Towards a standardized enterprise architecture: enhancing decision-making in oncology multidisciplinary team meetings

Abstract

This study proposes a novel enterprise architecture (EA) designed to enhance the efficiency and decision-making processes of multidisciplinary team meetings (MDTMs) in oncology by integrating advanced artificial intelligence (AI) technologies. The architecture addresses current inefficiencies in MDTMs, particularly the lack of real-time data integration and limited decision support, by providing a structured framework that improves interoperability and standardizes clinical workflows. Developed using the open group architecture framework (TOGAF) framework and the ArchiMate modelling language, this conceptual architecture lays the groundwork for future empirical research, offering a scalable solution that can be adapted to various healthcare settings. The AI component, centered on generative pretrained transformer (GPT) models, is designed to support oncologists by providing evidence-based treatment recommendations tailored to individual patient cases. Although the study focusses on the theoretical development of this architecture, it opens the door for subsequent empirical testing and validation, with the aim of ultimately improving patient outcomes and streamlined oncology care through enhanced decision support systems.