ArticleBMJ open2024
Akrivia Health Database-deep patient characterisation using a secondary mental healthcare dataset in England and Wales: cohort profile.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- The predicament of heritable confounders.Nature genetics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe Akrivia Health cohort was created to extract data from electronic health records in secondary mental health and dementia care services in England and Wales. The data are anonymised, structured and harmonised from the source electronic health records across a range of information technology systems, enabling for unified, privacy-preserving access for research purposes.
participantsThe cohort contains data from electronic health records for over 4.6 million patients in England and Wales, as of January 2024. The data are refreshed with regularity, and the dataset expands whenever a new healthcare provider joins the Akrivia network. 13% of the database are patients under 18 years old (n=590 160), 56% are adults 18-65 years old (n=2 631 690) and 31% are older people (n=1 422 609). About 11.5% are deceased (n=538 371). FINDINGS TO DATE: Structured data include patient demographics and service pathways. Akrivia Health also uses a bespoke natural language processing model to further extract the research-relevant information from free-text progress notes, including diagnoses, medications and clinical symptoms. This allows for an in-depth longitudinal description of patient journeys. FUTURE PLANS: The anonymised data can be accessed in collaboration with Akrivia Health, following the National Health Service guidelines and without requiring a separate ethics application. There is no planned end date for data collection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.