Non-technical Skills for Urology Trainees: A Double-Blinded Study of ChatGPT4 AI Benchmarking Against Consultant Interaction

Pears, Matthew; Wadhwa, Karan; Payne, Stephen R.; Hanchanale, Vishwanath; Elmamoun, Mamoun Hamid; Jain, Sunjay; Konstantinidis, Stathis Th; Rochester, Mark; Doherty, Ruth; Spearpoint, Kenneth; Ng, Oliver; Dick, Lachlan; Yule, Steven; Biyani, Chandra Shekhar

Abstract

Non-technical skills (NTS) are crucial in healthcare, encompassing cognitive and social skills that support technical ability. Traditional NTS training is evolving with the emergence of artificial intelligence (AI) models that can intelligently converse with their users, known as large language models (LLMs). This study investigated the capabilities and limitations of a popular model named generative pre-trained transformer 4 (GPT-4) in NTS training, comparing its performance to that of human evaluators. Urology trainees identified NTS events in simulated scenarios and discussed them in blinded feedback sessions with AI and human consultants. Experts assessed the blinded interaction data, providing quantitative ratings and qualitative evaluations using annotated transcripts. Wilcoxon signed-rank tests compared pre- and post-intervention ratings, whilst Mann–Whitney U tests compared post-intervention ratings between AI and human feedback. Thematic analysis identified strengths, limitations, and differences between AI and human feedback approaches. The AI model demonstrated significant strengths in reinforcing knowledge gathering (p = 0.04), providing accurate and evidence-based feedback (p = 0.013), conveying empathy (p = 0.021), and tailoring explanations to complexity (p = 0.002). However, human feedback excelled in language terminology (p = 0.003), complexity (p = 0.020), and fact-based feedback (p = 0.025). The study highlights the potential for AI to augment assessment of NTS training in healthcare. A blended approach utilising AI and human expertise may boost training efficacy.

Citation

Pears, M., Wadhwa, K., Payne, S. R., Hanchanale, V., Elmamoun, M. H., Jain, S., Konstantinidis, S. T., Rochester, M., Doherty, R., Spearpoint, K., Ng, O., Dick, L., Yule, S., & Biyani, C. S. (2024). Non-technical Skills for Urology Trainees: A Double-Blinded Study of ChatGPT4 AI Benchmarking Against Consultant Interaction. Journal of Healthcare Informatics Research, https://doi.org/10.1007/s41666-024-00180-7

• Journal Article Type: Article
• Acceptance Date: Nov 5, 2024
• Online Publication Date: Nov 14, 2024
• Publication Date: Nov 14, 2024
• Deposit Date: Nov 18, 2024
• Publicly Available Date: Nov 15, 2025
• Journal: Journal of Healthcare Informatics Research
• Print ISSN: 2509-4971
• Electronic ISSN: 2509-498X
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s41666-024-00180-7
• Keywords: Artificial intelligence · Healthcare education · Non-technical skills · Pedagogy · Simulation · Urology
• Public URL: https://nottingham-repository.worktribe.com/output/41924014
• Publisher URL: https://link.springer.com/article/10.1007/s41666-024-00180-7
• Additional Information: Received: 1 July 2024; Revised: 22 October 2024; Accepted: 5 November 2024; First Online: 14 November 2024