Abstract
The Critical Access Hospital (CAH) Program has offered Medicare cost-based reimbursement to small hospitals that meet certain eligibility criteria to improve their financial viability and quality of care. However, cost-based reimbursement has been associated with inefficiency in hospital operations. This study uses a two-stage approach and bootstrap procedures to examine the effects of environmental variables on the technical efficiency of CAHs. The two-stage approach with quality controls significantly improved statistical efficiency of parameter estimates in the second stage bootstrapped truncated regression relative to a similar model without quality controls. Overall, our results suggest that enhanced Medicare reimbursement may not have had detrimental effects on the technical efficiency of CAHs.
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Notes
Before January 2006, states were allowed to waive the distance requirement for hospitals that were declared “necessary providers” and qualify them for CAH conversion. Thus, some CAHs are quite close to other hospitals. For a detailed description of the CAH Program, see [1].
PPS pays a fixed price per case based on the diagnosis-related group (DRG), constraining hospitals to keep their unit costs below PPS rates in order to remain financially viable.
AHA data can be obtained from http://www.ahadataviewer.com/book-cd-products/AHA-Survey/, Area Resource file data from http://www.arf.hrsa.gov, and Hospital Compare quality data from http://www.hospitalcompare.hhs.gov.
CAHs voluntarily report quality measures to Hospital Compare and they do not have the financial incentives of PPS hospitals to consistently report quality information to CMS.
Alternatively, one can use a stochastic frontier model which, as a parametric approach, requires strong assumptions about the functional form and error distributions. Further, a stochastic frontier model cannot easily accommodate multiple outputs and inputs.
Many of the previous two-stage studies used a tobit (censored) regression in the second stage. However, Simar and Wilson showed that tobit is a misspecification under their statistical model.
Result estimated using Matlab after adopting from programs written for Simar and Zelenyuk [23].
We thank an anonymous reviewer for pointing out this issue.
References
Medicare Payment Advisory Commision (MedPAC) (2005) Report to Congress: issues in a modernized Medicare program, Washington, DC
Dalton K, Slifkin R, Poley S, Fruhbeis M (2003) Choosing to convert to Critical Access Hospital status. Health Care Financing Rev 25(1):115–132
Gianfrancesco FD (1990) The fairness of the PPS reimbursement methodology. Health Serv Res 25(1):1–23
McKay NL, Deily ME, Dorner FH (2002) Ownership and changes in hospital inefficiency, 1986–1991. Inquiry-J Health Car 39(4):388–399
Rosko MD, Mutter RL (2010) Inefficiency differences between Critical Access Hospitals and prospectively paid rural hospitals. J Health Polit Pol Law 35(1):95–126
Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econ 136(1):31–64
Farrell MJ (1957) The measurement of productive efficiency. J Roy Stat Soc Ser-Gen 120(3):253–290
Nayar P, Ozcan YA (2008) Data envelopment analysis comparison of hospital efficiency and quality. J Med Syst 32(3):193–199
Clement JP, Valdmanis VG, Bazzoli GJ, Zhao M, Chukmaitov A (2008) Is more better? An analysis of hospital outcomes and efficiency with a DEA model of output congestion. Health Care Manag Sc 11(1):67–77
Kneip A, Park BU, Simar L (1998) A note on the convergence of nonparametric DEA estimators for production efficiency scores. Economet Theor 14(6):783–793
Zelenyuk V, Zheka V (2006) Corporate governance and firm’s efficiency: the case of a transitional country, Ukraine. J Prod Anal 25(1–2):143–157
Demchuk P, Zelenyuk V (2009) Testing differences in efficiency of regions within a country: the case of Ukraine. J Prod Anal 32(2):81–102
Pink GH, Holmes GM, D’Alpe C, Strunk LA, McGee P, Slifkin RT (2006) Financial indicators for Critical Access Hospitals. J Rural Health 22(3):229–236
Pink GH, Holmes GM, Thompson RE, Slifkin RT (2007) Variations in financial performance among peer groups of Critical Access Hospitals. J Rural Health 23(4):299–305
Li PX, Schneider JE, Ward MM (2009) Converting to Critical Access status: how does it affect rural hospitals’ financial performance? Inquiry-J Health Car 46(1):46–57
Li PX, Schneider JE, Ward MM (2007) Effect of Critical Access Hospital conversion on patient safety. Health Serv Res 42(6):2089–2108
Casey MM, Moscovice I (2004) Quality improvement strategies and best practices in Critical Access Hospitals. J Rural Health 20(4):327–334
Ozgen H, Ozcan YA (2004) Longitudinal analysis of efficiency in multiple output dialysis markets. Health Care Manag Sci 7(4):253–261
Rosko MD, Mutter RL (2011) What have we learned from the application of stochastic frontier analysis to U.S. hospitals? Med Care Res Rev 68(1 suppl):75S–100S
Ozcan YA, Luke RD, Haksever C (1992) Ownership and organizational performance - a comparison of technical efficiency across hospital types. Med Care 30(9):781–794
Ferrier GD, Valdmanis V (1996) Rural hospital performance and its correlates. J Prod Anal 7(1):63–80
Rosko MD (2001) Impact of HMO penetration and other environmental factors on hospital X-inefficiency. Med Care Res Rev 58(4):430–454
Simar L, Zelenyuk V (2006) On testing equality of distributions of technical efficiency scores. Econ Rev 25(4):497–522
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Nedelea, I.C., Fannin, J.M. Technical efficiency of Critical Access Hospitals: an application of the two-stage approach with double bootstrap. Health Care Manag Sci 16, 27–36 (2013). https://doi.org/10.1007/s10729-012-9209-8
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DOI: https://doi.org/10.1007/s10729-012-9209-8