Journal of General Internal Medicine

, Volume 34, Issue 11, pp 2482–2489 | Cite as

Evaluation of Physician Network-Based Measures of Care Coordination Using Medicare Patient-Reported Experience Measures

  • Erika L. Moen
  • Julie P. W. Bynum
Original Research



There is significant promise in analyzing physician patient-sharing networks to indirectly measure care coordination, yet it is unknown whether these measures reflect patients’ perceptions of care coordination.


To evaluate the associations between network-based measures of care coordination and patient-reported experience measures.


We analyzed patient-sharing physician networks within group practices using data made available by the Centers for Medicare and Medicaid Services.


Medicare beneficiaries who provided responses to the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey in 2016 (data aggregated by physician group practice made available through the Physician Compare 2016 Group Public Reporting).

Main Measures

The outcomes of interest were patient-reported experience measures reflecting aspects of care coordination (CAHPS). The predictor variables of interests were physician group practice density (the number of physician pairs who share patients adjusting for the total number of physician pairs) and clustering (the extent to which sets of three physicians share patients).

Key Results

Four hundred seventy-six groups had patient-reported measures available. Patients’ perception of “Clinicians working together for your care” was significantly positively associated with both physician group practice density (Est (95 % CI) = 5.07(0.83, 9.33), p = 0.02) and clustering (Est (95 % CI) = 3.73(1.01, 6.44), p = 0.007). Physician group practice clustering was also significantly positively associated with “Getting timely care, appointments, and information” (Est (95 % CI) = 4.63(0.21, 9.06), p = 0.04).


This work suggests that network-based measures of care coordination are associated with some patient-reported experience measures. Evaluating and intervening on patient-sharing networks may provide novel strategies for initiatives aimed at improving quality of care and the patient experience.


physician networks network analysis care coordination Physician Compare CAHPS 



The authors would like to acknowledge Andrew Schaefer for his assistance in obtaining the Census data and RUCA codes used in the analyses.

Funding Information

This study was supported by NIH NIA P01AG019783 and NIH NIGMS P20GM104416.

Compliance with Ethical Standards

Conflict of Interest

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

Supplementary material

11606_2019_5313_MOESM1_ESM.docx (21 kb)
ESM 1 (DOCX 21 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). 2007. Accessed October 2018.
  2. 2.
    Schultz EM, Pineda N, Lonhart J, Davies SM, McDonald KM. A systematic review of the care coordination measurement landscape. BMC Health Serv Res 2013;13(1):443.CrossRefGoogle Scholar
  3. 3.
    Pollack CE, Weissman GE, Lemke KW, Hussey PS, Weiner JP. Patient Sharing Among Physicians and Costs of Care: A Network Analytic Approach to Care Coordination Using Claims Data. J Gen Intern Med 2012;28(3):459-465.CrossRefGoogle Scholar
  4. 4.
    Mandl KD, Olson KL, Mines D, Liu C, Tian F. Provider collaboration: cohesion, constellations, and shared patients. J Gen Intern Med 2014;29(11):1499-1505.CrossRefGoogle Scholar
  5. 5.
    Uddin S, Hamra J, Hossain L. Mapping and modeling of physician collaboration network. Stat Med 2013;32(20):3539-3551.CrossRefGoogle Scholar
  6. 6.
    Bynum JPW, Ross JS. A measure of care coordination? J Gen Intern Med 2013;28(3):336-338.CrossRefGoogle Scholar
  7. 7.
    Moen EL, Kapadia NS, O’Malley AJ, Onega T. Evaluating breast cancer care coordination at a rural National Cancer Institute Comprehensive Cancer Center using network analysis and geospatial methods. Cancer Epidemiol Biomark Prev 2019; 28(3):455-61.CrossRefGoogle Scholar
  8. 8.
    Moen EL, Austin AM, Bynum JP, Skinner JS, O’Malley AJ. An analysis of patient-sharing physician networks and implantable cardioverter defibrillator therapy. Health Serv Outcome Res Methodol 2016;16(3):132-153.CrossRefGoogle Scholar
  9. 9.
    Moen EL, Bynum JP, Austin AM, Skinner JS, Chakraborti G, O’Malley AJ. Assessing Variation in Implantable Cardioverter Defibrillator Therapy Guideline Adherence With Physician and Hospital Patient-sharing Networks. Med Care 2018; 56(4):350-7.PubMedPubMedCentralGoogle Scholar
  10. 10.
    Pollack CE, Weissman G, Bekelman J, Liao K, Armstrong K. Physician Social Networks and Variation in Prostate Cancer Treatment in Three Cities. Health Serv Res 2011;47(1pt2):380-403.CrossRefGoogle Scholar
  11. 11.
    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 2014;8(2):156-166.CrossRefGoogle Scholar
  12. 12.
    Landon BE, Keating NL, Barnett ML, et al. Variation in patient-sharing networks of physicians across the United States. - PubMed - NCBI. JAMA. 2012;308(3):265-273.CrossRefGoogle Scholar
  13. 13.
    Barnett ML, Christakis NA, O’Malley AJ, Onnela J-P, Keating NL, Landon BE. Physician Patient-sharing Networks and the Cost and Intensity of Care in US Hospitals. Med Care 2012;50:1-9.CrossRefGoogle Scholar
  14. 14.
    Uddin S, Hossain L, Kelaher M. Effect of physician collaboration network on hospitalization cost and readmission rate. Eur J Pub Health 2012;22(5):629-633.CrossRefGoogle Scholar
  15. 15.
    Ong M-S, Olson KL, Chadwick L, Liu C, Mandl KD. The Impact of Provider Networks on the Co-Prescriptions of Interacting Drugs: A Claims-Based Analysis. Drug Saf 2016:1-10.Google Scholar
  16. 16.
    Ong M-S, Olson KL, Cami A, et al. Provider Patient-Sharing Networks and Multiple-Provider Prescribing of Benzodiazepines. J Gen Intern Med 2015;31(2):164-171.CrossRefGoogle Scholar
  17. 17.
    Hollingsworth JM, Funk RJ, Garrison SA, et al. Association Between Physician Teamwork and Health System Outcomes After Coronary Artery Bypass Grafting. Circ Cardiovasc Qual Outcomes 2016;9(6):641-648.CrossRefGoogle Scholar
  18. 18.
    Barnett ML, Landon BE, O’Malley AJ, Keating NL, Christakis NA. Mapping Physician Networks with Self-Reported and Administrative Data. Health Serv Res 2011;46(5):1592-1609.CrossRefGoogle Scholar
  19. 19.
    Csárdi G, Nepusz T. The igraph software package for complex network research. Interjournal. Complex Systems:1695.Google Scholar
  20. 20.
    R Development Core Team. R: A language and environment for statistical computing.
  21. 21.
    Kamada T, Kawai S. An algorithm for drawing general undirected graphs. Inf Process Lett 1989;31(1):7-15.CrossRefGoogle Scholar
  22. 22.
    Landon, BE, Onnela JP, Keating NE, et al. Using Administrative Data to Identify Naturally Occurring Networks of Physicians. Med Care 2013;51(8): 715–21.CrossRefGoogle Scholar
  23. 23.
    Douthit N, Kiv S, Dwolatzky T, Biswas S. Exposing some important barriers to health care access in the rural USA. Public Health 2015;129(6):611-620.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2019

Authors and Affiliations

  • Erika L. Moen
    • 1
    • 2
  • Julie P. W. Bynum
    • 2
    • 3
    • 4
  1. 1.Department of Biomedical Data ScienceGeisel School of Medicine at DartmouthLebanonUSA
  2. 2.The Dartmouth Institute for Health Policy and Clinical PracticeHanoverUSA
  3. 3.Department of Internal MedicineUniversity of Michigan Medical SchoolAnn ArborUSA
  4. 4.Institute for Heathcare Policy and InnovationUniversity of MichiganAnn ArborUSA

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