[Data envelopment analysis for evaluating public hospitals in Brazilian state capitals].
OBJECTIVE
To apply the Data Envelopment Analysis (DEA) methodology for evaluating the performance of public hospitals, in terms of clinical medical admissions.


METHODS
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.


RESULTS
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%.


CONCLUSIONS
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.
Gonçalves
Antonio C
AC
Secretaria Municipal da Saúde, Rio de Janeiro, RJ, Brasil.
Noronha
Cláudio P
CP
Lins
Marcos P E
MP
Almeida
Renan M V R
RM
D001938:Brazil / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000401:mortality; D003365:Costs and Cost Analysis; D003627:Data Interpretation, Statistical; D017598:Efficiency, Organizational / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006779:Hospitals, Public / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay; D009369:Neoplasms / Q000401:mortality; D010272:Parasitic Diseases / Q000401:mortality; D010343:Patient Admission; D011320:Primary Health Care / Q000592:standards* / Q000706:statistics & numerical data; D014505:Urban Population / Q000706:statistics & numerical data