The next time you’re in a hospital room (hopefully just to visit someone), count the medical devices around you. Experts say a typical room has between 15 and 20 networked devices, and a large hospital, as a whole, can have as many as 85,000, all of them connected to wireless networks.[1]

What are all those devices doing? They’re collecting patient-generated data (PGD) and gathering data about themselves. PGD is “health-related data created, recorded, gathered, or inferred by or from patients or their designees to help address a health concern.”[2] After gathering that data, the devices probably send it to a cloud service or to an application running in the data center. And sometimes, that’s where it stops. The data can be impossible to sort and analyze without the right tools. Or it is done inefficiently and haphazardly, leaving much of the data’s value untapped.

But what if that data could be efficiently processed at the edge, where it is generated? What if that journey to the cloud or to the data center could be complemented in ways that added value? The possibilities are intriguing: real-time data and analytics could lead to instant insights and a more complete picture of long-term health, leading to better care decisions that benefit both the patient and the clinician.

Edge Computing: Where IoT Data Becomes Valuable

Edge computing is becoming more popular in healthcare as health IT organizations work to place the full power of PGD in clinicians’ hands. They do so by introducing more connected devices—in other words, by building out the Internet of Things (IoT) and the analysis capabilities at the edge.

IT leaders are rising to meet the challenge and the promise of edge computing. For example, Cisco and SAS announced last year their joint solution for sorting and analyzing data collected by IoT devices. Among other things, the solution “enables analytic models to run against data-in-motion with a sub-second response time, close to the devices and sensors creating the data. The analysis initiates alerts, and defines which data is pertinent to store and route forward.”

In other words, solutions like this are turning raw data into actionable information at the edge, saving valuable time. Clinics and hospitals have raw data in abundance (the 15–20 connected devices per room mentioned earlier). But that data is not valuable by itself until it can be processed and used to make better decisions for better outcomes. That’s what computing at the edge promises.

Here’s what a practical application of edge computing might look like: data from those 15–20 connected devices can be pulled into a single dashboard and combined with patient history from the electronic health record (EHR) to enable more evidence-based, effective care in the moment. For example, a clinician can treat a patient using a tablet that displays a dashboard of all the PGD collected by the connected devices. She can simultaneously query the analytics platform at the edge, which has the same PGD plus access to the patient’s historical data. Patients no longer need to wait for data-based results, which could reduce the number of visits.

Large healthcare organizations with IoT and big-data aspirations should look into edge computing to help ensure that all parties get the maximum value from the data produced by patients and connected medical devices.

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[1] “Edge Computing Uses IoT Devices for Fast Health IT Analytics.” April 2017.

[2] Deloitte Insights. “No appointment necessary: How the IoT and patient-generated data can unlock health care value.” August 2015.

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