G2

Bikash B.

JAVA FULL STACK INTERN

Computer Software

Small-Business (50 or fewer emp.)

10/27/2026

"Focus on the clinical benefits (Best for a nursing/medical perspective)"

5/5

What do you like best about VUNO Med-DeepCARS?

From a Clinical Operations perspective (Focus on reducing alarm fatigue) The best part about VUNO Med-DeepCARS is its ability to drastically reduce 'alarm fatigue' for our rapid response teams and ward nurses. Traditional early warning systems (EWS) often trigger too many false alarms, causing staff to become desensitized. DeepCARS provides a much higher level of predictive accuracy for cardiac arrest up to 24 hours in advance, while significantly lowering the daily false alarm rate compared to conventional scoring systems. It helps our staff focus their attention on the patients who actually need it most. Review collected by and hosted on G2.com.

What do you dislike about VUNO Med-DeepCARS?

The main drawback isn't really the software itself, but how much it relies on human consistency. Because DeepCARS generates its risk scores based on the four vital signs entered into the EMR, the prediction is only as good—and as timely—as the nursing staff's data entry. If a ward is understaffed and vital signs are delayed in being recorded into the system, the AI cannot provide that crucial early warning. It doesn't replace the need for vigilant, on-time patient monitoring. Review collected by and hosted on G2.com.

What problems is VUNO Med-DeepCARS solving and how is that benefiting you?

From a Clinical/Nursing perspective (Focus on patient safety and alarm fatigue) Problems Solved: Traditional Early Warning Scores (EWS) often generate too many false alarms, which can lead to "alarm fatigue" among the nursing staff. Additionally, predicting sudden clinical deterioration in general wards is incredibly difficult. Benefits: VUNO Med-DeepCARS significantly reduces the daily false alarm rate compared to conventional scoring systems. It provides a 0-100 risk score that predicts in-hospital cardiac arrest up to 24 hours in advance. Because it can predict a cardiac event an average of 15.8 hours before it actually occurs, our rapid response teams have the time they need to intervene proactively. Review collected by and hosted on G2.com.