By Benjamin Ross
July 19, 2019 | The potential the Internet of Things (IoT) presents in healthcare is obvious: myriad sensors relaying patient information and data directly to the researchers and physicians for fast, accurate diagnosis and treatment. However, the story of IoT's integration into healthcare is similar to the story of other technologies and their impact in the life sciences—long and slow-paced.
As a concept, IoT has been around for decades, Shawn Evans, Director of Intelligent Edge at Valorem Reply, a digital strategy firm, tells Diagnostics World News. The data collected from patients in real time is a gold mine waiting to be combed through, especially for clinical researchers.
"We have sensors everywhere," Evans says. "You can take patient and diagnostic data and merge it with real time activity data from these sensors, and now all of a sudden you can mine it and look for new insights that lead to quicker diagnoses."
The big boom of data occurred during the late 2000s, when cloud computing began to mature. The cloud is a key enabler for IoT, Evans explains, because it provides the processing power necessary for the large amounts of data being collected.
The rise of cloud computing also enabled big data, which provided IoT an enormous "silo" to aggregate data, says Evans.
"When you look at technology, you look for when certain technologies mature and converge at around the same time, and that's really where you see overnight change," Evans says. "For the first time [with the cloud], we had significant processing power that could be democratized. It wasn't just incredibly large organizations that had these huge server forms. Now everybody has access."
The challenge for the researcher is finding the relevant data, and seeing patterns in the patient's behavior that can lead to actionable insights. In the case of diagnostic data, the goal is for the researchers to be able to analyze data in real-time and provide insights directly to the patients on lifestyle choices such as diet or exercise.
Evans says the true value of the data, though, are the digital feedback loops the data can create once researchers are able to feed the data collected from IoT back into the process.
Digital feedback loops occur in four phases, according to Evans. First, the researchers collect the data from the sensors. Second, researchers use data analytics and AI to analyze and feed the information directly to the subject matter experts, such as the physician. The third phase involves planning, where researchers discuss and develop strategies to address certain patterns they see within the analyzed data. The final phase puts the researchers' plans into actions, updating procedures and training material. In the case of diagnostics, Evans says that would mean presenting the data directly to the patient or physician so that they can understand and take action on the insight.
"That's really where we're starting to see the potential with this technology," Evans explains, adding that IoT is crucial in the collection phase of the feedback loop. "If we can establish and create [a feedback loop], then no matter what the system or solution is, we'll be able to see rapid improvement in the amount of insight this data can provide."
Connecting the Dots
A decade after cloud computing enabled IoT on a more practical level, Evans says clinical researchers and pharma are still playing catch-up.
"Talking to pharma and speaking with vendors, I'm sensing a lot of them are at the point where they have significant amounts of data, but they haven't quite been able to connect the dots on what to do with that data."
Data sharing—or lack thereof—is a significant barrier to being able to create these digital feedback loops that allows providers to rapidly innovate. In fact, data silos are often culprit for a lot of the industry's slow pace in innovation.
"Data exchange is always going to be an issue," Evans says. He sees industry leaders adopting platforms for data collection in a similar way to people choosing smartphones.
"If you buy a smartphone, you're not just buying a phone. You're buying into an entire ecosystem."
These silos are often a result of regulation, Evans says. The regulatory landscape in healthcare is significantly different from any other vertical.
"One of the big barriers to IoT adoption in healthcare centers around providers learning how to properly navigate regulation to effectively implement it," Evans explains in an email. "For instance, a provider can't just take raw telemetry from devices/sensors and stream that to a non-HIPPA cloud provider—the data must first be scrubbed of everything that can tie the data back to a patient, and we can do that with a hybrid solution. Regulation also complicates data sharing and exchange, and requires us to be very careful in that we don't compromise patient data in the process."
Being able to take diagnostic data and create new value out of it is something Evans sees as an emerging trend, adding that he can see IoT beginning to gain traction in healthcare within the next one to two years. Others watching the market agree, predicting the world IoT healthcare market to reach $136.8 billion by 2021.