Cosma, Georgina, Singh, Mohit Kumar ORCID: 0000-0001-7736-5583 , Waterson, Patrick, Jun, Gyuchan Thomas and Back, Jonathan (2024) Unveiling disparities in maternity care: a topic modelling approach to analysing maternity incident investigation reports. In: Artificial Intelligence in Healthcare: First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part I. Lecture Notes in Computer Science (LNCS), 14975 . Springer, Cham, Switzerland, pp. 295-308. ISBN 978-3031672774; 978-3031672781 ISSN 0302-9743 (Print), 1611-3349 (Online) (doi:https://doi.org/10.1007/978-3-031-67278-1_23)

PDF (Accepted conference paper) 48173_SINGH_Unveiling_disparities_in_maternity_care_A_topic_modelling_approach_to_analysing_maternity_incident_investigation_reports.pdf - Accepted Version Restricted to Repository staff only Download (386kB) | Request a copy

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.

Item Type:	Conference Proceedings
Title of Proceedings:	Artificial Intelligence in Healthcare: First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part I
Additional Information:	Included in the following conference series: International Conference on AI in Healthcare
Uncontrolled Keywords:	topic modelling; maternity care; healthcare safety
Subjects:	H Social Sciences > H Social Sciences (General) R Medicine > R Medicine (General) R Medicine > RG Gynecology and obstetrics
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > Networks and Urban Systems Centre (NUSC) Greenwich Business School > School of Business, Operations and Strategy
Last Modified:	26 Sep 2024 11:37
URI:	http://gala.gre.ac.uk/id/eprint/48173

Actions (login required)

View Item

Downloads