MEASURING THE EFFICIENCY OF HUNGARIAN HOSPITALS BY DATA ENVELOPMENT ANALYSIS
Author(s)
Csákvári T1, Turcsanyi K1, Vajda R2, Danku N2, Ágoston I2, Boncz I3
1University of Pécs, Zalaegerszeg, Hungary, 2University of Pécs, Pécs, Hungary, 3Faculty of Health Sciences, University of Pécs, Pécs, Hungary
OBJECTIVES Hospitals are important cost elements of the Hungarian health care system. During the past decade, several health care reform affected the number of hospital beds in Hungary. The aim of our research is to analyse the efficiency of the Hungarian acute inpatient-care system. METHODS Data derived from the Hungarian nationwide health insurance database. We analyzed the technical- (TE) and scale efficiency (SE) of the Hungarian acute inpatient-care system (2003, 2006, 2010). The number of hospitals included into the study was 133 in 2003, 125 in 2006 and 93 in 2010. We chose four inputs and two outputs: the number of active hospital beds, the number of discharged patients, the number of one-day cases, completed days of nursing (inputs), average length of stay, DRG cost weights (outputs). The method we used for our calculations was Data Envelopment Analysis. RESULTS In 2003 both the technical and scale efficiency were high (TE: 96.9%; SE: 92.9%). To 2006 the situation deteriorated by some degree (TE: 96.6 %; SE: 80.3 %). By 2010 technical efficiency still did not show improvement (TE: 94.0 %), but scale efficiency increased (SE: 88.2 %). Usually the hospitals with higher number of beds are more efficient than the smaller units. CONCLUSIONS The effects of the performance volume limit did not improve the two values; however, the capacity decrease of 2007 did improve the scale efficiency to some extent. The Hungarian health care system needs to reduce the numbers of hospitals and rethink their functions, but needs to improve the size of them.
Conference/Value in Health Info
2014-11, ISPOR Europe 2014, Amsterdam, The Netherlands
Value in Health, Vol. 17, No. 7 (November 2014)
Code
PHP82
Topic
Health Policy & Regulatory
Topic Subcategory
Approval & Labeling, Health Disparities & Equity
Disease
Multiple Diseases