Participatory Digital Twins for Chronic Care: From Predictive Models to Shared Sensemaking

Area of Science:

• Artificial Intelligence in Healthcare
• Computational Modeling
• Digital Health

Background:

• Health digital twins are advanced computational models for chronic care management.
• Current digital twins are expert-oriented, limiting patient understanding and engagement.
• Chronic care requires patient participation in daily decisions and sustained engagement.

Purpose of the Study:

• To advocate for reframing digital twins as participatory systems for shared sensemaking.
• To propose a conceptual framework for participatory digital twins.
• To explore the role of generative AI in patient-clinician interaction.

Main Methods:

• Conceptual framework development for participatory digital twins.
• Integration of generative AI as a translation layer.
• Outlining design principles for participatory digital twins.

Main Results:

• Participatory digital twins can bridge computational models and lived experiences.
• Generative AI enables plain-language dialogue and "what-if" reasoning.
• Key design principles include visible uncertainty and negotiated care.

Conclusions:

• Participatory digital twins promote equitable, human-centered chronic care.
• Shifting focus to understanding, interaction, and trust enhances AI-enabled healthcare.
• Addressing privacy, consent, and access is crucial for equitable implementation.