Large Language Model Based Assessment of Clinical Reasoning Documentation in the Electronic Health Record Across Two Institutions (Preprint)
Authors: Schaye Verity , DiTullio David , Guzman Benedict , Vennemeyer Scott , Shih Hanniel , Reinstein Ilan , Weber Danielle E , Goodman Abbie , Wu Danny T. Y. , Sartori Daniel J. , Santen Sally A. , Gruppen Larry , Aphinyanaphongs Yindalon , Rafel Jesse Burk 
Published: October 2024

BACKGROUND

Clinical reasoning is an essential skill, yet physicians receive limited feedback. Artificial intelligence holds promise to fill this gap.

OBJECTIVE

We report the development of both named entity recognition (NER), logic-based and large language model (LLM)-based assessments of CR documentation in the electronic health record (EHR) across two institutions.

METHODS

Two note sets were retrieved from the EHR at each institution (NYU Grossman School of Medicine (NYU) and University of Cincinnati College of Medicine (UC)): 1) retrospective dataset comprised of internal medicine resident admission notes from July 2020-December 2021 (n=700 NYU notes, n=450 UC notes) and 2) prospective validation dataset from July 2023-December 2023 (n=155 NYU notes, n=92 UC notes). Using a validated human gold standard for assessment of CR documentation, the R-DEA tool, clinicians rated notes for D (differential diagnosis) and EA (explanation of reasoning) quality, each on 3-point scales (D0, D1, D2 and EA0, EA1, EA2). Model training occurred accordingly on the retrospective datasets: 1) NYU development of NER, logic-based model with validation at UC, 2) NYU fine tune training of LLM NYUTron (a BERT-like (Bidirectional Encoder Representation with Transformer) LLM with about 110 million parameters that has been pre-trained on 7.25 million clinical notes), 3) NYU fine tune training of LLM GatorTron (an open source LLM with 345 million parameters that was pre-trained on over 82 billion words of de-identified clinical text), 4) UC fine tune training of NYU fine-tuned GatorTron, and 5) UC fine tune training of GatorTron. The best performing models were validated with the prospective datasets and performance assessed with F1 scores for the NER, logic-based model and AUROC and AUPRC for the LLMs.

RESULTS

At NYU, the NYUTron models were the best performing. The D0 and D2 models with an AUROC 0.87, AUPRC 0.79 and AUROC 0.89, AUPRC 0.86, respectively. The D1 model did not have sufficient performance for implementation. The EA0 and EA1 models also did not have adequate performance so the approach pivoted to create a binary EA2 model (i.e. EA2 vs not EA2) which had excellent performance with an AUROC 0.85 and AUPRC 0.80.

At UC, the NER, D-logic-based model was the best performing D model. The F1-scores for the D model on the UC dataset were 0.80, 0.74, and 0.80 for D0, D1, D2, respectively. The UC fine tuning of NYU fine-tuned GatorTron EA2 model had an AUROC 0.75 and AUPRC 0.69.

CONCLUSIONS

This is the first study to our knowledge to demonstrate the use of LLMs for assessment of CR documentation quality in the EHR across two institutions. Lessons learned can help promote implementation of these technologies across institutions with ranges of technical resources and enhance feedback on the essential skill of CR.