Properties of healthcare teaming networks as a function of network construction algorithms

Abstract

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106–108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and Florida, likely due to seasonal residence patterns of Medicare beneficiaries. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications of our findings for selecting the algorithm best suited to the type of analysis to be performed.

A scoping review of patient-sharing network studies using administrative data

“…We identified two approaches to defining how many shared patients "counted" as a connection between two providers. One approach used a fixed threshold of shared patients (e.g., 1, 2, 5 shared patients) [23][24][25][26][27]. A second approach used relative thresholds.…”

“…As opposed to geographic distance (i.e., the physical distance between two providers), distance measures the level of connection or closeness between a pair of providers. Distance may be calculated as the shortest path (geodesic distance) between two nodes or longest path (diameter) of a network [23,27,57].…”

The Role of Organizational Affiliations in Physician Patient-Sharing Relationships

“…We follow the majority of the literature in this space which ignores temporal sequencing of physician visits, as we are most interested in the presence of a connection between physicians, rather than the specific order of the visits and because it is the most straightforward way to identify physician linkages. Alternative methods in which connections are constructed with directed links based on the order of visits lead to different network properties (Zand et al, 2017).…”

Medicare Radiology Group Network Market Share: Recent Characteristics and Trends

“…Referral data is frequently shared with ACOs, such as a risk-shared insurance database [6], but detailed databases are not typically widely or publicly available. To assess NMS, we used the CareSet DocGraph Hop Teaming dataset [10, 11], which is based on administrative claims data from the Centers for Medicare and Medicaid Services (CMS), where at least 10 unique Medicare beneficiaries are shared between two healthcare providers connected in a directed graph structure, one of the largest graph databases freely available. Unfortunately, this dataset does not include details of exam types, diagnosis codes, and whether the shared patients were explicitly referred (i.e., directed to a radiology group).…”