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Characterizing Physician Referral Networks with Ricci Curvature.

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This study uses network curvature to identify healthcare access barriers and efficacy indicators in the US. Novel methods reveal regional disparities in care delivery and patient outcomes.

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Area of Science:

  • Network Science
  • Healthcare Analytics
  • Computational Topology

Background:

  • Identifying systemic barriers to quality healthcare access in the US is challenging.
  • Key indicators of healthcare efficacy and regional disparities in care delivery require further understanding.

Purpose of the Study:

  • To introduce a novel application of network curvature to Physician Referral Networks.
  • To identify indicators of healthcare efficacy and regional disparities.
  • To present an open-source tool for analyzing healthcare network structures.

Main Methods:

  • Applied Forman-Ricci and Ollivier-Ricci curvature measures to Physician Referral Networks.
  • Utilized network topology to characterize network structure.
  • Integrated network features with local census data and healthcare effectiveness metrics.

Main Results:

  • Forman-Ricci and Ollivier-Ricci curvature measures show promise as indicators of healthcare efficacy.
  • Network curvature captures significant regional demographic features and variations in care delivery.
  • The study identified correlations between network structure, demographic data, and patient outcomes.

Conclusions:

  • Network curvature is a valuable tool for analyzing Physician Referral Networks and understanding healthcare disparities.
  • The developed open-source tool, apparent, facilitates the examination of complex relationships within healthcare systems.
  • Findings contribute to improving healthcare access and quality by identifying regional variations.