This study finds that it is possible to use claims data to map the connections between doctors caring for patients with prostate cancer. In each city, there was a substantial group of physicians who were connected to one another through the care of patients with localized prostate cancer. Within this large component, urologist subgroup provided additional information concerning the likelihood of patients to receive prostatectomy after accounting for individual patient-level characteristics and clustering by physicians. To our knowledge, network techniques have not been previously used to map cancer care delivery.
A major advantage of using a network approach in this setting is that it may not only shed light on the impact of formal institutional arrangements between physicians (e.g., practice structure) but also provide information on the informal relationships (e.g., “who knows who”) that may influence which patients see particular doctors. In integrated delivery systems and multispecialty group practices, the connections between physicians probably reflect this practice structure. However, a majority of PCPs work in small group practices (Bodenheimer and Pham 2010
) and are likely to use informal relationships and information channels to guide these decisions (Kinchen et al. 2004
; Forrest et al. 2006
). Our work suggests that subgroups and practices are related to one another, yet distinct. While diagnosing urologists in the same practice location were frequently (although not invariably) placed in the same subgroup, larger subgroups brought together doctors from multiple practices.
These connections between physicians may be associated with the exchange of information and diffusion of innovation (Coleman, Katz, and Menzel 1966
; Rogers 1995
; Valente 1996
). This exchange may occur directly between doctors communicating in the care of a given patient and indirectly communicating through patients relaying “messages” about their diagnosis and treatment. The network structure may also reflect other formal and informal ways that information is passed (e.g., through advice seeking, curbside consultations, and teaching conferences) (Kuo, Gifford, and Stein 1998
While we were unable to directly assess these mechanisms, it is probable that the connections between physicians in the setting of shared patients probably encompass a mix of these direct and indirect communications and formal and informal mechanisms.
In each city, a majority of urologists were connected to relatively few other doctors in their care of patients with localized prostate cancer, whereas a few were very highly connected. This skewed distribution is consistent with a power law or scale-free network, which is found in many larger networks (Barabasi and Albert 1999
). It is possible that the highly connected physicians may serve as opinion leaders (Lomas et al. 1991
; Soumerai, McLaughlin, and Gurwitz 1998
) and be used in interventions to facilitate the spread of norms and quality standards.
The data further revealed subgroups with significant clustering by patient race and socioeconomic status. This corresponds to the well-described segregation by race/ethnicity in the health care system (Smith 1998
). Prior literature has tended to focus on differences in PCPs who treat white and black patients (Bach et al. 2004
) and racial differences between hospitals (Barnato et al. 2005
; Groeneveld, Laufer, and Garber 2005
; Jha et al. 2007
; Pollack et al. in press). The current work extends this by examining the ways in which providers, primarily in the outpatient setting, cluster with one another. It is critical to examine whether subgroups have different quality of care and access to resources, as has been shown with hospitals that treat varying proportions of non-white patients (Jha et al. 2007
Physician relationships have received increasing attention as health care reform attempts to potentially modify and codify them under primary care medical home demonstration projects and accountable care organizations (ACOs) (Fisher et al. 2007
; Fisher, McClellan, and Bertko 2009
; Lee et al. 2010
). In these reforms, relationships between PCPs and specialists may become more clearly delineated and information flow improved. Network analysis may provide a way to model these relationships. For example, the approach may be used to demonstrate the extent to which physicians in the same ACO are currently clustered with one another in caring for patients, describe how differences in network structures may impact the ability of different ACOs to improve quality and control costs, and elucidate how reforms lead to changes in clustering over time. The observed clustering by patient race/ethnicity may also have ramifications for understanding how these reforms may work to ameliorate and/or exacerbate health care disparities (Pollack and Armstrong 2011
). In addition, these reforms are expected to function through improved care coordination. With few readily available measures of care coordination (McDonald, Sundaram, and Bravata 2007
), it is plausible that network approaches may be an important tool in developing such measures.
The use of claims data to construct networks presents several opportunities as well as key limitations. Typically, studies on networks rely on surveys designed to capture communication between members (Wasserman and Faust 1999
). Survey administration is costly and time consuming. Employing claims data increase the ability to generate networks and test how indirect connections (through patients and other physicians) may influence behavior. However, claims data lack direct information about referral processes and cannot examine the motivations and beliefs of PCPs, urologists, and patients.
This work has several additional limitations. First, Medicare claims are primarily restricted to people aged 65 and over. Although networks for younger patients may be different, prior studies have shown that urologist volume as calculated from Medicare data is highly correlated with total patient volume (Bach et al. 2004
). In addition, patients enrolled in Medicare HMOs and those who receive their care exclusively from the Veterans’ Affairs system were not included. The exclusion of these patients may alter the network structure by potentially decreasing the number of doctors and lowering the ties that exist between doctors, thus biasing estimates of network-level statistics (Kossinets 2006
). Second, we defined networks based on health referral regions to limit the scope, and due to constraints of the SEER-Medicare data. This may contribute to a “boundary specification problem” leading to biased measures of network structural features (Laumann, Marsden, and Prensky 1983
). It remains uncertain how the inclusion of patients living outside of (but potentially proximal to) a given health referral region would impact the observed classification of subgroups. It is important to use complementary data to compare and contrast network structural features and subgroup designation, ideally testing against all payor databases and varying the geographic specifications. Third, the data include only those men diagnosed with prostate cancer and excluded men who are in the networks but who do not have prostate cancer (e.g., men with an elevated PSA but negative prostate biopsy). We would expect care connections between PCPs and urologists to be similar because most men receive their cancer diagnosis after the referral has taken place. Fourth, obtaining accurate practice variables can be challenging. We relied on preferred office address; however, because physicians may work at multiple locations, it is unlikely that our definition captured the full extent of physician practices. Allowing for a more liberal matching process (assigning doctors with the same street address but different suite numbers to the same practice) produced similar results. Importantly, we were unable to capture whether doctors at different sites of care are affiliated with one another through the same health care delivery organization. In addition, we focused solely on the practice of the diagnosing urologist, recognizing the importance of examining how multispecialty groups and health care organizations influence network structures. Fifth, network algorithms were used to define physician subgroup. Although the Girvan-Newman algorithm is widely used, considerable controversy exists over which algorithm is best for determining community structure (Fortunato 2010
). Additional research is required to determine the optimal measurement of network structure among physicians and health care professionals and, if network structure is determined to be causally associated with treatment variation, to define its underlying mechanisms.
Along with clinical characteristics and patient preferences, variation in care for men with localized prostate cancer may also be related to the doctor a patient sees, how that doctor is connected to other doctors, and how the doctor fits in the overall network structure. Using claims data represents an important opportunity to study network structure. With prior studies suggesting that networks may amplify beneficial or deleterious behaviors (Christakis and Fowler 2007
; Christakis and Fowler 2008
; Fowler and Christakis 2008
), identifying the physician network structure is a crucial first step in understanding and modifying local variation in care.