This research was carried out under contract no. 230-99-0021 with the U.S. Department of Health and Human Services, Health Resources and Services Administration, with funding from the Health Resources and Services Administration, Agency for Healthcare Research and Quality, Centers for Medicare and Medicaid Services, and the National Institute of Nursing Research. Additional analysis was supported by grant no. HS09958 from the Agency for Healthcare Research and Quality. The preparation of this manuscript was supported in part by a Dissemination and Development Grant from Abt Associates Inc. to Dr. Mattke. At the time the research was conducted, Dr. Needleman and Ms. Stewart were at the Harvard School of Public Health and Dr. Mattke was at the Harvard School of Public Health and Abt Associates.
Measuring Hospital Quality: Can Medicare Data Substitute for All-Payer Data?
Article first published online: 18 DEC 2003
Health Services Research
Volume 38, Issue 6p1, pages 1487–1508, December 2003
How to Cite
Needleman, J., Buerhaus, P. I., Mattke, S., Stewart, M. and Zelevinsky, K. (2003), Measuring Hospital Quality: Can Medicare Data Substitute for All-Payer Data?. Health Services Research, 38: 1487–1508. doi: 10.1111/j.1475-6773.2003.00189.x
Address correspondence to Jack Needleman, Ph.D., Department of Health Services, UCLA School of Public Health, P.O. Box 951772, Los Angeles, CA 90095-1772. Peter I. Buerhaus, Ph.D., R.N., F.A.A.N., is the Valere Potter Professor and Senior Associate Dean for Research at Vanderbilt University School of Nursing, Nashville, TN. Soeren Mattke, M.D., DS.c, M.P.H., is with RAND Health in Arlington, VA. Maureen Stewart, B.A., is with Heller School for Social Policy and Management, Brandeis University, Waltham, MA. Katya Zelevinsky, B.A., is with the Department of Health Policy and Management, Harvard School of Public Health, Boston.
- Issue published online: 18 DEC 2003
- Article first published online: 18 DEC 2003
- Quality of care;
- reproducibility of results;
- nursing care
Objectives. 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.
Data Sources. 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.
Study Design. 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.
Data Collection/Extraction. 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.
Principal Findings. 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.