Digital Twin Ecosystem for Oncology Clinical Operations

TL;DR

This paper presents a novel digital twin framework for oncology clinical operations, integrating multiple specialized twins and aligning with NCCN guidelines to improve workflow efficiency and personalized patient care.

Contribution

It introduces a new digital twin ecosystem specifically designed for oncology, combining various digital twins and a dynamic knowledge base for clinical decision support.

Findings

Enhanced workflow efficiency in oncology clinics

Personalized care recommendations based on digital twins

Dynamic Cancer Care Path aligned with NCCN guidelines

Abstract

Artificial Intelligence (AI) and Large Language Models (LLMs) hold significant promise in revolutionizing healthcare, especially in clinical applications. Simultaneously, Digital Twin technology, which models and simulates complex systems, has gained traction in enhancing patient care. However, despite the advances in experimental clinical settings, the potential of AI and digital twins to streamline clinical operations remains largely untapped. This paper introduces a novel digital twin framework specifically designed to enhance oncology clinical operations. We propose the integration of multiple specialized digital twins, such as the Medical Necessity Twin, Care Navigator Twin, and Clinical History Twin, to enhance workflow efficiency and personalize care for each patient based on their unique data. Furthermore, by synthesizing multiple data sources and aligning them with the National Comprehensive Cancer Network (NCCN) guidelines, we create a dynamic Cancer Care Path, a continuously evolving knowledge base that enables these digital twins to provide precise, tailored clinical recommendations.