An augmented data envelopment analysis approach for designing a health service network

Document Type : Research Paper

Authors

Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran

Abstract

Background: In the healthcare systems, health centers are taken into consideration as the most important sector due to providing health care services to people. In this respect, the assessing of this center is of great importance. Therefore, there is a need for a performance evaluation system to evaluate both efficiency and effectiveness of human resource, processes, and programs of health centers to improve the competitive power.
Methods: To measure the efficiency and productivity of Decision Making Units (DMUs), Data Envelopment Analysis (DEA), which is a nonparametric technique, is considered as the most common tool and can be applied to compare the performance of health centers. However, being DMUs homogenous is one of the underlying assumptions of DEA which prevent us from devising this technique because health centers provide different services, and thus, they are incommensurable. To overcome this barrier, a novel DEA technique is developed to select the best locations for health centers of Iran’s healthcare system.
Results: A practical case study, that is designing the health service network for urban residents’ health center (towns) in Fars province, is incorporated into the proposed technique. Finally, the candidate locations for health centers are ranked in terms of efficiency using novel DEA technique, and then, the sensitivity analysis is conducted on final results.
Conclusion: The obtained results imply the high performance of the proposed technique in the ranking of efficient health centers in health care systems. Moreover, this technique introduces a comprehensive performance evaluation tool for health centers and also aids managers and decision-makers to more accurately plan for selecting the best candidate location for health centers along with saving the resources.

Keywords

References

  1. Zahiri B, Mousazadeh M, Bozorgi-Amiri A. A robust stochastic programming approach for blood collection and distribution network design. International Journal of Research in Industrial Engineering. 2014; 3(2):1.
  2. Mousazadeh M, Torabi SA, Pishvaee MS, Abolhassani F. Accessible, stable, and equitable health service network redesign: A robust mixed possibilistic-flexible approach. Transportation Research Part E: Logistics and Transportation Review. 2018; 111:113-29.
  3. Yasenovskiy V, Hodgson J. Hierarchical location-allocation with spatial choice interaction modeling. Annals of the Association of American Geographers. 2007; 97(3):496-511.
  4. Stummer C, Doerner K, Focke A, Heidenberger K. Determining location and size of medical departments in a hospital network: A multiobjective decision support approach. Health care management science. 2004; 7(1):63-71.
  5. Ndiaye M, Alfares H. Modeling health care facility location for moving population groups. Computers & Operations Research. 2008; 35(7):2154-61.
  6. Zhang W, Cao K, Liu S, Huang B. A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Computers, Environment and Urban Systems. 2016; 59:220-30.
  7. Harper PR, Shahani AK, Gallagher JE, Bowie C. Planning health services with explicit geographical considerations: a stochastic location–allocation approach. Omega. 2005; 33(2):141-52.
  8. Smith HK, Harper PR, Potts CN, Thyle A. Planning sustainable community health schemes in rural areas of developing countries. European Journal of Operational Research. 2009; 193(3):768-77.
  9. Güneş ED, Yaman H, Çekyay B, Verter V. Matching patient and physician preferences in designing a primary care facility network. Journal of the Operational Research Society. 2014; 65(4):483-96.
  10. Zhang Y, Berman O, Verter V. The impact of client choice on preventive healthcare facility network design. OR spectrum. 2012; 34(2):349-70.
  11. Beheshtifar S, Alimoahmmadi A. A multiobjective optimization approach for location‐allocation of clinics. International Transactions in Operational Research. 2015; 22(2):313-28.
  12. Griffin PM, Scherrer CR, Swann JL. Optimization of community health center locations and service offerings with statistical need estimation. IIE transactions. 2008; 40(9):880-92.
  13. Kim DG, Kim YD. A Lagrangian heuristic algorithm for a public healthcare facility location problem. Annals of Operations Research. 2013; 206(1):221-40.
  14. Galvão RD, Espejo LG, Boffey B, Yates D. Load balancing and capacity constraints in a hierarchical location model. European Journal of Operational Research. 2006; 172(2):631-46.
  15. Benneyan JC, Musdal H, Ceyhan ME, Shiner B, Watts BV. Specialty care single and multi-period location–allocation models within the Veterans Health Administration. Socio-economic planning sciences. 2012; 46(2):136-48.
  16. Syam SS, Côté MJ. A location–allocation model for service providers with application to not-for-profit health care organizations. Omega. 2010; 38(3-4):157-66.
  17. Song BD, Ko YD, Hwang H. The design of capacitated facility networks for long term care service. Computers & Industrial Engineering. 2015; 89:177-85.
  18. Galvao RD, Espejo LG, Boffey B. A hierarchical model for the location of perinatal facilities in the municipality of Rio de Janeiro. European Journal of Operational Research. 2002; 138(3):495-517.
  19. Mitropoulos P, Mitropoulos I, Giannikos I, Sissouras A. A biobjective model for the locational planning of hospitals and health centers. Health Care Management Science. 2006; 9(2):171-9.
  20. Mestre AM, Oliveira MD, Barbosa-Póvoa A. Organizing hospitals into networks: a hierarchical and multiservice model to define location, supply and referrals in planned hospital systems. OR spectrum. 2012; 34(2):319-48.
  21. Mitropoulos P, Mitropoulos I, Giannikos I. Combining DEA with location analysis for the effective consolidation of services in the health sector. Computers & Operations Research. 2013; 40(9):2241-50.
  22. Mestre AM, Oliveira MD, Barbosa-Póvoa AP. Location–allocation approaches for hospital network planning under uncertainty. European Journal of Operational Research. 2015; 240(3):791-806.
  23. Hosseini-Motlagh SM, Cheraghi S, Ghatreh Samani M. A robust optimization model for blood supply chain network design. International Journal of Industrial Engineering & Production Research. 2016; 27(4):425-44.
  24. Cheraghi S, Hosseini-Motlagh SM, Ghatreh Samani M. Integrated planning for blood platelet production: a robust optimization approach. Journal of Industrial and Systems Engineering. 2017; 10(special issue on healthcare):55-80.
  25. Issabakhsh M, Hosseini Ms, Ghatreh M. The robust periodic vehicle routing problem of the home healthcare of peritoneal dialysis patients.
  26. Ghatreh Samani M, Hosseini-Motlagh SM. A hybrid algorithm for a two-echelon location-routing problem with simultaneous pickup and delivery under fuzzy demand. International Journal of Transportation Engineering. 2017; 5(1):59-85.
  27. Samani MR, Hosseini-Motlagh SM. An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research. 2018; 1-50.
  28. Samani MR, Torabi SA, Hosseini-Motlagh SM. Integrated blood supply chain planning for disaster relief. International journal of disaster risk reduction. 2018; 27:168-88.
  29. Bashiri M, Khorasani H, Shiri M. Multi objective supply chain network design considering customer satisfaction. In2014 IEEE International Conference on Industrial Engineering and Engineering Management 2014; 923-927.
  30. Bashiri M, Shiri M, Bakhtiarifar MH. A robust desirability-based approach to optimizing multiple correlated responses. Int J Indus Eng Prod Res. 2015; 26(2):119-28.
  31. Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European journal of operational research. 1978; 2(6):429-44.
  32. Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science. 1984; 30(9):1078-92.
  33. Emrouznejad A, Yang GL. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences. 2018; 61:4-8.
  34. Hollingsworth B, Dawson PJ, Maniadakis N. Efficiency measurement of health care: a review of non‐parametric methods and applications. Health care management science. 1999; 2(3):161-72.
  35. Worthington AC. Frontier efficiency measurement in health care: a review of empirical techniques and selected applications. Medical care research and review. 2004; 61(2):135-70.
  36. Ersoy K, Kavuncubasi S, Ozcan YA, Harris II JM. Technical efficiencies of Turkish hospitals: DEA approach. Journal of Medical Systems. 1997; 21(2):67-74.
  37. Hajialiafzali H, Moss JR, Mahmood MA. Efficiency measurement for hospitals owned by the Iranian social security organisation. Journal of Medical Systems. 2007; 31(3):166-72.
  38. Andersen P, Petersen NC. A procedure for ranking efficient units in data envelopment analysis. Management science. 1993; 39(10):1261-4.
  39. Lee KH, Yang SB, Choi M. The association between hospital ownership and technical efficiency in a managed care environment. Journal of Medical Systems. 2009; 33(4):307-15.
  40. Caballer-Tarazona M, Moya-Clemente I, Vivas-Consuelo D, Barrachina-Martínez I. A model to measure the efficiency of hospital performance. Mathematical and computer modelling. 2010; 52(7-8):1095-102.
  41. Dotoli M, Epicoco N, Falagario M, Sciancalepore F. A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty. Computers & Industrial Engineering. 2015; 79:103-14.
  42. Shahhoseini R, Tofighi S, Jaafaripooyan E, Safiaryan R. Efficiency measurement in developing countries: application of data envelopment analysis for Iranian hospitals. Health services management research. 2011; 24(2):75-80.
  43. Lindlbauer I, Schreyögg J. The relationship between hospital specialization and hospital efficiency: do different measures of specialization lead to different results?. Health care management science. 2014; 17(4):365-78.
  44. Fragkiadakis G, Doumpos M, Zopounidis C, Germain C. Operational and economic efficiency analysis of public hospitals in Greece. Annals of Operations Research. 2016; 247(2):787-806.
  45. Rezaee MJ, Karimdadi A. Do geographical locations affect in hospitals performance? A multi-group data envelopment analysis. Journal of medical systems. 2015; 39(9):85.
  46. Costantino N, Dotoli M, Epicoco N, Falagario M, Sciancalepore F. Using cross-efficiency fuzzy Data Envelopment Analysis for healthcare facilities performance evaluation under uncertainty. In2013 IEEE International Conference on Systems, Man, and Cybernetics 2013; (pp. 912-917). IEEE.
  47. Ruiz JL, Sirvent I. Fuzzy cross-efficiency evaluation: a possibility approach. Fuzzy Optimization and Decision Making. 2017; 16(1):111-26.
  48. Hatam N. The role of Data Envelopment Analysis (DEA) pattern in the efficiency of social security hospitals in Iran. Iranian Red Crescent Medical Journal. 2008;10(3):208.
  49. Kiadaliri AA, Jafari M, Gerdtham UG. Frontier-based techniques in measuring hospital efficiency in Iran: a systematic review and meta-regression analysis. BMC health services research. 2013;13(1):312.
  50. Abadi NY, Noori S, Haeri A. The use of resource allocation approach for hospitals based on the initial efficiency by using data envelopment analysis. Journal of Health Management and Informatics. 2017;4(4):101-6.
  51. Lam KF. In the determination of weight sets to compute cross-efficiency ratios in DEA. Journal of the Operational Research Society. 2010; 61(1):134-43.
  52. Wu J, Sun J, Liang L, Zha Y. Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications. 2011; 38(5):5162-5.
  53. Wu J, Chu J, Sun J, Zhu Q, Liang L. Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Computers & Industrial Engineering. 2016; 93:143-51.
  54. Wu J, Chu J, Sun J, Zhu Q. DEA cross-efficiency evaluation based on Pareto improvement. European Journal of Operational Research. 2016; 248(2):571-9.
  55. Haeri A, Rezaie K, Amalnick MS. Using multi-objective DEA to assess the overall and partial performance of hierarchical resource Utilization. Research Journal of Applied Sciences, Engineering and Technology. 2013;5(4):1213-24.
  56. Rezaee MS, Haeri A, Noori S. Automotive Vendor's Performance Evaluation and Improvement Plan Presentation by Using a Data Envelopment Analysis. International Journal of Engineering. 2018;31(2):374-81.
  57. Peykani P, Mohammadi E, Jabbarzadeh A, Jandaghian A. Utilizing robust data envelopment analysis model for measuring efficiency of stock, a case study: Tehran Stock Exchange.
  58. Peykani P, Mohammadi E, Pishvaee MS, Rostamy-Malkhalifeh M, Jabbarzadeh A. A novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity and credibility-based approaches. RAIRO-Operations Research. 2018;52(4):1445-63.
  59. Peykani P, Mohammadi E. Interval network data envelopment analysis model for classification of investment companies in the presence of uncertain data. Journal of Industrial and Systems Engineering. 2018; 11:63-72.
International Journal of Hospital Research
Volume 7, Issue 2
May 2018
Pages 62-80

Files

Share

How to cite

Statistics