Comparing human vs. machine-assisted analysis to develop a new approach for Big Qualitative Data Analysis

Journal article

Background: The exponential growth of Big Qualitative (Big Qual) data in healthcare research presents methodological challenges for traditional analysis approaches. This study evaluates the effectiveness of machine-assisted analysis using artificial intelligence (AI) tools compared to human-only analysis for processing large-scale qualitative datasets, using the Royal College of Anaesthetists’ 7th National Audit Project (NAP7) baseline survey as a test case. Methodology/Principal Findings: We conducted a comparative methodological study analysing 5,196 free-text responses about peri-operative cardiac arrest experiences. Three researchers established a human-coded reference standard following SRQR guidelines. We then applied machine-assisted analysis using Pulsar for exploratory analysis and Caplena for sentiment and thematic analysis, evaluating performance against the human gold standard using STARD-AI reporting standards. Performance metrics included accuracy, precision, recall, F1-scores, and Cohen’s Kappa, with confidence intervals calculated using bootstrap resampling. Machine-assisted analysis substantially reduced analysis time, with particularly dramatic improvements in theme identification speed. The machine-assisted approach achieved good thematic and sentiment classification accuracy compared to the human reference standard, though human analysis identified an emergent ‘ambiguous’ sentiment category that current AI tools cannot accommodate, highlighting limitations in commercial platforms’ flexibility for inductive analysis. Conclusions/Significance: Machine-assisted analysis offers substantial efficiency gains with acceptable accuracy trade-offs for large-scale qualitative data analysis. However, human expertise remains essential for capturing nuanced meanings, identifying emergent categories, and providing domain-specific interpretation. This hybrid approach represents a viable methodology for Big Qual research, though current AI tools’ constraints in accommodating emergent classification schemes remain a limitation. Our findings establish benchmarks for future development of more flexible AI systems adapted to qualitative research paradigms.

Authors: Martin, S; Beecham, E; Kursumovic, E; Armstrong, RA; Cook, TM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Public Library of Science
• Journal: PLOS Digital Health
• Volume: 5
• Issue: 2
• Pages: e0000576
• Article number: e0000576
• Publication date: 2026-02-25
• Acceptance date: 2026-02-08
• DOI: 10.1371/journal.pdig.0000576
• EISSN: 2767-3170
• ISSN: 2767-3170
• Language: English