A new study, published today 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. The researchers from King's College London, King's College Hospital NHS Foundation Trust (KCH), and Guy's and St Thomas' Hospital NHS Foundation Trust (GSTT), used NLP algorithms 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 patients In the same way social media posts can be tracked and aggregated by 'hashtags', the researchers detected words or phrases that were 'trending' in electronic health records at KCH and GSTT, during key stages of the COVID-19 pandemic last year. For instance, 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.