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.

Patients collectively made Long Covid – and cognate term ‘long-haul Covid’ – in the first months of the pandemic. Patients, many with initially ‘mild’ illness, used various kinds of evidence and advocacy to demonstrate a longer, more complex course of illness than laid out in initial reports from Wuhan. Long Covid has a strong claim to be the first illness created through patients finding one another on social media: it moved from patients, through various media, to formal clinical and policy channels in just a few months. This initial mapping of Long Covid – by two patients with this illness – focuses on actors in the UK and USA and demonstrates how patients marshalled epistemic authority. Patient knowledge needs to be incorporated into how COVID-19 is conceptualised, researched, and treated.

Background: Case registers have been used extensively in mental health research. Recent developments in electronic medical records, and in computer software to search and analyse these in anonymised format, have the potential to revolutionise this research tool.

SummaryBackgroundAuditory hallucinations—or voices—are a common feature of many psychiatric disorders and are also experienced by individuals with no psychiatric history. Understanding of the variation in subjective experiences of hallucination is central to psychiatry, yet systematic empirical research on the phenomenology of auditory hallucinations remains scarce. We aimed to record a detailed and diverse collection of experiences, in the words of the people who hear voices themselves.MethodsWe made a 13 item questionnaire available online for 3 months. To elicit phenomenologically rich data, we designed a combination of open-ended and closed-ended questions, which drew on service-user perspectives and approaches from phenomenological psychiatry, psychology, and medical humanities. We invited people aged 16–84 years with experience of voice-hearing to take part via an advertisement circulated through clinical networks, hearing voices groups, and other mental health forums. We combined qualitative and quantitative methods, and used inductive thematic analysis to code the data and χ2 tests to test additional associations of selected codes.FindingsBetween Sept 9 and Nov 29, 2013, 153 participants completed the study. Most participants described hearing multiple voices (124 [81%] of 153 individuals) with characterful qualities (106 [69%] individuals). Less than half of the participants reported hearing literally auditory voices—70 (46%) individuals reported either thought-like or mixed experiences. 101 (66%) participants reported bodily sensations while they heard voices, and these sensations were significantly associated with experiences of abusive or violent voices (p=0·024). Although fear, anxiety, depression, and stress were often associated with voices, 48 (31%) participants reported positive emotions and 49 (32%) reported neutral emotions. Our statistical analysis showed that mixed voices were more likely to have changed over time (p=0·030), be internally located (p=0·010), and be conversational in nature (p=0·010).InterpretationThis study is, to our knowledge, the largest mixed-methods investigation of auditory hallucination phenomenology so far. Our survey was completed by a diverse sample of people who hear voices with various diagnoses and clinical histories. Our findings both overlap with past large-sample investigations of auditory hallucination and suggest potentially important new findings about the association between acoustic perception and thought, somatic and multisensorial features of auditory hallucinations, and the link between auditory hallucinations and characterological entities.FundingWellcome Trust.

BackgroundElectronic health records (EHRs) provide enormous potential for health research but also present data governance challenges. Ensuring de-identification is a pre-requisite for use of EHR data without prior consent. The South London and Maudsley NHS Trust (SLaM), one of the largest secondary mental healthcare providers in Europe, has developed, from its EHRs, a de-identified psychiatric case register, the Clinical Record Interactive Search (CRIS), for secondary research.MethodsWe describe development, implementation and evaluation of a bespoke de-identification algorithm used to create the register. It is designed to create dictionaries using patient identifiers (PIs) entered into dedicated source fields and then identify, match and mask them (with ZZZZZ) when they appear in medical texts. We deemed this approach would be effective, given high coverage of PI in the dedicated fields and the effectiveness of the masking combined with elements of a security model. We conducted two separate performance tests i) to test performance of the algorithm in masking individual true PIs entered in dedicated fields and then found in text (using 500 patient notes) and ii) to compare the performance of the CRIS pattern matching algorithm with a machine learning algorithm, called the MITRE Identification Scrubber Toolkit – MIST (using 70 patient notes – 50 notes to train, 20 notes to test on). We also report any incidences of potential breaches, defined by occurrences of 3 or more true or apparent PIs in the same patient’s notes (and in an additional set of longitudinal notes for 50 patients); and we consider the possibility of inferring information despite de-identification.ResultsTrue PIs were masked with 98.8% precision and 97.6% recall. As anticipated, potential PIs did appear, owing to misspellings entered within the EHRs. We found one potential breach. In a separate performance test, with a different set of notes, CRIS yielded 100% precision and 88.5% recall, while MIST yielded a 95.1% and 78.1%, respectively. We discuss how we overcome the realistic possibility – albeit of low probability – of potential breaches through implementation of the security model.ConclusionCRIS is a de-identified psychiatric database sourced from EHRs, which protects patient anonymity and maximises data available for research. CRIS demonstrates the advantage of combining an effective de-identification algorithm with a carefully designed security model. The paper advances much needed discussion of EHR de-identification – particularly in relation to criteria to assess de-identification, and considering the contexts of de-identified research databases when assessing the risk of breaches of confidential patient information.