School of Medicine, University of St. Gallen (HSG), Switzerland.
Institute for Implementation Science in Health Care, University of Zurich.
Stud Health Technol Inform. 2024 Aug 22;316:791-795. doi: 10.3233/SHTI240530.
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 labor-intensive. 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 semi-automated 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.
为了解决医疗保健领域持续存在的挑战，从患者和医疗保健专业人员等利益相关者那里获取第一手经验和观点至关重要。然而，目前收集、分析和解释定性数据（如访谈）的过程既缓慢又耗费大量人力。为了加快这一过程并提高效率，自动化方法旨在提取有意义的主题并加速解释，但当前的方法（如主题建模）会降低原始数据的丰富度。在这里，我们评估大型语言模型是否可用于支持定性访谈数据的半自动解释。我们比较了一种基于大型语言模型的新方法与主题建模方法，并在两个不同的定性访谈数据集上与手动识别的主题进行了比较。这项探索性研究发现，大型语言模型有可能支持更广泛地将人类观点纳入可持续医疗保健系统的发展中。
