| Title |
Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems
|
|---|---|
| Published in |
Health Care Management Science, January 2016
|
| DOI | 10.1007/s10729-015-9350-2 |
| Pubmed ID | |
| Authors |
Nathaniel D. Bastian, Tahir Ekin, Hyojung Kang, Paul M. Griffin, Lawrence V. Fulton, Benjamin C. Grannan |
| 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 73 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 12% |
| Student > Ph. D. Student | 7 | 10% |
| Student > Doctoral Student | 6 | 8% |
| Professor | 6 | 8% |
| Researcher | 5 | 7% |
| Other | 13 | 18% |
| Unknown | 27 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 13 | 18% |
| Business, Management and Accounting | 11 | 15% |
| Economics, Econometrics and Finance | 6 | 8% |
| Social Sciences | 3 | 4% |
| Medicine and Dentistry | 3 | 4% |
| Other | 8 | 11% |
| Unknown | 29 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 January 2016.
All research outputs
#18,434,182
of 22,837,982 outputs
Outputs from Health Care Management Science
#206
of 285 outputs
Outputs of similar age
#284,412
of 393,723 outputs
Outputs of similar age from Health Care Management Science
#2
of 10 outputs
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