Combining multiple sources of relationships in a network to advance understanding of physicians' beliefs regarding peer-effects.

Authors

Yifan Zhao, Carly A Bobak, Megan A Murphy, Olivia Sacks, Lili Liu, Natasha Ray, Amber E Barnato, A James O'Malley

Published in

Health services & outcomes research methodology. Mar 20, 2025. Epub Mar 20, 2025.

Abstract

Patient-sharing physician networks are increasingly recognized as valuable tools for examining physician relationships in healthcare research. However, very few studies have examined the reliability of such networks and summary measures derived from them in relation to directly measured physician relationships. In this paper, we evaluate the level of congruence between a survey-based network derived from survey responses to specific name-generator questions and a patient-sharing network derived from claims data. We also examine the association of summary measures derived from either network with physicians' beliefs about peer influence in medical practice. Statistical models with hierarchical and multiple-membership structures were used to estimate the strength of the associations. We found that a survey measure indicating whether a physician was nominated by others was statistically significantly associated with their survey reported beliefs about peer influence. We also observed notable associations between the physicians' structural importance in the network reflected in their eigenvector and betweenness centrality in the patient-sharing network and their beliefs about peer influence. This study of multi-source network relational information advances our understanding of physician survey responses and yields more precise predictions of physician beliefs toward peer-influence than either data source alone. Overall, we found that patient-sharing networks are an important alternative to directly measured survey-based name-generator questions in health services research and other applications. While patient-sharing networks recover some of the information in directly measured peer physician nominations, they also contain distinct information that is helpful for interpreting healthcare insights.

PMID:
40881966
Bibliographic data and abstract were imported from PubMed on 30 Aug 2025.

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