Health care policy makers and administrators frequently wish to compare the performance of health care facilities. Indeed, 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
). We have developed a methodology for developing customized, nonmutually exclusive peer groups, with a given health care entity being the reference point or center of a unique comparison group. This methodology can be applied to health care systems, regions, clinics, or pharmacies. Our nearest neighbor methodology differs from traditional cluster analysis in a number of ways. Most conventional cluster analyses result in a set of mutually exclusive peer groups, which are almost invariably of varying sizes. Also, the number of facilities/entities in each peer group is determined during the creation of the groups. In contrast, our nearest neighbor methodology creates peer groups that are not mutually exclusive and each entity is the reference point and center of its group. In addition, the number of peers in a group is determined at the discretion of the researchers.
Nearest neighbor peer groups can be nondiscrete, in that medical center A can be a member of medical center B's peer group but not the converse, depending on how group membership is defined. If membership is defined based on the number of facilities, then varying Euclidean distances between medical centers and their peers can lead to this nondiscrete quality. Indeed, this lack of discreteness in the resulting peer groups illustrates the flexibility of the nearest neighbor method in individualizing the groups to a given reference medical center. However, if membership is defined by Euclidean distance to furthest peer or total density/diffusion of the group, this nondiscrete quality will not occur. In our exploration of the novel nearest neighbor peer groups, we found that the maximum Euclidean distance from reference point to furthest peer facility did not increase substantially as the number of members in a group increases. Instead, even when the membership of peer groups was increased eight-fold, the Euclidean distance for the plurality of peer groups was not even doubled.
In this research, we also provided a comparison of peer groups formed from using the nearest neighbor methodology to those created using standard cluster analysis. Such a comparison is difficult and somewhat tenuous, as the different methodologies do not yield results that lend themselves to obvious comparisons. In the comparisons we did perform, we found that the median diffusion of nearest neighbor peer groups with 15 members was approximately equivalent to the median diffusion of peer groups created using cluster analysis (median peer group size was 13). Thus, as a rough approximation, it appears that the nearest neighbor methodology produces peer groups that are at least as dense as those created using cluster analysis that is designed to maximize group density. However, for every given peer group from cluster analysis, the (approximate) same-sized nearest neighbor set of peer groups, constructed using a number-based determination of membership, had a maximum group diffusion that was larger. Thus, some peer groups created using the nearest neighbor method and including a fixed number of facilities will comprise facilities that are not very similar.
The likely reason for the maximum diffusion of the nearest neighbor methodology peer group being greater than a comparably sized cluster analysis generated peer group (when a fixed number of facilities is used to form nearest neighbor peer groups) is because of the inclusion in the nearest neighbor groups of facilities that are not very similar to other facilities in that group. We can, in fact, identify seven facilities that are most responsible for the highest RSS scores. These medical centers are dissimilar to the other centers on most of the characteristics that we used to create the peer groups.
No peer grouping methodology can alleviate the concern that some medical centers will not have close peers. However, with the nearest neighbor methodology, we specifically know the Euclidean distance from a given medical center to its nearest peer(s). Therefore, a researcher or user of this methodology can decide a priori, based on a comparison with other peer distances or on some other calculation, the maximum distance they would consider for neighbors. In this way, medical centers that do not have any appropriate peers can be identified, and users can decide whether or not to include them in comparisons.
There are a couple of important issues that potential users of our methodology must address when they consider constructing their own peer groups. Of first importance is the question as to which characteristics to include. As discussed, the variables that should be used for peer group construction will be dependent on the entities being grouped and the purpose of the groupings. For our analyses, we focused upon structural characteristics, such as academic affiliation and number of beds, that were not readily changed by managers, and thus represent somewhat fixed constraints on performance. Second, although not included in the analysis here for clarity of presentation, it is possible to weight the characteristics that are used in terms of importance. Weights could be applied in a variety of ways; one way would be to multiply the desired weights to the squared characteristic values before they are summed and the square root taken. Thus, higher importance can be placed on specific variables that users believe should be more influential in forming the groups, such as rural location or other characteristics. Third, as discussed throughout, users of this methodology have discretion in choosing how to form final peer groups. Groups can be formed based on a certain number of members, if a specific number of comparators are desired. Alternatively, if a specific similarity or “closeness” of peers is desired, that can be specified to determine peer membership. Finally, users of our methodology should be aware that certain types of statistical analyses using the groups might not be valid. For example, the use of random effects models is questionable because the hospitals in a group cannot be considered a random sample of a larger population. Moreover, the same analysis on different groups that contain one or more of the same hospitals may yield different results. These issues highlight the need for further research on the use of significance testing with peer groups determined by this method.
One of the advantages of the nearest neighbor method is that the peer groups are more refined than often-used groupings, reflecting the multidimensional diversity of health care providers. Also, the multiple characteristics and dimensions in the methods account for the myriad complex factors that contribute to health care provider performance. While some health care managers might find traditional lists of peers to be relatively clear-cut (as, for example, they may show rural versus urban, or teaching versus nonteaching institutions), the nearest neighbor method can incorporate as many characteristics that leaders deem critical. This method also facilitates the use of continuous variables as measures.
Managers of hospital and health care systems struggle at times to achieve mutually acceptable means of evaluating and comparing hospitals under their leadership. Some researchers argue that comparing hospitals only to their peers, rather than all hospitals, perversely lowers the standards for those medical centers whose characteristics may be generally associated with lower quality performance (Romano 2004
). However, this perspective is countered by the practical consideration that health care facilities or systems may have structural and patient-based differences that cannot be changed but do affect financial or quality outcomes. An advantage of the nearest neighbor peer group methodology is that it may strengthen organizational buy-in for use of peer groups for use in comparisons of financial performance, efficiency, and quality. As the use of benchmarking and quality reviews increases and evolves, the methods presented here may facilitate the formation of peer groups for comparison.