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The impact of TennCare on hospital efficiency

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Abstract

This study measures the effect of TennCare, a Medicaid managed care reform initiated in 1994, on the efficiency of hospitals in Tennessee. We apply a multiple-output stochastic frontier approach to a panel dataset that represents all short-term acute care hospitals operating in Tennessee for 1990–2001 and find a modest gain in operating efficiency overall. Our results also reveal that the effect of reform on hospital efficiency varies significantly with the admitting hospital’s TennCare patient load and whether the hospital is located in an urban or rural area. During the study period, high-TennCare hospitals in urban areas saw efficiency gains in the 4 years immediately after the implementation of the program while high-TennCare hospitals in rural areas had significant efficiency losses. The effects immediately following the program’s implementation on low-TennCare urban and rural hospitals are similar to those experienced by hospitals with high-TennCare admissions but the magnitude of the effects are much smaller. Policymakers considering large scale reforms of this type should be careful to take into consideration the likely differential responses from urban and rural hospitals that are prone to differ in payer mix and capacity to improve efficiency.

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Notes

  1. Safety net hospitals provide a significant level of care to low-income and uninsured individuals.

  2. This review benefits from an excellent review article by Hollingsworth [42].

  3. For a detailed discussion of the strengths and weaknesses of SFA and DEA techniques for modeling efficiency of health care firms, see Jacobs et al. [51].

  4. The Baccouche and Kouki paper studies Tunisian manufacturing firms, not hospitals.

  5. The restriction of the sample to firms with non-zero values for outpatient visits is necessitated by the choice of functional form, which requires the log transformation of all outputs and input prices. In addition, for hospitals to be directly comparable, the frontier must be defined based on a set of firms who produce a similar set of outputs using a similar set of inputs. Hospitals with zero levels of outpatient visits are likely to be producing a different product with a different set of inputs than hospitals that are producing both inpatient and outpatient care.

  6. Other authors have used the county as the market area for hospital care. See, for example, Friedman and Basu [60], Laditka et al. [61], Laditka and Laditka [62], and Laditka and Laditka [63].

  7. We were only able to obtain Medicare case-mix data from 1993–2001. Given the lack of data, we assigned 1993 value for the Medicare case-mix Index for each hospital to the same hospital in 1990, 1991, and 1992. When considering the correlation between the Medicare case-mix Index and the Medicare case-mix Index lagged one period for 1993–2001, we find that the correlation is .95271 for our data. This suggests that there is little variation in this measure over time at the hospital level.

  8. The mortality rate is based on the number of deaths in the hospital divided by the number of admissions. Unfortunately, we only have the number of deaths for 1990–1999. So, for 2000 and 2001, we assign the 1999 value for the mortality rate to each hospital.

  9. In the data, we have an indicator for JCAHO accreditation, which has been used by prior researchers as a quality indicator. However, it is omitted because it is highly correlated with the indicator for medical school affiliation, and the vast majority of observations were for accredited hospitals. As will be discussed in the concluding section of this paper, the omission of a meaningful quality measure is a weakness of our paper.

  10. To our knowledge, there were no other significant structural changes, such as changes in Certificate-of-Need laws in Tennessee, that could significantly affect hospitals over the period considered.

  11. The specification tests follow suggestions outlined in Coelli et al. [56]. The generalized likelihood-ratio statistic associated with the null hypothesis involving a test of γ = 0 has a mixed chi-square distribution. The hypothesis test on the second-order coefficients from the translog uses a generalized likelihood ratio test,

  12. The tests for pooling lead to this division of urban and rural hospitals, as described below.

  13. The were some changes in ownership over time that are reflected in the proportions of different facility types in different periods. When examining the data from 1990–2001, we found 25 instances in which hospital changed ownership type. Of these, there was a net increase over time of two not-for-profit hospitals, a net loss of three for-profit hospitals, and no net change in government-owned facilities. The net increase in not-for-profit entities was split between urban and rural areas (one hospital each). The net loss in for-profit facilities involved one rural and two urban hospitals. There was a two hospital increase in rural government hospitals and a two hospital decrease in urban government hospitals.

  14. The sample was also split into facilities with above and below median levels of outpatient TennCare/Medicaid visits and the analyses were conducted using those samples. The results were not qualitatively different from those presented in Section 4 and, therefore, are not presented.

  15. The critical values were obtained from Kodde and Palm [72].

  16. We tested for stationarity of the dependent variable for the full sample using the test developed by Maddala and Wu [73] for use with an unbalanced panel. The null hypothesis for this test is that the series is non-stationary. We tested the model with one lag, the model with one lag and a drift, and the model with one lag and a trend. In all three cases, the null hypothesis of non-stationarity was strongly rejected (p < 0.0001).

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Authors and Affiliations

  1. The Methodist LeBonheur Center for Healthcare Economics, Fogelman College of Business and Economics, The University of Memphis, Memphis, TN, 38152, USA

    Cyril F. Chang

  2. Department of Economics, Belk College of Business, University of North Carolina at Charlotte, Charlotte, NC, 28223-0001, USA

    Jennifer L. Troyer

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  1. Cyril F. Chang
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Correspondence to Jennifer L. Troyer.

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Chang, C.F., Troyer, J.L. The impact of TennCare on hospital efficiency. Health Care Manag Sci 12, 201–216 (2009). https://doi.org/10.1007/s10729-008-9084-5

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