RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews
Satpreet Singh, Kevin Jiang, Kanchan Bhasin, Ashutosh Sabharwal, Nidal J. Moukaddam, Ankit Patel · 2024
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, 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 expertguided 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.