  This study proposes a novel, integrative framework for patient-centered data
science in the digital health era. We developed a multidimensional model that
combines traditional clinical data with patient-reported outcomes, social
determinants of health, and multi-omic data to create comprehensive digital
patient representations. Our framework employs a multi-agent artificial
intelligence approach, utilizing various machine learning techniques including
large language models, to analyze complex, longitudinal datasets. The model
aims to optimize multiple patient outcomes simultaneously while addressing
biases and ensuring generalizability. We demonstrate how this framework can be
implemented to create a learning healthcare system that continuously refines
strategies for optimal patient care. This approach has the potential to
significantly improve the translation of digital health innovations into
real-world clinical benefits, addressing current limitations in AI-driven
healthcare models.
