A South African first of its kind tool is transforming patient care locally by leveraging Netcare’s advanced electronic medical records system and abundant clinical data. This will help clinicians identify risk of deterioration from common causes earlier among intensive care patients so that treatment can start sooner.
Doctors in Netcare ICUs now have the support of a scientifically developed algorithm that predicts a person’s risk of deteriorating from causes including heart failure, respiratory instability or compromise, infection, sepsis or acute cardiac arrhythmia.
“The prediction algorithm uses automatically recorded real-time heart rate, blood pressure, oxygen saturation and respiratory rate data to detect a person’s chance of deteriorating. This provides vital information that doctors can use to commence therapy much earlier, when such interventions tend to be most effective,” explains Professor Reitze Rodseth, head of clinical data innovation and research at Netcare.
Underpinned by complex mathematics, the artificial neural network applies a machine learning algorithm to analyse electronic information on patients’ vital signs and anticipate the risk of a patient deteriorating eight to ten hours before it is otherwise clinically identifiable.
International collaboration
The sophisticated algorithm was developed over years by researchers from institutions including Netcare, Charité – Universitätsmedizin Berlin, one of the largest academic hospitals in Europe, Telehealth Competence Centre (TCC) in Germany, the University of KwaZulu-Natal, University of Minnesota School of Medicine and Emory University School of Medicine in Atlanta, US and DigitalOn Tech from South Africa.
The algorithm, which goes live in all Netcare ICUs from 28 May 2025, provides an early indication to the treating doctors who are able to assess and adjust medication to prevent their patients’ condition from deteriorating. In the case of sepsis, which is the body’s reaction to an overwhelming infection, this is critical as treatment is especially time sensitive for securing the best outcomes for the patient.
“Crucially, this means that we are supporting doctors to identify this risk hours in advance, and commence potentially lifesaving treatment much earlier – providing an opportunity to address this leading cause of clinical deterioration before it progresses,” says Dr Richard Friedland, co-author of the article published in The Journal of Clinical Medicine and CEO of the Netcare Group.
Turning back the clock
“This insight is of considerable human and clinical value, as once sepsis has reached the stage where the person is showing symptoms, the risk of mortality is tragically as high as 20%. Turning back the clock for the initiation of sepsis treatment with this advanced machine learning algorithm provides clinicians the opportunity to address this leading risk for intensive care patients earlier,” Dr Friedland says.
Digital integration of medical equipment and devices in ICUs and theatres is already well established in all Netcare hospitals through the international award-winning CareOn electronic medical records system, another South African healthcare first that Netcare initiated. With approval from the South African Health Products Regulatory Authority (SAPHRA), this prediction algorithm has been embedded into the CareOn system.
“Strategically, our Group-wide digitisation focus laid a foundation that now enables us to use this technology meaningfully in the clinical setting. The data derived from digitising our operating platforms informs the development and implementation of innovative analytics and algorithms that informs doctors’ decisions at the bedside, improving quality and safety of care, as well as cost-effectiveness,” adds Dr Anchen Laubscher, group medical director of Netcare.
“The World Health Organization notes that treatment of sepsis is most effective when initiated early, and this evidence based tool seeks to provide an early warning system for doctors, adding an extra layer of protection that is especially significant for persons at higher risk of infection and sepsis, including the elderly, long-term ICU patients and people with certain co-morbidities,” she explains.
During the pilot phase, limited to certain Netcare ICUs, it was observed that even among patients whose vital signs were not yet abnormal enough to typically warrant concern, the machine learning algorithm was able to identify an increased risk in the quick sequential organ failure assessment (qSOFA) score, prompting doctors to evaluate this new information as part of their management of intensive care patients for timeous treatment.
Now, the system has been implemented in all Netcare ICUs as part of an ethics approved clinical study to measure the impact of this technology. The system always protects the privacy of patients, and no personal data ever leaves Netcare’s strictly safeguarded information technology environment.
“We believe early warning systems such as this risk prediction tool represents the future of proactive healthcare. Through strategically harnessing evidence-based medicine that is data driven and digitally enabled, we see this groundbreaking application of the algorithm in our ICUs as another important step in our mission to provide the best and safest care, centred on the needs of the individual,” Dr Friedland concludes.