  This study applies Natural Language Processing techniques, including Latent
Dirichlet Allocation, to analyse anonymised maternity incident investigation
reports from the Healthcare Safety Investigation Branch. The reports underwent
preprocessing, annotation using the Safety Intelligence Research taxonomy, and
topic modelling to uncover prevalent topics and detect differences in maternity
care across ethnic groups. A combination of offline and online methods was
utilised to ensure data protection whilst enabling advanced analysis, with
offline processing for sensitive data and online processing for non-sensitive
data using the `Claude 3 Opus' language model. Interactive topic analysis and
semantic network visualisation were employed to extract and display thematic
topics and visualise semantic relationships among keywords. The analysis
revealed disparities in care among different ethnic groups, with distinct focus
areas for the Black, Asian, and White British ethnic groups. The study
demonstrates the effectiveness of topic modelling and NLP techniques in
analysing maternity incident investigation reports and highlighting disparities
in care. The findings emphasise the crucial role of advanced data analysis in
improving maternity care quality and equity.
