Imaging Practice Patterns: Referral Network Analysis of a Single State of Origination

Radiology Research Funding: Current State and Future Opportunities

Funding for research has become increasingly difficult to obtain in an environment of decreasing clinical revenue, increasing research costs, and growing competition for federal and nonfederal funding sources. This paper identifies critical requirements to build and sustain a successful radiology research program (eg, key personnel and leadership, research training and mentorship, infrastructure, institutional and departmental funding or support), reviews the current state of available funding for radiology (including federal, nonfederal, philanthropy, crowdfunding, and industry), and describes promising opportunities for future funding (eg, health services, comparative effectiveness, and patient-centered outcomes research). The funding climate, especially at the federal level, changes periodically, so it is important to have radiology-specific organizations such as the American College of Radiology and the Academy of Radiology Research serving as our key advocates. Key to obtaining any funding, no matter what the source, is a well-formulated grant proposal, so a review of opportunities specifically available to radiologists to develop and hone their grant-writing skills is provided. Effective and sustained funding for radiology research has the potential to cultivate young researchers, bolster quality research, and enhance health care. Those interested in pursuing research need to be aware of the ever-changing funding landscape, research priority areas, and the resources available to them to succeed. To succeed, radiology researchers need to think about diversification and flexibility in their interests, developing multidisciplinary and multi-institutional projects, and engaging a broader base of stakeholders that includes patients.

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, 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.