Maria M. Hofmarcher

Measuring Hospital Efficiency in Austria – A DEA Approach

We investigate the evolution of efficiency and productivity in the hospital sector of an Austrian province for the time period 1994–1996. We use panel data to design non-parametric frontier models (Data Envelopment Analysis) and compare efficiency scores and time patterns of efficiency across medical fields. As health outcomes hardly can be measured in a direct way we make use of two different approaches for output measurement: In a first approach, we employ the number of case mix-adjusted discharges and of inpatient days, in a second we use credit points, which are calculated in course of the newly introduced diagnosis related group-type financing system. We calculate and compare individual efficiency scores for hospital wards as decision making units (DMU) in specified medical fields. To our knowledge the calculation of ward-specific efficiency scores has not up till now been the unit of non-parametric efficiency analysis. Our two models find different results: Model 1 with conservative output measurement calculates an average efficiency level of 96%, while model 2 with credit points for output measurement puts average efficiency at 70%. Whereas average efficiency in model 1 hardly changes and in model 2 increases modestly in the period 1994–1996, a closer look at single hospitals displays a variety of different efficiency developments over time.

Health Status Transitions

This paper was prepared as Work Package IV of the AHEAD project – Ageing, Health Status and the Determinants of Health Expenditure – which has received financing from the European Commission under the 6th Research Framework Programme. The purpose of this work package was to build up a picture of the movements in health status of the elderly population of each country in the EU by age and sex. Residential care and death were considered as well as states of health. Due to the scarcity of data regarding residential care, however, we calculated transition probabilities between the different states of health only for Belgium, Germany and UK. In addition, we calculated healthy life expectancies for those three countries. The calculations/estimations were derived from various micro- and macro-data sources (e.g. ECHP), and build upon results of WP III. The Stone-algorithm was used as a means of calibration.