CogStack – an information retrieval, extraction and natural language processing platform – has been successful in the latest round of the Artificial Intelligence in Health and Care Award for innovative use of Natural Language Processing (NLP) to transform and improve research, planning and care.
The platform, originally developed by researchers at the NIHR Maudsley BRC in partnership with the University College London Hospitals NHS Foundation Trust BRC, uses artificial intelligence to reveal important data locked in patient’s health records to support clinical decision making and healthcare research.
First used at King’s College Hospital, the Award will see the technology used across five NHS Trusts – BHP founder member University Hospitals Birmingham and:
- University College London Hospitals
- Guys & St Thomas Hospital
- King’s College Hospital
- South London & Maudsley Hospital
The Artificial Intelligence in Health and Care Award is one of the NHS AI Lab programmes, led by NHSX. The competitive award scheme is run by the Accelerated Access Collaborative (AAC) in partnership with the National Institute for Health Research (NIHR).
Using Natural Language Processing to unlock health records
Electronic health record systems are important for both patient care and for research into improving healthcare. However, records can often be difficult to access and contain incomplete and unstructured data such as narrative text.
Narrative text is challenging to analyse, and the process of assigning standardised codes to records based on particular words, conditions or treatments, also known as “Clinical Coding” is normally performed manually by NHS staff. Clinical coding is a vital part of nearly all delivery of care, planning and service improvement for healthcare but is time-consuming, expensive and carries a risk of mistakes.
With the government funding received from the AI Award, this CogStack project will use a type of Artificial Intelligence called natural language processing to read clinical language in the NHS and assess the magnitude of efficiency savings and improved performance if clinical coding were reduced by this technology.
Professor Simon Ball, UHB Chief Medical Officer, said:
“Using CogStack gives us a real opportunity to find out more about tailored treatments, and ensure we can offer our patients individualised and improved care.
“By reading doctors notes and summarising them in a format that is easily understandable, we aim to reduce admin time and ensure information about people’s symptoms, tests, and treatments are shared securely and appropriately.
“We’re looking forward to working with colleagues in London to implement the CogStack platform here at UHB and hopefully improve patient care across the wider NHS in time.
Claire Rymer, Performance and Operational Informatics Manager at UHB, added: “Clinical coders are trained to analyse clinical statements and assign standard codes to them, which can often be a time consuming and complex process.
“The data produced by this coding forms an integral part of health information management and is relied upon for informing research, epidemiological studies, healthcare resource allocation and national reporting.
“The CogStack project gives us a fantastic opportunity to redefine the process of clinical coding, which we believe will lead to richer, standardised data summaries that improve patient care and ensure clinical time is used as well as possible.”
Patients and the public are central to this project and informed its design. A coordinated, cross-site PPIE group will provide input to the CogStack learnings, building a patient involvement toolkit, ensuring public members are equipped to review NLP projects, and can embed patient priorities into the future use of this tool.
The AI Award
The AI Award is one of the programmes that make up the NHS AI Lab, led by NHSX and delivered in partnership with the Accelerated Access Collaborative (AAC) and National Institute for Health Research (NIHR).
The Award aims to increase the impact of AI-driven technologies to help solve clinical and operational challenges across the NHS and care settings. It will speed up the most promising technologies through the regulatory process by building an evidence base to demonstrate the effectiveness and safety of AI-driven technologies in health and social care.