Graph Network Techniques to Model and Analyze Emergency Department Patient Flow

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

Mathematics, 2022, vol. 10, issue 9, 1-21

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.

Keywords: emergency department; graph database; graph analytics; time-varying graph (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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