We studied 4,586,044 Medicare beneficiaries from 51 HRRs who were seen by 68,288 physicians practicing in those HRRs. The randomly sampled HRRs are distributed across all regions of the country and include urban and rural locations (). The characteristics of all included physicians and patients are presented in . The mean physician age was 48.8 years and about 80% were male. Among the Medicare patients, the mean age was 71 and 40% were male. The distribution of the number of shared patients between linked physicians for the entire dataset is depicted in .
Description of Physicians, Their Medicare Beneficiaries, and Network Characteristics stratified by HRR
After applying the relative thresholding rule (keeping only ties with strength in the top 20th percentile for each physician), the mean number of patients shared per 100 Medicare beneficiaries across the entire sample was 27.3. Network attributes are depicted graphically in , which shows scatter plots of the network topological characteristics of interest versus the network size for the included HRRs. The network measures fall into two distinct categories: those with a strong dependence on network size (adjusted degree, clustering, number of shared patients) and those less associated with network size (relative PCP and medical specialist centrality). Network characteristics across the geographic regions also are shown in . Substantial variation was observed across HRRs. For example, the number of included physicians ranged from 135 in Minot, ND (with 1,568 ties) to 8,197 in Boston (392,582 ties). Physician adjusted degree is much higher in Boston (the average physician was connected to 51.4 other physicians per 100 Medicare patients cared for in Boston versus 11.7 in Minot), whereas clustering is greater in Minot (.62 in Minot versus .48 in Boston -- the clustering coefficient ranges from 0–1 and quantifies the proportion of physicians who, in addition to being connected to a given physician, are also connected to one another). As noted above, these network characteristics also were strongly associated with network size. Other variation cannot be explained by the general relationship to network size, however, such as the greater relative betweenness centrality of specialists in Minot vs. Boston (Specialists are over 5 times more central than PCPs in Minot whereas in Boston they are only 1.6 times as central), meaning that certain structural aspects of physician networks are not simply functions of network size.
Graphical depictions of networks for two HRR’s are presented in . For descriptive purposes, hospital affiliation and specialty are presented in two separate graphs. In St. Paul, MN, there are many ties between physicians in different hospitals, with primary care physicians centering their patient sharing around a pool of medical and surgical specialists in multiple hospitals. Alternatively, in Albuquerque, NM, network connections are mostly confined within hospitals, and connections are generally confined to their hospital.
Figure 3 Depictions of two networks: Albuquerque, NM (panels A and B, ~1000 physicians) and Minneapolis/St. Paul, MN (Panels C and D, ~1700 physicians). On the left (panels A and C), hospital affiliations are coded (each hospital is represented by a different (more ...)
Graphical depictions of networks for two HRR’s are presented in . Networks are pictorially represented using “spring embedder” methods, which position objects with stronger connections (i.e., physicians with more shared patients) in closer physical proximity within the network. In St. Paul, MN, there are many ties between physicians in different hospitals, with primary care physicians centering their patient sharing around a pool of medical and surgical specialists in multiple hospitals. Thus, although physicians are clustered according to their principal hospital affiliation, the close proximity of the clusters is indicative of multiple ties across hospitals. Alternatively, in Albuquerque, NM, network connections are mostly confined within hospitals, and connections are generally confined to their hospital. Consequently, the hospital clusters in Albuquerque are more distinct and separated in space.
Factors Associated with Network Ties
Among all physicians and ties (rather than just the 20% of strongest ties), across the 51 HRRs, male physicians were more likely to have ties with other male physicians (65.1% of connected physician pairs were male-male versus 54.6% of unconnected physician pairs, p<.001), but female physicians were less likely to have ties with other female physicians (3.8% of connected physician pairs versus 6.4% of unconnected physician pairs, p<.001) (). Physicians with ties were also closer in age (mean difference of 11.5 years for those with ties versus 12.5 for those without, p<.001). Patterns varied by physician specialty as well. Although most (69.2%) connected physician pairs were from different hospitals, virtually all unconnected physician pairs (96.0%) were from different hospitals (p<.001). Connected physician pairs were also more likely to be in close geographic proximity. The mean distance for connected pairs was 13.2 miles versus 24.2 miles for unconnected pairs (p<.001). Connected physicians also had more similar practice intensity as measured by ETGs (a difference of .29 for linked physicians versus a difference of .31 for unlinked physicians, p<.001).
Characteristics of physicians’ patient populations were also associated with the presence of ties between physicians. Across all physician racial and ethnic groups, connected physicians had more similar racial composition of their patient panels (net of any shared patients) than unconnected physicians. For instance, connected physician pairs had an average difference of 8.8 points in the percentage of black patients in their two patient panels compared with a difference of 14.0 percentage points for unconnected physician pairs (p<.001). Similarly, differences in mean patient age and percent Medicaid patients were also smaller for connected physicians than unconnected physicians. Medical comorbidities (measured by the HCC score) were also more similar, suggesting that connected physicians had more similar patients in terms of clinical complexity than unconnected physicians. All of these results were confirmed in multivariable regression models as shown in the right column of .
Physicians thus tend to cluster together along attributes that characterize their own backgrounds and the clinical circumstances of their patients. Of note, we observed similar patterns when repeating the analyses using logistic regression after applying the thresholding criteria.