Client Overview
A group of multi-specialty hospitals situated in various cities decided to upgrade their patient monitoring processes. Because waiting on manual logs and alerts was common, important tasks were delayed, and both patients and workers were affected.
eInnosys introduced a complete IoT-based system for monitoring vital signs remotely, sending automatic updates, and effortlessly merging with the EHR platform. As a result, monitoring accuracy improved by 35%, the time needed for emergency response was reduced by 25%, and productivity went up by 30%.
About the Company
Challenges
Manual Monitoring Delays
Many vital signs were not properly collected, so nurses did not always discover critical health emergencies on time.
No Real-Time Alerts
Quick drops in blood pressure or decreases in oxygen were usually spotted too late, making it take longer for help to arrive.
Isolated Devices and Systems
Even though each department used different monitoring tools, none of them connected with the central EHR, which delayed care coordination.
High Staff Workload
Shifting some of the responsibilities to staff members led to greater stress and less attention to patients because of the continuous logging.
Our IoT-Driven Solutions
IoT-Enabled Vital Monitoring Devices
The patients had direct heart rate, blood pressure, oxygen, and vital monitoring with continuous data transmission wirelessly to the cloud.
Real-Time Alerts and Escalation System
Made an automated system that sends out mobile and desktop alerts to doctors and emergency staff when patients’ vital signs jump beyond critical points.
Centralized Dashboard and EHR Integration
All sensors were linked to a single dashboard, which connected to the hospital’s EHR in real time with the help of HL7 and FHIR standards.
Predictive Analytics for ICU and Chronic Care
Make use of AI to monitor live sensor readings and a patient’s records, anticipating risks such as sepsis or a decline in health condition.
Tech Stack
IoT Devices & Firmware : Raspberry Pi, Arduino, BLE Sensors
Cloud Platform : Microsoft Azure IoT Hub, Azure Functions
AI/ML Frameworks : Scikit-learn, Keras
Visualization Tools : Power BI, Grafana
Integration Protocols : HL7, FHIR
Business Impact
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