Developed by AI and data scientist Professor Georgina Cosma and human factors and complex systems expert Professor Patrick Waterson, the tool analyses maternity incident reports to highlight key human factors – such as communication, teamwork, and decision-making – that may have impacted care outcomes, providing insights into areas that could benefit from additional support.
When an adverse maternity incident occurs in England, detailed investigation reports are produced to identify opportunities for learning and enhancing safety.
These reports provide valuable insights into clinical aspects that impacted care, such as health conditions, procedures, and tests. However, identifying the human factors involved is often more challenging, as they tend to be complex and nuanced.
Currently, experts must carry out manual reviews to extract human factor insights from incident reports. This process is resource-intensive, time-consuming, and relies on individual interpretation and expertise, which can lead to varying conclusions.
The AI tool addresses these challenges by identifying and categorising human factors in reports quickly and consistently. Its standardised approach allows it to analyse multiple reports and identify recurring factors, helping pinpoint areas that would benefit most from additional support.
The AI model was trained and tested on data from 188 real maternity incident reports. It successfully identified human factors in each report and analysed them collectively, providing insights into where extra support could improve outcomes.
“AI has transformed our analysis of maternity safety reports. We’ve uncovered crucial insights far quicker than manual methods,” said Professor Cosma.
“This has enabled us to gather a comprehensive understanding of where there are areas for improvement in maternity care, and these insights can help identify ways to enhance patient safety and improve outcomes for mothers and babies.”
Insights from the reports
Teamwork and communication emerged as the most frequently identified human factors across all the analysed reports, underscoring the importance of effective collaboration and clear information exchange among healthcare professionals and patients in promoting safety and quality in maternity care.
The analysis also emphasised the importance of thorough patient evaluations – including assessments, investigations, and screenings – as well as the impact of individual patient characteristics, such as birth history and conditions like pre-eclampsia, on care outcomes.
The AI tool identified challenges related to medical technology use and staff performance, indicating that ongoing training and support could improve care outcomes. It also provided insights into how COVID-19 affected maternity services, underscoring the need for adaptability in practices.
The analysis also i