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Article Excerpt
Measuring and reporting health care facility performance via clinical measures of quality has become a major strategic initiative in improving the quality of care for Medicare and other health care payers and delivery systems (e.g., Centers for Medicare and Medicaid Services 2008). Establishing appropriate peer groups (i.e., grouping entities by similarity on specific characteristics) for such comparisons can help health care leaders and administrators make equitable comparisons across hospitals or health systems. Peer groups can be used in health care systems for a variety of purposes including promoting fair allocation of resources, evaluating efficiency or financial performance, as well as assessing quality of care and outcomes. For these evaluations, peer groups help control for systematic risk and constraints presented by various influences on a hospital's finances or clinical outcomes (Ellis and McGuire 1988; Stefos, LaVallee, and Holden 1992). These constraints and risk are generally not easy for administrators to change within a reasonable time. Hence, the rationale for peer groups is to place hospitals or health systems facing similar structural and patient characteristics together and facilitate "like-to-like" fair comparisons.
In the health care field, peer groups have become key elements of hospital industry quality benchmarking analyses (Zodet and Clark 1996; Chen et al. 1999; Austin et al. 2004; Solucient 2006). Washington State has incorporated hospital peer groups in its hospital rate schedule for state-funded care (Holubkov et al. 1998). Canada has developed regional health peer groups within its provinces, where the peer group is a "cluster of health regions that have similar social and economic health determinants" (Statistics Canada 2002; MacNabb 2003). The World Health Organization Regional Office for Europe uses peer groups as a tool to assess quality improvement internationally in European hospitals (World Health Organization 2007).
However, although often used, peer groups as commonly constructed have several characteristics that may not be desirable. Peer groups customarily have mutually exclusive membership, with characteristics or attributes defined in categories (e.g., categories of bed size, urban versus rural, teaching versus nonteaching, categories of patient severity or case mix). Hence, peer groups may have some members on the "edge" of a group with respect to certain characteristics, making comparisons with group members appear imbalanced or unfair. For example, a hospital with 152 beds may be placed in a group where bed size is 150+ beds, and nearly all other hospitals in the group have a much greater number of beds. As hospitals are increasingly scrutinized in quality and financial evaluations, they might be concerned about being on the edge in terms of characteristics that are used to define the groups. In addition, traditional cluster analysis, a common tool for creating peer groups in hospital systems (Klastorin 1982; Alexander, Evashwick, and Rundall 1984; Stefos, LaVallee, and Holden 1992), may create groups of widely varying sizes. It is not uncommon for clustering analysis to produce groups with as little as one member, or as many as dozens of members.
To counter these potentially undesirable characteristics of peer groups, we sought to develop a new methodology for constructing peer groups using the Department of Veterans Affairs (VA) medical centers as the sample population of facilities. The VA is an ideal place to explore the development of a new methodology for creating peer groups. The VA consists of 21 regional Networks with 137 medical centers and hundreds of outpatient clinics. The VA medical centers vary widely in size, patient illness burden, geographic location, and numerous other characteristics. This variation is essential for exploring peer groupings, as systems with too similar facilities may not be amenable to formation of distinct peer groups. In addition, the development of valid peer groups in the VA is essential to ensure fairness in funding allocation across medical centers (Stefos, LaVallee, and Holden 1992) and for comparison of efficiency and quality of care.
In this research, our main objective was to develop and explore a novel methodology that can be used to create peer groups in any health care system. Our work is innovative in that, unlike peer groups based on traditional cluster analysis, our methodology develops a peer group customized to each medical center by identifying the "nearest neighbor" medical centers, according to the selected characteristics for comparison. We use a Euclidean distance measure as the metric to evaluate "nearness." Thus, each medical center is the hub of its own peer group, but will also appear in peer groups of other medical centers. In this paper, we explore the characteristics of our "nearest neighbor" peer groups and compare with peer groups constructed using traditional cluster analysis.
METHODS
Sample and Characteristics for Peer Group Development
Our sample of facilities was composed of 133 VA medical centers, excluding sites in Puerto Rico, the Philippines, New Orleans, LA, and Biloxi, MS. (Puerto Rico and the Philippines were excluded because of incomplete data, while New Orleans and Biloxi were excluded due to substantial disruptions in service due to hurricanes during the period of study.) To create our peer groups, we used characteristics that reflected patient complexity, academic mission, medical research, and size (Stefos, LaVallee, and Holden 1992; Hogan, Franzini, and Boex 2000; Maciejewski et al. 2002; Larson and Fleishman 2003; Wasserman et al. 2003). For example, our patient-level measures included patient illness burden and veteran reliance on the VA (versus Medicare) for health services among...
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