University Hospital Southampton is leading a pioneering research project about how machine learning - a form of artificial intelligence that provides systems the ability to learn and improve from data, can prevent deaths, not just in the UK, but globally.
Dr Daniels, consultant in respiratory at University Hospital Southampton (UHS), comments: Hospitals run on machines using monitoring devises that read patients vital signs like blood pressure, heart rate, breathing rate, and countless other factors. Currently, nurses monitor patients, taking observations throughout the day and night. The higher the score, the sicker the patient is. Doctors are called for deteriorating patients. This is called an early warning score system.
Since its introduction, this approach has improved the quality of care, however it can sometimes identify a deteriorating patient too late. When assessing a patient, doctors need to consider huge amounts of information from many different sources in a short space of time.
In the UK, nearly 80% of patients who experience cardiac arrest in hospital dont survive. Experts estimate that around 50% of those patients could have been saved with more accurate prognosis and earlier warning.
Dr Daniels continues: Machine learning is a field of computer science using statistical techniques to give computers the ability to learn with data without being explicitly programmed how to give the best answer.
It used to be a concept from science fiction, but today machine learning has already become integrated into many aspects of daily life such as mobile technology, satellite navigation, and much more.
Machine learning can use every piece of digitally available data to give a highly personalised, accurate risk score. It has the power to translate this data into actionable information to aid human judgement to help staff make correct and timely decisions and to decrease the chance of errors.
It eliminates reliance on one size fits all clinical practice, giving recommendations for diagnosis, prognosis and treatment that is personalised to the individual patient.
An initial study carried out by the team found that the machine learning algorithm they had produced outperformed the current National early warning system.
Southampton Hospital Charity would like to further Dr Daniels work by enabling him to compare his algorithm to real time data from UHS. If shown to be superior, it be carefully integrated into clinical practice for all 150,000 adult inpatients at UHS.
Potentially, there are around 13 million adult inpatients in UK hospitals every year who could benefit long-term.
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