Researchers at Loughborough University have developed an AI tool designed to enhance maternity care outcomes by analyzing incident reports to identify key human factors affecting safety.

This innovative tool quickly and consistently categorizes human factors, drawing insights from 188 real maternity incident reports to pinpoint recurring issues.

The research aligns with the findings of the Ockenden Review, which emphasized the urgent need for improvements in maternity care quality and safety.

Among the critical human factors identified, teamwork and communication emerged as essential elements for enhancing the safety and quality of maternity care.

The AI analysis also highlighted the importance of thorough patient evaluations and the impact of individual patient characteristics on care outcomes.

There are indications that certain human factors may disproportionately affect mothers from ethnic minority groups, although further research is needed due to limited ethnicity data in existing reports.

Challenges related to medical technology and staff performance were noted, suggesting a need for ongoing training and support to address these issues.

Additionally, the tool provided insights into the effects of COVID-19 on maternity services, emphasizing the need for adaptability in care practices.

The researchers are seeking funding to refine the AI model using a larger dataset and to collaborate with hospitals and health organizations for broader application.

Dr. Jonathan Back from the Health Services Safety Investigations Body remarked on the potential of this research to identify inequalities in maternity care and to maximize learning from investigations.

Traditionally, the extraction of human factor insights from incident reports has been a resource-intensive and subjective process, often leading to inconsistent conclusions.

The findings from this research have been published in the International Journal of Population Data Science, contributing to the understanding of maternal safety and health outcomes.