Understanding how clinicians arrive at decisions in actual practice settings is vital for advancing personalized, evidence-based care. However, systematic analysis of qualitative decision data poses challenges.

We analyzed transcribed interviews with Hebrew-speaking clinicians on decision processes using natural language processing (NLP). Word frequency and characterized terminology use, while large language models (ChatGPT from OpenAI and Gemini by Google) identified potential cognitive paradigms.

Word frequency analysis of clinician interviews identified experience and knowledge as most influential on decision-making. NLP tentatively recognized heuristics-based reasoning grounded in past cases and intuition as dominant cognitive paradigms. Elements of shared decision-making through individualizing care with patients and families were also observed. Limited Hebrew clinical language resources required developing preliminary lexicons and dynamically adjusting stopwords. Findings also provided preliminary support for heuristics guiding clinical judgment while highlighting needs for broader sampling and enhanced analytical frameworks.

This study represents the first use of integrated qualitative and computational methods to systematically elucidate clinical decision-making. Findings supported experience-based heuristics guiding cognition. With methodological enhancements, similar analyses could transform global understanding of tailored care delivery. Standardizing interdisciplinary collaborations on developing NLP tools and analytical frameworks may advance equitable, evidence-based healthcare by elucidating real-world clinical reasoning processes across diverse populations and settings.

Clinical decision-making

Natural language processing (NLP)

Heuristics-based reasoning

Shared decision-making

Healthcare informatics

AI in medicine

© 2025 The Author(s). Published by Elsevier Inc.