Knowledge-Based Decision Support in Healthcare via Near Field Communication

Area of Science:

• Medical Informatics
• Healthcare Technology
• Internet of Things (IoT)

Background:

• Automatic identification technologies are recognized for healthcare benefits.
• Current systems often lack depth for complex medical tasks.
• Need for advanced systems to support knowledge-intensive healthcare applications.

Purpose of the Study:

• To present an innovative Decision Support System (DSS) leveraging Near Field Communication (NFC).
• To semantically enhance NFC for storing patient and medication data.
• To enable intelligent decision support for caregivers.

Main Methods:

• Developed a DSS based on a semantically enhanced Near Field Communication (NFC) standard.
• Stored annotated medication descriptions and patient case history in NFC transponders.
• Integrated a lightweight reasoning engine for incompatibility detection and therapy suggestion.
• Validated the framework with a rheumatology case study and performance tests.

Main Results:

• Demonstrated a working prototype of the DSS.
• Preliminary performance tests support the practical feasibility of the approach.
• The system is independent of back-end infrastructures.
• Automated reasoning on NFC-extracted knowledge enables effective decision support.

Conclusions:

• The proposed DSS framework offers a feasible solution for enhanced healthcare decision-making.
• NFC-based automated reasoning supports critical medical tasks in diverse settings.
• Applicable in hospitals, first aid, ambulance transport, rehabilitation, and home care.