Helping hospitals predict COVID-19 surges
King’s Health Partners informatics tool, CogStack, could help hospitals plan for surges in COVID-19 cases, based on notes recorded electronically by doctors.
A new study, published in Nature Digital Medicine, found that ‘natural language processing’ (NLP) of information routinely recorded by doctors – as part of patients’ electronic health records – reveal vital trends that could help clinical teams forecast and plan for surges in patients.
This study used King’s Health Partners informatics tool, CogStack to implement NLP. Using NLP algorithms, researchers from King’s College London, King’s College Hospital and Guy’s and St Thomas’ Hospital NHS Foundation Trusts were able to translate the electronic notes made by doctors into a standardised, structured set of medical terms that could be analysed by a computer.
Tracking trends in patient symptoms
Researchers detected re-occurring words or phrases in electronic health records at King’s College Hospital and Guy’s and St Thomas’ NHS Foundation Trusts during key stages of the COVID-19 pandemic last year. For example, they tracked the number of patient records containing keywords for symptomatic COVID-19, such as ‘dry cough’, ‘fever’ or ‘pneumonia’. Throughout the pandemic, hospital doctors have entered patient symptoms and test results into electronic health records, which are used to track the spread of COVID-19 at a national level. However, these records can contain incomplete and unstructured data, that is difficult to access and analyse.
By analysing the text using NLP, the researchers were able to produce real-time maps to signal which symptoms were most frequently recorded by doctors, and these signals closely mirrored patterns of positive laboratory tests reported by each hospital. Clear spikes were visible in March 2020, for instance, during the first wave of COVID-19 cases, and in subsequent waves.
Providing advance warning for hospitals
The study indicates that these signals provide a real-time situational report of activity levels in a hospital and up to four days advance warning for hospitals. This could help hospitals more effectively prepare for surges in COVID-19 admissions.
A strong association was also identified between the trending signals and regional tracking of COVID-19 admissions in London hospitals. In addition, they found that as new COVID-19 symptoms emerged nationally, these symptoms were then more frequently recorded by doctors at King’s College Hospital and Guy’s and St Thomas’ NHS Foundation Trusts.
Dr James Teo, Clinical Director of AI at King’s College Hospital and Guy’s and St Thomas’ NHS Foundation Trusts, said:
By teaching computers how to read and understand doctors’ notes, we hope to reveal important patterns and trends that could help in the fight against COVID-19 and other diseases.
Tracking word trends in electronic health records offers an additional method for studying disease and healthcare activity, in a way that is very easy and cost-effective to run. While this method was shown to be effective in two individual hospital Trusts, the approach could be scaled up to a regional or even national level with the right privacy safeguards.
CogStack is an information retrieval and extraction platform. It implements open-source enterprise search, natural language processing, analytics, and visualisation technologies to unlock the health record and assist in clinical decision making and research.
Using the CogStack platform in this study allowed researchers to interrogate complex sets of data extremely rapidly, providing a real-time feed of what is happening in a particular hospital, allowing clinical teams to prepare for incoming patients.
Prof Richard Dobson, Head of the Department of Biostatistics and Health Informatics, National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre (BRC)said:
The CogStack platform allows us to extract information from deep within hospital records at King’s College Hospital NHS Foundation Trust in near real time. This means we can anticipate likely increases in pressure on the system before receiving information such as test results, giving clinical teams time to react and prepare in advance.
The platforms behind this research are funded by the NIHR Maudsley BRC, Health Data Research UK, UK Research and Innovation, London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare, Innovate UK, the NIHR Applied Research Collaboration South London, the NIHR University College London Hospitals Biomedical Research Centre and King’s College London.