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Wayland, J.D. ; Funk, R.J.* ; Rieck, B.

Characterizing physician referral networks with Ricci Curvature.

In: (Pediatric and Lifespan Data Science). Springer, 2025. 1-16 (Comm. Comp. Info. Sci. ; 2386 CCIS)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 1865-0929
e-ISSN 1865-0937
Konferenztitel Pediatric and Lifespan Data Science
Zeitschrift Communications in Computer and Information Science
Quellenangaben Band: 2386 CCIS, Heft: , Seiten: 1-16 Artikelnummer: , Supplement: ,
Verlag Springer
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)