Distributed guidelines (DiG): a software framework for extending automated health decision support to the general...

Area of Science:

• Health Informatics
• Consumer Health Informatics
• Knowledge Engineering

Background:

• Personal health record (PHR) systems generate vast amounts of raw health data.
• A significant challenge is the semantic gap between raw data and meaningful health information for the general population.
• Lack of accessible, codifiable expert knowledge hinders consumer empowerment in healthcare.

Purpose of the Study:

• To present a framework and methodology for creating PHR systems.
• To bridge the semantic gap between individuals and their health data.
• To empower consumers through better understanding and utilization of their health information.

Main Methods:

• Development of a novel approach to encode expert knowledge into machine-interpretable components called "consumer guidelines."
• Encoding guidelines for easy sharing, extension, and modification.
• Utilizing a processing engine (Health-Guru) to execute guidelines against PHR data for personalized risk assessment.

Main Results:

• Successfully encoded expert knowledge into reusable "consumer guidelines."
• Demonstrated the capability of guidelines to be distributed and executed.
• Enabled personalized health risk assessments based on individual PHR data.

Conclusions:

• The proposed framework and methodology effectively transform raw health data into meaningful information.
• "Consumer guidelines" provide a scalable solution for encoding and utilizing expert knowledge in PHR systems.
• This approach facilitates consumer empowerment and potentially improves healthcare outcomes through personalized risk assessment.