Integrating an openEHR-based personalized virtual model for the ageing population within HBase

Area of Science:

• Gerontology
• Health Informatics
• Data Management

Background:

• Frailty is a prevalent clinical syndrome in the aging population, increasing risks for falls, hospitalization, disability, and mortality.
• Effective monitoring and prevention strategies are crucial for health and social care planning in aging populations.
• Existing electronic health record (EHR) systems lack sufficient orientation towards the specific needs of the aging demographic.

Purpose of the Study:

• To leverage the openEHR framework for semantic interoperability in representing frailty in the aging population.
• To establish a methodology for adopting or developing openEHR archetypes for frailty.
• To propose a framework for mapping openEHR archetypes to a NoSQL database (HBase) for integrated data storage.

Main Methods:

• Utilized the openEHR framework for semantic interoperability in frailty data representation.
• Developed and adopted openEHR archetypes tailored for describing frailty in older adults.
• Created a one-to-one mapping framework between openEHR archetypes and HBase for data integration.

Main Results:

• Defined 22 clinically meaningful parameters for frailty in older adults.
• Successfully implemented openEHR methodology, using 22 archetypes, modifying/reusing 2, and developing 28 new ones.
• Established two HBase tables for storing frailty data through the mapping procedure.

Conclusions:

• Designed and integrated an openEHR-based virtual patient model into an HBase storage system.
• The developed framework enhances data integration and leverages the strengths of openEHR and HBase.
• This system provides a foundation for future development of decision support systems using openEHR's Guideline Definition Language.