Abstract
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 the dynamics of 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.
Original language | English (US) |
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Title of host publication | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
Editors | Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3812-3823 |
Number of pages | 12 |
ISBN (Electronic) | 9781665439022 |
DOIs | |
State | Published - 2021 |
Event | 2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States Duration: Dec 15 2021 → Dec 18 2021 |
Publication series
Name | Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Conference
Conference | 2021 IEEE International Conference on Big Data, Big Data 2021 |
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Country/Territory | United States |
City | Virtual, Online |
Period | 12/15/21 → 12/18/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- Hodge theory
- health care
- topological data analysis