kang3

  • 1Department of Industrial & Manufacturing Engineering, The Pennsylvania State University, 362 Leonhard Building, University Park, PA, 16802, USA. ndbastian@psu.edu.

Health Care Management Science
|January 9, 2016
PubMed
Summary
This summary is machine-generated.

A new Stochastic Multi-Objective Auto-Optimization Model (SMAOM) improves resource allocation in fixed-input health systems. This model enhances system-wide technical efficiency by 18% compared to traditional methods.

Keywords:
Health systemsMilitary medicineMulti-objective optimizationPerformance measurementProductivity analysisResource allocationStochastic programming

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Area of Science:

  • Health Systems Management
  • Operations Research
  • Stochastic Optimization

Background:

  • Managing hospitals in fixed-input systems like the U.S. Military Health System (MHS) presents challenges due to numerous facilities and resource uncertainties.
  • Effective resource allocation is crucial for optimizing performance within these complex environments.

Purpose of the Study:

  • To introduce a Stochastic Multi-Objective Auto-Optimization Model (SMAOM) for improved resource allocation decision-making in fixed-input health systems.
  • To automatically identify optimal re-allocation of system input resources at the hospital level.

Main Methods:

  • Development and application of the Stochastic Multi-Objective Auto-Optimization Model (SMAOM).
  • Utilized hospital-level data from 128 MHS hospitals (Air Force, Army, Navy) from 2009-2013.
  • Compared SMAOM results against the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model.

Main Results:

  • SMAOM application to the MHS increased expected system-wide technical efficiency by 18% compared to the DEA model.
  • The model effectively accounts for uncertainty in health system inputs and outputs.
  • SMAOM demonstrated greater robustness against data outliers and sampling errors than traditional DEA methods.

Conclusions:

  • SMAOM offers a superior approach to resource allocation in fixed-input health systems, enhancing efficiency and managing uncertainty.
  • The method provides valuable decision support for the Defense Health Agency (DHA) in integrating clinical and business processes.
  • SMAOM facilitates better resource sharing and system-wide standardization across military services.