Measuring hospital quality: can medicare data substitute for all-payer data?
OBJECTIVE
To assess whether adverse outcomes in Medicare patients can be used as a surrogate for measures from all patients in quality-of-care research using administrative datasets.


METHODS
Patient discharge abstracts from state data systems for 799 hospitals in 11 states. National MedPAR discharge data for Medicare patients from 3,357 hospitals. State hospital staffing surveys or financial reports. American Hospital Association Annual Survey.


METHODS
We calculate rates for 10 adverse patient outcomes, examine the correlation between all-patient and Medicare rates, and conduct negative binomial regressions of counts of adverse outcomes on expected counts, hospital nurse staffing, and other variables to compare results using all-patient and Medicare patient data.


METHODS
Coding rules were established for eight adverse outcomes applicable to medical and surgical patients plus two outcomes applicable only to surgical patients. The presence of these outcomes was coded for 3 samples: all patients in the 11-state sample, Medicare patients in the 11-state sample, and Medicare patients in the national Medicare MedPAR sample. Logistic regression models were used to construct estimates of expected counts of the outcomes for each hospital. Variables for teaching, metropolitan status, and bed size were obtained from the AHA Annual Survey.


RESULTS
For medical patients, Medicare rates were consistently higher than all-patient rates, but the two were highly correlated. Results from regression analysis were consistent across the 11-state all-patient, 11-state Medicare, and national Medicare samples. For surgery patients, Medicare rates were generally higher than all-patient rates, but correlations of Medicare and all-patient rates were lower, and regression results less consistent.


CONCLUSIONS
Analyses of quality of care for medical patients using Medicare-only and all-patient data are likely to have similar findings. Measures applied to surgery patients must be used with more caution, as those tested only in Medicare patients may not provide results comparable to those from all-patient samples or across different samples of Medicare patients.
Needleman
Jack
J
Department of Health Services, UCLA School of Public Health, 90095-1772, USA.
Buerhaus
Peter I
PI
Mattke
Soeren
S
Stewart
Maureen
M
Zelevinsky
Katya
K
D000368:Aged; D003625:Data Collection; D006302:Health Services Research / Q000379:methods; D006761:Hospitals / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D016015:Logistic Models; D006278:Medicare / Q000592:standards* / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D014481:United States