Harnessing Language Models for Streamlined Post-Colonoscopy Patient Management: A Novel Approach

Background and Aims ChatGPT, an advanced language model, is increasingly utilized in diverse fields, including medicine. This study explores using ChatGPT to optimize post-colonoscopy management by providing guideline-based recommendations, addressing low adherence rates and timing issues. Methods In this proof-of-concept study twenty clinical scenarios were prepared as structured reports and free text notes, and ChatGPT's responses were evaluated by two senior gastroenterologists. Adherence to guidelines and accuracy were assessed, and inter-rater agreement was calculated using Fleiss' kappa coefficient. Results ChatGPT exhibited 90% adherence to guidelines and 85% accuracy, with a very good inter-rater agreement (Fleiss' kappa coefficient of 0.84, p<0.01). ChatGPT handled multiple variations and descriptions, and crafted concise patient letters. Conclusion Results suggest that ChatGPT could aid healthcare providers in making informed decisions and improve adherence to post-colonoscopy surveillance guidelines. Future research should investigate integrating ChatGPT into electronic health record systems and evaluating its effectiveness in different healthcare settings and populations.