Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals

Kawsar Noor; Lukasz Roguski; Xi Bai; Alex Handy; Roman Klapaukh; Amos Folarin; Luis Romao; Joshua Matteson; Nathan Lea; Leilei Zhu; Folkert W Asselbergs; Wai Keong Wong; Anoop Shah; Richard JB Dobson

doi:10.2196/38122

Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals

Kawsar Noor, Lukasz Roguski, Xi Bai, Alex Handy, Roman Klapaukh, Amos Folarin, Luis Romao, Joshua Matteson, Nathan Lea, Leilei Zhu, Folkert W Asselbergs, Wai Keong Wong, Anoop Shah, Richard JB Dobson

Epic Systems Corporation
UCL Hospitals NHS Foundation Trust
Univ London, Birkbeck University London, University College London, University of London Royal Veterinary College, University of London, St Georges University London, Imperial College London, Kings College London, Royal Holloway University London, Queen Mary University London, Div Populat Hlth Sci & Educ

Research output: Contribution to journal › Article › peer-review

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174 Downloads (Pure)

Abstract

BACKGROUND: As more health care organizations transition to using electronic health record (EHR) systems, it is important for these organizations to maximize the secondary use of their data to support service improvement and clinical research. These organizations will find it challenging to have systems capable of harnessing the unstructured data fields in the record (clinical notes, letters, etc) and more practically have such systems interact with all of the hospital data systems (legacy and current).

OBJECTIVE: We describe the deployment of the EHR interfacing information extraction and retrieval platform CogStack at University College London Hospitals (UCLH).

METHODS: At UCLH, we have deployed the CogStack platform, an information retrieval platform with natural language processing capabilities. The platform addresses the problem of data ingestion and harmonization from multiple data sources using the Apache NiFi module for managing complex data flows. The platform also facilitates the extraction of structured data from free-text records through use of the MedCAT natural language processing library. Finally, data science tools are made available to support data scientists and the development of downstream applications dependent upon data ingested and analyzed by CogStack.

RESULTS: The platform has been deployed at the hospital, and in particular, it has facilitated a number of research and service evaluation projects. To date, we have processed over 30 million records, and the insights produced from CogStack have informed a number of clinical research use cases at the hospital.

CONCLUSIONS: The CogStack platform can be configured to handle the data ingestion and harmonization challenges faced by a hospital. More importantly, the platform enables the hospital to unlock important clinical information from the unstructured portion of the record using natural language processing technology.

Original language	English
Article number	e38122
Pages (from-to)	e38122
Journal	JMIR Medical Informatics
Volume	10
Issue number	8
DOIs	https://doi.org/10.2196/38122
Publication status	Published - 24 Aug 2022

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Deployment of a Free-Text_NOOR_Publishedonline24August2022_GOLD VoR (CC BY)Final published version, 342 KBLicence: CC BY

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Noor, K., Roguski, L., Bai, X., Handy, A., Klapaukh, R., Folarin, A., Romao, L., Matteson, J., Lea, N., Zhu, L., Asselbergs, F. W., Wong, W. K., Shah, A., & Dobson, R. JB. (2022). Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals. JMIR Medical Informatics, 10(8), e38122. Article e38122. https://doi.org/10.2196/38122

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title = "Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals",

abstract = "BACKGROUND: As more health care organizations transition to using electronic health record (EHR) systems, it is important for these organizations to maximize the secondary use of their data to support service improvement and clinical research. These organizations will find it challenging to have systems capable of harnessing the unstructured data fields in the record (clinical notes, letters, etc) and more practically have such systems interact with all of the hospital data systems (legacy and current).OBJECTIVE: We describe the deployment of the EHR interfacing information extraction and retrieval platform CogStack at University College London Hospitals (UCLH).METHODS: At UCLH, we have deployed the CogStack platform, an information retrieval platform with natural language processing capabilities. The platform addresses the problem of data ingestion and harmonization from multiple data sources using the Apache NiFi module for managing complex data flows. The platform also facilitates the extraction of structured data from free-text records through use of the MedCAT natural language processing library. Finally, data science tools are made available to support data scientists and the development of downstream applications dependent upon data ingested and analyzed by CogStack.RESULTS: The platform has been deployed at the hospital, and in particular, it has facilitated a number of research and service evaluation projects. To date, we have processed over 30 million records, and the insights produced from CogStack have informed a number of clinical research use cases at the hospital.CONCLUSIONS: The CogStack platform can be configured to handle the data ingestion and harmonization challenges faced by a hospital. More importantly, the platform enables the hospital to unlock important clinical information from the unstructured portion of the record using natural language processing technology.",

author = "Kawsar Noor and Lukasz Roguski and Xi Bai and Alex Handy and Roman Klapaukh and Amos Folarin and Luis Romao and Joshua Matteson and Nathan Lea and Leilei Zhu and Asselbergs, {Folkert W} and Wong, {Wai Keong} and Anoop Shah and Dobson, {Richard JB}",

note = "Funding Information: RJBD is supported by the following: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London, London, United Kingdom; (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement 116074, which receives support from the European Union{\textquoteright}s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and European Society of Cardiology; (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London; (6) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare; and (7) the NIHR Applied Research Collaboration South London (NIHR ARC South London) at King{\textquoteright}s College Hospital National Health Service Foundation Trust. Funding Information: This study has been supported by the National Institute for Health Research University College London Hospitals Biomedical Research Center, in particular, by the National Institute for Health Research (NIHR) University College London Hospitals/University College London Biomedical Research Centre Clinical and Research Informatics Unit. Publisher Copyright: {\textcopyright} Kawsar Noor, Lukasz Roguski, Xi Bai, Alex Handy, Roman Klapaukh, Amos Folarin, Luis Romao, Joshua Matteson, Nathan Lea, Leilei Zhu, Folkert W Asselbergs, Wai Keong Wong, Anoop Shah, Richard JB Dobson. Originally published in JMIR Medical Informatics.",

year = "2022",

month = aug,

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doi = "10.2196/38122",

language = "English",

volume = "10",

pages = "e38122",

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Noor, K, Roguski, L, Bai, X, Handy, A, Klapaukh, R, Folarin, A, Romao, L, Matteson, J, Lea, N, Zhu, L, Asselbergs, FW, Wong, WK, Shah, A & Dobson, RJB 2022, 'Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals', JMIR Medical Informatics, vol. 10, no. 8, e38122, pp. e38122. https://doi.org/10.2196/38122

TY - JOUR

T1 - Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital

T2 - CogStack at University College London Hospitals

AU - Noor, Kawsar

AU - Roguski, Lukasz

AU - Bai, Xi

AU - Handy, Alex

AU - Klapaukh, Roman

AU - Folarin, Amos

AU - Romao, Luis

AU - Matteson, Joshua

AU - Lea, Nathan

AU - Zhu, Leilei

AU - Asselbergs, Folkert W

AU - Wong, Wai Keong

AU - Shah, Anoop

AU - Dobson, Richard JB

N1 - Funding Information: RJBD is supported by the following: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, United Kingdom; (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome Trust; (3) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement 116074, which receives support from the European Union’s Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and European Society of Cardiology; (4) the NIHR University College London Hospitals Biomedical Research Centre; (5) the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London; (6) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare; and (7) the NIHR Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital National Health Service Foundation Trust. Funding Information: This study has been supported by the National Institute for Health Research University College London Hospitals Biomedical Research Center, in particular, by the National Institute for Health Research (NIHR) University College London Hospitals/University College London Biomedical Research Centre Clinical and Research Informatics Unit. Publisher Copyright: © Kawsar Noor, Lukasz Roguski, Xi Bai, Alex Handy, Roman Klapaukh, Amos Folarin, Luis Romao, Joshua Matteson, Nathan Lea, Leilei Zhu, Folkert W Asselbergs, Wai Keong Wong, Anoop Shah, Richard JB Dobson. Originally published in JMIR Medical Informatics.

PY - 2022/8/24

Y1 - 2022/8/24

N2 - BACKGROUND: As more health care organizations transition to using electronic health record (EHR) systems, it is important for these organizations to maximize the secondary use of their data to support service improvement and clinical research. These organizations will find it challenging to have systems capable of harnessing the unstructured data fields in the record (clinical notes, letters, etc) and more practically have such systems interact with all of the hospital data systems (legacy and current).OBJECTIVE: We describe the deployment of the EHR interfacing information extraction and retrieval platform CogStack at University College London Hospitals (UCLH).METHODS: At UCLH, we have deployed the CogStack platform, an information retrieval platform with natural language processing capabilities. The platform addresses the problem of data ingestion and harmonization from multiple data sources using the Apache NiFi module for managing complex data flows. The platform also facilitates the extraction of structured data from free-text records through use of the MedCAT natural language processing library. Finally, data science tools are made available to support data scientists and the development of downstream applications dependent upon data ingested and analyzed by CogStack.RESULTS: The platform has been deployed at the hospital, and in particular, it has facilitated a number of research and service evaluation projects. To date, we have processed over 30 million records, and the insights produced from CogStack have informed a number of clinical research use cases at the hospital.CONCLUSIONS: The CogStack platform can be configured to handle the data ingestion and harmonization challenges faced by a hospital. More importantly, the platform enables the hospital to unlock important clinical information from the unstructured portion of the record using natural language processing technology.

AB - BACKGROUND: As more health care organizations transition to using electronic health record (EHR) systems, it is important for these organizations to maximize the secondary use of their data to support service improvement and clinical research. These organizations will find it challenging to have systems capable of harnessing the unstructured data fields in the record (clinical notes, letters, etc) and more practically have such systems interact with all of the hospital data systems (legacy and current).OBJECTIVE: We describe the deployment of the EHR interfacing information extraction and retrieval platform CogStack at University College London Hospitals (UCLH).METHODS: At UCLH, we have deployed the CogStack platform, an information retrieval platform with natural language processing capabilities. The platform addresses the problem of data ingestion and harmonization from multiple data sources using the Apache NiFi module for managing complex data flows. The platform also facilitates the extraction of structured data from free-text records through use of the MedCAT natural language processing library. Finally, data science tools are made available to support data scientists and the development of downstream applications dependent upon data ingested and analyzed by CogStack.RESULTS: The platform has been deployed at the hospital, and in particular, it has facilitated a number of research and service evaluation projects. To date, we have processed over 30 million records, and the insights produced from CogStack have informed a number of clinical research use cases at the hospital.CONCLUSIONS: The CogStack platform can be configured to handle the data ingestion and harmonization challenges faced by a hospital. More importantly, the platform enables the hospital to unlock important clinical information from the unstructured portion of the record using natural language processing technology.

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DO - 10.2196/38122

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SN - 2291-9694

VL - 10

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JO - JMIR Medical Informatics

JF - JMIR Medical Informatics

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M1 - e38122

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Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals

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