Applying Large Language Models to Interpret Qualitative Interviews in Healthcare
Type
book section
Date Issued
2024-08-22
Author(s)
Abstract
To address the persistent challenges in healthcare, it is crucial to incorporate firsthand experiences and perspectives from stakeholders such as patients and healthcare professionals. However, the current process of collecting, analyzing and interpreting qualitative data, such as interviews, is slow and laborintensive. To expedite this process and enhance efficiency, automated approaches aim to extract meaningful themes and accelerate interpretation, but current approaches such as topic modeling reduce the richness of the raw data. Here, we evaluate whether Large Language Models can be used to support the semiautomated interpretation of qualitative interview data. We compare a novel approach based on LLMs to topic modeling approaches and to manually identified themes across two different qualitative interview datasets. This exploratory study finds that LLMs have the potential to support incorporating human perspectives more widely in the advancement of sustainable healthcare systems.
Keywords
Healthcare
information technology
clinical information systems
healthcare professionals
qualitative research
thematic analysis
topic modeling
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Wosny M., 2024, Applying Large Language Models to Interpret Qualitative Interviews in Healthcare.pdf
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Main Article
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Format
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