Applying Large Language Models for Surgical Case Length Prediction

Abstract

Importance: Accurate prediction of surgical case duration is critical for operating room (OR) management, as inefficient scheduling can lead to reduced patient and surgeon satisfaction while incurring considerable financial costs. Objective: To evaluate the feasibility and accuracy of large language models (LLMs) in predicting surgical case length using unstructured clinical data compared to existing estimation methods. Design, Setting, and Participants: This was a retrospective study analyzing elective surgical cases performed between January 2017 and December 2023 at a single academic medical center and affiliated community hospital ORs. Analysis included 125 493 eligible surgical cases, with 1950 used for LLM fine-tuning and 2500 for evaluation. An additional 500 cases from a community site were used for external validation. Cases were randomly sampled using strata to ensure representation across surgical specialties. Exposures: Eleven LLMs, including base models (GPT-4, GPT-3.5, Mistral, Llama-3, Phi-3) and 2 fine-tuned variants (GPT-4 fine-tuned, GPT-3.5 fine-tuned), were used to predict surgical case length based on clinical notes. Main Outcomes and Measures: The primary outcome was average error between predicted and actual surgical case length (wheels-in to wheels-out time). The secondary outcome was prediction accuracy, defined as predicted length within 20% of actual duration. Results: Fine-tuned GPT-4 achieved the best performance with a mean absolute error (MAE) of 47.64 minutes (95% CI, 45.71-49.56) and R2 of 0.61, matching the performance of current OR scheduling (MAE, 49.34 minutes; 95% CI, 47.60-51.09; R2, 0.63; P = .10). Both GPT-4 fine-tuned and GPT-3.5 fine-tuned significantly outperformed current scheduling methods in accuracy (46.12% and 46.08% vs 40.92%, respectively; P < .001). GPT-4 fine-tuned outperformed all other models during external validation with similar performance metrics (MAE, 48.66 minutes; 95% CI, 45.31-52.00; accuracy, 46.0%). Base models demonstrated variable performance, with GPT-4 showing the highest performance among non-fine-tuned models (MAE, 59.20 minutes; 95% CI, 56.88 - 61.52). Conclusion and Relevance: The findings in this study suggest that fine-tuned LLMs can predict surgical case length with accuracy comparable to or exceeding current institutional scheduling methods. This indicates potential for LLMs to enhance operating room efficiency through improved case length prediction using existing clinical documentation.

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