Leveraging Large Language Models to Enhance Regional Genomic Medicine eConsults

/Abstract Primary care physicians (PCPs) have limited genetics knowledge, including knowing if and when to order genetic/genomic testing and which tests to order. While genetics and other specialty societies have created robust clinical practice guidelines to define the appropriate genetic evaluation for defined indications, they are typically complex and PCPs lack sufficient time or expertise to assess the need for genetics consultation or testing for a specific patient. Further, given the ever-increasing and evolving medical knowledge base, PCPs lack time to stay current with guideline updates. As a result, PCPs may make clinical decisions that are not fully informed and result in substandard care, with potentially suboptimal clinical outcomes. To address these issues, we propose to leverage large language models (LLM) to create an AI assistant tool to help PCPs and clinical genetics professionals enhance guideline-concordant genetic eConsultations. Our approach is superior to creating clinical decision support rules, which have not been robust and must be manually updated whenever guidelines change. Aim 1 focuses on developing methods to combine large language models with clinical practice genetics guidelines for quality, semi-automated eConsult recommendations. In doing so, we will develop extensible methods for digitizing guidelines so that LLMs can access them to determine if a given patient meets criteria for ordering a specific test, whether a genetics consultation is warranted and other tasks. Our second aim is to demonstrate the usability of a prototype among PCPs. Using user-centered design principles, we will build a Web-based Chatbot prototype of our innovation. We will then recruit PCPs and perform usability reviews, including administering a usability questionnaire. We will also obtain qualitative feedback from PCPs. If we are successful, we will demonstrate the feasibility of our tool to enable PCPs to make informed clinical decisions at the point of care, including ordering genetic testing, which will enhance eConsultations with clinical genetics professionals, including reducing the time a patient has to wait for consultation and treatment, and ultimately improving clinical outcomes. Project Number: 1R43HG014149-01 | Fiscal Year: 2025 | NIH Institute/Center: National Human Genome Research Institute (NHGRI) | Principal Investigator: Howard Isenstein | Institution: CARE PROGRESS, LLC, BETHESDA, MD | Award Amount: $400,618 | Activity Code: R43 | Study Section: Special Emphasis Panel[ZHG1 HGR-W (J1)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11096353