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. 2022 Aug 24;10(8):e38122.
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  1   2   3   4 , Lukasz Roguski  1   2   3 , Xi Bai  1   2   3 , Alex Handy  1   2   3   4 , Roman Klapaukh  4 , Amos Folarin  1   2   4   5   6 , Luis Romao  2   3   4 , Joshua Matteson  7 , Nathan Lea  2   3   4 , Leilei Zhu  3 , Folkert W Asselbergs  2   3 , Wai Keong Wong  3 , Anoop Shah  1   2   3   4 , Richard Jb Dobson  1   2   3   4   5   6
Affiliations

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

Kawsar Noor et al. JMIR Med Inform. .

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.

Keywords: clinical support; electronic health record system; information retrieval; natural language processing; text mining.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
An overview of the CogStack platform as deployed at University College London Hospitals (UCLH). EHR: electronic health record; NLP: natural language processing.
Figure 2
Figure 2
The Kibana interface being used to conduct keyword searches.
See this image and copyright information in PMC

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