Natural language processing-driven knowledge graphs for transformative public health intelligence and research...

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Clinical Data Analysis

Background:

• Urgent and emergency care produces vast clinical data, but valuable research information is often trapped in unstructured free-text notes.
• Current analysis methods fail to capture patient reasons for presentation, demand drivers, or unavailable care, disproportionately affecting patients with complex care needs.
• Existing data initiatives focus on record linkage or event prediction, not on understanding the underlying clinical reasoning for patient crises.

Purpose of the Study:

• To propose a novel three-layer knowledge graph architecture for urgent and emergency care.
• To leverage natural language processing and artificial intelligence to extract and structure critical clinical information.
• To make patterns of unmet need and care barriers visible for service planning and governance.

Main Methods:

• A three-layer knowledge graph architecture is proposed:
• Layer 1: Connects individual patient contacts into comprehensive trajectories across services and time.
• Layer 2: Recovers clinical detail lost in current reductive coding practices.
• Layer 3: Captures the reasoning behind patient presentations, including care barriers and unmet needs.

Main Results:

• Advances in natural language processing and artificial intelligence make the proposed architecture feasible.
• Component technologies show strong evidence, though the integrated architecture requires validation.
• Implementation requires defined validation, governance, and infrastructure.

Conclusions:

• The proposed knowledge graph architecture offers a pathway to transform data volume into actionable knowledge.
• It provides a method to identify and understand patterns of unmet healthcare needs.
• The necessary technologies exist, making the visibility of this crucial information a matter of implementation design.