Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era

TL;DR

This paper introduces an integrative, patient-centered data science framework that combines diverse data types and advanced AI techniques to improve clinical outcome prediction and healthcare personalization in the digital health era.

Contribution

It presents a novel multidimensional model integrating clinical, patient-reported, social, and omic data with AI, advancing personalized healthcare and continuous learning systems.

Findings

Framework effectively combines multiple data sources.

AI models improve outcome prediction accuracy.

System supports continuous healthcare improvement.

Abstract

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.