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Impulse response function analysis of the impacts of hospital accreditations on hospital efficiency

  • Fengyi Lin
  • Yung-Jr Deng
  • Wen-Min LuEmail author
  • Qian Long Kweh
Article
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Abstract

Improving hospital efficiency is an emerging area of interest among policy makers in the new global economy’s healthcare system. To ensure accurate efficiency analyses, we consider the nonhomogeneous input/output characteristics of various hospital departments, particularly the Department of Medicine, Department of Surgery, and Department of Other Specialist Medicine. These departments employ co-inputs to produce nonhomogeneous outputs. Specifically, we employ data envelopment analysis to evaluate the efficiency of 15 veterans hospitals in Taiwan. Empirical results show that the performance of the Department of Surgery has higher quality than that of the Department of Medicine and Department of Other Specialist Medicine. In addition, we include another data science technique, namely, impulse response function analysis. The findings indicate that “the New Hospital Accreditation” introduced in 2007 and revised in 2011 improved the efficiency of all departments in their respective first year of introductions. By contrast, the efficiencies of the Department of Surgery and Department of Other Specialist Medicine immediately decreased in the second year after the introductions.

Keywords

Impulse response function Data envelopment analysis Hospital efficiency Hospital accreditation Nonhomogeneous departments 

MSC Codes:

68M20 90C39 
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Notes

References

  1. 1.
    Zhang X, Tone K, Lu Y (2018) Impact of the local public hospital reform on the efficiency of medium-sized hospitals in Japan: an improved slacks-based measure data envelopment analysis approach. Health Serv Res 53(2):896–918CrossRefGoogle Scholar
  2. 2.
    Lynch JR, Ozcan YA (1994) Hospital closure: an efficiency analysis. J Healthc Manag 39(2):205Google Scholar
  3. 3.
    Ozcan YA, McCue MJ (1996) Development of a financial performance index for hospitals: DEA approach. J Oper Res Soc 47(1):18–26CrossRefGoogle Scholar
  4. 4.
    Ozcan YA (2008) Health care benchmarking and performance evaluation. Springer, USCrossRefGoogle Scholar
  5. 5.
    Ozcan YA, Lins ME, Lobo MSC, Da Silva ACM, Fiszman R, Pereira BB (2010) Evaluating the performance of Brazilian university hospitals. Ann Oper Res 178(1):247–261CrossRefGoogle Scholar
  6. 6.
    Chou T-H, Ozcan YA, White KR (2012) Technical and scale efficiencies of Catholic hospitals: does a system value of stewardship matter? In: Advanced decision making methods applied to health care. Springer, pp 83–101Google Scholar
  7. 7.
    Ozcan YA, Legg JS (2014) Performance measurement for radiology providers: a national study. Int J Healthc Technol Manag 14(3):209–221CrossRefGoogle Scholar
  8. 8.
    Narcı HÖ, Ozcan YA, Şahin İ, Tarcan M, Narcı M (2015) An examination of competition and efficiency for hospital industry in Turkey. Health Care Manag Sci 18(4):407–418CrossRefGoogle Scholar
  9. 9.
    DePuccio MJ, Ozcan YA (2017) Exploring efficiency differences between medical home and non-medical home hospitals. Int J Healthc Manag 10(3):147–153CrossRefGoogle Scholar
  10. 10.
    Ozcan YA, Khushalani J (2017) Assessing efficiency of public health and medical care provision in OECD countries after a decade of reform. CEJOR 25(2):325–343CrossRefGoogle Scholar
  11. 11.
    Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444CrossRefGoogle Scholar
  12. 12.
    Cook WD, Harrison J, Imanirad R, Rouse P, Zhu J (2013) Data envelopment analysis with nonhomogeneous DMUs. Oper Res 61(3):666–676CrossRefGoogle Scholar
  13. 13.
    Li Y, Lei X, Morton A (2018) Performance evaluation of nonhomogeneous hospitals: the case of Hong Kong hospitals. Health Care Manag Sci:1–14Google Scholar
  14. 14.
    Kapelko M, Lansink AO, Stefanou SE (2015) Analyzing the impact of investment spikes on dynamic productivity growth. Omega 54:116–124CrossRefGoogle Scholar
  15. 15.
    Nunamaker TR (1983) Measuring routine nursing service efficiency: a comparison of cost per patient day and data envelopment analysis models. Health Serv Res 18(2, Part 1):183Google Scholar
  16. 16.
    Sherman HD (1984) Hospital efficiency measurement and evaluation: empirical test of a new technique. Med Care 22(10):922–938CrossRefGoogle Scholar
  17. 17.
    Banker RD, Conrad RF, Strauss RP (1986) A comparative application of data envelopment analysis and translog methods: an illustrative study of hospital production. Manag Sci 32(1):30–44CrossRefGoogle Scholar
  18. 18.
    Linna M (1998) Measuring hospital cost efficiency with panel data models. Health Econ 7(5):415–427CrossRefGoogle Scholar
  19. 19.
    Giuffrida A, Gravelle H (2001) Measuring performance in primary care: econometric analysis and DEA. Appl Econ 33(2):163–175CrossRefGoogle Scholar
  20. 20.
    Worthington AC (2004) Frontier efficiency measurement in health care: a review of empirical techniques and selected applications. Med Care Res Rev 61(2):135–170CrossRefGoogle Scholar
  21. 21.
    Hollingsworth B (2008) The measurement of efficiency and productivity of health care delivery. Health Econ 17(10):1107–1128CrossRefGoogle Scholar
  22. 22.
    Huang Y-GL (1990) An application of data envelopment analysis: measuring the relative performance of Florida general hospitals. J Med Syst 14(4):191–196CrossRefGoogle Scholar
  23. 23.
    Sexton TR, Leiken AM, Nolan AH, Liss S, Hogan A, Silkman RH (1989) Evaluating managerial efficiency of veterans administration medical centers using data envelopment analysis. Med Care 27(12):1175–1188CrossRefGoogle Scholar
  24. 24.
    Valdmanis VG (1990) Ownership and technical efficiency of hospitals. Med Care 28(6):552–561CrossRefGoogle Scholar
  25. 25.
    White KR, Ozcan YA (1996) Church ownership and hospital efficiency. J Healthc Manag 41(3):297Google Scholar
  26. 26.
    Chirikos TN, Sear AM (2000) Measuring hospital efficiency: a comparison of two approaches. Health Serv Res 34(6):1389–1408Google Scholar
  27. 27.
    Ferrier GD, Rosko MD, Valdmanis VG (2006) Analysis of uncompensated hospital care using a DEA model of output congestion. Health Care Manag Sci 9(2):181–188CrossRefGoogle Scholar
  28. 28.
    Hua Z, Bian Y, Liang L (2007) Eco-efficiency analysis of paper mills along the Huai River: an extended DEA approach. Omega 35(5):578–587CrossRefGoogle Scholar
  29. 29.
    Barbetta GP, Turati G, Zago AM (2007) Behavioral differences between public and private not-for-profit hospitals in the Italian national health service. Health Econ 16(1):75–96CrossRefGoogle Scholar
  30. 30.
    Liu C, Laporte A, Ferguson BS (2008) The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations. Health Econ 17(9):1073–1087CrossRefGoogle Scholar
  31. 31.
    Veihola M, Aroviita P, Kekomäki R, Linna M, Sintonen H (2008) Discarded cellular components and the technical efficiency of component preparation. Eur J Health Econ 9(4):325–331CrossRefGoogle Scholar
  32. 32.
    Nayar P, Ozcan YA (2008) Data envelopment analysis comparison of hospital efficiency and quality. J Med Syst 32(3):193–199CrossRefGoogle Scholar
  33. 33.
    Chu H-L, Wang C-C, Shiu SF (2009) Effect of participating in Taiwan quality Indicator project on hospital efficiency in Taiwan. J Health Care Finance 35(4):32–41Google Scholar
  34. 34.
    Chang S-J, Hsiao H-C, Huang L-H, Chang H (2011) Taiwan quality indicator project and hospital productivity growth. Omega 39(1):14–22CrossRefGoogle Scholar
  35. 35.
    Kounetas K, Papathanassopoulos F (2013) How efficient are Greek hospitals? A case study using a double bootstrap DEA approach. Eur J Health Econ 14(6):979–994CrossRefGoogle Scholar
  36. 36.
    Dowd B, Swenson T, Kane R, Parashuram S, Coulam R (2014) Can data envelopment analysis provide a scalar index of ‘value’? Health Econ 23(12):1465–1480CrossRefGoogle Scholar
  37. 37.
    Ferrera JMC, Cebada EC, Zamorano LRM (2014) The effect of quality and socio-demographic variables on efficiency measures in primary health care. Eur J Health Econ 15(3):289–302CrossRefGoogle Scholar
  38. 38.
    Castelli A, Street A, Verzulli R, Ward P (2015) Examining variations in hospital productivity in the English NHS. Eur J Health Econ 16(3):243–254CrossRefGoogle Scholar
  39. 39.
    Gascón F, Lozano J, Ponte B, de la Fuente D (2017) Measuring the efficiency of large pharmaceutical companies: an industry analysis. Eur J Health Econ 18(5):587–608CrossRefGoogle Scholar
  40. 40.
    Jordà Ò (2005) Estimation and inference of impulse responses by local projections. Am Econ Rev 95(1):161–182CrossRefGoogle Scholar
  41. 41.
    Furceri D, Zdzienicka A (2012) How costly are debt crises? J Int Money Financ 31(4):726–742CrossRefGoogle Scholar
  42. 42.
    Bernal-Verdugo LE, Furceri D, Guillaume D (2013) Banking crises, labor reforms, and unemployment. J Comp Econ 41(4):1202–1219CrossRefGoogle Scholar
  43. 43.
    Kuiper WE, Lansink AGO (2013) Asymmetric price transmission in food supply chains: impulse response analysis by local projections applied to US broiler and pork prices. Agribusiness 29(3):325–343CrossRefGoogle Scholar
  44. 44.
    Chung H, Fang P, Bao C, Shih W (2008) Evaluating operative performance of clinical section in hospital using data envelopment analysis-a case of regional teaching hospital. J Healthc Manag 9(1):36–52Google Scholar
  45. 45.
    Donabedian A (1980) The definition of quality and approaches to its assessment (explorations in quality assessment and monitoring)s. Health Administration Press, Ann ArborGoogle Scholar
  46. 46.
    Landon BE, Epstein AM (2001) For-profit and not-for-profit health plans participating in Medicaid. Health Aff 20(3):162–171CrossRefGoogle Scholar
  47. 47.
    Pilyavsky AI, Aaronson WE, Bernet PM, Rosko MD, Valdmanis VG, Golubchikov MV (2006) East–west: does it make a difference to hospital efficiencies in Ukraine? Health Econ 15(11):1173–1186CrossRefGoogle Scholar
  48. 48.
    Chang H, Chang W-J, Das S, Li S-H (2004) Health care regulation and the operating efficiency of hospitals: evidence from Taiwan. J Account Public Policy 23(6):483–510CrossRefGoogle Scholar
  49. 49.
    Chang H, Cheng M-A, Das S (2004) Hospital ownership and operating efficiency: evidence from Taiwan. Eur J Oper Res 159(2):513–527CrossRefGoogle Scholar
  50. 50.
    Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092CrossRefGoogle Scholar
  51. 51.
    Cooper WW, Li S, Seiford LM, Tone K, Thrall RM, Zhu J (2001) Sensitivity and stability analysis in DEA: some recent developments. J Prod Anal 15(3):217–246CrossRefGoogle Scholar
  52. 52.
    de Castro Lobo MS, Ozcan YA, da Silva AC, Lins MPE, Fiszman R (2010) Financing reform and productivity change in Brazilian teaching hospitals: Malmquist approach. CEJOR 18(2):141–152CrossRefGoogle Scholar
  53. 53.
    Kacak H, Ozcan YA, Kavuncubasi S (2014) A new examination of hospital performance after healthcare reform in Turkey: sensitivity and quality comparisons. Int J Public Policy 10(4–5):178–194CrossRefGoogle Scholar
  54. 54.
    Teulings CN, Zubanov N (2014) Is economic recovery a myth? Robust estimation of impulse responses. J Appl Econ 29(3):497–514CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Business Management at the College of ManagementNational Taipei University of TechnologyTaipei CityTaiwan
  2. 2.Department of Financial ManagementNational Defense UniversityTaipeiTaiwan
  3. 3.Faculty of ManagementCanadian University DubaiDubaiUnited Arab Emirates

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