Striving towards efficiency in the Greek hospitals by reviewing case mix classifications.
In order to verify the efficiency level of Greek public hospitals, this paper evaluates the most recent indicators. Relevant data were collected from the two following databases: (a) hospitals' utilisation data generally and per clinical speciality [Ministry of Health, Athens, (Data based) 1995]; (b) Patients' and hospitals' characteristics per diagnosis [National Statistical Office, Athens, (Data based) 1993]. As explanatory variables, the study examines supply and demand factors following case mix classifications. Firstly, average length of stay (ALOS) and secondly, cost per case were regressed as dependent variables. The study highlights the extent of variability across hospitals for different groups of patients with the same condition. The results specify the most important factors that affect ALOS and cost pertaining to efficiency. Per speciality analysis shows occupancy, size-type of the hospital, beds and doctors per speciality, access and use of outpatient services, and surgical operations, etc. as the most significant factors. Per disease-diagnosis analysis shows age of over 65 years, gender, residence, marital status, surgical operation and insurance as the most important factors. General cost analysis in all National Health Systems (NHS) hospitals shows that economies of scale appear in: (a) district and/or specialised hospitals of 250-400 beds; (b) regional and/or teaching hospitals of over but near to 400 beds. Consequently, the author determines the 'Greek' Diagnostic Related Groups (DRGs), based on the cost per clinical speciality in the nine basic specialities and on the cost per diagnosis of the top 15 diagnoses. Further to the scientific results, such studies will enhance much necessary discussions on the organisation of service delivery and financing, by following case mix classification.
Polyzos
Nicholas M
NM
Onassis Cardiac Surgery Center, 356, Sygrou 356 Ave, 176 74, Kallithea, Greece. polyzos@ocsc.gr
D000293:Adolescent; D000328:Adult; D000368:Aged; D001509:Bed Occupancy / Q000706:statistics & numerical data; D002648:Child; D002675:Child, Preschool; D003953:Diagnosis-Related Groups / Q000145:classification* / Q000191:economics; D004470:Economics, Medical; D017598:Efficiency, Organizational / Q000191:economics / Q000706:statistics & numerical data*; D005260:Female; D006115:Greece; D006301:Health Services Needs and Demand; D006302:Health Services Research; D017721:Hospital Costs / Q000145:classification; D006779:Hospitals, Public / Q000458:organization & administration / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008511:Medicine / Q000706:statistics & numerical data; D008875:Middle Aged; D012044:Regression Analysis; D013038:Specialization; D013222:State Medicine / Q000458:organization & administration; D014600:Utilization Review