Estimating technical efficiency in the hospital sector with panel data: a comparison of parametric and non-parametric techniques.
BACKGROUND
Policy makers are increasingly interested in developing performance indicators that measure hospital efficiency. These indicators may give the purchasers of health services an additional regulatory tool to contain health expenditure.


OBJECTIVE
Using panel data, this study compares different parametric (econometric) and non-parametric (linear programming) techniques for the measurement of a hospital's technical efficiency.


METHODS
This comparison was made using a sample of 17 Italian hospitals in the years 1996-9.


RESULTS
Highest correlations are found in the efficiency scores between the non-parametric data envelopment analysis under the constant returns to scale assumption (DEA-CRS) and several parametric models. Correlation reduces markedly when using more flexible non-parametric specifications such as data envelopment analysis under the variable returns to scale assumption (DEA-VRS) and the free disposal hull (FDH) model. Correlation also generally reduces when moving from one output to two-output specifications.


CONCLUSIONS
This analysis suggests that there is scope for developing performance indicators at hospital level using panel data, but it is important that extensive sensitivity analysis is carried out if purchasers wish to make use of these indicators in practice.
Siciliani
Luigi
L
Department of Economics and Related Studies, University of York, Heslington, York, UK. ls24@york.ac.uk
D003363:Cost Control; D003627:Data Interpretation, Statistical; D017598:Efficiency, Organizational / Q000706:statistics & numerical data*; D005377:Financial Management, Hospital / Q000706:statistics & numerical data; D006302:Health Services Research / Q000379:methods*; D006739:Hospital Administration / Q000191:economics / Q000592:standards*; D006801:Humans; D007558:Italy; D008330:Management Audit / Q000379:methods*; D017059:Models, Econometric; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric