Business Problem
Delays in detecting life-threatening conditions dueto lack of lacked real-time insights and predictive capabilities posed risks topatient safety and limited the ability to respond proactively.
Actions Taken
· Developed an IoT-based monitoring systemusing a network of sensors to collect continuous data.
· Streamed real-time sensor data to acloud-based platform for analysis.
· Applied analytics and machine learning modelsto detect anomalies and identify threshold breaches.
· Enabled automated activation of alarms incritical situations.
Outcomes Achieved
· Enhanced patient safety through real-timemonitoring and faster response times.
· Improved detection of abnormal conditionsbefore escalation.
· Optimized resource allocation and reducedoperational downtime.
· Enabled data-driven facility management andlong-term performance improvement.
