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The health informatics cohort enhancement project (HICE): using routinely collected primary care data to identify people with a lifetime diagnosis of psychotic disorder

Alexis Economou1, Michelle Grey1, Joanna McGregor1, Nick Craddock2, Ronan A Lyons1, Michael J Owen2, Vaughn Price1, Sue Thomson1, James TR Walters2 and Keith Lloyd1*

Author Affiliations

1 Centre for Health Information Research & Evaluation, ILS2, College of Medicine, Swansea University, Swansea, SA2 8PP, UK

2 MRC Centre for Neuropsychiatric Genetics and Genomics and Neuroscience and Mental Health Research Institute, Cardiff University, Heath Park, Cardiff CF14 4XN, UK

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BMC Research Notes 2012, 5:95  doi:10.1186/1756-0500-5-95

Published: 14 February 2012



We have previously demonstrated that routinely collected primary care data can be used to identify potential participants for trials in depression [1]. Here we demonstrate how patients with psychotic disorders can be identified from primary care records for potential inclusion in a cohort study. We discuss the strengths and limitations of this approach; assess its potential value and report challenges encountered.


We designed an algorithm with which we searched for patients with a lifetime diagnosis of psychotic disorders within the Secure Anonymised Information Linkage (SAIL) database of routinely collected health data. The algorithm was validated against the "gold standard" of a well established operational criteria checklist for psychotic and affective illness (OPCRIT). Case notes of 100 patients from a community mental health team (CMHT) in Swansea were studied of whom 80 had matched GP records.


The algorithm had favourable test characteristics, with a very good ability to detect patients with psychotic disorders (sensitivity > 0.7) and an excellent ability not to falsely identify patients with psychotic disorders (specificity > 0.9).


With certain limitations our algorithm can be used to search the general practice data and reliably identify patients with psychotic disorders. This may be useful in identifying candidates for potential inclusion in cohort studies.