Address correspondence to Laura A. Petersen, M.D., M.P.H., Health Services Research and Development (152), Houston Veterans Affairs Medical Center, 2002 Holcombe Blvd., Houston, TX 77030; e-mail: firstname.lastname@example.org. Margaret M. Byrne, Ph.D., is with the Department of Epidemiology and Public Health, University of Miami, Miami, FL. Christina N. Daw, M.P.H., Ph.D., Tracy H. Urech, M.P.H., and Kenneth Pietz, Ph.D., are with the, Health Services Research and Development Center of Excellence, Houston VA Medical Center, and Section for Health Services Research, Baylor College of Medicine, Houston, TX. Harlan A. Nelson, M.S., is with the InVentive Clinical Inc., Indianapolis, IN.
Method to Develop Health Care Peer Groups for Quality and Financial Comparisons Across Hospitals
Article first published online: 4 NOV 2008
© Health Research and Educational Trust
Health Services Research
Volume 44, Issue 2p1, pages 577–592, April 2009
How to Cite
Byrne, M. M., Daw, C. N., Nelson, H. A., Urech, T. H., Pietz, K. and Petersen, L. A. (2009), Method to Develop Health Care Peer Groups for Quality and Financial Comparisons Across Hospitals. Health Services Research, 44: 577–592. doi: 10.1111/j.1475-6773.2008.00916.x
- Issue published online: 12 MAR 2009
- Article first published online: 4 NOV 2008
- Peer groups;
- cluster analysis;
- nearest neighbor;
- Euclidean distance;
- quality of care comparisons
Objective. To develop and explore the characteristics of a novel “nearest neighbor” methodology for creating peer groups for health care facilities.
Data Sources. Data were obtained from the Department of Veterans Affairs (VA) databases.
Statistical Methods and Findings. Peer groups are developed by first calculating the multidimensional Euclidean distance between each of 133 VA medical centers based on 16 facility characteristics. Each medical center then serves as the center for its own peer group, and the nearest neighbor facilities in terms of Euclidean distance comprise the peer facilities. We explore the attributes and characteristics of the nearest neighbor peer groupings. In addition, we construct standard cluster analysis-derived peer groups and compare the characteristics of groupings from the two methodologies.
Conclusions. The novel peer group methodology presented here results in groups where each medical center is at the center of its own peer group. Possible advantages over other peer group methodologies are that facilities are never on the “edge” of a group and group size—and thus group dispersion—is determined by the researcher. Peer groups with these characteristics may be more appealing to some researchers and administrators than standard cluster analysis and may thus strengthen organizational buy-in for financial and quality comparisons.