Abstract
To apply the Data Envelopment Analysis (DEA) methodology for evaluating the performance of public hospitals, in terms of clinical medical admissions. The efficiency of the hospitals was measured according to the performance of decision-making units in relation to the variables studied for each hospital, in the year 2000. Data relating to clinical medical admissions in hospitals within the public system in Brazilian state capitals and Federal District (mortality rate, mean length of stay, mean cost of stay and disease profile) were analyzed. The canonical correlation analysis technique was introduced to restrict the variation range of the variables used. The constant returns to scale model was used to generate scores that would enable assessment of the efficiency of the units. From the scores obtained, these cities were classified according to their relative performance in the variables analyzed. It was sought to correlate between the classification scores and the exogenous variables of the expenditure on primary care programs per inhabitant and the human development index for each state capital. In the hospitals studied, circulatory diseases were the most prevalent (23.6% of admissions), and the mortality rate was 10.3% of admissions. Among the 27 state capitals, four reached 100% efficiency (Palmas, Macapá, Teresina and Goiânia), seven were between 85 and 100%, ten were between 70 and 85% and ten had efficiency of less than 70%. The tool utilized was shown to be applicable for evaluating the performance of public hospitals. It revealed large variations among the Brazilian state capitals in relation to clinical medical admissions.
Highlights
The tool utilized was shown to be applicable for evaluating the performance of public hospitals
Data envelopment analysis (DEA), which was introduced by Charnes et al[3] in 1978 and extended by Banker et al[1] (1984), provides a representation of the structure formed by decision-making units (DMUs), with inputs and outputs that are defined in such a way as to be able to assess the relative efficiency of these DMUs
The database was formed by admissions to SUS hospitals in the country’s state capitals in 2000, and the data were obtained from the SUS hospital information system (Datasus).* The DEA was performed using the Frontier Analyst Professional software.** The canonical weights, canonical correlation, restriction intervals for the weights of the variables and the other statistical procedures were generated in the Statistica software
Summary
Data envelopment analysis (DEA), which was introduced by Charnes et al[3] in 1978 and extended by Banker et al[1] (1984), provides a representation of the structure formed by decision-making units (DMUs), with inputs and outputs that are defined in such a way as to be able to assess the relative efficiency of these DMUs. This efficiency is defined from the observed performance of the DMUs in relation to the variables analyzed It is an empirical measurement and not a theoretical or conceptual reference.[10,11] This means that its scores are a comparison measurement that is more appropriate than the more commonly used indicators (e.g. number of procedures per time period or mortality rates), which may be highly dependent on the specific characteristics of a population. This method establishes a “common region” on the basis of the data (variables) of the DMUs, thereby creating an efficiency index that reflects the importance of each variable for each DMU. DEA has recently been used in the health sector for establishing reference standards for hospitals, clinics or health services, in developing countries.[3,4,7,9,12,13] In Brazil, one of the rare studies using this methodology was carried out in 2001, to compare university and general hospitals in the municipality of Rio de Janeiro.[11]
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