Creating personalised clinical pathways by semantic interoperability with electronic health records

Area of Science:

• Health Informatics
• Artificial Intelligence in Medicine
• Clinical Decision Support Systems

Background:

• Clinical pathways (CPs) are crucial for healthcare quality but face challenges in treatment personalization and dynamic adaptability.
• Current CPs often lack the flexibility to cater to individual patient needs, limiting their practical application.

Purpose of the Study:

• To enhance the practicality of clinical pathways (CPs) through semantic interoperability.
• To integrate knowledge-based CPs with semantic electronic health records (EHRs) for improved treatment personalization.

Main Methods:

• Utilized SPARQL to extract patient data from semantic EHRs.
• Integrated patient data into a CP ontology using OWL.
• Employed SWRL rules within the Jena framework for reasoning and customizing standardized CPs.

Main Results:

• Demonstrated CP customization for acute appendicitis using semantic interoperability.
• Generated personalized care plans by analyzing patient history from semantic EHRs.
• Successfully handled exceptions during CP execution, proving technical feasibility.

Conclusions:

• The study improves the personalized practicality of standardized clinical pathways.
• Establishes a foundation for developing independent clinical pathway systems.