Introduction: Large-language models can help extract information from clinical notes, making them potentially useful for research in ulcerative colitis. However, it remains unclear if these models will scale well in practice. Methods: We analyzed the performance and cost of programmatically using GPT-4 to abstract Mayo endoscopic subscores (MES) from 499 colonoscopy reports using different prompting strategies. Results: Zero-shot prompting, where GPT-4 is instructed without examples, was most accurate (83.55%) and cost-effective ($0.097/note). Discussion: Using GPT-4 to automatically curate the MES and other variables is a practical strategy for quantifying UC activity and measuring improvements to clinical care.

VAR receives research support from Alnylam, Takeda, Merck, Genentech, Blueprint Medicines, Stryker, Mitsubishi Tanabe, and Janssen. He also is a shareholder of ZebraMD. RPY has nothing to disclose.

Research reported in this publication was supported by the National Library of Medicine of the National Institutes of Health under Award Number K99LM014099, the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR001872, as well as the UCLA Clinical and Translational Science Institute through grant number UL1TR001881. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. This research project has benefitted from the Microsoft Accelerate Foundation Models Research (AFMR) grant program through which leading foundation models hosted by Microsoft Azure along with access to Azure credits were provided to conduct the research.

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