Go with the Flow? A Large-Scale Analysis of Health Care Delivery Networks in the United States Using Hodge Theory
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Abstract (arXiv)
Health care delivery is a collaborative process, requiring close coordination among networks of providers with specialized expertise. Yet in the United States, care is often spread across multiple disconnected providers (e.g., primary care physicians, specialists), leading to fragmented care delivery networks, and contributing to higher costs and lower quality. While this problem is well known, there are relatively few quantitative tools available for characterizing care delivery networks at scale, thereby inhibiting deeper understanding of care fragmentation and efforts to address it. In this, study, we conduct a large-scale analysis of care delivery networks across the United States using the discrete Hodge decomposition, an emerging method of topological data analysis. Using this technique, we decompose networks of patient flows among physicians into three orthogonal subspaces: gradient (acyclic flow), harmonic (global cyclic flow), and curl (local cyclic flow). We document substantial variation in the relative importance of each subspace, suggesting that there may be systematic differences in the organization of care delivery networks across health care markets. Moreover, we find that the relative importance of each subspace is predictive of local care cost and quality, with outcomes tending to be better with greater curl flow and worse with greater harmonic flow.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Oct 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.
5 papers.
- 2022Simplicial Convolutional Filterscited by 18
- 2023Topological Point Cloud Clusteringcited by 5
- 2024Beyond symmetrization: effective adjacency matrices and renormalization for (un)singed directed graphscited by 0
- 2024Characterizing Physician Referral Networks with Ricci Curvaturecited by 0
- 2026Topological Signal Processing: An Application-Oriented Tutorialcited by 0
- 2026Topological Signal Processing: An Application-Oriented Tutorial18 May 2026
- 2024Characterizing Physician Referral Networks with Ricci Curvature27 Aug 2024
- 2024Beyond symmetrization: effective adjacency matrices and renormalization for (un)singed directed graphs3 Jun 2024
- 2023Topological Point Cloud Clustering29 Mar 2023
- 2022Simplicial Convolutional Filters27 Jan 2022