A Collaborative Decision Support Tool for Managing Chronic Conditions.

Area of Science:

• Digital Health
• Health Informatics
• Artificial Intelligence in Healthcare

Background:

• Chronic conditions like stroke require continuous management and patient involvement.
• Effective collaboration between patients, carers, and healthcare professionals is crucial for managing chronic conditions.
• Existing tools may not adequately integrate diverse data sources or support complex decision-making processes.

Purpose of the Study:

• To assess the feasibility of a decision support tool for stroke patient self-management.
• To develop a system integrating wellness sensor data, electronic health records, and clinical guidelines.
• To enhance patient-provider collaboration through novel computational and dialogue methods.

Main Methods:

• Integration of data from commercial wellness sensors, electronic health records, and clinical guidelines.
• Utilization of computational argumentation for data tracking, conflict resolution, and recommendations.
• Development of an argumentation-based dialogue system for patient interaction.
• Implementation as a smart device application with personalized dashboards and a conversational chatbot.

Main Results:

• The proposed system demonstrates feasibility in supporting chronic condition management.
• Integration of diverse data sources enhances the comprehensiveness of health information.
• Computational argumentation effectively resolves data conflicts and generates recommendations.
• Argumentation-based dialogue facilitates patient engagement and understanding.

Conclusions:

• The developed decision support tool shows promise for improving self-management in stroke patients.
• The novel integration of data and argumentation techniques offers a robust approach to personalized healthcare.
• Smart device applications with conversational agents can empower patients in managing their health conditions.