Estimation of direct cost and resource allocation in intensive care: correlation with Omega system.
OBJECTIVE
An instrument able to estimate the direct costs of stays in Intensive Care Units (ICUs) simply would be very useful for resource allocation inside a hospital, through a global budget system. The aim of this study was to propose such a tool.


METHODS
Since 1991, a region-wide common data base has collected standard data of intensive care such as the Omega Score, Simplified Acute Physiologic Score, length of stay, length of ventilation, main diagnosis and procedures. The Omega Score, developed in France in 1986 and proved to be related to the workload, was recorded on each patient of the study.


METHODS
Eighteen ICUs of Assistance Publique-Hôpitaux de Paris (AP-HP) and suburbs.


METHODS
1) Hundred twenty-one randomly selected ICU patients; 2) 12,000 consecutive ICU stays collected in the common data base in 1993.


METHODS
1) On the sample of 121 patients, medical expenditure and nursing time associated with interventions were measured through a prospective study. The correlation between Omega points and direct costs was calculated, and regression equations were applied to the 12,000 stays of the data base, leading to estimated costs. 2) From the analytic accounting of AP-HP, the mean direct cost per stay and per unit was calculated, and compared with the mean associated Omega score from the data base. In both methods a comparison of actual and estimated costs was made.


RESULTS
The Omega Score is strongly correlated to total direct costs, medical direct costs and nursing requirements. This correlation is observed both in the random sample of 121 stays and on the data base' stays. The discrepancy of estimated costs through Omega Score and actual costs may result from drugs, blood product underestimation and therapeutic procedures not involved in the Omega Score.


CONCLUSIONS
The Omega system appears to be a simple and relevant indicator with which to estimate the direct costs of each stay, and then to organise nursing requirements and resource allocation.
Sznajder
M
M
Department of Public Health & Medical Information, Hôpital Ambroise Parè, Boulogne, France.
Leleu
G
G
Buonamico
G
G
Auvert
B
B
Aegerter
P
P
Merlière
Y
Y
Dutheil
M
M
Guidet
B
B
Le Gall
J R
JR
D003422:Critical Care / Q000145:classification* / Q000191:economics; D016208:Databases, Factual / Q000706:statistics & numerical data; D004180:Direct Service Costs / Q000706:statistics & numerical data*; D017050:Episode of Care; D005602:France; D015276:Health Care Rationing; D006801:Humans; D007362:Intensive Care Units / Q000191:economics*; D016018:Least-Squares Analysis; D007902:Length of Stay / Q000191:economics / Q000706:statistics & numerical data; D016014:Linear Models; D017059:Models, Econometric; D009741:Nursing Staff, Hospital / Q000191:economics; D010561:Personnel Staffing and Scheduling / Q000191:economics / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012494:Sampling Studies