PURPOSE: This study investigates the use of artificial intelligence (AI)
to expedite and enhance qualitative analysis research in medical education,
specifically through evaluating clinicians' experiences in a flourishing
program aimed at promoting humanism among healthcare professionals.
METHODS: Clinician focus group transcripts from a six-month program on
character, flourishing, and practical wisdom were analyzed by AI using Gemini
1.5 Pro through the Northwell AI Hub with a structured prompt to identify key
themes, excluding program logistics. Independently, two student researchers
manually coded the transcripts, reconciling discrepancies before finalizing
results. Comparisons were drawn between AI and human-coded themes.
RESULTS: AI identified four themes across 65 quotes: Personal Growth &
Self-Reflection (45.4%), Connectedness & Community (15.8%), Application
& Success (13.7%), and Character Strengths (26.4%). Human analysis
identified six themes across 71 quotes: Introspection (32.4%), Growth
(19.7%), Belonging (14%), Leadership (22.5%), Empathy (7%), and Resilience
(4.2%). While AI demonstrated efficiency in identifying more quotes quickly,
human analysis provided nuanced distinctions among themes.
CONCLUSIONS: AI serves as a beneficial supplement for initial theme
identification, offering efficiency in theme identification processes, yet
lacking contextual depth in certain themes like Empathy & Resilience,
which human analysis captured. Future studies should address AI prompt
variations and explore different AI models to refine AI's role in qualitative
medical education research.