Evaluation of the HCFA model for the analysis of mortality following hospitalization.
From 1987 through 1990, the Health Care Financing Administration (HCFA) evaluated variations in the mortality rates experienced by patients admitted to hospitals participating in the Medicare program. This study was conducted to evaluate the adequacy of the model used for that purpose. Detailed clinical data were gathered on 42,773 patients admitted to 84 statistically selected hospitals. The effect of risk adjustment using the HCFA model, which is based on claims data, was compared to a risk-adjustment model based on physiologic and clinical data. Models that include claims data were markedly superior to those containing only demographic characteristics in predicting the probability of patient death, and the addition of clinical data resulted in further improvement. The correlation of ranks of hospitals based on a model that uses only the claims data and on one that uses, in addition, clinical data, was .91. As a screen for the identification of "high (mortality) outlier" hospitals, the claims model had moderate sensitivity (81 percent) and specificity (79 percent), a high negative predictive value (90 percent), and a low positive predictive value (64 percent) when compared to the clinical model. The two mortality models gave similar results when used to determine which structural characteristics of hospitals were related to mortality rates: hospitals with a higher proportion of registered nurses or board-certified physician specialists, or with a greater level of access to high-technology equipment had lower risk-adjusted mortality rates. These data suggest that the current claims-based risk-adjustment procedure may satisfactorily be used to characterize variations in mortality rates associated with hospitalization. The procedure could also be used as a basis for further epidemiological analyses of factors that affect the probability of patient death. However, it does not positively identify outlier hospitals as providers of problematic care.
Krakauer
H
H
Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814-4799.
Bailey
R C
RC
Skellan
K J
KJ
Stewart
J D
JD
Hartz
A J
AJ
Kuhn
E M
EM
Rimm
A A
AA
D014487:Centers for Medicare and Medicaid Services, U.S.; D005069:Evaluation Studies as Topic; D006302:Health Services Research / Q000379:methods*; D017052:Hospital Mortality; D006761:Hospitals / Q000145:classification; D006801:Humans; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data; D016015:Logistic Models; D008499:Medical Records / Q000706:statistics & numerical data; D006278:Medicare / Q000706:statistics & numerical data; D015233:Models, Statistical; D011336:Probability; D012307:Risk Factors; D014481:United States / Q000453:epidemiology