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Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource
  1. Gayan Perera1,
  2. Matthew Broadbent2,
  3. Felicity Callard3,
  4. Chin-Kuo Chang1,
  5. Johnny Downs1,
  6. Rina Dutta1,
  7. Andrea Fernandes1,
  8. Richard D Hayes1,
  9. Max Henderson1,
  10. Richard Jackson1,
  11. Amelia Jewell1,
  12. Giouliana Kadra1,
  13. Ryan Little2,
  14. Megan Pritchard1,
  15. Hitesh Shetty2,
  16. Alex Tulloch1,
  17. Robert Stewart1
  1. 1King's College London (Institute of Psychiatry, Psychology and Neuroscience), London, UK
  2. 2South London and Maudsley NHS Foundation Trust, London, UK
  3. 3Durham University, Durham, UK
  1. Correspondence to Professor Robert Stewart; robert.stewart{at}


Purpose The South London and Maudsley National Health Service (NHS) Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register and its Clinical Record Interactive Search (CRIS) application were developed in 2008, generating a research repository of real-time, anonymised, structured and open-text data derived from the electronic health record system used by SLaM, a large mental healthcare provider in southeast London. In this paper, we update this register's descriptive data, and describe the substantial expansion and extension of the data resource since its original development.

Participants Descriptive data were generated from the SLaM BRC Case Register on 31 December 2014. Currently, there are over 250 000 patient records accessed through CRIS.

Findings to date Since 2008, the most significant developments in the SLaM BRC Case Register have been the introduction of natural language processing to extract structured data from open-text fields, linkages to external sources of data, and the addition of a parallel relational database (Structured Query Language) output. Natural language processing applications to date have brought in new and hitherto inaccessible data on cognitive function, education, social care receipt, smoking, diagnostic statements and pharmacotherapy. In addition, through external data linkages, large volumes of supplementary information have been accessed on mortality, hospital attendances and cancer registrations.

Future plans Coupled with robust data security and governance structures, electronic health records provide potentially transformative information on mental disorders and outcomes in routine clinical care. The SLaM BRC Case Register continues to grow as a database, with approximately 20 000 new cases added each year, in addition to extension of follow-up for existing cases. Data linkages and natural language processing present important opportunities to enhance this type of research resource further, achieving both volume and depth of data. However, research projects still need to be carefully tailored, so that they take into account the nature and quality of the source information.


This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See:

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