ML Tool Alerts Doctors to Patients’ Deteriorating Condition 2-8 Hours in Advance

With the massive amount of data in electronic medical records (EMRs) comes the potential for better patient care. For example, the information from the data can be used to help medical staff make decisions that can prevent a patient’s deterioration from adverse events and acute illness. Up until recently, and still in some hospitals, patient data was not available electronically, restricting the capacity to develop digital tools to benefit from it. Now, a study to develop a machine learning tool which provides an early warning to medical professionals of a patient’s deteriorating condition has shown that the early warning deterioration alerts can be set to monitor patients two to eight hours before they are triggered by current clinical criteria.

The machine learning tool developed by scientists from the Commonwealth Scientific and Industrial Research Organization (CSIRO, Canberra, Australia), Australia's national science agency, will allow medical professionals to now use the data contained in EMRs to predict when a patient’s vital signs such as blood pressure or temperature are likely to reach a danger zone, triggering patient decline. The CSIRO scientists are now in discussion with partners for a clinical trial to explore how the alerts work and how they can be best implemented into clinical workflows.

Image: Early warning deterioration alerts can be set to monitor patients two to eight hours before they are triggered by current clinical criteria (Photo courtesy of Pexels)

“Until now there hasn’t been a way to harness all the data in the EMR to predict patient health. This new tool has the potential to transform the day-to-day functioning of health systems,” said CSIRO scientist Dr. Sankalp Khanna. “When applied to a test cohort of 18,648 patient records, the tool achieved 100% for prediction windows two to eight hours in advance for patients that were identified at 95%, 85% and 70% risk of deterioration.”

“Our scientists hold expertise in transforming data into useable information to help guide clinical choices. The new tool also sets out the reasons for the warning, which can guide the choice of intervention,” added Dr. Khanna “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. 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.”

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