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Ontology for Overcrowding Management in Emergency Department.

Khouloud Fakhfakh Maala1,2, Sarah Ben-Othman1, Laetitia Jourdan1

  • 1Centre de Recherche en Informatique Signal et Automatique de Lille, CRIStAL, UMR 9189, Central Lille, F-59000 Lille, France.

Studies in Health Technology and Informatics
|June 8, 2022
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Summary
This summary is machine-generated.

Emergency department (ED) overcrowding is a global issue. This study introduces a new domain ontology (EDOMO) and overcrowding estimation score (OES) to detect, manage, and resolve ED overcrowding effectively.

Keywords:
Domain OntologyEmergency DepartmentsOvercrowding management

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Ontology Engineering

Background:

  • Emergency department (ED) overcrowding is a persistent global challenge.
  • Existing scoring systems aid in problem detection but lack integrated management solutions.
  • Effective resource management and performance indicator ranking are crucial for addressing overcrowding.

Purpose of the Study:

  • To develop a novel domain ontology (EDOMO) for detecting and managing ED overcrowding.
  • To introduce a new overcrowding estimation score (OES) for critical situation identification and severity assessment.
  • To create a system capable of reasoning, ranking ED resources, and evaluating performance indicators based on environmental factors.

Main Methods:

  • Development of the EDOMO, a domain ontology for ED overcrowding.
  • Integration of a new overcrowding estimation score (OES) within the ontology.
  • Utilizing a four-year real-world database from Lille University Hospital Center (LUHC).
  • Implementation of SWRL rules for semantic reasoning on domain knowledge.

Main Results:

  • The EDOMO successfully models comprehensive domain knowledge related to ED overcrowding.
  • The system demonstrates the capability to detect critical situations and specify overcrowding levels.
  • The proposed ontology facilitates the proposal of targeted solutions for overcrowding management.
  • Evaluation confirms the completeness and functional enhancement potential of the EDOMO for ED operations.

Conclusions:

  • The EDOMO, coupled with the OES, offers a robust framework for detecting and managing ED overcrowding.
  • This ontology enables advanced semantic reasoning for improved ED resource allocation and performance.
  • The developed system has the potential to significantly enhance the overall functioning and efficiency of emergency departments.