Implemented an Azure-native real-time monitoring and analytics platform to process IoT healthcare data, enabling faster clinical insights, improved patient outcomes, and scalable data-driven decision-making.
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The challenge
The organisation relied on an on-premises application to monitor high-risk newborns, which introduced delays in data processing and limited real-time visibility. Monitoring data from IoT devices was not processed quickly enough, leading to delays in identifying critical events. The system also faced scalability constraints, infrastructure limitations, and high operational overhead, making it difficult to expand across multiple hospitals and support growing data volumes.
The approach
Designed and implemented a cloud-native solution leveraging Microsoft Azure and AI services to modernise the existing monitoring platform. The approach included migration from on-premises infrastructure to Azure, enabling real-time data ingestion and processing from IoT devices. AI and analytics capabilities were applied to process and analyse monitoring data, while Azure data services provided scalable, secure, and highly available infrastructure. Automated pipelines and modern DevOps practices ensured efficient deployment, monitoring, and continuous improvement of the platform.
The outcome
Delivered a real-time monitoring platform capable of significantly reducing delays in detecting critical patient events, improving responsiveness in neonatal care. The solution enabled near real-time data processing, enhanced clinical decision-making, and reduced manual workload for healthcare staff. The platform now supports multiple hospitals at scale, provides governed datasets for analytics, and establishes a robust foundation for future AI-driven healthcare innovation and expansion.
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