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.

翻译：本研究应用自然语言处理技术（包括潜在狄利克雷分布）分析来自医疗安全调查机构的匿名产科事件调查报告。报告经过预处理、采用安全情报研究分类法进行标注，并通过主题建模揭示普遍主题及不同种族群体间产科护理的差异。研究采用离线与在线相结合的方法确保数据保护的同时实现高级分析：敏感数据采用离线处理，非敏感数据使用`Claude 3 Opus`语言模型进行在线处理。通过交互式主题分析与语义网络可视化技术，提取并展示主题话题，呈现关键词间的语义关系。分析揭示了不同种族群体间的护理差异，其中黑人、亚裔与英国白人群体呈现出显著不同的关注焦点。本研究证明了主题建模与自然语言处理技术在分析产科事件调查报告及揭示护理差异方面的有效性。研究结果强调了高级数据分析在提升产科护理质量与公平性方面的关键作用。