Novel Development of LLM Driven mCODE Data Model for Improved Clinical Trial Matching to Enable Standardization and Interoperability in Oncology Research
This addresses the problem of manual, time-consuming clinical trial matching in oncology for healthcare systems, though it appears incremental as it builds on existing FHIR and mCODE standards with LLM enhancements.
The paper tackles the problem of inefficient data standardization and interoperability in cancer care by developing an LLM-driven mCODE data model framework that transforms unstructured patient data into standardized formats. Results show accuracy rates of over 92% for data standardization and up to 90% for medical code mapping, outperforming existing LLMs like GPT-4 and Claude 3.5 which average 77%.
Each year, the lack of efficient data standardization and interoperability in cancer care contributes to the severe lack of timely and effective diagnosis, while constantly adding to the burden of cost, with cancer costs nationally reaching over $208 billion in 2023 alone. Traditional methods regarding clinical trial enrollment and clinical care in oncology are often manual, time-consuming, and lack a data-driven approach. This paper presents a novel framework to streamline standardization, interoperability, and exchange of cancer domains and enhance the integration of oncology-based EHRs across disparate healthcare systems. This paper utilizes advanced LLMs and Computer Engineering to streamline cancer clinical trials and discovery. By utilizing FHIR's resource-based approach and LLM-generated mCODE profiles, we ensure timely, accurate, and efficient sharing of patient information across disparate healthcare systems. Our methodology involves transforming unstructured patient treatment data, PDFs, free-text information, and progress notes into enriched mCODE profiles, facilitating seamless integration with our novel AI and ML-based clinical trial matching engine. The results of this study show a significant improvement in data standardization, with accuracy rates of our trained LLM peaking at over 92% with datasets consisting of thousands of patient data. Additionally, our LLM demonstrated an accuracy rate of 87% for SNOMED-CT, 90% for LOINC, and 84% for RxNorm codes. This trumps the current status quo, with LLMs such as GPT-4 and Claude's 3.5 peaking at an average of 77%. This paper successfully underscores the potential of our standardization and interoperability framework, paving the way for more efficient and personalized cancer treatment.