Enhancing Clinical Decision Support for Precision Medicine: A Data-Driven Approach

Abstract

Precision medicine has emerged as a transformative approach aimed at tailoring treatment to individual patients, moving away from the traditional one-size-fits-all model. However, Clinical decision support systems encounter challenges, particularly in terms of data aspects. In response, our study proposes a data-driven framework rooted in Simon’s decision-making model. This framework leverages advanced technologies such as artificial intelligence and data analytics to enhance clinical decision-making in precision medicine. By addressing limitations and integrating AI and analytics, our study contributes to the advancement of optimal clinical decision-making practices in precision healthcare.