Ontology for Overcrowding Management in Emergency Department

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.