The healthcare sector has a large public-sector presence throughout Europe. It features significant regulatory guidance and intervention. It is critical for European society’s well-being. The industry has many large and small healthcare facilities, with data-sharing needs among them, as well as unique privacy concerns. It also has a large amount of mobility of staff, patients, medications, dangerous materials, and valuable hospital assets that need tracking, coordination, and control. This sector extensively uses connected devices, asset tracking, patient monitoring and analytics; healthcare facilities are good candidates for deploying local edge computing resources for security, privacy, and efficiency.

The number of IoT active connections in healthcare in the EU during 2019 stood at 2.79 million and is expected to reach 10.34 million by 2025. In sum, healthcare is critical for society, a strong candidate for diverse CEI use cases, a subject of significant regulation and policy attention, and a very different example than the other industries discussed, all of which make it important in the context of CEI.

Spotlight use case categories

Hospital Asset Tracking

Hospitals can track and manage valuable assets to improve patient care, reduce losses, and optimise resource utilisation. This use case integrates with other hospital systems and enables digitalisation. Edge computing is commonly used, but a mix of edge and cloud resources is utilised.

Bedside Telemetry

IoT-driven bedside monitoring allows continuous patient monitoring and automated alerts for faster response. It improves patient care and efficiency with limited staff. The system relies on sensors, edge infrastructure for data analysis and alerts, and cloud computing for training models and management.

AI-Assisted Diagnosis and Treatment

AI systems analyse diverse patient data to assist healthcare providers in early detection and personalised treatment. It improves health outcomes and reduces costs. This use case is in the early stages of development and relies on AI technology. Local data analysis is done with edge computing, while cloud-based systems are used to analyse and train AI models.