IJPDS International Journal of Population Data Science

A team overseeing the Clinical Record Interactive Search (CRIS) mental health case register at the South London and Maudsley NHS Foundation Trust have created an effective environment of transparency around the use of mental health data for research. In a new open-access article published in the International Journal of Population Data Science (IJPDS), the process that the team undertook to achieve transparency throughout the entire data journey, from data collection to data use and through to dissemination of research findings, is described as an exemplar for others to follow.

Transparency in the use of routinely collected mental health data for research is essential in maintaining public support and trust. It is also important for supporting the sharing of information and data resources amongst the academic community. The National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC) CRIS system is a de-identified case register of electronic medical records for mental health patients from the South London and Maudsley Trust. South London and Maudsley is one of the largest providers of secondary mental health care in Europe, providing mental health services for the London boroughs of Lambeth, Southwark, Croydon, and Lewisham, as well as some national specialist services. 

Transparency in the use of this data has been a founding principle of CRIS right from its inception. Patient and public involvement formed an integral part of the development of the CRIS system, informing the original CRIS build in parallel with technical developments. This has continued throughout the past 17 years with a comprehensive communications plan that aims to target all CRIS stakeholders including patients, carers, academic researchers, NHS staff, and the general public.

The communications activities for CRIS focus on three specific areas of transparency designed to cover the journey of information from data collection through to research outputs.

1. Transparency in Existence ensures that people are aware of the existence of CRIS. Leaflets and posters are made available in waiting rooms and team bases across the Trust, regular drop-in sessions are held with members of the CRIS Team, and there is a CRIS website maudsleybrc.nihr.ac.uk/cris for public access.
2. Transparency in Use ensures that stakeholders are aware of how the data are being used within CRIS and provides opportunities to get involved and give input on its use. The team host patient and carer advisory groups and have created a publicly available data use registry which includes all approved projects. There have also been targeted activities for data linkage projects such as a podcast series.
3. Transparency in Output supports the dissemination of findings from CRIS projects and includes activities such as open access journal publications, seminars where researchers are invited to present their findings, and a video campaign showcasing some of the work being carried out using CRIS which was released across multiple social media platforms.

Amelia Jewell, lead author from the South London and Maudsley added, “There have been challenges to maintaining transparency in CRIS over the years, including ensuring that activities are varied enough to reach all stakeholders, particularly harder to reach groups. However, greater transparency has led to more opportunities for researchers to engage with patients and the CRIS model has been widely accepted by researchers and patients alike.”

 

Click to read the full article

Amelia Jewell, Research Informatics and Governance Lead, South London and Maudsley NHS Foundation Trust

Jewell, A., Broadbent, M., Delaney-Pope, C., Pritchard, M., Woods, H. and Stewart, R. (2025) “Transparency in the existence, use, and output of a mental health data resource: a descriptive paper from the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC) Clinical Record Interactive Search (CRIS) Platform”, International Journal of Population Data Science, 10(2). doi: 10.23889/ijpds.v10i2.2945.