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How can edge computing improve healthcare data operations?

Edge computing can help healthcare organizations augment their infrastructure to take advantage of diversifying data management.
By admin
Apr 19, 2022, 9:55 AM

With terabytes of data moving through the average healthcare system every day, CIOs are constantly in search of ways to reduce latency and avoid disruptions while maintaining ironclad cybersecurity.

Cloud computing has proven a highly valuable tool in this effort, but there are additional ways to enhance the agility, accessibility, and efficiency of health data infrastructure, especially as health systems add more and more devices to their Internet of Things (IoT).

Edge computing is one of the most promising solutions for complementing existing cloud infrastructure and helping healthcare organizations maximize their technical resources.

What is edge computing?

Defined by IBM as “a distributed computing framework that brings enterprise applications closer to data sources, such as IoT devices or local edge servers,” edge computing can produce improved response times and better bandwidth to complement existing cloud-based infrastructure efforts.

Edge computing is about physical proximity as much as anything else.  The gains may be calculated in fractions of a second, but it literally takes less time to complete a process geographically close to device rather than sending the data to a central server located hundreds or thousands of miles away.

By processing as much data as possible close to the device, or the “edge” of the network, organizations can better manage the flow of data to and from their centralized servers, optimizing their cloud bandwidth and keeping resources more available for other critical tasks.

What are some use cases for edge computing?

Healthcare systems are full of devices.  From bedside monitors and robotic surgical equipment to smartphones, laptops, and even implantable devices, these tools simply must work correctly all the time with no exceptions.  

Increasingly, patient monitoring tools and provider workflow software are using resource-intensive machine learning and artificial intelligence algorithms to analyze information and inform decision-making.

Edge computing allows those calculations to take place on the device itself, or on a local edge server, instead of requiring devices to send data to a larger central hub for processing.  Distributed processing makes the entire network more resilient to points of failure while increasing the speed and availability of datapoints that must be available within milliseconds.

For example, edge computing is extremely helpful for supporting telehealth and remote patient monitoring initiatives, particularly in rural areas where broadband connections can be spotty and 4G or 5G networks are still scarce.  

With edge computing, devices can synthesize and analyze data locally, then transmit the information to a centralized location when the network is stable or most available.  This can ensure seamless, immediate access to results for local users while preventing network overload every time a transaction occurs.

How can healthcare organizations embrace edge computing?

Most health systems already use some form of edge computing, whether they are aware of it or not, but few organizations have a concrete strategy for maximizing the potential of distributed processing.

To make more informed decisions about the edge, organizations should start by mapping out their existing infrastructure and identifying the devices and data points most suitable for decentralized processing.  Pinpoint which data elements need to be sent to more powerful processing hubs for enhanced analytics or security reasons and which can be successfully handled by edge devices.

Consider prioritizing use cases tied to specific business goals that have standardized architecture and low-risk outcomes, then use these lessons to expand to other areas.  

By integrating edge computing techniques with existing cloud-based infrastructure, healthcare organizations can increase their agility and reduce unwanted latency while keeping device end-users appropriately informed and satisfied.

 


Jennifer Bresnick is a journalist and freelance content creator with a decade of experience in the health IT industry. Her work has focused on leveraging innovative technology tools to create value, improve health equity, and achieve the promises of the learning health system.


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