Comparing Shared Patient Networks Across Payers

• Published in: Journal of general internal medicine : JGIM Vol. 34; no. 10; pp. 2014 - 2020
• Main Authors: Trogdon, Justin G., Weir, W. H., Shai, S., Mucha, P. J., Kuo, T. M., Meyer, A. M., Stitzenberg, K. B.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2019; Springer Nature B.V
• Subjects: Cancer; Clustering; Cohort Studies; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - therapy; Community Networks - statistics & numerical data; Continuity of Patient Care - statistics & numerical data; Eigenvectors; Government programs; Health care; Humans; Insurance; Insurance Claim Review - statistics & numerical data; Internal Medicine; Medical personnel; Medicare; Medicare - statistics & numerical data; Medicine; Medicine & Public Health; Network analysis; North Carolina; Patients; Physicians; Practice Patterns, Physicians' - statistics & numerical data; Radiation; Registries; Social networks; Social organization; United States; North Carolina; United States; network analysis; colorectal cancer
• ISSN: 0884-8734; 1525-1497
• DOI: 10.1007/s11606-019-04978-9

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.