This is the first large-scale analysis to explore how the structure of patient-sharing relationships among physicians is related to care patterns within hospitals. In addition, we present a novel method for using readily available administrative data to construct networks of physicians that will be useful for studying physician practice patterns.14
We find that the structure of physician patient-sharing networks is significantly associated with Medicare spending and care patterns. Higher adjusted degree is associated with higher spending and health care utilization even after adjusting for hospital characteristics. In contrast, higher PCP relative centrality is associated with lower spending and utilization. These results are consistent with the hypothesis that network measures reflective of poorer coordination of care within hospitals are associated with higher costs and care intensity.
We found that hospitals with physicians whose patients see a broader array of other doctors (higher adjusted degree) have higher levels of spending. They also use more hospital care, physician visits, and imaging. These associations may reflect the difficulty of care coordination as physicians have to manage information from an increasing number of colleagues, which could be either a cause or an effect of increased health care utilization.
Another possible explanation for this phenomenon might be that hospitals whose physicians have high median adjusted degree have sicker patients (who see more physicians), leading to higher costs and utilization of services. Our methods make this unlikely for two reasons. First, our outcome measures are risk-adjusted to reflect similar patient populations, so differences in costs are not reflective of differences in burden of illness.13
Second, the adjusted degree measure is distinct from the number of physicians that patients see. The difference between a broad and focused network of physicians among the doctors caring for patients is the factor measured by the median adjusted degree.
In contrast, a network measure that likely reflects greater coordination of care, PCP relative centrality, was associated with lower imaging and test spending in addition to fewer ICU days and specialist visits. These findings build upon prior state-level analyses showing that states with more PCPs have lower costs,44
but extend this work to more formal network analysis considering the relative location of PCPs within a network of their colleagues. Interestingly, PCP relative centrality did not have a significant association with costs and utilization in non-urban hospitals. One possible interpretation of this result is that urban hospitals without primary care centered networks may be more likely to use readily-available specialist services, whereas in non-urban areas, this may not be as relevant because of less access to specialists.45
Further research is needed to understand the interaction between PCP centrality in urban and non-urban settings, but this approach could provide insight into how PCPs might best be utilized to contain costs and care utilization.
A prior study showed that the average primary care physician shares patients with approximately 99 other physicians based at 53 other practices per 100 Medicare patients treated.31
That analysis, however, was based on patients assigned to individual primary care physicians. We demonstrate that, when considering all patients being cared for by all physicians, including both PCPs and specialists, physicians are connected to 155–281 doctors per 100 Medicare patients shared with other doctors. This network-based perspective illustrates the challenge of care coordination among physicians.
Our study is subject to several limitations. First, we ascertained network structure based on the presence of shared patients using administrative data. While this technique has been validated,14
we nevertheless cannot know what information or behaviors, if any, pass across the ties defined by shared patients. In addition, our data are cross-sectional and only included elderly patients insured by the Medicare program. The local network of physicians and patients in a hospital or region is likely to be in flux, and future analyses would be enhanced by longitudinal data. Furthermore, the sample of hospitals we used is representative of larger, urban hospitals rather than all US hospitals. However, because the sample included a full range of hospital sizes, and because our focus is on the relationship between variables (not population aggregates or means), the representativeness should be less of a concern. Also, we used risk-adjusted hospital-level data on costs and care intensity averaged over 2001–2005 for our outcome measures while our networks were mapped with 2006 data. This discrepancy, however, would tend to bias our results towards the null.
Next, our main dependent variables were calculated using several years of data from the Medicare program by the Dartmouth Atlas of Healthcare. Although others have noted the possibility of inadequate risk adjustment or failure to account for differences in the prices paid for services in different regions,46,47
substantial variation in spending remains even after further risk adjustment.3,4
In addition, our models include several hospital-level characteristics that are likely to be associated with unmeasured case-mix, including size, urban versus rural location, and teaching hospital affiliation. With regards to prices, although the spending measures were not adjusted for regional payment differences, regional variation is reduced only modestly when taking prices into account.47
Moreover, our six utilization measures (e.g. hospitalizations) would not be affected by price differences and the findings for these outcomes serve to validate the findings we observed for spending.
Lastly, due to the observational design of this study, our results should not necessarily be interpreted as causal. Further work is needed to determine the causal mechanisms underlying these associations. In addition, though we adjusted for numerous covariates, we cannot rule out the possibility of unobserved confounders that could help explain the mechanisms driving the associations we observe. These unmeasured confounders could reflect local medical culture and market dynamics.
In summary, we studied a large sample of physician networks to examine how network structures reflect health care in a national sample of hospitals. This analysis highlights the importance of physician relationship networks – networks that are embedded in institutional structures and that may inform health policy and physician workforce management. We demonstrate that the characteristics of physician networks affiliated with a hospital are correlated with a hospital’s performance in a manner consistent with the hypothesis that poorer coordination of care is associated with greater spending and care intensity.