'AI-powered system predicts patient deterioration up to 16 hrs in advance'

Healthtech firm Dozee's AI-powered system alerted patients' health deterioration about 16 hours in advance and continuous monitoring saved 10% of healthcare practitioners' time

In a nation with 2 million hospital beds, where approximately 1.9 million patients in general wards rely on manual spot checks for monitoring, healtech firm Dozee’s AI-Powered remote patient monitoring and early warning system (EWS) has the potential to transform care across 95 per cent of hospital capacity, according to a study published in journal Frontiers in Medical Technology. The study was conducted at King George’s Medical University (KGMU) and is one of the largest observational studies of its kind in Indian tertiary care. The study said Dozee’s technology enables delivering life-saving continuous monitoring that ensures healthcare at a fraction of the cost of ICU services.

 

 

This study highlights the ability of the technology to predict patient health deterioration up to 16 hours in advance. This provides healthcare professionals with a critical window to intervene early and potentially save lives.

 

The observational study monitored over 700 patients across 85,000 hours and demonstrated how Dozee’s EWS can transform traditional manual processes. By delivering alerts up to 16 hours before a critical event, Dozee’s system empowers healthcare professionals to act earlier, improving patient outcomes while saving healthcare practitioners 2.4 hours per staff member per day. The study analysed key metrics including alert sensitivity, specificity, average time from initial alert to deterioration. It also included healthcare practitioners' activity.

 

In many Indian hospitals, continuous monitoring is limited to ICUs, leaving general wards—where the majority of patients reside—vulnerable to undetected clinical deterioration. This study demonstrates that Dozee’s EWS bridges this gap by continuously tracking vitals such as heart rate, respiratory rate, and blood pressure. The results showed that Dozee's EWS predicted patient deterioration in 67 per cent to 94 per cent of cases, allowing healthcare providers to intervene long before conditions became critical. This early detection holds the potential to save 21 lakh lives annually and reduce healthcare costs by Rs 6,400 crore.

 

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 provides 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,” said Dandu.

 

Dr Jean-Louis Teboul, a leading intensivist and critical care expert from Paris-Saclay University, emphasised the global implications of the study, “What we have achieved in India has the potential to reshape healthcare globally. The challenges may differ, but the need for equitable, timely, and affordable care remains universal.”

 

Gaurav Parchani, CTO and Co-Founder of Dozee said the results of this study demonstrates technology’s ability to transform healthcare, making it more efficient, accessible, and equitable. “We’re not just solving a problem for India but laying the groundwork for global healthcare solutions,” said Parchani.