Automated clinical pathway standardization using SNOMED CT- based semantic relatedness.

Area of Science:

• Health Informatics
• Medical Terminology Standardization
• Clinical Pathway Computerization

Background:

• Healthcare institutions face increasing patient loads and workloads, necessitating efficient and cost-effective care delivery.
• Clinical pathways offer a promising solution for reducing costs and optimizing resource use, but are often paper-based.
• Computerizing clinical pathways requires standardizing terminology to align with digital systems, a process currently manual and difficult to automate.

Purpose of the Study:

• To introduce automation into the clinical pathway standardization process.
• To develop a system that assists physicians in standardizing clinical pathway terminology using SNOMED CT.
• To enhance the efficiency and accuracy of integrating clinical pathways into health information systems.

Main Methods:

• Development of a semantic score-based algorithm for automated SNOMED CT term searching.
• Implementation of the algorithm into a software system with a graphical user interface for physician use.
• Validation of the system and algorithm using SNOMED CT ontology and case studies.

Main Results:

• The algorithm achieved a maximum search space reduction of 98.9% in single iterations and an overall average reduction of 71.3%.
• The system enables physicians to locate relevant SNOMED CT terms precisely and efficiently.
• Case studies demonstrated the effectiveness of human-guided automation in clinical pathway standardization.

Conclusions:

• Automating clinical pathway standardization is feasible and beneficial for healthcare informatics.
• The developed system significantly improves the speed and accuracy of terminology standardization.
• Human-guided automation represents a promising methodology for advancing clinical pathway computerization.