“5G and edge computing will enable the low-latency, real-time guaranteed conditions necessary to use IoT devices for patient monitoring and at-home care. For rural patients unable to access the care provided in larger metropolitan facilities, this could be a game-changer.”
Greg Chiasson, Principal, Capital Projects & Infrastructure (Technology, Media and Telecommunications), PwC US
Centralization has been a predominant paradigm in healthcare computing for the past several decades. Originally, this meant that provider organizations operated large, centralized data centers that housed mainframes and servers designed to serve as the industry’s computing workhorses. Recent technological advancements are starting to shift that paradigm, however.
Edge computing, through on-site sensors and devices, as well as last-mile edge equipment that connects to those devices, allows data processing and analysis to happen close to the digital interaction. Rather than using centralized cloud or on-premises infrastructure, these distributed tools at the edge offer the same quality of data processing but without latency issues or massive bandwidth use. It seems clear that edge computing will play an essential role in health care. But what is it? And why is it a significant development?
First, some basics – Edge computing is the practice of capturing, storing, processing, and analyzing data near the client, where the data is generated, instead of in a centralized data-processing warehouse. Hence, the data is stored at intermediate points at the ‘edge’ of the network, rather than always at the central server or data center.” The concept dates back to the 1990s when Akamai solved the challenge of Web traffic congestion by introducing Content Delivery Network (CDN) solutions. The technology involved network nodes storing static cached media information at locations closer to end-users. Today, edge computing takes this concept further, introducing computational capabilities into nodes at the network edge to process information and deliver services.
How is edge computing related to cloud computing? – Think of edge as an extension of the cloud rather than a replacement. Cloud computing is the concept of storing, processing, and analyzing large amounts of data on remote servers or “data centers,” usually online. Data centers are often located remotely where data is processed and collected, resulting in a period between collection and processing or “high delays.” While the lag of time is usually only a few hundred milliseconds, it makes a massive difference to time-sensitive applications. In addition, a large amount of data moving up and down the network poses significant difficulties to bandwidth. This can reduce the speed of data processing and transfer. This back-to-back period could mean the difference between life and death in time-sensitive applications, especially in health care. So, edge computing is used to process time-sensitive data, while cloud computing is used to process data that is not time-driven.
Why is edge computing important for health care? – In the healthcare industry, data is growing at an astounding rate. A recent Dell Technologies global survey found that healthcare and life sciences data had grown by 878 percent over the previous two years — with no slowdown in sight. A significant percentage of this data is coming from the 10–15 connected devices that are often found at the typical hospital bedside. At the same time, healthcare providers are also collecting a mountain of patient data generated from connected wearable medical devices, like smartwatches and mobile wellness applications. Whether it’s on a health worker’s tablet, a wearable device, an ingestible sensor, or a mobile app, computing at the “edge” of the network is essential for speed, scale, and performance. The challenge now is putting all of this data to work to improve diagnostic and patient care processes to contribute to better patient outcomes.
What are some key use cases for edge computing in health care? – In connected healthcare, distributed analytics unlocks insights from data collected from IoT devices to help healthcare providers see beyond episodic patient visits. Edge computing broadens the field of vision, creating a continuous real-time patient record that helps providers shift from reactive to proactive care. A clear view of the power of Edge computing emerges in use case examples that illustrate how the healthcare equation changes when the analytics are brought to the data, including:
Rural medicine – Providing quality healthcare to isolated rural areas has been a challenge throughout history. Even today, with innovations in telemedicine and more readily accessible health data, medical providers have struggled to deliver fast, quality care to people who live far from hospitals and have limited internet access. Traditional healthcare databases face significant challenges here due to connectivity issues, but combining IoT medical devices and edge computing applications can make it easier to overcome these difficulties.
Patient-Generated Health Data – A range of IoT medical devices such as wearable sensors, blood glucose monitors, and healthcare apps have become far more common over the last decade, all of them collecting massive amounts of Patient-Generated Health Data (PGHD) that makes it possible for medical professionals to diagnose problems better and monitor patient health over long periods. The enormous amount of data produced by these IoT edge devices may be valuable. Still, it’s also creating a challenge for the healthcare providers tasked with managing it and keeping it secure. Edge computing applications have the potential to solve this data problem. By retaining much of the critical processing tasks on the devices located on the edge of the network, healthcare IT architectures can still benefit from gathering health-related data while also getting the rapid, real-time analytics that can predict and respond to health emergencies.
Improving the patient experience – Going to the hospital doesn’t have to be an unpleasant or frustrating experience. From smart devices that allow people to check-in for appointments whenever they like to notifications that guide them through an unfamiliar facility to find the appropriate office, IoT medical devices are among the key edge computing use cases that can potentially transform the healthcare industry’s customer experience completely.
Improving the supply chain – Sensor-equipped IoT edge devices have the potential to revolutionize the way medical facilities manage their inventories. Devices gathering data on usage patterns can utilize predictive analytics to determine when the hardware will likely fail. Inventory management based on intelligent RFID tags can eliminate time-consuming paperwork and manual ordering. Fleet vehicles equipped with GPS and other sensors can track the location of critical shipments in real-time. For organizations struggling to control rising costs, IoT healthcare supply chain innovations offer an opportunity to gain operational efficiencies on the margins and represent one of the more compelling edge computing use cases.
Improved patient safety and monitoring – Computer vision solutions can monitor acute patient safety and longer-term medical compliance to reduce readmissions. Examples include cameras and sensors that monitor patient and staff compliance with hand sanitization policies to reduce infection rates, devices that ensure discharge instructions are fully followed, telesitters to improve patient safety and reduce fall risk in post-acute care step-down patients, and connected pill bottles that confirm medical adherence.
Enhanced pharmaceutical drug supply chain safety – Edge and IoT devices and sensors can reduce the risks inherent in the healthcare supply chain, including temperature-related and counterfeit risks. Examples include devices that continuously monitor temperature changes in vaccines during transportation to ensure that safe temperature range is maintained, RFID sensors that track medication from the point of manufacturing to the point of consumption, and GPS-enabled shipping containers that improve inventory and waste management and distinguish between goods in transit and goods stolen.
New opportunities to enhance precision medicine research – Sensor-generated data, combined with medical-grade software applications, make it possible to treat rare medical conditions previously too expensive to address. Examples include wearables and other sensors integrated into the clinical trial process to expedite study completion and improve clinical compliance and reporting, along with digital therapeutic capabilities, such as applications that allow for the automatic collection and use of individual health data.
Where we are today – The sky’s the limit when it comes to the opportunities to use edge computing in health care, says Paul Savill, senior vice president of product management and services at technology company Lumen, especially as health systems work to reduce costs by shifting testing and treatment out of hospitals and into clinics, retail locations, and homes. That is not simply buzzword-driven hype: covid-19, for example, has laid bare the need for health-care options outside the doctor’s office or hospital. There are hundreds of healthcare uses that rely on low-latency, remote, real-time results, from pop-up clinics and cancer-screening centers to patient-monitoring systems, including pacemakers and insulin pumps.
My take – While some technology applications are still in their early stages, edge computing will ultimately help solve problems that cloud computing cannot. Edge computing can act as the “glue” that takes the benefits of exponential technologies like 5G cellular technology, The Internet of Medical Things, digital health sensors, and remote patient monitoring and combines them into a comprehensive tool that can be applied at the point of care to improve clinical outcomes and increase the quality of life for patients everywhere.
But, edge computing in healthcare is domain-specific and needs support from healthcare organizations. It is necessary to work with domain experts to ensure that the system can play in real life. Healthcare organizations will need to bring all stakeholders to the table to discuss their requirements and needs, so they have a better chance of moving forward to applying edge computing to healthcare domains.
The widespread adoption of healthcare IoT devices and edge computing will make people more aware of their health status. It will also make advanced healthcare resources available in remote areas via telemedicine. The ability for caregivers to regularly keep tabs on their patients will reduce the rehospitalization rate significantly. As collaborative edge computing and machine learning can preprocess data and generate meaningful analytics in real-time, caregivers will spend less time collecting and analyzing data and more time caring for their patients.