Graph Network Techniques to Model and Analyze Emergency Department Patient Flow

Iris Reychav, Roger McHaney, Sunil Babbar, Krishanthi Weragalaarachchi, Nadeem Azaizah, Alon Nevet

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 demon-strated 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 bet-ter understand overall patient satisfaction during their journey through the emergency department.

Original languageEnglish
Article number1526
JournalMathematics
Volume10
Issue number9
DOIs
StatePublished - 1 May 2022

Keywords

  • emergency department
  • graph analytics
  • graph database
  • time-varying graph

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