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



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


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


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


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.


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.


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.


network analysis colorectal cancer 



Centers for Medicare and Medicaid Services


International Classification of Diseases, 9th Revision


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)


  1. 1.
    McDonald KM, Sundaram V, Bravata DM, et al. Closing the quality gap: a critical analysis of quality improvement strategies (vol. 7: care coordination). AHRQ Technical Reviews. Rockville (MD), 2007Google Scholar
  2. 2.
    Committee on Quality of Health Care in America IoM. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington (DC); 2001Google Scholar
  3. 3.
    Iglehart JK. No place like home--testing a new model of care delivery. N Engl J Med 359:1200–2, 2008CrossRefGoogle Scholar
  4. 4.
    Jackson GL, Powers BJ, Chatterjee R, et al. Improving patient care. The patient centered medical home. A systematic review. Ann Intern Med 158:169–78, 2013CrossRefGoogle Scholar
  5. 5.
    Salz T, Oeffinger KC, McCabe MS, et al. Survivorship care plans in research and practice. CA Cancer J Clin 62:101–17, 2012CrossRefGoogle Scholar
  6. 6.
    Auerbach DI, Liu H, Hussey PS, et al. Accountable care organization formation is associated with integrated systems but not high medical spending. Health Aff (Millwood) 32:1781–8, 2013CrossRefGoogle Scholar
  7. 7.
    Hussain T, Chang HY, Veenstra CM, et al. Collaboration between surgeons and medical oncologists and outcomes for patients with stage III colon cancer. J Oncol Pract 11:e388–97, 2015CrossRefGoogle Scholar
  8. 8.
    Pollack CE, Frick KD, Herbert RJ, et al. It’s who you know: patient-sharing, quality, and costs of cancer survivorship care. J Cancer Surviv 8:156–66, 2014CrossRefGoogle Scholar
  9. 9.
    Pollack CE, Lemke KW, Roberts E, et al. Patient sharing and quality of care: measuring outcomes of care coordination using claims data. Med Care 53:317–23, 2015Google Scholar
  10. 10.
    Pollack CE, Weissman GE, Lemke KW, et al. Patient sharing among physicians and costs of care: a network analytic approach to care coordination using claims data. J Gen Intern Med 28:459–65, 2013CrossRefGoogle Scholar
  11. 11.
    Barnett ML, Christakis NA, O'Malley J, et al. Physician patient-sharing networks and the cost and intensity of care in US hospitals. Med Care 50:152–60, 2012CrossRefGoogle Scholar
  12. 12.
    Landon BE, Onnela JP, Keating NL, et al. Using administrative data to identify naturally occurring networks of physicians. Med Care 51:715–21, 2013CrossRefGoogle Scholar
  13. 13.
    Uddin S, Hossain L, Kelaher M. Effect of physician collaboration network on hospitalization cost and readmission rate. Eur J Public Health 22:629–33, 2012CrossRefGoogle Scholar
  14. 14.
    Casalino LP, Pesko MF, Ryan AM, et al. Physician networks and ambulatory care-sensitive admissions. Med Care 53:534–41, 2015CrossRefGoogle Scholar
  15. 15.
    Iwashyna TJ, Christie JD, Moody J, et al. The structure of critical care transfer networks. Med Care 47:787–93, 2009CrossRefGoogle Scholar
  16. 16.
    Barnett ML, Landon BE, O'Malley AJ, et al. Mapping physician networks with self-reported and administrative data. Health Serv Res 46:1592–609, 2011CrossRefGoogle Scholar
  17. 17.
    Landon BE, Keating NL, Barnett ML, et al. Variation in patient-sharing networks of physicians across the United States. JAMA 308:265–73, 2012Google Scholar
  18. 18.
    Trogdon JG, Chang Y, Shai S, et al. Care coordination and multispecialty teams in the care of colorectal cancer patients. Med Care 56:430–435, 2018Google Scholar
  19. 19.
    Dafny LS, Hendel I, Marone V, et al. Narrow networks on the health insurance marketplaces: prevalence, pricing, and the cost of network breadth. Health Affairs 36:1606–1614, 2017CrossRefGoogle Scholar
  20. 20.
    Meyer AM, Olshan AF, Green L, et al. Big data for population-based cancer research: the integrated cancer information and surveillance system. N C Med J 75:265–9, 2014Google Scholar
  21. 21.
    Blondel VD, Guillaume JL, Lambiotte R, et al. Fast unfolding of communities in large networks. J Stat Mech Theory Exp, 2008Google Scholar
  22. 22.
    Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci U S A 103:8577–8582, 2006CrossRefGoogle Scholar
  23. 23.
    Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E 69, 2004Google Scholar
  24. 24.
    Weir WH, Emmons S, Gibson R, et al: Post-processing partitions to identify domains of modularity optimization. Algorithms 10, 2017Google Scholar
  25. 25.
    Centers for Medicare & Medicaid Services. Oncology care model; 2016Google Scholar
  26. 26.
    Agha L, Ericson KM, Geissler KH, et al. Team formation and performance: evidence from healthcare referral networks, in Research NBoE (ed): NBER Working Paper 24338. Cambridge, MA, 2018Google Scholar
  27. 27.
    Pham HH, O'Malley AS, Bach PB, et al. Primary care physicians’ links to other physicians through Medicare patients: the scope of care coordination. Ann Intern Med 150:236–42, 2009CrossRefGoogle Scholar
  28. 28.
    Fisher ES, Wennberg DE, Stukel TA, et al. The implications of regional variations in Medicare spending. Part 2: health outcomes and satisfaction with care. Ann Intern Med 138:288–98, 2003CrossRefGoogle Scholar

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

Personalised recommendations