For decades now, the healthcare industry has been undergoing rapid change. New technologies have made everything from R&D, to diagnostics, to appointment setting much easier, allowing providers to treat more people more effectively than ever. As fast as things have changed in the recent past, change is about to accelerate to light speed as four technologies converge to disrupt the industry: the IoT, 5G, AI, and edge computing. In this post, we take a look at current adoption trends for each of these technologies and how they will work together to impact the future of healthcare.
#1 Growth in healthcare IoT
IoT (Internet of Things) is taking off in a big way across numerous industries, including healthcare. When Forbes and Intel teamed up to study the impact of the IoT on seven different industries, they found that 55% of healthcare executives already had robust IoT initiatives underway.
Healthcare IoT covers many different devices such as intelligent, connected equipment, tablets and smartphones devices used by mobile providers, and wearable technologies from smartbands (e.g., Fitbit, Apple Watch, and Garmin Activity Monitors) to glucose monitors and insulin pumps.
It’s hard to say how many devices there are in the healthcare IoT. Some estimates approach 20 billion or more. The global smartband market alone shipped more than 43 million units in 2017. I think it’s safe to say that the impact of these devices on the industry will be significant. Here are just a few of the ways the IoT may disrupt the healthcare industry.
- Personal monitoring devices encourage people to be more “health-conscious” and responsible for their own well-being. Insurance companies are beginning to encourage this with discounts for subscribers willing to use these devices to follow a healthy lifestyle.
- Managing chronic illnesses will become easier and more cost-effective. Instead of making frequent trips to a doctor’s office, doctors will be able to remotely monitor a patient’s condition for changes. Telemedicine is expected to grow at a CAGR of 16.5% from 2017 to 2023.
- Wearable devices can detect warning signs earlier, allowing people to seek treatment sooner and improving outcomes.
- Remote patient monitoring (RPM) can save money for an industry struggling to hold down costs. Studies estimate that remote monitoring of chronic conditions could save as much as $200 billion over the next 25 years in the US alone, and that RPM can reduce costs for elder care in rural areas by 25%.
#2 5G networks will enhance overall healthcare experience
Most people reading this probably have at least a basic understanding of 5G’s advantages. We remember what happened when our phones went from using 3G networks to 4G networks. Suddenly we could stream Netflix and NFL football to our smartphones. 5G is set to have a much broader impact on healthcare.
“More than just speed, 5G is also about capacity. Sometimes those get confused with each other, so to clarify, let’s revisit the analogy of a car on the highway. If 5G is that car going 100 miles an hour, then 4G is like that car going 90 miles per hour or maybe even 80 miles per hour. So, there is a difference in speed, but if 4G is like a four-lane highway, 5G is like a 16-lane highway."
- Dominic Romeo, TierPoint Director of Product Management
Read the interview with Dominic Romeo: How Will 5G Affect Edge Computing?
With the proliferation of healthcare IoT devices, we’re going to need that capacity. It’s no longer just 10 million people all binge-watching Game of Thrones at the same time. Now we have physicians, nurses, and other healthcare providers monitoring patients and offering telemedicine services to as many as a dozen patients a day. 5G’s 16-lane super-highway can reduce the lag times that threaten to impact both the patient experience and quality of care. (Arguably more important to our overall well-being than the need to suffer through while GOT buffers for a few seconds.)
#3 Artificial Intelligence (AI) stands out in healthcare
Before getting into the impact of AI on healthcare, we need to differentiate it from a related technology: analytics. Analytics is the use of data to inform decision-making. Given the proliferation of data gathered by wearable devices, we could have included analytics in our list of industry disruptors. However, because analytics is a component of AI, we decided to focus on the more recent, at least from a commercial perspective, innovation.
As computing power and the amount of data available increase exponentially, the line between AI and analytics can look rather blurry. The main difference is that analytics analyzes data according to a defined model but takes no action. For example, a doctor may gather ever-increasing amounts of data about a patient and use a program to analyze patterns in the data and advise her about a possible course of treatment. But, she still makes the final decision on how to proceed.
With AI, the doctor is removed from the equation (at least theoretically) in a couple of ways. First, AI uses machine learning to adjust its conclusions in the same way the doctor might use her benefit of experience to interpret the data differently than the program. Second, full AI can take action.
As someone once said, “We’re all just an experiment of one.” How one person’s vital signs should be interpreted and treated is unique to his or her physiology. For example, a blood pressure of 110/55 could be completely normal in one person but indicate serious dehydration in another. Wearable technology fitted with AI could monitor the individual’s vital signs, analyze them, and learn from them to adjust the model of what “optimal health” looks like for that person. Not since the days of house calls have flesh and bone doctors had the time to get to know their patients at this level. (Maybe not even then.)
Second, when vital signs go awry, AI can take action by advising the individual about the actions they should take to correct matters. If a chronic condition, such as diabetes, is being monitored, the system may even administer treatment.
#4 Edge computing drives new healthcare IT
Finally, edge computing ties the previous three disruptive technologies together. Edge computing simply means moving the computing power closer to the end-user. The main benefit of edge computing is that it significantly reduces latencies – what most people think of as “wait time.”
Let’s say you suffer from an irregular heartbeat, so you wear a device that monitors your heart rhythms. Contrary to what the commercials would have you believe, heart rhythms can be fairly irregular without indicating a serious condition. If that data had to travel from your device to a central data center for processing, it would severely impact your experience and ability to leverage the data. If it were a serious condition, it might even be too late by the time you got the results. Edge computing puts the ability to analyze the data on the device itself, allowing you to get the results much faster.
That’s a pretty simple example of edge computing in action. As AI becomes more commonplace, edge computing will become even more critical in allowing intelligent systems to analyze data, reinterpret models, and take action all within a matter of seconds, if not milliseconds, potentially saving lives.
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