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Generative AI for thematic analysis in a maternal health study: coding semistructured interviews using large language

Shan Qiao1,2, Xingyu Fang3,4,5, Junbo Wang6

  • 1Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.

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Summary

Generative artificial intelligence (GenAI) can efficiently code qualitative interview data, reducing coding time by 81% with over 80% accuracy. Further research is needed to address limitations in this automated thematic analysis approach.

Keywords:
CodingGenerative AIInductive codingMaternal healthThematic analysis

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Area of Science:

  • Qualitative Research Methods
  • Computational Linguistics
  • Artificial Intelligence in Healthcare

Background:

  • Thematic analysis of qualitative data relies on coding semistructured interview transcripts.
  • Manual coding is labor-intensive and time-consuming, hindering efficient data analysis.
  • Generative artificial intelligence (GenAI) offers potential solutions to improve qualitative coding efficiency.

Purpose of the Study:

  • To propose and evaluate a computational pipeline using GenAI for automatic theme extraction from interview transcripts.
  • To assess the efficiency and accuracy of GenAI in inductive coding compared to manual methods.

Main Methods:

  • Leveraged ChatGPT for inductive coding of interview transcripts from maternity care providers.
  • Developed structured prompts to guide ChatGPT in generating and summarizing codes without a predefined scheme.
  • Evaluated GenAI performance by comparing AI-generated codes with manually derived codes.

Main Results:

  • GenAI demonstrated significant promise in detecting and summarizing codes from qualitative interview data.
  • ChatGPT achieved over 80% accuracy in inductive coding tasks.
  • GenAI reduced the time required for coding by an impressive 81%.

Conclusions:

  • GenAI models can efficiently process language data for semantic identification in qualitative research.
  • Challenges including potential inaccuracies, biases, and privacy concerns require careful consideration.
  • Future research should focus on refining GenAI models to enhance reliability and address limitations for qualitative applications.