A recent study published in international journal ‘Frontiers in Medical Technology’ has highlighted the impact of AI-powered early warning system (EWS) that could predict patient health deterioration up to 16 hours in advance, thereby providing healthcare professionals with a critical window to intervene early and potentially save lives.
One of the largest observational studies of its kind in Indian tertiary care, the study was conducted at King George’s Medical University (KGMU), Lucknow. The research team studied the impact using health AI startup Dozee’s AI-Powered Remote Patient Monitoring and Early Warning System.
In an event organised on Friday, October 25, a team from KGMU and Dozee’s clinical research team presented the key findings of the study and its implications.
Dr Himanshu Dandu, Professor in the Department of Medicine at KGMU, emphasised the technology’s potential to enhance critical care in resource-constrained environments.
“This system enables early detection and continuous patient monitoring, providing a scalable and affordable solution tailored to the demands of healthcare systems facing heavy patient loads. The ability to detect signs of patient health deterioration can significantly improve their survival rates,” Dr Dandu said.
In a nation with 2 million hospital beds, where approximately 1.9 million patients in general wards rely on manual spot checks for monitoring, Dozee’s AI-powered EWS has the potential to transform care across 95 percent of hospital capacity, delivering life-saving continuous monitoring that ensures world-class healthcare at a fraction of the cost of ICU services, said Gaurav Parchani, CTO & co-founder of Dozee.
According to him, this early detection holds the potential to save 21 lakh lives annually and reduce healthcare costs by Rs 6,400 crore.
The study monitored over 700 patients across 85,000 hours.
