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Cited by 134Electronic Health Records SystemsBiomedical Text Mining and OntologiesMedical Coding and Health Information

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital

Open access
Jackson, Richard, Kartoglu, Ismail, Stringer, Clive, Gorrell, Genevieve, Roberts, Angus, Song, Xingyi, Wu, Honghan, Agrawal, Asha, Lui, Kenneth, Groza, Tudor, Lewsley, Damian, Northwood, Doug, Folarin, Amos, Stewart, Robert, Dobson, Richard·2018·BMC Medical Informatics and Decision Making
DOI 10.1186/s12911-018-0623-9PMC PMC6020175

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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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Fig. 1 CogStack Architecture and Dataflow All components can be deployed via the Docker containerisation software. 1. New job execution Master instance of CogStack identifies new data in Trust Data Sources at intermittent intervals. 2. Partitioning The job is partitioned into a user definable number of work units. 3a. Derive the freetext content Extract plain and/or formatted text from common proprietary document binary formats (performing OCR where necessary), using the Tika Library to enable the downstream processing of high value unstructured data elements. 3b. Supplement the text content with meta-data Filter and de-normalise a subset of the structured clinical data to provide a patient orientated, transparent representation of high value metadata concepts. For example, this might include calculated fields to represent patient age at document date, first part of postcode and ethnicity and lab results. 3c. De-identification Transform the resulting text documents into de-identified text documents, by masking personal health identifiers via the use of the Cognition de-identification algorithms. This is necessary to address governance concerns associated with the secondary use of patient data. Identifiers in structured data can be excluded via SQL query, according to business requirements. 4. Information Extraction Apply generic clinical IE pipelines to derive additional structured data from free text and supplement the quantity of available structured data at the point of query. 5. Indexing Build a JSON object from the resulting structured and unstructured data, which can then be readily be indexed into an Elasticsearch cluster. 6. Visualisation The Kibana suite provides a range of attractive options for viewing, aggregating and dash-boarding the loaded data
Fig. 1 CogStack Architecture and Dataflow All components can be deployed via the Docker containerisation software. 1. New job execution Master instance of CogStack identifies new data in Trust Data Sources at intermittent intervals. 2. Partitioning The job is partitioned into a user definable number of work units. 3a. Derive the freetext content Extract plain and/or formatted text from common proprietary document binary formats (performing OCR where necessary), using the Tika Library to enable the downstream processing of high value unstructured data elements. 3b. Supplement the text content with meta-data Filter and de-normalise a subset of the structured clinical data to provide a patient orientated, transparent representation of high value metadata concepts. For example, this might include calculated fields to represent patient age at document date, first part of postcode and ethnicity and lab results. 3c. De-identification Transform the resulting text documents into de-identified text documents, by masking personal health identifiers via the use of the Cognition de-identification algorithms. This is necessary to address governance concerns associated with the secondary use of patient data. Identifiers in structured data can be excluded via SQL query, according to business requirements. 4. Information Extraction Apply generic clinical IE pipelines to derive additional structured data from free text and supplement the quantity of available structured data at the point of query. 5. Indexing Build a JSON object from the resulting structured and unstructured data, which can then be readily be indexed into an Elasticsearch cluster. 6. Visualisation The Kibana suite provides a range of attractive options for viewing, aggregating and dash-boarding the loaded dataCC BY · PubMed Central
Fig. 2 Kibana interface loaded with pseudo-data
Fig. 2 Kibana interface loaded with pseudo-dataCC BY · PubMed Central

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  1. Jackson R, Kartoglu I. A Open Pipeline for Masking Patient Identifiers in Electronic Health Records, The Farr Institute International Conference 2015. 2015.

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Metadata

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
Publisher
Springer Science and Business Media LLC
Published
2018-06-25
Volume
18
Issue
1
Pages
47-47
ISSN
1472-6947 (ISSN-L)
DOI
10.1186/s12911-018-0623-9
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
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
  • Health Sciences › Medicine › Health Informatics › 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

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