The effect of managed care on ICU length of stay: implications for medicare.
OBJECTIVE
To determine whether insurance status (managed care vs traditional commercial and Medicare) influences resource consumption (as measured by length of stay [LOS]) in the intensive care unit (ICU).


METHODS
Retrospective analysis of the 1992 Massachusetts state hospital discharge database, using prospectively developed and validated risk-stratification models.


METHODS
All nonfederal hospitals in Massachusetts.


METHODS
Of all adult hospitalizations where an ICU stay was incurred (n=104270), we selected those covered by 1 of 4 payer groups (n=88050): (1) commercial fee-for-service (patients aged <65 years); (2) commercial managed care (patients aged <65 years); (3) traditional Medicare (patients aged >/=65 years); and (4) Medicare-sponsored managed care (patients aged >/=65 years).


METHODS
Mean ICU LOS.


METHODS
The ICU LOS regression models were constructed using split-halves validation to adjust for differences in age, sex, severity of illness, diagnosis, discharge status, and payer. Separate models were constructed for those younger than 65 years and those aged 65 years or older. Robustness of the models was explored using goodness of fit and correlation. The effect of payer on hospital mortality was also explored using logistic regression. Observed minus predicted mean ICU LOS and mortality rates were correlated with managed care penetration at the hospital level.


RESULTS
The ICU LOS models performed well (R2=0.84 and R2L [likelihood ratio statistic]=0.92 for the development set, and R2=0.83 and R2L=0.89 for the validation set). Significant covariables affecting LOS included age, severity of principal illness, comorbidity, reason for admission, and discharge status (P<.001 for each). Among the cohort younger than 65 years (n=27805), although unadjusted mean ICU LOS was shorter (2.9 vs 3.43 days; P<.05) for those covered by managed care organizations, payer status had no independent effect on ICU LOS (P=.48). Among those older than 65 years, there was neither a difference in unadjusted ICU LOS (3.94 vs 3.88 days; P>/=.05) nor an independent effect of payer on ICU LOS (P=.35). Unadjusted mortality was lower among managed care patients (3.9% vs 5.1% in patients aged <65 years [P<.05] and 8.7% vs 12.1% in patients aged > or = 65 years [P<.05]). Age, severity of principal diagnosis, comorbidity, and reason for admission significantly influenced mortality (P<.001). After controlling for these factors with the mortality model (R2L=0.92 and 0.89, C statistic [12 df]=8.45 and 17.58, and P=.75 and .13 [where a large P reflects good agreement] for the development and validation sets, respectively), payer continued to have a small but significant effect on mortality (odds ratios ranging from 1.67 at 0.1% probability of death to 1.11 at 30% probability of death.) Managed care penetration among the commercially insured varied across hospitals (n=82) from 0% to 68%. There was no correlation between managed care penetration and either ICU LOS (R2=0.04; P=.09) or mortality (R2=0.0; P=.88).


CONCLUSIONS
Though patients covered under managed care consume fewer ICU resources, this appears to be primarily attributable to a difference in patient-related factors. Thus, as managed care case mix changes in the future to include sicker and older patients, the initial advantages of reduced resource consumption may diminish.
Angus
D C
DC
Health Delivery and Systems Evaluation Team, Department of Anesthesiology and Critical Care Medicine, University of Pittsburgh, PA 15213-2582, USA.
Linde-Zwirble
W T
WT
Sirio
C A
CA
Rotondi
A J
AJ
Chelluri
L
L
Newbold
R C
RC
Lave
J R
JR
Pinsky
M R
MR
D000328:Adult; D000368:Aged; D003953:Diagnosis-Related Groups; D018588:Fee-for-Service Plans; D005260:Female; D017052:Hospital Mortality; D006783:Hospitals, State; D006801:Humans; D007348:Insurance, Health; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay; D008297:Male; D008329:Managed Care Programs / Q000191:economics / Q000639:trends; D008404:Massachusetts; D006278:Medicare; D008875:Middle Aged; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design; D012189:Retrospective Studies; D016019:Survival Analysis; D014481:United States