Characterizing Physician Referral Networks with Ricci Curvature

Jeremy Wayland, Russell J. Funk, Bastian Rieck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Identifying (a) systemic barriers to quality healthcare access and (b) key indicators of care efficacy in the United States remains a significant challenge. To improve our understanding of regional disparities in care delivery, we introduce a novel application of curvature, a geometrical-topological property of networks, to Physician Referral Networks. Our initial findings reveal that Forman-Ricci and Ollivier-Ricci curvature measures, which are known for their expressive power in characterizing network structure, offer promising indicators for detecting variations in healthcare efficacy while capturing a range of significant regional demographic features. We also present apparent, an open-source tool that leverages Ricci curvature and other network features to examine correlations between regional Physician Referral Networks structure, local census data, healthcare effectiveness, and patient outcomes.

Original languageEnglish (US)
Title of host publicationPediatric and Lifespan Data Science - First International Conference, IPLDSC 2024, Revised Selected Papers
EditorsLouis Ehwerhemuepha, Adam Kalawi, Terence Sanger, Mark Hoffman
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1-16
Number of pages16
ISBN (Print)9783031883453
DOIs
StatePublished - 2025
Event1st International Conference on Pediatric and Lifespan Data Science, IPLDSC 2024 - Anaheim, United States
Duration: May 23 2024May 24 2024

Publication series

NameCommunications in Computer and Information Science
Volume2386 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference1st International Conference on Pediatric and Lifespan Data Science, IPLDSC 2024
Country/TerritoryUnited States
CityAnaheim
Period5/23/245/24/24

Bibliographical note

Publisher Copyright:
© The Author(s) 2025.

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