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Digital Twin Ecosystem for Oncology Clinical Operations

Himanshu Pandey, Akhil Amod, Shivang, Kshitij Jaggi, Ruchi Garg, Abheet Jain, Vinayak Tantia

TL;DR

A novel digital twin framework specifically designed to enhance oncology clinical operations is introduced, which combines 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.

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.

Digital Twin Ecosystem for Oncology Clinical Operations

TL;DR

A novel digital twin framework specifically designed to enhance oncology clinical operations is introduced, which combines 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.

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.
Paper Structure (14 sections, 5 figures)

This paper contains 14 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Cancer Care Path
  5. Digital Twin Framework
  6. Twin Applications for Oncology
  7. Medical Necessity Twin
  8. Clinical History Twin
  9. Care Navigator Twin
  10. Case Study: Ovarian Cancer Diagnosis
  11. Cancer Care Graph
  12. Diagnosis Journey
  13. Conclusion
  14. Future Work

Figures (5)

  • Figure 1: An illustration of the Digital Twin Framework highlighting its various capabilities and their integration with the knowledge base.
  • Figure 2: Illustrating the various agents that comprise the Medical Necessity Twin, highlighting how they interact and address each item on the clinical guideline checklist.
  • Figure 3: Different twins in Digital Twin ecosystem leveraging knowledge base and interacting with other twins for optimal efficiency
  • Figure 4: A visual representation of Cancer Care Graph built for Ovarian Cancer Diagnosis
  • Figure 5: An example checklist from Anthem listing medical necessity conditions for CA-125 testing