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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.17650 |
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| _version_ | 1866910620881780736 |
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| author | Pandey, Himanshu Amod, Akhil Shivang Jaggi, Kshitij Garg, Ruchi Jain, Abheet Tantia, Vinayak |
| author_facet | Pandey, Himanshu Amod, Akhil Shivang Jaggi, Kshitij Garg, Ruchi Jain, Abheet Tantia, Vinayak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17650 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Digital Twin Ecosystem for Oncology Clinical Operations Pandey, Himanshu Amod, Akhil Shivang Jaggi, Kshitij Garg, Ruchi Jain, Abheet Tantia, Vinayak Artificial Intelligence Computation and Language 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. |
| title | Digital Twin Ecosystem for Oncology Clinical Operations |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2409.17650 |