This thesis explores how data-enabled design can be used to create personalized care pathways for chronic patients while reducing the burden on healthcare providers. The research focuses on three diseases colorectal cancer, pulmonary fibrosis, and sarcoidosis, and investigates how unstructured patient narratives from online peer-support communities can be transformed into clinically relevant behavioral insights.

Over 13,000 anonymized patient posts were collected, pre-processed, and analyzed using a GPT-4o-assisted semantic clustering and verification pipeline. A multi-step methodology combined natural language processing, chain-of-verification prompting, behavioral science frameworks, and manual review to generate detailed patient behavior profiles. These profiles captured goals, motivations, and challenges, and were validated through co-creation sessions with clinicians from Erasmus MC. The sessions refined the outputs and mapped patient behavior across different stages of the care journey. These behaviors were further thematically clustered into eight cross-condition patient archetypes.

These archetypes informed the design of two complementary interventions. The first is a patient-facing mobile application that supports goal setting based on quality of life, mood and symptom journaling, and symbolic peer support. The second is a clinician-facing dashboard, integrated into the HIX electronic health record, that visualizes behavioral trends alongside clinical data and generates AI-assisted care modules for review and assignment.

The outcomes include a reproducible data-to-design pipeline, validated behavioral archetypes, high-fidelity prototypes of patient and clinician tools, and a Vision 2040 roadmap for phased national implementation. This work demonstrates that patient-authored narratives contain actionable behavioral signals that, when combined with clinical expertise, can guide adaptive, behavior-aware care planning. The thesis contributes a practical framework for embedding these insights into decision-support systems that strengthen personalization, preserve clinician autonomy, and enhance patient engagement at scale.