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Patient data records can be convoluted and sometimes incomplete, meaning doctors don’t always have all the information they need readily available. Added to this is the fact that medical professionals can’t possibly keep up with the barrage of case studies, research papers, trials and other cutting-edge developments coming out of the industry.
New York City-based NYU Langone Health has come up with a novel approach to tackle these challenges for the next generation of doctors.
The academic medical center — which comprises NYU Grossman School of Medicine and NYU Grossman Long Island School of Medicine, as well as six inpatient hospitals and 375 outpatient locations — has developed a large language model (LLM) that serves as a respected research companion and medical advisor.
Every night, the model processes electronic health records (EHR), matching them with relevant research, diagnosis tips and essential background information that it then delivers in concise, tailored emails to residents the following morning. This is an elemental part of NYU Langone’s pioneering approach to medical schooling — what it calls “precision medical education” that uses AI and data to provide highly customized student journeys.
“This concept of ‘precision in everything’ is needed in healthcare,” Marc Triola, associate dean for educational informatics and director of the Institute for Innovations in Medical Education at NYU Langone Health, told VentureBeat. “Clearly the evidence is emerging that AI can overcome many of the cognitive biases, errors, waste and inefficiencies in the healthcare system, that it can improve diagnostic decision-making.”
How NYU Langone is using Llama to enhance patient care
NYU Langone is using an open-weight model built on the latest version of Llama-3.1-8B-instruct and the open-source Chroma vector database for retrieval-augmented generation (RAG). But it’s not just accessing documents — the model is going beyond RAG, actively employing search and other tools to discover the latest research documents.
Each night, the model connects to the facility’s EHR database and pulls out medical data for patients seen at Langone the previous day. It then searches for basic background information on diagnoses and medical conditions. Using a Python API, the model also performs a search of related medical literature in PubMed, which has “millions and millions of papers,” Triola explained. The LLM sifts through reviews, deep-dive papers and clinical trials, selecting a couple of the seemingly most relevant and “puts it all together in a nice email.”
Early the following morning, medical students and internal medicine, neurosurgery and radiation oncology residents receive a personalized email with detailed patient summaries. For instance, if a patient with congestive heart failure had been in for a checkup the previous day, the email will provide a refresher on the basic pathophysiology of heart conditions and information about the latest treatments. It also offers self-study questions and AI-curated medical literature. Further, it may give pointers about steps the residents could take next or actions or details they may have overlooked.
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