The strategy, which comes as England continues to miss government targets for reducing maternal and infant deaths, sets out how MNSI will use its extensive dataset of more than 4,300 investigations to move from explaining past events to identifying early warning signs and supporting trusts to intervene sooner. Improved analytical capabilities, alongside AI, will support NHS trusts to identify emerging risks sooner and guide where action is needed to prevent harm and address longstanding disparities.

The strategy centres on three priorities:

• Excellence focuses on maintaining the highest standards in investigation quality while building advanced analytical capability through a dedicated data analysis team.
• Impact involves using AI to support earlier action and working closely with other national partners to ensure insights drive improvements across the system.
• Relationships strengthen MNSI's partnership with families and communities, ensuring those experiencing the poorest outcomes are central to shaping priorities and change.

As part of this work, MNSI will create a Family Voices Group to guide investigation priorities and embed family insight throughout the programme, so that lived experience drives improvements in maternity and newborn safety. 

MNSI will also generate deeper intelligence to help all NHS trusts identify and manage maternity and newborn safety risks earlier and more proactively. This work will continue to place equity and the experiences of families most affected by harm at the centre of efforts to strengthen safety across the country.

Sandy Lewis, MNSI programme director, said: ‘By using better analytical approaches alongside AI to understand emerging patterns and risks earlier, NHS trusts will have a clearer picture of where to focus resources so they can act sooner and prevent harm, especially for communities still experiencing the worst outcomes.'

Louise Page, MNSI clinical director, added: ‘The statistics on racial disparities are not abstract; they represent women, babies and families from Black and Asian communities who continue to face worse outcomes. Our transformation must confront this directly. By combining AI with the perspectives of the communities most affected, we can target prevention efforts more effectively and ensure improvements make a real difference where they are needed most.'