CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital
Open accessPublished version of a preprint
Abstract
BACKGROUND: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King's College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present. RESULTS: To date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King's College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall. CONCLUSION: We describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.
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About 80% of patient data are found in free-text format.
“because about 80% of patient data are found in freetext format.1,2”
From Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study (2024) · cites this paper as [1]
The Background says that nearly all clinical information is recorded as unstructured clinical narrative, including free-text notes, discharge summaries, and referral letters. It does not give an 80% estimate, so it supports the general point but not the claimed figure. No page number is available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredStructured datasets can provide a narrow view of a patient's journey because about 80% of patient data are found in free-text format.
“However, structured datasets are not always available and, even when they are, they can provide a narrow view of a patient's journey, because about 80% of patient data are found in freetext format.1,2”
From Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study (2024) · cites this paper as [1]
The Background says that most clinical information is recorded as unstructured narrative, supporting the point that structured datasets can give a limited view. It does not give the claimed figure of about 80%; searches for that figure and its wording found no supporting passage in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents
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national initiative in data science for health: an evaluation of the UK Farr Institute
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Text Mining in Healthcare: Bringing Structure to Electronic Health Records
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Improving personalized healthcare with automated longitudinal EHR analysis
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From Learning About Machines to Machine Learning: Applications for Mental Health Rehabilitation
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Risk Factors Extraction from Clinical Texts based on Linked Open Data
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- Authors
- Jackson, R., Kartoglu, I., Stringer, C., Gorrell, G., Roberts, A., Song, X., Wu, H., Agrawal, A., Lui, K., Groza, T., Lewsley, D., Northwood, D., Folarin, A., Stewart, R., & Dobson, R.
- Affiliations
- King's College London (GB)
- InterDigital (United Kingdom) (GB)
- Farr Institute (GB)
- King's College Hospital (GB)
- Garvan Institute of Medical Research (AU)
- Corresponding author
- Jackson, Richard
- ORCID
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2018-06-25
- Volume
- 18
- Issue
- 1
- Pages
- 47-47
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC6020175
- OpenAlex
- W2949308662
- Cited by
- 134
- References
- 39
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 12 · 2023: 18 · 2022: 13 · 2021: 28 · 2020: 15 · 2019: 13 · 2018: 2
- FWCI
- 18.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, public-domain)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (MR/K006584/1)
- UK Infrastructure for Large-scale Clinical Genomics Research (MC_PC_14089)
- National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust
- National Institute for Health Research (NIHR) Biomedical Research Centre at University College London Hospital
- NHS England Enablement
- Horizon 2020 Framework Programme (644753)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- medical information retrieval, clinical text extraction, health information systems, National Health Service, information governance, de-identification, medical data processing, open source software, genomic medicine, clinical text mining
- MeSH terms
- United Kingdom, Hospitals, Humans, National Health Programs, Natural Language Processing, Information Storage and Retrieval, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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