Data model for personalized patient health guidelines: an exploratory study.

Area of Science:

• Health Informatics
• Medical Information Systems
• Patient Education

Background:

• Practitioner guidelines offer comprehensive disease information but are often too complex for patients.
• Patients frequently seek guideline information to understand their health conditions.
• Bridging the gap between expert guidelines and patient comprehension is crucial for effective self-management.

Purpose of the Study:

• To design an information model for patient portals that links personal health information to relevant guideline content.
• To create a method for individualizing guideline information based on patient needs and preferences.
• To enhance patient access to understandable and actionable health information.

Main Methods:

• Developed a twelve-class concept information model.
• Integrated patient information needs and preferences into the model design.
• Manually reviewed and annotated patient medical records to validate the model's applicability.

Main Results:

• The information model successfully identified relevant guideline concepts within patient medical records.
• All twelve classes of concepts were present in at least one patient record.
• Seven concept classes were found in over half of the annotated patient records, demonstrating broad relevance.

Conclusions:

• The developed information model effectively links patient data to pertinent guideline information.
• This approach facilitates the delivery of individualized, relevant health information to patients through portals.
• The model supports personalized patient education by connecting health records to expert clinical guidelines.