Background: Synoptic reporting, the documenting of clinical information in a structured manner, is known to improve patient care by reducing errors, increasing readability, interoperability, and report completeness. Despite its advantages, manually synthesizing synoptic reports from narrative reports is expensive and error prone when the number of structured fields are many. While the recent revolutionary developments in Large Language Models (LLMs) have significantly advanced natural language processing, their potential for innovations in medicine is yet to be fully evaluated. Objectives: In this study, we explore the strengths and challenges of utilizing the state-of-the-art language models in the automatic synthesis of synoptic reports. Materials and Methods: We use a corpus of 7,884 cancer related, narrative pathology reports, which have annotated reference synoptic reports from Mayo Clinic EHR. Using these annotations as a reference, we reconfigure the state-of-the-art large language models, such as LLAMA-2, to generate the synoptic reports. Our annotated reference synoptic reports contain 22 unique data elements. To evaluate the accuracy of the reports generated by the LLMs, we use several metrics including the BERT F1 Score and verify our results by manual validation. Results: We show that using fine-tuned LLAMA-2 models, we can obtain BERT Score F1 of 0.86 or higher across all data elements and BERT F1 scores of 0.94 or higher on over 50% (11 of 22) of the questions. The BERT F1 scores translate to average accuracies of 76% and as high as 81% for short clinical reports. Conclusions: We demonstrate successful automatic synoptic report generation by fine-tuning large language models.

The authors have declared no competing interest.

This study was supported by the National Institutes of Health (NIH) NIGMS grant number R00GM135488. XJ is CPRIT Scholar in Cancer Research (RR180012), and he was supported in part by Christopher Sarofim Family Professorship, UT Stars award, UTHealth start-up, the National Institutes of Health (NIH) under award number R01AG066749, R01LM013712, R01LM014520, R01AG082721, R01AG066749, U01AG079847, U24LM013755, U01CA274576, U54HG012510, 1OT2OD032581-02-420, 1OT2OD032581-02-211, 1OT2OD032581-02-164, OT2OD032701 and the National Science Foundation (NSF) #2124789. ZH was supported in part by NIH under award number R21LM013911, P01AA029547, R01AG064529, the Agency for Healthcare Research and Quality grant R21HS029969, and the U.S. Environmental Protection Agency under award number 84063201.

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Due to Mayo Clinic policy and to protect privacy, the raw electronics health records data cannot be shared. The data presented in the figures and the code used to train the models is available here: https://github.com/bioIKEA/SynopsisGPT

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