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Healthcare data analytics at the edge due to have major impact

The healthcare industry has historically been plagued by outdated technology and glacial processes. But healthcare data analytics at the edge stands to reduce costs and improve patient care.

When it comes to IT, healthcare is one of the most challenging industries. For decades, healthcare has been plagued by aging technology, manual processes, regulatory requirements and practitioners resistant to digitization. As a result, the healthcare industry—despite clear need—has trailed other sectors in adoption of new and necessary technology.

But new technologies like the Internet of Things (IoT) and edge computing are bringing healthcare into the now—and fast. Better patient care requires technologies like smartphones and IoT-connected devices. These devices can improve communication between healthcare practitioners and their patients and provide essential real-time data on patient health. These devices are also data- and bandwidth-hungry.

As a result, industries like healthcare have had to seek out new architectures to accommodate this data and device overload. Enter edge computing, which reduces the need to send data to centralized data centers in the cloud. Instead, processing can be achieved at the edge, eliminating the need for data to travel to the cloud and back. Edge computing thus eliminates latency and performance issues that can slow mobile apps, IoT data gathering and other processes.

But this marriage of new technology architectures and devices—what we’ll call healthcare analytics at the edge—is just starting to take hold. Let’s take a look at how it might progress.

The new edge computing infrastructure

A new digital architecture is necessary for all industries, not just healthcare, to make use of data insight. It’s a more complex, but also a richer, landscape. It has more potential for efficiency, productivity, robustness and agility. It involves new and innovative networking infrastructure that is still in its infancy but finding champions.

The need for edge computing is simple: IT management is now marked by the proliferation of devices, data and users. The solution is, in principle, just as simple: put a new layer of processing between them. Doing so would boost the available computing power for devices, particularly for those engaged in IoT functions, ramp up the bandwidth and manage the increased traffic. This new layer eliminates the need for data to travel back and forth to a cloud-based data center, which can create latency problems. Data processing at the edge may also boost security and provide much-needed intelligence to business processes through artificial intelligence at the edge.

Healthcare data analytics at the edge: The benefits

Over the past several years, healthcare has adopted technologies, including IoT-connected devices, to monitor at-risk patients with a variety of conditions, from cancer to diabetes. Wearables – Fitbits, smartwatches, mobile apps for exercise and diet – offer meaningful data for enterprise healthcare systems. Wearables, and the data analytics they generate on heartrate and other health indicators, can have valuable effects on preventive healthcare, provider and payor efficiency, and the predictive analytics that the healthcare enterprise embraces as mission-critical, across roles. This data can improve patient health and outcomes and reduce healthcare costs.

Patients can track their own activity, receiving prompts, notifications and suggestions from wearables. When these devices are connected to the edge, devices can also receive real-time input from edge-based systems that augment suggestions and predictions with artificial intelligence (AI)-based awareness of impending negative outcomes.

In the future, a smartwatch will warn you that your heart and blood pressure are too high during your run through the hills. It will also consult with the edge in real time and let you know that, based on your recent food intake, the temperature outdoors, and your medical history, you’re more at risk for a stroke event. Healthcare data analytics at the edge isn’t a crystal ball, but it’s predictive, intelligent and attuned to specific circumstances.

Healthcare data analytics at the edge: The challenges

This is where the healthcare industry can take the lead in IT modernization, extending its systems to the edge to reduce costs and improve patient care.

Despite these inviting benefits, however, there are some substantial hurdles. More sources of input into enterprise healthcare systems inevitably means more security risk for those systems; millions of devices connecting to cloud systems that used to talk only to other cloud systems means many more opportunities for attack. And it’s not just IoT. Edge systems themselves, by definition, exist beyond enterprise firewalls, and must thus be independently secured.

IoT devices have historically suffered from a lack of standardization that has further exacerbated security concerns. Healthcare, retail, manufacturing and consumer smart-home technologies use different standards and technologies.

As a result, IoT devices each have their own firmware update processes and security protocols. Healthcare, of course, requires rigorous security standards to satisfy the Health Insurance Portability and Accountability Act (HIPAA). Which standards among the many can satisfy HIPAA requirements? Will partner organizations in healthcare chains share data through the edge, and, if so, how can they do so securely? Another key issue is how blockchain technologies, a secure digital ledger, might help transmit data securely.

While there is a good deal to sort out in terms of secure transfer of patient data through connected devices and edge computing, the healthcare industry stands to gain a great deal. Effective use of IoT devices at the edge could introduce efficiency, productivity, cost reduction and, most of all, better patient care to an industry long resistant to IT advancements.

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Scott Robinson

Scott Robinson is director of business intelligence at Lucina Health in Louisville, Ky.