Advertisement

Health Care Management Science

, Volume 14, Issue 1, pp 22–35 | Cite as

Efficiency and quality of care in nursing homes: an Italian case study

  • Giulia Garavaglia
  • Emanuele Lettieri
  • Tommaso Agasisti
  • Silvano Lopez
Article

Abstract

This study investigates efficiency and quality of care in nursing homes. By means of Data Envelopment Analysis (DEA), the efficiency of 40 nursing homes that deliver their services in the north-western area of the Lombardy Region was assessed over a 3-year period (2005–2007). Lombardy is a very peculiar setting, since it is the only Region in Italy where the healthcare industry is organised as a quasi-market, in which the public authority buys health and nursing services from independent providers—establishing a reimbursement system for this purpose. The analysis is conducted by generating bootstrapped DEA efficiency scores for each nursing home (stage one), then regressing those scores on explanatory variables (stage two). Our DEA model employed two input (i.e. costs for health and nursing services and costs for residential services) and three output variables (case mix, extra nursing hours and residential charges). In the second-stage analysis, Tobit regressions and the Kruskall–Wallis tests of hypothesis to the efficiency scores were applied to define what are the factors that affect efficiency: (a) the ownership (private nursing houses outperform their public counterparts); and (b) the capability to implement strategies for labour cost and nursing costs containment, since the efficiency heavily depends upon the alignment of the costs to the public reimbursement system. Lastly, even though the public institutions are less efficient than the private ones, the results suggest that public nursing homes are moving towards their private counterparts, and thus competition is benefiting efficiency.

Keywords

Efficiency Quality Data envelopment analysis Nursing homes 

References

  1. 1.
    Goodson J, Wooseung J (2008) Assessing nursing home care quality through Bayesian networks. Health Care Manage Sci 11:382–392CrossRefGoogle Scholar
  2. 2.
    Shimshak DG, Lenard ML, Klimberg RK (2009) Incorporating quality into data envelopment analysis of nursing home performance: a case study. Omega 37:672–685CrossRefGoogle Scholar
  3. 3.
    Lee RH, Bott MJ, Gajewski B, Lee Taunton R (2009) Modeling efficiency at the process level: an examination of the care planning process in nursing homes. Health Serv Res 44(1):15–32CrossRefGoogle Scholar
  4. 4.
    Zhang NJ, Unruh L, Wan TTH (2008) Has the Medicare prospective payment system led to increased nursing home efficiency? Health Serv Res 43(3):1043–1061CrossRefGoogle Scholar
  5. 5.
    Laine J, Harriet Finne-Soveri U, Bjorkgren M, Linna M, Noro A, Hakkinen U (2005) The association between quality of care and technical efficiency in long-term care. Int J Qual Health C 17(3):259–267CrossRefGoogle Scholar
  6. 6.
    Laine J, Linna M, Noro A, Hakkinen U (2005) The cost efficiency and clinical quality of institutional long-term care for the elderly. Health Care Manage Sci 8:149–156CrossRefGoogle Scholar
  7. 7.
    Laine J, Linna M, Hakkinen U, Noro A (2005) Measuring the productive efficiency and clinical quality of institutional long-term care for the elderly. Health Econ 14:245–256CrossRefGoogle Scholar
  8. 8.
    Castle NG, Engberg J, Lave J, Fisher A (2009) Factors associated with increasing nursing home closures. Health Serv Res 44(3):1088–1109CrossRefGoogle Scholar
  9. 9.
    Bjorkgren MA, Hakkinen U, Miika L (2001) Measuring efficiency of long-term care units in Finland. Health Care Manage Sci 4:193–200CrossRefGoogle Scholar
  10. 10.
    Chesteen S, Helgheim B, Randall T, Wardell D (2005) Comparing quality of care in non-profit and for-profit nursing homes: a process perspective. J Oper Manage 23(2):229–242CrossRefGoogle Scholar
  11. 11.
    Tortosa MA, Granell R (2002) Nursing home voucher in Spain: a Valencian experience. Ageing Soc 22(6):669–687CrossRefGoogle Scholar
  12. 12.
    Castle N, Engbert J, Liu D (2007) Have nursing home compare quality measure scores changed over time in response to competition? Qual Saf Health Care 16:185–191CrossRefGoogle Scholar
  13. 13.
    Sainfort F, Ramsay J, Monato H Jr (1995) Conceptual and methodological sources of variation in measurement of nursing home quality: an evaluation of 24 models and an empirical study. Med Care Res Rev 52(1):60–87CrossRefGoogle Scholar
  14. 14.
    Donabedian A (1988) The quality of care: how can it be assessed? J Am Med Assoc 260(12):1743–1748CrossRefGoogle Scholar
  15. 15.
    Center for Health Systems Research and Analysis (2001) QI definitions. Online; accessed 12-April-2005; http://www.chsra.wisc.edu/CHSRA/PIP_ORYX_LTC/QI_Matrix/main.htm].
  16. 16.
    Nyman JA, Bricker DL (1989) Profit incentives and technical efficiency in the production of nursing home care. Rev Econ Stat 71(4):586–594CrossRefGoogle Scholar
  17. 17.
    Kooreman P (1994) Nursing home care in The Netherlands: a nonparametric analysis. J Health Econ 13:301–316CrossRefGoogle Scholar
  18. 18.
    Anderson RI, Shelton Weeks H, Hobbs BK, Webb JR (2003) Nursing homes quality, chain affiliation, profit status and performance. J Real Estate Res 25(1):43–60Google Scholar
  19. 19.
    Ozcan Y, Wogen SE, Wen Mau L (1998) Efficiency evaluation of skilled nursing facilities. J Med Syst 22(4):211–224CrossRefGoogle Scholar
  20. 20.
    Cooper WW, Seiford LM, Tone K (2006) Data envelopment analysis. Springer-Verlag, New YorkGoogle Scholar
  21. 21.
    Linn MW, Gurel L, Linn BS (1977) Patient outcome as a measure of quality of nursing home care. Am J Public Health 67(4):337–344CrossRefGoogle Scholar
  22. 22.
    Gertler PJ (1989) Subsidies, quality, and the regulation of nursing homes. J Public Econ 38:33–52CrossRefGoogle Scholar
  23. 23.
    Zinn JS, Aaronson WE, Rosko MD (1994) Strategic groups, performance, and strategic response in the nursing home industry. Health Serv Res 29(2):187–205Google Scholar
  24. 24.
    Spector WD, Takada HA (1991) Characteristics of nursing homes that affect resident outcome. J Ageing Health 3:427–454CrossRefGoogle Scholar
  25. 25.
    Health Care Financing Administration (2000) Report to Congress: appropriateness of minimum nurse staffing ratios in nursing homes, SummerGoogle Scholar
  26. 26.
    Banker RD, Chang H, Cooper WW (1996) Simulation studies of efficiency, return to scale and misspecification with nonlinear functions in DEA. Ann Oper Res 66:233–253Google Scholar
  27. 27.
    Wilson PW (2008) FEAR: a software package for frontier efficiency analysis with R. Socio-Econ Plann Sci 42:247–254CrossRefGoogle Scholar
  28. 28.
    Simar L, Wilson PW (1999) Of course we can bootstrap DEA scores! But does it mean anything? Logic trumps wishful thinking. J Prod Anal 11:93–97CrossRefGoogle Scholar
  29. 29.
    Simar L, Wilson PW (2000) Statistical inference in nonparametric frontier models: the state of the art. J Prod Anal 13:49–78CrossRefGoogle Scholar
  30. 30.
    Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econom 136:31–64CrossRefGoogle Scholar
  31. 31.
    Pilyavsky AI, Aaronson WE, Bernet PM, Rosko MD, Valdmanis VG, Golubchikov MV (2006) Health Econ 15:1173–1186CrossRefGoogle Scholar
  32. 32.
    Blank JLT, Valdmanis VG (2009) Environmental factors and productivity on dutch hospitals: a semi-parametric approach. Health Care Management Science, Published onlineGoogle Scholar
  33. 33.
    Borge LE, Harldsvik M (2009) Efficiency potential and determinants of efficiency: an analysis of the care for the elderly sector in Norway. Int Tax Public Financ 16:468–486CrossRefGoogle Scholar
  34. 34.
    Norman M, Stoker B (1991) Data envelopment analysis, the assessment of performance. Wiley, New JerseyGoogle Scholar
  35. 35.
    Blank JLT, Eggink E (2009) A quality-adjusted cost function in a regulated industry: the case of Dutch nursing homes. Health Care Manage Sci 4:201–211CrossRefGoogle Scholar
  36. 36.
    Kleinsorge K, Karney D (1992) Management of nursing homes using data envelopment analysis. Socio-Econ Plann Sci 26(1):57–71CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Giulia Garavaglia
    • 1
  • Emanuele Lettieri
    • 1
  • Tommaso Agasisti
    • 1
  • Silvano Lopez
    • 2
  1. 1.Department of Management, Economics and Industrial EngineeringPolitecnico di MilanoMilanoItaly
  2. 2.ASL Milano 1LegnanoItaly

Personalised recommendations