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Comparing Shared Patient Networks Across Payers

  • Justin G. TrogdonEmail author
  • W. H. Weir
  • S. Shai
  • P. J. Mucha
  • T. M. Kuo
  • A. M. Meyer
  • K. B. Stitzenberg
Article

Abstract

Background

Measuring care coordination in administrative data facilitates important research to improve care quality.

Objective

To compare shared patient networks constructed from administrative claims data across multiple payers.

Design

Social network analysis of pooled cross sections of physicians treating prevalent colorectal cancer patients between 2003 and 2013.

Participants

Surgeons, medical oncologists, and radiation oncologists identified from North Carolina Central Cancer Registry data linked to Medicare claims (N = 1735) and private insurance claims (N = 1321).

Main Measures

Provider-level measures included the number of patients treated, the number of providers with whom they share patients (by specialty), the extent of patient sharing with each specialty, and network centrality. Network-level measures included the number of providers and shared patients, the density of shared-patient relationships among providers, and the size and composition of clusters of providers with a high level of patient sharing.

Results

For 24.5% of providers, total patient volume rank differed by at least one quintile group between payers. Medicare claims missed 14.6% of all shared patient relationships between providers, but captured a greater number of patient-sharing relationships per provider compared with the private insurance database, even after controlling for the total number of patients (27.242 vs 26.044, p < 0.001). Providers in the private network shared a higher fraction of patients with other providers (0.226 vs 0.127, p < 0.001) compared to the Medicare network. Clustering coefficients for providers, weighted betweenness, and eigenvector centrality varied greatly across payers. Network differences led to some clusters of providers that existed in the combined network not being detected in Medicare alone.

Conclusion

Many features of shared patient networks constructed from a single-payer database differed from similar networks constructed from other payers’ data. Depending on a study’s goals, shortcomings of single-payer networks should be considered when using claims data to draw conclusions about provider behavior.

KEY WORDS

network analysis colorectal cancer 

Abbreviations

CMS

Centers for Medicare and Medicaid Services

ICD-9

International Classification of Diseases, 9th Revision

Notes

Funding Source

This study was supported by the Cancer Information and Population Health Resource (CIPHR), UNC Lineberger Comprehensive Cancer Center, with funding provided by the University Cancer Research Fund (UCRF) via the State of North Carolina.

Author Contributions

JGT, PJM, AMM, and KBS conceptualized the work and led the design and interpretation of the data. AMM led the acquisition of the data. SS, WHW, and TMK led the analysis of the data. All authors drafted and revised the manuscript, approved the final draft for submission, and agree to be accountable for all aspects of the work.

Compliance with Ethical Standards

Financial Disclosure

No financial disclosures were reported by the authors of this paper.

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Supplementary material

11606_2019_4978_MOESM1_ESM.docx (781 kb)
ESM 1 (DOCX 780 kb)

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Copyright information

© Society of General Internal Medicine 2019

Authors and Affiliations

  • Justin G. Trogdon
    • 1
    • 2
    Email author
  • W. H. Weir
    • 3
    • 4
  • S. Shai
    • 5
  • P. J. Mucha
    • 3
  • T. M. Kuo
    • 2
  • A. M. Meyer
    • 6
  • K. B. Stitzenberg
    • 2
    • 7
  1. 1.Department of Health Policy and Management, Gillings School of Global Public Health University of North Carolina at Chapel HillChapel HillUSA
  2. 2.Lineberger Comprehensive Cancer CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Carolina Center for Interdisciplinary Applied Mathematics, Department of MathematicsUniversity of North Carolina at Chapel HillChapel HillUSA
  4. 4.Curriculum in Bioinformatics and Computational BiologyUniversity of North Carolina at Chapel HillChapel HillUSA
  5. 5.Department of Mathematics and Computer ScienceWesleyan UniversityMarionUSA
  6. 6.IQVIASaint-PrexSwitzerland
  7. 7.Division of Surgical Oncology and Endocrinology Surgery, Department of SurgeryUniversity of North Carolina at Chapel HillChapel HillUSA

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