The Laryngoscope · 2025
Surgeon, Trainee, or GPT? A Blinded Multicentric Study of AI-Augmented Operative Notes.
СтатьяМногоцентровое исследованиеСравнительное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Hack Sholem, Attal Rebecca, Locatelli Giacomo, Scotta Gianluca, Maniaci Antonino, Parisi Federica Maria, van der Poel Nicolien, Van Daele Margot, Garcia-Lliberos Ainhoa, Rodriguez-Prado Cristina, Chiesa-Estomba Carlos Miguel, Andueza-Guembe Maider, Cobb Pollara, Zalzal Habib G, Saibene Alberto Maria
Аннотация
OBJECTIVES: Clear, complete operative documentation is essential for surgical safety, continuity of care, and medico-legal standards. Large language models such as ChatGPT offer promise for automating clinical documentation; however, their performance in operative note generation, particularly in surgical subspecialties, remains underexplored. This study aimed to compare the quality, accuracy, and efficiency of operative notes authored by a surgical resident, attending surgeon, GPT alone, and an attending surgeon using GPT as a writing aid.
METHODS: Five publicly available otolaryngologic procedures were selected. For each procedure, four operative notes were generated, one by a resident, one by an attending, one by GPT alone, and one by a hybrid of attending plus GPT. Ten blinded otolaryngologists (five residents, five attendings) independently reviewed all 20 notes. Reviewers scored each note across eight domains using a five-point scale, assigned a final approval rating, and provided qualitative feedback. Writing time was recorded to assess documentation efficiency.
RESULTS: Hybrid notes written by an attending surgeon with GPT assistance received the highest average domain scores and the highest "as is" approval rate (79%), outperforming all other groups. GPT-only notes were the fastest to generate but had the lowest approval rate (23%) and the highest incidence of both omissions and overdocumentation. Writing time was significantly reduced in both AI-assisted groups compared to human-only authorship. Inter-rater reliability among reviewers was moderate to high across most domains.
CONCLUSION: In this limited dataset, hybrid human-AI collaboration outperformed both human-only and AI-only authorship in operative documentation. These findings support GPT-assisted documentation to improve operative note efficiency and consistency.
LEVEL OF EVIDENCE: N/A.
METHODS: Five publicly available otolaryngologic procedures were selected. For each procedure, four operative notes were generated, one by a resident, one by an attending, one by GPT alone, and one by a hybrid of attending plus GPT. Ten blinded otolaryngologists (five residents, five attendings) independently reviewed all 20 notes. Reviewers scored each note across eight domains using a five-point scale, assigned a final approval rating, and provided qualitative feedback. Writing time was recorded to assess documentation efficiency.
RESULTS: Hybrid notes written by an attending surgeon with GPT assistance received the highest average domain scores and the highest "as is" approval rate (79%), outperforming all other groups. GPT-only notes were the fastest to generate but had the lowest approval rate (23%) and the highest incidence of both omissions and overdocumentation. Writing time was significantly reduced in both AI-assisted groups compared to human-only authorship. Inter-rater reliability among reviewers was moderate to high across most domains.
CONCLUSION: In this limited dataset, hybrid human-AI collaboration outperformed both human-only and AI-only authorship in operative documentation. These findings support GPT-assisted documentation to improve operative note efficiency and consistency.
LEVEL OF EVIDENCE: N/A.