  In healthcare, thousands of safety incidents occur every year, but learning
from these incidents is not effectively aggregated. Analysing incident reports
using AI could uncover critical insights to prevent harm by identifying
recurring patterns and contributing factors. To aggregate and extract valuable
information, natural language processing (NLP) and machine learning techniques
can be employed to summarise and mine unstructured data, potentially surfacing
systemic issues and priority areas for improvement. This paper presents
I-SIRch:CS, a framework designed to facilitate the aggregation and analysis of
safety incident reports while ensuring traceability throughout the process. The
framework integrates concept annotation using the Safety Intelligence Research
(SIRch) taxonomy with clustering, summarisation, and analysis capabilities.
Utilising a dataset of 188 anonymised maternity investigation reports annotated
with 27 SIRch human factors concepts, I-SIRch:CS groups the annotated sentences
into clusters using sentence embeddings and k-means clustering, maintaining
traceability via file and sentence IDs. Summaries are generated for each
cluster using offline state-of-the-art abstractive summarisation models (BART,
DistilBART, T5), which are evaluated and compared using metrics assessing
summary quality attributes. The generated summaries are linked back to the
original file and sentence IDs, ensuring traceability and allowing for
verification of the summarised information. Results demonstrate BART's
strengths in creating informative and concise summaries.
