This consortium distributes a Nature published, custom BERT-style large language model called NYUTron that is built on 7.25 million clinical notes and 9.5 years of data. Our goal is to enable a community around NYUTron and develop use cases together to demonstrate real patient value.

NYUTron, which was developed by NYU Langone researchers, reads a complete history and physical or discharge summary note to achieve state-of-the-art performance for estimating an inpatient’s risk of death, calculating length of hospital stay, 30-day readmission, comorbidity index, and insurance denials. The Nature publication has been accessed 86,000 times since its publication on June 7, 2023.

A core finding of the work is that we can accelerate the model lifecycle from weeks to days using a foundation large language model like NYUTron. Specifically, feature extraction is no longer necessary and foundation models can learn the relevant features for a task directly. In other words, we can train a model with medical notes such as a discharge summary and directly learn this patient’s readmission risk (as opposed to identifying individual features and then calculating risk).

About the Consortium

NYU Langone has organized the Consortium for Operational Medical AI (COMAI) to be comprised of non-profit hospitals and academic medical centers throughout the United States and internationally that can join together in a cooperative effort to use, study, and develop NYUTron; to develop a better understanding of clinical large language models and operational medical AI; and to facilitate improvement of the implementation and use of NYUTron and other clinical large language models for the purpose of quality improvement, safety, and health care efficiency for the ultimate benefit of patients and health care providers.

Reasons to Join

  • Starter set of 5 models:  
    • predicting inpatient mortality from the history and physical,
    • predicting insurance denial from discharge summary,  
    • predicting 30-day readmission from the discharge summary,  
    • predicting length of stay from the history and physical,
    • classifying Charlson Comorbidity from the history and physical. 
  • Access to an inventory of use cases from consortium members that run classification and prediction tasks on clinical text and notes. 
  • Software infrastructure to build text-based models in hours vs months.  
  • A community for best practices and co-learning. 
  • Organizational best practices for building up expertise and personnel to build, develop, and deploy fine-tuned large language models.

Objectives

COMAI has been created to establish partnerships with non-profit and academic medical institutions to promote a mission of innovation in the clinical large language models and operational medical AI.  As a member of COMAI, the member is expected to play an important role in the research, education, and innovation goals of COMAI including expanding and demonstrating the scientific, technological, and operational feasibility of innovative methodologies and applications governing clinical large language models. 

Eligibility

Any non-profit hospital or academic medical center.

Member Commitment

Membership in COMAI is $100,000 per year, for a three (3) year term. Renewals and/or extensions are subject to the terms of the membership agreement.