The A.I. Doctor is In – Application of Large Language Models as Prediction Engines for Improving the Healthcare System
Predictive Healthcare Analytics
Physicians grapple with challenging healthcare decisions, navigating extensive information from scattered records like patient histories and diagnostic reports. Current clinical predictive models, often reliant on structured inputs from electronic health records (EHR) or clinician entries, create complexities in data processing and deployment. To overcome this challenge, a team of researchers at NYU developed NYUTron, an effective large language model (LLM)-based system, which is now integrated into clinical workflows at the NYU Langone Health System. Using natural language processing (NLP), it reads and interprets physicians’ notes and electronic orders, trained on both structured and unstructured EHR text. NYUTron’s effectiveness was demonstrated across clinical predictions like readmission (an episode when a patient who had been discharged from a hospital is admitted again), mortality (death of a patient), and comorbidity (the simultaneous presence of two or more diseases or medical conditions in a patient) as well as operational tasks like length of stay and insurance denial within the NYU Langone Health System. Reframing medical predictive analytics as an NLP problem, the team’s study showcases the capability of LLM to serve as universal prediction engines for diverse medical tasks.