Abstract
Three problems impede the assessment of hospital pharmacy efficiency. First, although multiple efficiency indicators are utilized to measure a large variety of activities, it has not been possible to validly measure overall efficiency. Second, there have been no widely-used clinical activity indicators, so key outputs often have not been accounted for. Third, there has been no effective methodology for identifying when declines in efficiency are normal random variations and when they represent true decreases in performance. This paper presents a procedure that simultaneously addresses these three problems. It analyzes data from a group of U.S. hospital pharmacies that collect an inclusive set of clinical and distributional indicators. It employs Data Envelopment Analysis to develop comprehensive efficiency measures from the numerous outputs and inputs. It applies statistical Panel Data Analysis to estimate confidence intervals within which each pharmacy’s true efficiency resides, and to develop control charts for signaling when a pharmacy’s efficiency has declined by more than can be attributed to random variation. This integrated efficiency evaluation system is transferable to other hospital pharmacy systems, thereby offering decision makers a better way of measuring, controlling and improving hospital pharmacy efficiency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Gupta, S. R., Wojtynek, J. E., Walton, S. M., Botticelli, J. T., Shields, K. L., Quad, J. E., and Schumock, G. T., Pharmacy staffing and productivity. Am. J. Health. Syst. Pharm. 64 (22)2323–2324, 2006.
Nayar, P., and Ozcan, Y., Data envelopment analysis comparison of hospital efficiency and quality. J. Med. Syst. 32 (3)193–199, 2008.
Kirigia, J., Emrouznejad, A., Cassoma, B., Asbu, E., and Barry, S., A performance assessment method for hospitals: the case of municipal hospitals in Angola. J. Med. Syst. 32 (6)509–519, 2008.
Polisena, J., Laporte, A., Coyte, P., and Croxford, R., Performance evaluation in home and community care. J. Med. Syst. 2009 (in press), doi:10.1007/s10916-008-9240-y.
Lee, K. H., Yang, S. B., and Choi, M., The association between hospital ownership and technical efficiency in a managed care environment. J. Med. Syst. 2009 (in press), doi:10.1007/s10916-008-9192-2.
Kontodimopoulos, N., Papathanasiou, N., Tountas, Y., and Niakas, D., Separating managerial inefficiency from influences of the operating environment: an application in dialysis. J. Med. Syst. 2009 (in press), doi:10.1007/s10916-009-9252-2.
Schumock, G. T., Shields, K. L., Walton, S. M., and Barnum, D. T., Data envelopment analysis—a method for comparing hospital pharmacy productivity. Am. J. Health. Syst. Pharm. 2009 (in press).
Charnes, A., Cooper, W. W., and Rhodes, E., Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2 (6)429–444, 1978.
Andersen, P., and Petersen, N. C., A procedure for ranking efficient units in data envelopment analysis. Manag. Sci. 39 (10)1261–1265, 1993.
Gupta, S. R., Wojtynek, J. E., Walton, S. M., Botticelli, J. T., Shields, K. L., Quad, J. E., and Schumock, G. T., Association between hospital size and pharmacy department productivity. Am. J. Health. Syst. Pharm. 64 (9)937–944, 2007.
Breen, R., Regression models: censored, sample-selected, or truncated data. Sage, Thousand Oaks, 1996.
Tulkens, H., and Vanden Eeckaut, P., Nonparametric efficiency, progress and regress measures for panel-data—methodological aspects. Eur. J. Oper. Res. 80 (3)474–499, 1995.
Barnum, D. T., and Gleason, J. M., Biases in technical efficiency scores caused by intra-input aggregation: mathematical analysis and a DEA application using simulated data. Appl. Econ. 38:1593–1603, 2006.
Barnum, D. T., and Gleason, J. M., Technical efficiency bias in data envelopment analysis caused by intra-output aggregation. Appl. Econ. Lett. 14 (9)623–626, 2007.
Hsiao, C., Analysis of panel data. Cambridge University Press, Cambridge, 2003.
Wooldridge, J. M., Econometric analysis of cross section and panel data. MIT Press, Cambridge, 2002.
Greene, W. H., Econometric analysis, 6th edition. Prentice Hall, Upper Saddle River, 2008.
Frees, E. W., Longitudinal and panel data: analysis and applications in the social sciences. Cambridge University Press, Cambridge, 2004.
Baltagi, B. H., Econometric analysis of panel data. Wiley, West Sussex, 2008.
StataCorp, Stata Base Reference Manual Release 10. StataCorp, College Station, 2007.
Carroll, R. J., and Ruppert, D., Transformation and weighting in regression. Chapman and Hall, New York, 1988.
Friedman, M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32:675–701, 1937.
Frees, E. W., Assessing cross-sectional correlation in panel data. J. Econometrics. 69 (2)393–414, 1995.
De Hoyos, R. E., and Sarafidis, V., Testing for cross-sectional dependence in panel-data models. Stata J. 6 (4)482–496, 2006.
Barnum, D. T., and Gleason, J. M., Measuring efficiency in allocating inputs among outputs with DEA. Appl. Econ. Lett. 13 (6)333–336, 2006.
Barnum, D. T., and Gleason, J. M., Technical efficiency bias caused by intra-input aggregation in data envelopment analysis. Appl. Econ. Lett. 12 (13)785–788, 2005.
Driscoll, J. C., and Kraay, A. C., Consistent covariance matrix estimation with spatially dependent panel data. Rev. Econ. Stat. 80 (4)549–560, 1998.
Hoechle, D., Robust standard errors for panel regressions with cross-sectional dependence. Stata J. 7 (3)281–312, 2007.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Barnum, D.T., Shields, K.L., Walton, S.M. et al. Improving the Efficiency of Distributive and Clinical Services in Hospital Pharmacy. J Med Syst 35, 59–70 (2011). https://doi.org/10.1007/s10916-009-9341-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10916-009-9341-2