Information analytics for healthcare service discovery

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Ontology Engineering

Background:

• Current healthcare service provision models struggle with patient-centricity, particularly in referral processes.
• Referrals often lack a consensual decision-making process between General Practitioners (GPs) and patients.
• Existing systems do not adequately support personalized patient needs in service discovery.

Purpose of the Study:

• To present an ontology-enabled healthcare service provision model.
• To facilitate joint decision-making between patients and GPs for referral choices.
• To enhance patient-centricity in healthcare service discovery and provision.

Main Methods:

• Development of an ontology-enabled healthcare service provision model.
• Definition of three stakeholder profile types representing varied requirements.
• Implementation of healthcare service discovery processes: need articulation, matching, and best-fit service selection.

Main Results:

• The model enables patients and GPs to jointly decide on referral decisions.
• Personalized information and iterative processes are utilized for coherent analysis.
• The system accommodates evolving requirements over time.

Conclusions:

• Ontology-enabled systems can significantly improve patient-centric healthcare referrals.
• Collaborative decision-making models enhance the suitability of healthcare service provision.
• This approach offers a flexible and personalized framework for healthcare service discovery.