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Healthcare AI, IoT Require Legacy Data Integration, Edge Computing

As organizations embrace healthcare AI and the IoT, they need to consider their legacy applications and how to integrate them for the digital transformation and edge computing.

Healthcare AI and IoT

Source: Thinkstock

By Elizabeth O'Dowd

- MapR Technologies and C3 IoT announced their partnership to bring an end-to-end solution for healthcare artificial intelligence (AI) and Internet of Things (IoT) applications.

The partnership will address the demand of enterprise architecture for intelligent applications based on AI and IoT. The partnership aims to provide faster time to value and broader deployment options and will allow customers to leverage a combination of on-premises data center, public cloud, and edge computing.

The companies emphasize the importance of the integration of intelligent applications with legacy data to the digital transformation. Their solution supports legacy applications and multi-tenancy so organizations can maintain their existing mission-critical systems while they introduce new AI and IoT applications and development tools into their IT infrastructure.

The C3 IoT and MapR platform and others like it are necessary because organizations cannot rip and replace their entire IT infrastructure when they want to make significant upgrades. The latter need a technology that will support legacy applications while integrating new and more advanced ones into the IT infrastructure.

AI and IoT are some of the most transformative technology at the moment, according to MapR CEO Matt Mills. Both technologies need to work with legacy applications to build a solid foundation for the digital transformation.

AI and the IoT are significant as enterprise architecture shifts from a centralized model to a decentralized approach. Edge computing is emerging in healthcare as organizations continue to introduce connected devices in their health IT ecosystems.

Healthcare organizations are adopting more analytics initiatives for population health and research purposes. IoT devices are producing more data which makes AI necessary for the data to be analyzed and made actionable.

Organizations are also looking into using AI and IoT devices to diagnose patients at the point of care, which should give the patient results quicker and also save organizations money as a result of patients no longer needing to return for a second appointment to review results.

A clinician’s mobile device is the edge between the patient who is the data source and the cloud. A clinician treating a patient with a tablet will be able to enter patient data into the analytics platform at the edge where it is processed and displayed in near real-time.

Edge computing is needed in enterprises with a substantial amount of IoT devices, including the push from cloud services, pull from IoT, and the change from data consumer to producer, the report explained.  Edge computing lets organizations explore the true value of the data collected by IoT devices by using it effectively.

The push from cloud services is the result from organizations placing more commuting tasks in the cloud. While this method is efficient because of the computing power offered by the cloud, data is being produced at the edge in quantities that are too large to be passed to the cloud due to bandwidth restrictions.

The volume of IoT devices and the data they constantly produce causes a bottleneck for cloud-based computing. Cloud-based computing is more efficient than edge computing, but bandwidth restrictions make edge computing the better choice for near real-time analytics. 

To take advantage of edge computing, organizations need a platform that will bridge legacy applications to more advanced AI technology so the data can be used without totally rebuilding the app right away.

Large healthcare organizations with current or future big data analytics prospects need to look into edge computing to ensure that clinicians and patients will get the best response times from the data they produce. However, organizations need to keep their legacy applications in mind while they plan for the future to ensure that they can use all of their applications and data effectively.