RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews
This addresses the time-consuming and labor-intensive analysis of mental health interviews for healthcare researchers, though it is incremental as it builds on existing LLM methods.
The authors tackled the problem of manually analyzing semi-structured mental health interviews by developing RACER, an LLM-based pipeline that automatically extracts themes from transcripts, achieving 72% agreement with human evaluators compared to 77% human inter-rater agreement.
Semi-structured interviews (SSIs) are a commonly employed data-collection method in healthcare research, offering in-depth qualitative insights into subject experiences. Despite their value, the manual analysis of SSIs is notoriously time-consuming and labor-intensive, in part due to the difficulty of extracting and categorizing emotional responses, and challenges in scaling human evaluation for large populations. In this study, we develop RACER, a Large Language Model (LLM) based expert-guided automated pipeline that efficiently converts raw interview transcripts into insightful domain-relevant themes and sub-themes. We used RACER to analyze SSIs conducted with 93 healthcare professionals and trainees to assess the broad personal and professional mental health impacts of the COVID-19 crisis. RACER achieves moderately high agreement with two human evaluators (72%), which approaches the human inter-rater agreement (77%). Interestingly, LLMs and humans struggle with similar content involving nuanced emotional, ambivalent/dialectical, and psychological statements. Our study highlights the opportunities and challenges in using LLMs to improve research efficiency and opens new avenues for scalable analysis of SSIs in healthcare research.