Hospital benchmarking analysis and the derivation of cost indices
AbstractThis paper reports work undertaken for the UK Department of Health to explore approaches to measuring and comparing hospital productivity. The purpose of the cost indices produced in this paper has been to use them to derive productivity scores for English NHS Trusts in order to benchmark them against one another to help identify poorer performers. The work builds on previous deterministic ‘efficiency indices’ by using statistical regression adjustment techniques. This work describes the derivation of three cost indices (CCI, 2CCI and 3CCI), each with increasing adjustment in terms of case mix, factor prices and environmental factors. The analysis uses data for the year 1995/6 and specifically examines acute Trusts. The CCI cost index is a deterministic index that takes into account case mix as measured by Healthcare Resource Groups (HRGs) and inpatient, first outpatient and accident and emergency (A & E) activity. It is a weighted index of actual / expected costs where expected costs are measured as average national costs per respective attendance. 2CCI takes factors into account such as additional adjustments for case mix, age and gender mix, transfers in and out of the hospital, inter-specialty transfers, local labour and capital prices and teaching and research costs for which Trusts might be over or under compensated. The 3CCI makes additional adjustments over and above those in the 2CCI for hospital capacity, including number of beds, and number of sites, scale of inpatient and non-inpatient activity and scope of activity. It therefore tries to capture institutional characteristics amenable to change in the long, but not the short run. 2CCI and 3CCI indices are obtained from a short-run regression model using CCI as the dependent variable, and productivity scores are obtained from the residuals of the regressions. The results suggest that the statistical adjustments reduce estimates of productivity variation between providers considerably, such that there is relatively little difference between providers in terms of fully adjusted (short-run) productivity scores (3CCI). This suggests that savings from bringing poorer performers up to those with higher productivity scores, may in fact be quite small. In the long run there may be more scope for productivity enhancement and savings than in the short run, by optimising capacity and activity levels. Productivity benchmarking results should always be tempered against judgements on the quality and effectiveness of service provision which these indices are currently unable to measure. Implicitly equating high cost to inefficiency, as these indices do, may also be problematic. The paper suggests that the use of panel data and the application of alternative methodologies (such as stochastic frontiers and Data Envelopment Analysis) would be a valuable way to extend this work.
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Bibliographic InfoPaper provided by Centre for Health Economics, University of York in its series Working Papers with number 174chedp.
Length: 38 pages
Date of creation: Nov 1999
Date of revision:
cost index; productivity;
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- Farley, Dean E., 1989. "Measuring casemix specialization and the concentration of diagnoses in hospitals using information theory," Journal of Health Economics, Elsevier, vol. 8(2), pages 185-207, June.
- White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
- Andrew Street, 2003. "How much confidence should we place in efficiency estimates?," Health Economics, John Wiley & Sons, Ltd., vol. 12(11), pages 895-907.
- Rowena Jacobs, 2000. "Alternative methods to examine hospital efficiency: Data Envelopment Analysis and Stochastic Frontier Analysis," Working Papers 177chedp, Centre for Health Economics, University of York.
- Neil Soderlund & Rowena Jacobs, 2001. "Towards panel data specifications of efficiency measures for English acute hospitals," Working Papers 185chedp, Centre for Health Economics, University of York.
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