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Effective consumer centred healthcare incorporates consumer and clinician perspectives into decision making, in addition to traditional quantitative measures. This information is usually captured in qualitative data that requires manual analysis. Healthcare systems often lack resources to systematically incorporate qualitative feedback into decision making. Semi-automated content analysis tools, such as Leximancer, provide an efficient and objective alternative to time consuming manual content analysis (MCA). Literature on the validity of Leximancer in healthcare is sparse. This study seeks to validate Leximancer against MCA on a broad emotive conversational dataset gathered in a healthcare setting. At the outset of the COVID-19 pandemic, a large Australian hospital and health service conducted interactive webcasts with staff to provide updates and answer questions. A manual thematic analysis and a Leximancer content analysis were conducted independently on 20 webcast transcripts. The findings were compared, along with the time required to the complete each analysis. The Leximancer analysis identified nine concepts, while the manual analysis identified 12 concepts. The Leximancer concepts mapped to five of the concepts identified in the manual analysis, which accounted for 74% of mentions tagged in the text through the manual analysis. Leximancer missed concepts which required an emotional or contextual interpretation. The Leximancer analysis took 21 hours (excluding time to learn the program), compared to 73 hours for the manual analysis. Semi-automated content analysis provides an efficient alternative to manual qualitative data analysis, shifting it from a small-scale research activity to a more routine operational activity, albeit with some limitations. This is critical to be able to utilise at scale the rich narratives from consumers and clinicians in healthcare decision making.
Engstrom et al. (Fri,) studied this question.