Patient and Public Involvement and Engagement
Cross Cutting – Patient and Public Involvement and Engagement
Our patient advisory group has developed an innovative PPI structure, to ensure the study aims and outcomes reflect patient and public priorities.
During the development award, we engaged with 42 people with experiences of long-term health conditions, social inequalities, and from ethnic minority groups.
Our PPI network facilitate engagement with 30 of these individuals throughout the course of the project.
Meetings are planned for key time-points during the project to ensure questions are relevant and findings interpretable.
Training activities include jargon busting sessions on AI and healthcare data.
PPI partners and researchers are offered training to enable mutual understanding and effective engagement.
We have also employed a mixed methods approach to document PPIE involvement within AI-Multiply including: an impact log, a qualitative and quantitative survey based on the CUBE Framework, elements of the PiiAF Framework, and PPIE session feedback documents.
We are analysing this data using a ‘follow a thread’ approach to identify and integrate key issues and challenges from across each data source. This data will be supplemented with one-to-one interviews with work package researchers and PPIE members to enhance our understanding of the impact and process of public involvement across the project. The findings will form guidance and publications outlining how best to implement PPIE within similar large AI and big data projects and highlighting any barriers and issues that we have encountered.