Health Care Management Science

, Volume 20, Issue 2, pp 265–275 | Cite as

Assessing overall, technical, and scale efficiency among home health care agencies

  • Vivian G. Valdmanis
  • Michael D. Rosko
  • Hervé Leleu
  • Dana B. Mukamel


While home health care agencies (HHAs) play a vital role in the production of health, little research has been performed gauging their efficiency. Employing a robust approach to data envelopment analysis (DEA) we assessed overall, technical, and scale efficiency on a nationwide sample of HHAs. After deriving the three efficiency measures, we regressed these scores on a variety of environmental factors. We found that HHAs, on average, could proportionally reduce inputs by 28 % (overall efficiency), 23 % (technical efficiency) and 6 % (scale efficiency). For-profit ownership was positively associated with improvements in overall efficiency and technical efficiency and chain ownership was positively associated with global efficiency. There were also state-by-state variations on all the efficiency measures. As home health becomes an increasingly important player in the health care system, and its share of national health expenditures increases, it has become important to understand the cost structure of the industry and the potential for efficiencies. Therefore, further research is recommended as this sector continues to grow.


Home health agencies Efficiency Data envelopment analysis 


  1. 1.
    Centers for Medicare & Medicaid Services (2015) Home health services. Accessed 1 Dec 2014
  2. 2.
    Centers for Medicare & Medicaid Services. National Health Expenditure Historical Data 1960–2012. Available via Centers for Medicare & Medicaid Services Accessed 1 Dec 2014
  3. 3.
    Medicare Payment Advisory Committe (MedPAC) (2014) A Data Book - Health Care Spending and the Medicare Program June 2014. Accessed 12 Nov 2014
  4. 4.
    Centers for Medicare & Medicaid SerVices (2012). National Health Expenditure Projections 2011–2021. Accessed 26 July 2012
  5. 5.
    Centers for Disease Control and Prevention. National Home and Hospice Care Survey (NHHCS) 2007. Available via Accessed 5 Dec 2014
  6. 6.
    Dudzinski CS, Erekson OH, Ziegert AL (1998) Estimating an hedonic translog cost function for the home health care industry. Appl Econ 30(9):1259–1267CrossRefGoogle Scholar
  7. 7.
    Gonzales TI (1997) An empirical study of economies of scope in home healthcare. Health Serv Res 32(3):313–324Google Scholar
  8. 8.
    Hay JW, Mandes G (1984) Home health care cost-function analysis. Health Care Financ Rev 5(3):111–116Google Scholar
  9. 9.
    Kass DI (1987) Economies of scale and scope in the provision of home health services. J Health Econ 6(2):129–146CrossRefGoogle Scholar
  10. 10.
    Nyman JA, Dowd BE (1991) Cost function analysis of medicare policy: are reimbursement limits for rural home health agencies sufficient? J Health Econ 10(3):313–327CrossRefGoogle Scholar
  11. 11.
    Nyman JA, Svetlik MA (1989) Does the average cost of home health care vary with case mix? Public Health Rep 104(4):335–341Google Scholar
  12. 12.
    Mukamel DB, Fortinsky RH, White A, Harrington C, White LM, Ngo-Metzger Q (2014) The policy implications of the cost structure of home health agencies. Medicare Medicaid Res Rev 4(1):E1–E23CrossRefGoogle Scholar
  13. 13.
    Ozcan Y (2014) Health care benchmarking and performance evaluation: an assessment using Data Envelopment Analysis (DEA). Springer, New YorkCrossRefGoogle Scholar
  14. 14.
    Valdmanis V (1992) Sensitivity analysis for DEA models: an empirical example using public vs. NFP hospitals. J Publ Econ 48(2):185–205CrossRefGoogle Scholar
  15. 15.
    Ozcan Y, Woge S, Mau LW (1998) Efficiency evaluation of skilled nursing facilities. J Med Syst 22(4):211–224CrossRefGoogle Scholar
  16. 16.
    Rosko M, Chilingerian J, Zinn J, Aaronson W (1995) The effects of ownership, operating environment, and strategic choices on nursing home efficiency. Med Care 33(10):1001–1021CrossRefGoogle Scholar
  17. 17.
    Ozcan Y, Cotter J (1994) An assessment of efficiency of area agencies on aging in Virginia through data envelopment analysis. Gerontologist 34(3):363–370CrossRefGoogle Scholar
  18. 18.
    Ferrier G, Valdmanis V (2003) Exploring psychiatric hospital performance using data envelopment analysis and cluster analysis. J Econ Med 20(3):143–153Google Scholar
  19. 19.
    Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44:49–61CrossRefGoogle Scholar
  20. 20.
    Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econ 136:31–64CrossRefGoogle Scholar
  21. 21.
    Politis DN, Romano JP (1994) large sample confidence regions based on subsamples under minimal assumptions. Ann Stat 22:2031–2050CrossRefGoogle Scholar
  22. 22.
    Politis DN, Romano JP (1999) Subsampling. Springer, New YorkCrossRefGoogle Scholar
  23. 23.
    Politis DN, Romano JP, Wolf M (2001) On the asymptotic theory of subsampling. Stat Sin 11:1105–1124Google Scholar
  24. 24.
    Bickel PJ, Sakov A (2008) On the choice of m in the m out of n bootstrap and confidence bounds for extrema. Stat Sin 18:967–985Google Scholar
  25. 25.
    Kneip A, Simar L, Wilson PW (2008) asymptotics and consistent bootstraps for dea estimators in non-parametric frontier models. Econ Theory 24:1663–1697CrossRefGoogle Scholar
  26. 26.
    Kneip A, Simar L, Wilson PW (2011) a computationally efficient, consistent bootstrap for inference with non-parametric dea estimators. Comput Econ 38:483–515CrossRefGoogle Scholar
  27. 27.
    Simar L, Wilson PW (2011) Inference by the m out of n bootstrap in nonparametric frontier models. J Prod Anal 36:33–53CrossRefGoogle Scholar
  28. 28.
    Färe R, Grosskopf S, Lovell CAK (1994) Production frontiers. Cambridge University Press, CambridgeGoogle Scholar
  29. 29.
    Farrell M (1957) The measurement of productive efficiency. J R Stat Soc Ser A, General 120:253–281CrossRefGoogle Scholar
  30. 30.
    Madigan EA, Fortinsky RH (2004) Interrater reliability of the outcomes and assessment information set: results from the field. Gerontologist 44(5):689–692CrossRefGoogle Scholar
  31. 31.
    Tullai-McGuinness S, Madigan EA, Fortinsky RH (2009) Validity testing the Outcomes and Assessment Information Set (OASIS). Home Health Care Ser Q 28(1):45–57CrossRefGoogle Scholar
  32. 32.
    Grosskopf SV, Valdmanis V (1987) Measuring hospital performance: a nonparametric approach. J Health Econ 6(2):89–107CrossRefGoogle Scholar
  33. 33.
    Madigan E, Schott D, Matthews C (2001) Rehospitalization among home healthcare patients: results of a prospective study. Home Health Nurse 19(5):298–305CrossRefGoogle Scholar
  34. 34.
    Mutter R, Valdmanis V, Rosko M (2010) High versus lower quality hospitals: a comparison of environmental characteristics and technical efficiency. Health Serv Outcome Res Method 10:134–153CrossRefGoogle Scholar
  35. 35.
    Rosko M (1999) Impact of internal and external environmental pressures on hospital inefficiency,”. Health Care Mgt Sci 2:63–74CrossRefGoogle Scholar
  36. 36.
    Rosko M (2001) Impact of HMO penetration and other environmental factors on hospital x-inefficiency. Med Care Res Rev 58(4):430–454CrossRefGoogle Scholar
  37. 37.
    Kelly D, Amburgey TL (1991) Organizational inertia and momentum: a dynamic model of strategic change. Acad Manag J 34(3):591–612CrossRefGoogle Scholar
  38. 38.
    Hollingsworth B (2003) Non-parametric and parametric applications measuring efficiency in health care. Health Care Manag Sci 6(4):203–218CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Vivian G. Valdmanis
    • 1
    • 2
  • Michael D. Rosko
    • 3
    • 4
  • Hervé Leleu
    • 2
  • Dana B. Mukamel
    • 5
  1. 1.Department of EconomicsGrand Valley State UniversityGrand RapidsUSA
  2. 2.CNRS, IÉSEG School of Management, Univ. Lille, UMR 9221-LEM Lille Economie ManagementLilleFrance
  3. 3.School of Business AdministrationWidener UniversityChesterUSA
  4. 4.School of ManagementUniversity of St. AndrewsSt. AndrewsUK
  5. 5.Medicine-Division of General Internal MedicineUniversity of CaliforniaIrvineUSA

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