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Graph Network Techniques to Model and Analyze Emergency Department Patient Flow

Author

Listed:
  • Iris Reychav

    (Industrial Engineering & Management, Ariel University, Ariel 40700, Israel)

  • Roger McHaney

    (Management Information Systems, Kansas State University, Manhattan, KS 66506, USA)

  • Sunil Babbar

    (Information Technology and Operations Management, Florida Atlantic University, Boca Raton, FL 33431, USA)

  • Krishanthi Weragalaarachchi

    (Data Analytics, Kansas State University, Manhattan, KS 66506, USA)

  • Nadeem Azaizah

    (Industrial Engineering & Management, Ariel University, Ariel 40700, Israel)

  • Alon Nevet

    (Beilinson Hospital, Rabin Medical Center, Petah Tikva 4941492, Israel)

Abstract

This article moves beyond analysis methods related to a traditional relational database or network analysis and offers a novel graph network technique to yield insights from a hospital’s emergency department work model. The modeled data were saved in a Neo4j graphing database as a time-varying graph (TVG), and related metrics, including degree centrality and shortest paths, were calculated and used to obtain time-related insights from the overall system. This study demonstrated the value of using a TVG method to model patient flows during emergency department stays. It illustrated dynamic relationships among hospital and consulting units that could not be shown with traditional analyses. The TVG approach augments traditional network analysis with temporal-related outcomes including time-related patient flows, temporal congestion points details, and periodic resource constraints. The TVG approach is crucial in health analytics to understand both general factors and unique influences that define relationships between time-influenced events. The resulting insights are useful to administrators for making decisions related to resource allocation and offer promise for understanding impacts of physicians and nurses engaged in specific patient emergency department experiences. We also analyzed customer ratings and reviews to better understand overall patient satisfaction during their journey through the emergency department.

Suggested Citation

  • Iris Reychav & Roger McHaney & Sunil Babbar & Krishanthi Weragalaarachchi & Nadeem Azaizah & Alon Nevet, 2022. "Graph Network Techniques to Model and Analyze Emergency Department Patient Flow," Mathematics, MDPI, vol. 10(9), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1526-:d:807621
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    References listed on IDEAS

    as
    1. Xiaogang Qi & Lifang Liu & Guoyong Cai & Mande Xie, 2015. "A Topology Evolution Model Based on Revised PageRank Algorithm and Node Importance for Wireless Sensor Networks," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-7, September.
    2. Daniel M Bean & Clive Stringer & Neeraj Beeknoo & James Teo & Richard J B Dobson, 2017. "Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-16, October.
    Full references (including those not matched with items on IDEAS)

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