Unveiling Disparities in Maternity Care: A Topic Modelling Approach to Analysing Maternity Incident Investigation Reports

TL;DR

This paper employs NLP and topic modelling to analyze maternity incident reports, revealing ethnic disparities in care and demonstrating the utility of advanced text analysis for healthcare quality improvement.

Contribution

It introduces a novel application of topic modelling and semantic analysis to uncover disparities in maternity care across ethnic groups.

Findings

Identified distinct care focus areas for different ethnic groups.

Demonstrated effectiveness of NLP techniques in healthcare incident analysis.

Highlighted disparities in maternity care among ethnic populations.

Abstract

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.