The Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) are transforming clinical decision making with intelligent healthcare analytics. These solutions relieve busy clinicians by processing real-time data from medical sensors and wearable devices to detect conditions like sepsis, skin cancer, antibiotic resistance, neurodegenerative diseases, chronic illnesses, and more.
Cloud computing delivers the high performance and capacity needed to quickly process large data sets to identify health conditions requiring intervention. However, moving huge volumes of data to the cloud is impractical in some cases. For example, rural clinics often lack wide area network (WAN) telecommunications bandwidth to upload or download large medical image files to and from the cloud. Even large urban hospital systems lack the bandwidth to transmit readings from multiple sensors on hundreds of thousands of hospital beds. And for image storage, per-gigabyte cloud data fees add up quickly.
•Supporting clinical decision making
•Predicting disease for proactive intervention
•Simplifying compliance with privacy regulations
•Standardizing data formats for centralized analytics and population studies