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.