Customizable Rule-Based System for Enhanced Clinical Decision-Making in Dementia Risk Reduction

Area of Science:

• Digital health interventions
• Clinical decision support systems
• Dementia risk reduction

Background:

• Clinical decision-making in multidomain interventions for dementia risk reduction requires personalized approaches.
• Existing Clinical Trial Management Systems (CTMS) may lack the flexibility for dynamic, patient-specific rule creation.
• Tailored digital tools are increasingly important for managing complex health conditions like dementia.

Purpose of the Study:

• To develop and integrate a customizable rule-based engine into the LETHE CTMS.
• To enhance clinical decision-making for dementia risk reduction interventions.
• To enable health professionals to create and personalize rules based on patient data and clinical knowledge.

Main Methods:

• Development of a customizable rule-based engine using a JSON-based system.
• Integration of the rule engine into the LETHE Clinical Trial Management System (CTMS).
• Collaborative development approach involving health professionals.
• Application within the LETHE project, a randomized control trial with 156 participants.

Main Results:

• Successful development and integration of a flexible, rule-based engine.
• Demonstrated potential for personalized interventions in dementia risk reduction.
• Facilitated precise rule creation and implementation by health professionals.
• The system was applied in a multi-center randomized control trial.

Conclusions:

• The customizable rule-based engine significantly enhances clinical decision-making for dementia risk reduction.
• Tailored digital interventions integrated into CTMS show promise for improving clinical outcomes.
• The system's flexibility supports personalized patient care and evolving clinical knowledge.
• Future work will focus on expanding usability and automating patient-specific tasks.