Properties of Healthcare Teaming Networks as a Function of Network Construction Algorithms

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other. Most healthcare service network models have been constructed from patient claims data, using billing claims to link patients with providers. The data sets can be quite large, 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. To address this issue, we compared the properties of healthcare networks constructed using different algorithms and the 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 found that each algorithm produced networks with substantially different topological properties. Provider networks adhered to a power law, and organization networks to 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 greatly altered measures of vertex prominence such as the betweenness centrality. We identified patterns in the distance patients travel between network providers, and most strikingly between providers in the Northeast United States and Florida. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications for selecting the algorithm best suited to the type of analysis to be performed.Comment: With links to comprehensive, high resolution figures and networks via figshare.co

Visualizing nationwide variation in medicare Part D prescribing patterns

Abstract Background To characterize the regional and national variation in prescribing patterns in the Medicare Part D program using dimensional reduction visualization methods. Methods Using publicly available Medicare Part D claims data, we identified and visualized regional and national provider prescribing profile variation with unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction techniques. Additionally, we examined differences between regionally representative prescribing patterns for major metropolitan areas. Results Distributions of prescribing volume and medication diversity were highly skewed among over 800,000 Medicare Part D providers. Medical specialties had characteristic prescribing patterns. Although the number of Medicare providers in each state was highly correlated with the number of Medicare Part D enrollees, some states were enriched for providers with > 10,000 prescription claims annually. Dimension-reduction, hierarchical clustering and t-SNE visualization of drug- or drug-class prescribing patterns revealed that providers cluster strongly based on specialty and sub-specialty, with large regional variations in prescribing patterns. Major metropolitan areas had distinct prescribing patterns that tended to group by major geographical divisions. Conclusions This work demonstrates that unsupervised clustering, dimension-reduction and t-SNE visualization can be used to analyze and visualize variation in provider prescribing patterns on a national level across thousands of medications, revealing substantial prescribing variation both between and within specialties, regionally, and between major metropolitan areas. These methods offer an alternative system-wide and pattern-centric view of such data for hypothesis generation, visualization, and pattern identification

2013 Provider-Provider Medicare Network Maps - Binning Method

These images contain healthcare maps created using a binning algorithm from the 2013 Medicare Part B claims data set.  These are essentially a social network map of US providers-provider connections via shared patients in 2013.  The plots are arranged in ascending order of the distance between providers, and each image represents a network map within that distance range

2013 Organization-Organization Medicare Network - Trace-Route Algorithm

These images contain healthcare maps, created using a trace-route mapping algorithm, from the 2013 Medicare Part B claims data set. These maps are essentially a social network diagram of how medicare network organizations are connected to each other by shared patients. The plots are arranged in ascending order of the distance between organizations, and each image represents a network map within that distance range.<div><br></div><div>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175876#ack<br></div

Medicare 2013 Provider and Organization Networks

Provider-Provider and Organization-Organization networks created with different algorithms from the Medicare Part  B 2013 Limited Claims Data Set.  These files are of the form:<div><br></div><div>{NPI1, NPI2, shared patients, total visits for shared patients}</div><div><br></div><div>All networks are HIPAA compliant, censored for total shared patients greater than or equal to 11 patients.  </div><div><br></div><div>Networks are named for the algorithm used to produce them, whether they are provider-provider or organization-organization networks, and the temporal frame parameter {30, 60, 90, 180, 365 days}.</div><div><br></div

2013 Organization-Organization Medicare Maps - Binning Method

These images contain healthcare maps created using a binning algorithm from the 2013 Medicare Part B claims data set.  The plots are arranged in ascending order of the distance between organizations, and each image represents a network map within that distance range

2013 Provider-Provider Medicare Network - Sliding Window Algorithm

These images contain healthcare maps, created using a sliding window algorithm, from the 2013 Medicare Part B claims data set. The plots are arranged in ascending order of the distance between providers, and each image represents a network map within that distance range.<div><br></div><div>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175876#ack<br></div

2013 Provider-Provider Medicare Network - Trace-Route Algorithm

These images contain healthcare maps, created using a trace-route algorithm, from the 2013 Medicare Part B claims data set. This is essentially a social network map of providers connected by caring for common patients. The plots are arranged in ascending order of the distance between providers, and each image represents a network map within that distance range.<div><br></div><div>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175876#ack<br></div

2013 Organization-Organization Medicare Network - Sliding Window Algorithm

These images contain healthcare maps created, using a sliding temporal window network construction algorithm, from the 2013 Medicare Part d claims data set. The plots are arranged in ascending order of the distance between healthcare organizations, and each image represents a network map within that distance range. <div><br></div><div>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175876#ack</div