Exploring the Feasibility of GPT-4 as a Data Extraction Tool for Renal Surgery Operative Notes

Abstract

Introduction: GPT-4 is a large language model with potential for multiple applications in urology. Our study sought to evaluate GPT-4's performance in data extraction from renal surgery operative notes.

Methods: GPT-4 was queried to extract information on laterality, surgery, approach, estimated blood loss, and ischemia time from deidentified operative notes. Match rates were determined by the number of "matched" data points between GPT-4 and human-curated extraction. Accuracy rates were calculated after manually reviewing "not matched" data points. Cohen's kappa and the intraclass coefficient were used to evaluate interrater agreement/reliability.

Results: Our cohort consisted of 1498 renal surgeries from 2003 to 2023. Match rates were high for laterality (94.4%), surgery (92.5%), and approach (89.4%), but lower for estimated blood loss (77.1%) and ischemia time (25.6%). GPT-4 was more accurate for estimated blood loss (90.3% vs 85.5% human curated) and similarly accurate for laterality (95.2% vs 95.3% human curated). Human-curated accuracy rates were higher for surgery (99.3% vs 93% GPT-4), approach (97.9% vs 90.8% GPT-4), and ischemia time (95.6% vs 30.7% GPT-4). Cohen's kappa was 0.96 for laterality, 0.83 for approach, and 0.71 for surgery. The intraclass coefficient was 0.62 for estimated blood loss and 0.09 for ischemia time.

Conclusions: Match and accuracy rates were higher for categorical variables. GPT-4 data extraction was particularly error prone for variables with heterogenous documentation styles. The role of a standard operative template to aid data extraction will be explored in the future. GPT-4 can be utilized as a helpful and efficient data extraction tool with manual feedback.

Keywords: artificial intelligence; kidney cancer; natural language processing.

Figure 1:

Examples of zero-shot prompts inputted into GPT-4 for the initial data extraction.

Figure 2:

Examples of few-shot prompts given to GPT-4.

Figure 3:

GPT-4 explaining how it is conducting the data extraction.

Figure 4:

Examples of GPT-4 modifying its extraction logic after manual feedback.