Analysis of the U.S. Patient Referral Network


  • Author: Chuankai An, A. James O'Malley, Daniel N. Rockmore and Corey D. Stock
  • Date: 06 February 2019
  • Copyright: Image copyright of Patrick Rhodes

In a paper published in Statistics in Medicine, the authors analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009 to 2015. In these networks, two physicians are linked if a patient encounters both of them within a specified time interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state‐level data as well as a core‐periphery structure.

The paper is available here and the authors explain their findings in further detail below:

Analysis of the U.S. patient referral network

Chuankai An, A. James O'Malley, Daniel N. Rockmore and Corey D. Stock

Statistics in Medicine, Volume 37, Issue 5, 28 February 2018, pages 847-866


thumbnail image: Analysis of the U.S. Patient Referral Network

This paper aims to investigate the structure over a 6-year period of the U.S. “patient referral network”, a network of physicians, in which two physicians are linked if they have shared a patient within a 30-day window. This network is unique for its large size and longitudinal measurement.

A patient referral network encodes patterns of information and expertise sharing as well as collaboration in and across the physician community. Tools and metrics from social network analysis help to uncover several interesting patterns at both the state and national levels. Statistical regression analysis between network measurements and public healthcare metrics in all 50 states are applied to show hidden connections. Power law distributions and a core-periphery structure exist in most state-level networks. A so-called small-world structure and a “gravity law" are found in some national and state level networks. Some physicians play the role of hubs for interstate referral with larger numbers of referred patients and more physicians in collaboration.

The mining of referral network patterns shows the power of integrating big data computational power and social network analysis. The patterns validated by statistical methods also illustrate the potential for providing new insights into the healthcare system and opportunities or mechanisms for catalyzing improvements. These findings may be useful for policymakers and hospitals in their efforts to balance healthcare service among different areas. Future work may consider individual patient data and clinical factors when accessing the association of referral network to patient treatment outcome.

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