Australian scientists have demonstrated a diagnostic tool that can provide an early warning to medical professionals that a patient’s condition is deteriorating.
Researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) have developed a machine learning tool that can analyse a patient’s medical records in order to predict when a patient’s condition will deteriorate.
In a study published in the journal Nature: Scientific Reports, the CSIRO team showed that the early warning system can identify complications in patients up to eight hours before they would normally meet clinical criteria.
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The algorithm uses data contained in a patient’s electronic medical records (EMRs) and measures things such as blood pressure, temperature and other vital signs.
Combining the data allows the program to make accurate predictions about when these measurements will likely exceed acceptable levels and signal patient deterioration.
Dr Sankalp Khanna, lead author of the study, says the technology has the potential to revolutionise patient care for a number of conditions.
“Until now there hasn’t been a way to harness all the data in the EMR to predict patient health,” he says.
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“This new tool has the potential to transform the day-to-day functioning of health systems.
“When applied to a test cohort of 18,648 patient records, the tool achieved 100 per cent sensitivity for prediction windows two to eight hours in advance for patients that were identified at 95 per cent, 85 per cent and 70 per cent risk of deterioration.”
Alerting doctors and nurses early to a deteriorating patient gives the best chance of successfully intervening in a timely manner.
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“The alerts warn medical staff when a patient is at risk of deterioration, leading to possible death, cardiac arrest, or unplanned admission to ICU. The tool can notify of the need for clinical intervention,” Dr Khanna says.
“Clinical decision support tools such as these are a pre-emptive solution that can provide medical staff with an opportunity to intervene earlier to prevent adverse patient outcomes.”
Dr David Cook, intensive care unit staff specialist at Princess Alexandra Hospital, says the algorithm provides a practical way to manage unexpected patient deterioration across a large hospital such as his.
“It is done without process duplication, nor does it interfere with established best practice systems, which are used to recognise sick and deteriorating ward patients.”
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