IITian Innovates AI-Based Early Warning System for Critical Patient Care

By Sunil Sonkar
2 Min Read
IITian Innovates AI-Based Early Warning System for Critical Patient Care

Hospitals can be busy places. Sometimes there just aren’t enough nurses to go around to look after the patients. The situation can lead to critical patient data, like blood pressure and heart rate, being overlooked because nurses can’t be there throughout the day and night across weeks and months.


To tackle such serious problem, Gaurav Parchani, a graduate of IIT Indore, has started a company that uses Artificial Intelligence (AI) to solve the staffing shortage issue and simultaneously improve patient monitoring. He initially worked on making racing cars faster, but later realized he could make a big impact in healthcare by applying technology as there are similarities between health tech and racing car tech.

This newly developed medical technology operates with just a single sensor placed on the patient’s bed. It handles all monitoring tasks seamlessly. The sensor continuously conducts comprehensive tests on the patient and displays the data on a nearby screen. Simultaneously, this valuable information is transmitted to the central computer system of the hospital. Moreover, should a patient’s vital signs deviate from the expected norms, such as an increase in blood pressure or indications of a potential heart issue on the ECG, an immediate alert is triggered.

Under-bed sensors can detect blood pressure, heart rate and breathing rate seamlessly. Meanwhile, three wireless devices worn by the patient monitor oxygen levels, ECG and temperature as well. This means patients don’t have to keep calling for help every time they need to move around the hospital.

This AI-based technology is already being used in over 300 hospitals and it covers around 8,000 beds in India. Hospitals like King George Medical College in Lucknow and PGI in Chandigarh have placed these sensors in their beds. Data from Chennai’s Apollo Hospital and Lucknow’s King George Medical College show that this system has helped save 80 percent of patients by spotting changes in their condition up to 8 hours before things get worse.

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