“Having providers who are trained and have empathy is life-saving”: Improving primary care communication through thematic analysis with ChatGPT and human expertise

Abstract

In the rapidly evolving field of healthcare research, Artificial Intelligence (AI) and conversational models like ChatGPT (Conversational Generative Pre-trained Transformer) offer promising tools for data analysis. The aim of this study was to: 1) apply ChatGPT methodology alongside human coding to analyze qualitative health services feedback, and 2) examine healthcare experiences among lesbian, gay, bisexual, transgender, and queer (LGBTQ+) patients (<i>N</i> = 41) to inform future intervention. The hybrid approach facilitated the identification of themes related to affirming care practices, provider education, communicative challenges and successes, and environmental cues. While ChatGPT accelerated the coding process, human oversight remained crucial for ensuring data integrity and context accuracy. This hybrid method promises significant improvements in analyzing patient feedback, providing actionable insights that could enhance patient-provider interactions and care for diverse populations. Innovation: This study is the first to combine ChatGPT with human coding for rapid thematic analysis of LGBTQ+ patient primary care experiences.

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