Health Care Management Science

, Volume 20, Issue 2, pp 246–264 | Cite as

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

  • Nathaniel D. Bastian
  • Tahir Ekin
  • Hyojung Kang
  • Paul M. Griffin
  • Lawrence V. Fulton
  • Benjamin C. Grannan
Article

Abstract

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. 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 model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 – 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

Keywords

Multi-objective optimization Stochastic programming Resource allocation Performance measurement Productivity analysis Health systems Military medicine 

Notes

Acknowledgments

This work was supported by the National Science Foundation under Grant No. DGE1255832. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, Pennsylvania State University, Texas State University, University of Virginia, Georgia Institute of Technology, Texas Tech University, or Virginia Military Institute.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Nathaniel D. Bastian
    • 1
  • Tahir Ekin
    • 2
  • Hyojung Kang
    • 3
  • Paul M. Griffin
    • 4
  • Lawrence V. Fulton
    • 5
  • Benjamin C. Grannan
    • 6
  1. 1.Department of Industrial & Manufacturing EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.McCoy College of Business AdministrationTexas State UniversitySan MarcosUSA
  3. 3.Department of Systems & Information EngineeringUniversity of VirginiaCharlottesvilleUSA
  4. 4.School of Industrial & Systems EngineeringGeorgia Institute of TechnologyAtlantaUSA
  5. 5.Rawls College of Business AdministrationTexas Tech UniversityLubbockUSA
  6. 6.Department of Economics & BusinessVirginia Military InstituteLexingtonUSA

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