Risk adjustment methods can affect perceptions of outcomes.
When comparing outcomes of medical care, it is essential to adjust for patient risk, including severity of illness. A variety of severity measures exist, but perceptions of outcomes may differ depending on how severity is defined. We used two severity-adjustment approaches to demonstrate that comparisons of outcomes across subgroups of patients can vary dramatically depending on how severity is assessed. We studied two approaches: model 1 was the admission MedisGroups score; model 2 was computed from age and 12 chronic conditions defined by diagnosis codes. Although common summary measures of model performance (R-squared and C) both suggested that model 1 is a better predictor of in-hospital death than model 2, the weaker model consistently produced more accurate expectations by payer class and age group. Using model 1 for severity adjustment suggested that Medicare patients did substantially worse than expected and Medicaid patients substantially better. In contrast, use of model 2 found Medicare patients doing as expected, but Medicaid patients faring poorly.
Iezzoni
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LI
Department of Medicine, Harvard Medical School, Beth Israel Hospital, Boston, MA 02215.
Shwartz
M
M
Ash
A S
AS
Mackiernan
Y
Y
Hotchkin
E K
EK
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003627:Data Interpretation, Statistical; D003953:Diagnosis-Related Groups / Q000145:classification; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008484:Medicaid; D006278:Medicare; D008875:Middle Aged; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000401:mortality / Q000628:therapy; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012307:Risk Factors; D012720:Severity of Illness Index; D014481:United States / Q000453:epidemiology