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arXiv · 2402.02656

RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews

Satpreet Harcharan Singh·Kevin Jiang·Kanchan Bhasin·Ashutosh Sabharwal·Nidal Moukaddam·Ankit B Patel
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Abstract

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.

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Satpreet Harcharan Singh, Kevin Jiang, Kanchan Bhasin, Ashutosh Sabharwal, Nidal Moukaddam, Ankit B Patel. 2024-02-05. RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews. https://arxiv.org/abs/2402.02656

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