Digital Twin Ecosystem for Oncology Clinical Operations

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.

Advancing Healthcare Automation: Multi-Agent Systems for Medical Necessity Justification

This paper explores the application of Swarm-Structured Multi-Agent Systems (MAS) to establish medical necessity, a process that involves a systematic review of patient-specific medical structured and unstructured data against clinical guidelines. We addressed this complex task by decomposing it into smaller, more manageable sub-tasks. Each sub-task is handled by a specialized AI agent. We conduct a systematic study of the impact of various prompting strategies on these agents and benchmark different Large Language Models (LLMs) to determine their accuracy in completing these tasks. Additionally, we investigate how these agents can provide explainability, thereby enhancing trust and transparency within the system.