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.
Highlights
Network science can provide key insights into healthcare systems including patient referral patterns [1,2,3,4,5,6,7,8,9,10,11,12], provider communities associated with better healthcare outcomes, or specific drug prescribing patterns [13,14,15]
The analysis presented here is compliant with Center for Medicare Services (CMS) current cell size suppression policy as well as all data exclusivity requirements contained in the CMS Limited Data Set Data Use Agreement
Our focus here is the comparison of topology and properties of the healthcare network graphs built using three algorithmic methods: (1) a sliding temporal frame algorithm similar to that currently used to construct Medicare networks by the Center for Medicare Services [28, 29], (2) a temporal binning method which captures all possible relationships within a given time span, and (3) a trace-route algorithm [21, 22] that builds networks based on sequential sequence of provider visits
Summary
Network science can provide key insights into healthcare systems including patient referral patterns [1,2,3,4,5,6,7,8,9,10,11,12], provider communities associated with better healthcare outcomes, or specific drug prescribing patterns [13,14,15]. The research questions suited to network science methods typically fall into three categories: 1) network topology; 2) patient flow; and 3) provider clustering. Network topology questions include investigations of network structure and properties, such as the effect of the rules and constraints under which provider teams organize (i.e. referral bias, geographic proximity, insurance network restrictions)[16] or identifying providers with high levels of influence. Provider clustering can identify highly collaborative groups of providers associated with specific patient outcomes. Such work is crucial for identifying provider groups (e.g. communities, k-cliques or k-clans) with good outcomes for patients with complex conditions, such as cancer, heart failure or kidney disease [17,18,19]
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