title: Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.
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.
authors.affiliation: Department of Industrial & Manufacturing Engineering, The Pennsylvania State University, 362 Leonhard Building, University Park, PA, 16802, USA. ndbastian@psu.edu.
authors.forename: Nathaniel D
authors.initials: ND
authors.lastname: Bastian
authors.affiliation: McCoy College of Business Administration, Texas State University, 601 University Drive, San Marcos, TX, 78666, USA.
authors.forename: Tahir
authors.initials: T
authors.lastname: Ekin
authors.affiliation: Department of Systems & Information Engineering, University of Virginia, 151 Engineer's Way, Charlottesville, VA, 22904, USA.
authors.forename: Hyojung
authors.initials: H
authors.lastname: Kang
authors.affiliation: School of Industrial & Systems Engineering, Georgia Institute of Technology, 755 Ferst Drive, NW, Atlanta, GA, 30332, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Griffin
authors.affiliation: Rawls College of Business Administration, Texas Tech University, 703 Flint Ave, Lubbock, TX, 79410, USA.
authors.forename: Lawrence V
authors.initials: LV
authors.lastname: Fulton
authors.affiliation: Department of Economics & Business, Virginia Military Institute, 345 Scott Shipp Hall, Lexington, VA, 24450, USA.
authors.forename: Benjamin C
authors.initials: BC
authors.lastname: Grannan
mesh_terms: D003657:Decision Making; D017598:Efficiency, Organizational; D006761:Hospitals; D006801:Humans; D040841:Resource Allocation; D035501:Uncertainty
keywords: Health systems; Military medicine; Multi-objective optimization; Performance measurement; Productivity analysis; Resource allocation; Stochastic programming