A model to compute the medical cost of patients in intensive care

Objective: Our objective was to identify, among the information routinely c ollected on patients in intensive care units (ICUs), data that determine th e total cost for a given patient. Design: We developed a model that could help physicians in medical ICUs to estimate the cost of care for their patients when no cost data were availab le at the individual patient level. Setting: A Medical ICU. Patients and participants: The model was developed using a random sample of 73 patients admitted to the medical ICU in 1996 and 1997, validated by ano ther random sample of 29 patients admitted during the same period. Interventions: The actual medical variable cost per patient was computed fr om data on the total resources used (excluding personnel and fixed costs), collected from the patients' records plus pharmacy, laboratory and blood ba nk logs. The explanatory variables tested were: length of stay, nursing wor kload, severity of condition, and procedures recorded by a score [omega (Om ega)] including 3 components related to the frequency of procedure use. The model was constructed in a stepwise fashion, assuming a linear relation. E quations were tested on the basis of the residual mean square; criteria for inclusion and elimination of variables were the level of its partial regre ssion coefficient and medical criteria. The model was validated by analysis of variance of the regression on a second population of 29 patients using the F-test. Main outcome measures and results: The median length of stay was 7 days (ra nge: 3 to 22 days). Mortality rate was 25%. Median medical variable cost wa s pound 805 (mean medical variable cost was pound 1738, total cost was poun d 6279). The variables selected in the multiple regression model as relevan t predictors of medical costs were: procedures recorded only once during th e ICU stay irrespective of their reiteration (Omega(1)), procedures recorde d every time they an performed (Omega(2)), procedures recorded daily in the ICU (Omega(3)) and the presence or absence of an invasive procedure (Kc). The final equation, calibrated with r(2) of 0.826 and p > 0.0001, was: medi cal cost (pound) = 23 Omega(1) + 53 Omega(2) + 8 Omega(3) + 2352Kc + 96. Th e validation with the other sample of 29 patients compared actual to predic ted costs. Analysis of variance of the regression from the model was r(2) = 0.596 (p > 0.05). Conclusions: Our standardised cost model is a possible approach to allow co mparison of medical costs within and between ICUs.