Black women are five times more likely to die in the UK as a result of complications in their pregnancy than White women. Women of Black heritage are around 83% more likely to suffer a ‘near miss’ in childbirth. Research also found that Black children have a 121% increased risk of stillbirth and a 50% increased risk of neonatal death. At the moment, we do not have an accurate picture of the causes contributing to these statistics. Research points to biological factors (e.g., obesity or the birth history of mothers) as one cause of poor outcomes in maternity. Similarly, social and economic factors (e.g., language barriers, unemployment) are thought to play a role. Finally, the quality of care (e.g., poor communication with the mother, lack of knowledge on health issues) given to Black mothers and families is likely to be a factor. Incidents of maternal death have been reported in recent news: Black women four times more likely to die in childbirth ; Black women in UK four times more likely to die in pregnancy and childbirth
The project is led by Dr Patrick Waterson from Loughborough University’s Human Factors and Complex Systems Group in the School of Design and Creative Arts and Dr Georgina Cosma, an expert in Artificial Intelligence and Data Science from the Department of Computer Science, in close collaboration with the Healthcare Safety Investigation Branch (HSIB) with funding from the Health Foundation and NHSX – via NIHR.
The HSIB has carried out more than 2,000 investigations over the last few years into things that go wrong during pregnancy and birth. Loughborough University in collaboration with HSIB will develop an AI-based system for analysing maternity investigation reports and extracting data, based on a set of codes, that is able to identify factors which contribute to harm during pregnancy and birth. The system will use ‘machine learning’ to probe deeper into how different factors – regional, biological, clinical, care quality – interact, influence one another and lead to harm. Ultimately the AI-based computer program will help us design specific ways in which maternal care for these at risk groups be improved.