Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era
It addresses limitations in AI-driven healthcare models for improving patient care outcomes in the digital health era, though it appears incremental as it builds on existing data integration approaches.
This study proposed an integrative framework for patient-centered data science that combines clinical, patient-reported, social, and multi-omic data to create digital patient representations, aiming to optimize multiple outcomes and improve translation of digital health innovations into clinical benefits.
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.