Introduction: Recourse allocation is very important in today’s highly competitive environment to enhance the quality and reduce costs due to limited resources and unlimited needs of the society. The aim of this study was to implement resource allocation in order to improve the efficiency of hospital.Method: This is a mixed method study. The data used in this paper are secondary data related to the 30 large acute and general hospitals in the US. Bed, service mix, full-time equivalent (FTE), and operational expenses are input indicators in hospital, and adjusted admissions and outpatient visits are output indicators. Using goal programming (GP) model and data envelopment analysis (DEA) model with a common weights, we suggest three scenarios for resource allocation and budget allocation. “Resource allocation based on efficiency”, “budget allocation based on efficiency” and “two stage allocation of budget”. The first scenario was used for allocating the resources and the second and third ones for allocating budget to decision making units (DMUs). The data were analyzed by LINGO software.Results: Before the allocation, four hospitals were efficient and the efficiency of six hospitals was less than 50%, but after allocation, in the first case of the first scenario 14 hospitals, 11 hospitals in the second case of the first scenario, 24 hospitals in the second scenario and 17 hospitals in the third scenario were efficient, and it is an important point that after the allocation, efficiency of all hospitals increased.Conclusion: This study can be useful for hospital administrators; it can help them to allocate their resource and budget and increase the efficiency of their hospitals.Keywords: Efficiency, Hospitals, Resource allocation, Budgets
Lotfi F, Kalhor R, Bastani P, Shaarbafchi Zadeh N, Eslamian M, Dehghani MR, et al. Various Indicators for the Assessment of Hospitalsâ Performance Status: Differences and Similarities. Iranian Red Crescent Medical Journal. 2014;16(4).
Shinjo D, Aramaki T. Geographic distribution of healthcare resources, healthcare service provision, and patient flow in Japan: A cross sectional study. Social Science & Medicine. 2012;75(11):1954-63.
Farrell MJ. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society Series A (General). 1957;120(3):253.
Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research. 1978;2(6):429-44.
Golany B, Tamir E. Evaluating Efficiency-Effectiveness-Equality Trade-Offs: A Data Envelopment Analysis Approach. Management Science. 1995;41(7):1172-84.
Cook WD, Kress M. Characterizing an equitable allocation of shared costs: A DEA approach. European Journal of Operational Research. 1999;119(3):652-61.
Cook WD, Zhu J. Allocation of shared costs among decision making units: a DEA approach. Computers & Operations Research. 2005;32(8):2171-8.
Amirteimoori A, Kordrostami S. Allocating fixed costs and target setting: A dea-based approach. Applied Mathematics and Computation. 2005;171(1):136-51.
Beasley JE. Allocating fixed costs and resources via data envelopment analysis. European Journal of Operational Research. 2003;147(1):198-216.
Du J, Cook WD, Liang L, Zhu J. Fixed cost and resource allocation based on DEA cross-efficiency. European Journal of Operational Research. 2014;235(1):206-14.
Yang C-C. Measuring health indicators and allocating health resources: a DEA-based approach. Health Care Management Science. 2016.
Lotfi FH, Hatami-Marbini A, Agrell PJ, Aghayi N, Gholami K. Allocating fixed resources and setting targets using a common-weights DEA approach. Computers & Industrial Engineering. 2013;64(2):631-40.
Cook WD, Kress M. A Data Envelopment Model for Aggregating Preference Rankings. Management Science. 1990;36(11):1302-10.
Kuosmanen T, Cherchye L, Sipiläinen T. The law of one price in data envelopment analysis: Restricting weight flexibility across firms. European Journal of Operational Research. 2006;170(3):735-57.
Liu F-HF, Hsuan Peng H. Ranking of units on the DEA frontier with common weights. Computers & Operations Research. 2008;35(5):1624-37.
Amin GR, Toloo M. Finding the most efficient DMUs in DEA: An improved integrated model. Computers & Industrial Engineering. 2007;52(1):71-7.
Li X, Cui J. A Comprehensive Dea Approach for the Resource Allocation Problem based on Scale Economies Classification. Journal of Systems Science and Complexity. 2008;21(4):540-57.
Bi G, Ding J, Luo Y, Liang L. Resource allocation and target setting for parallel production system based on DEA. Applied Mathematical Modelling. 2011;35(9):4270-80.
Davoodi A, Rezai HZ. Common set of weights in data envelopment analysis: a linear programming problem. Central European Journal of Operations Research. 2011;20(2):355-65.
Ozcan YA, Health care benchmarking and performance evaluation: an assessment using Data Envelopment Analysis (DEA), 2nd ed. New York: Springer; 2014. P 150
Yazdian Hossein Abadi, N., Noori, S., & Haeri, A. (2017). The use of resource allocation approach for hospitals based on the initial efficiency by using data envelopment analysis. Health Management & Information Science, 4(4), 101-106.
MLA
Nahid Yazdian Hossein Abadi; Siamak Noori; Abdorrahman Haeri. "The use of resource allocation approach for hospitals based on the initial efficiency by using data envelopment analysis", Health Management & Information Science, 4, 4, 2017, 101-106.
HARVARD
Yazdian Hossein Abadi, N., Noori, S., Haeri, A. (2017). 'The use of resource allocation approach for hospitals based on the initial efficiency by using data envelopment analysis', Health Management & Information Science, 4(4), pp. 101-106.
VANCOUVER
Yazdian Hossein Abadi, N., Noori, S., Haeri, A. The use of resource allocation approach for hospitals based on the initial efficiency by using data envelopment analysis. Health Management & Information Science, 2017; 4(4): 101-106.