There are a number of technological developments rapidly coming into view that will surprise most of us — not only for the leaps in capability, but for the speed at which they will work themselves into our professional and personal lives. Here are four that will accelerate the application of new technologies not in decades, but in a couple of years. Many have come into reality already.
Speeding on the information highway
Imagine you are running late. You open the door to go to your car, but the sky is cloudy; you think, should I take an umbrella? You open the weather radar app on your phone, and all you see is the agonizingly slow rendering, pixel by pixel. Is it your phone? Or is it your service — because you are on 4G or, worse, 3G connectivity.
5G will be a significant leap in performance and the backbone communication model integral to the Internet of Things. If you were an unfortunate soul trying to pull a movie down over 3G, it would take 26 hours. With 4G, that same two-hour film takes about six minutes. With 5G, it will be viewable in 3.6 seconds.
Big machines to move big data
Most of what we can do with new tech can be done with today’s computing capabilities. We can build algorithms and certain machine learning capabilities, but when we contemplate the full potential of intelligent machines, we are going to need a quantum leap in computing.
The concept of quantum computing was created over forty years ago, and depending on your sources you will hear either that this is vapor or that the world is on the verge of an incredible breakthrough in computational power. In the latter view, these systems will be so powerful that within a few years a single machine will be as capable as all the supercomputers in the world — and a year or two after that, able to solve in moments a problem that would take a classical computer 14 billion years.
There are functioning machines in various places. D-Wave, Google, and IBM have all touted their successes. In fairness, these exotic devices are still lab creatures and not yet free in the wild. The big question is when the migration from fascinating lab experiment to full commercial application occurs. Some predict decades, but that seems dismissive of how quickly exotic tech has become ubiquitous elsewhere. The first iPhone launched ten years ago; the original chip ran at 412 MHz, while the A11 runs at 2.35 GHz — nearly a six-fold improvement, in a phone.
Even if quantum computing is not mainstream in the near term, advances in classical computing, data management, and application development will still move us lightyears ahead in months and years, not decades.
Internet of (clinical) things
The FDA cleared AliveCor’s Kardia device as the first consumer wearable delivering clinically accurate EKG results. There are connected clinical devices emerging almost every day: spirometers, scales, blood pressure cuffs, glucose monitors, thermometers — even water bottles that measure volume consumed and report it back. Think about bariatric surgery patients and the number one reason for readmission. Rather than keep the list running, imagine that any device that captures data can evolve into a connected clinical device.
Artificial intelligence without all the buzz
In the near term we do not require intelligent machines. We merely require current technology to operate complex algorithms developed by clinical teams and IT professionals — to drive remote monitoring systems and to collect, calculate, store, analyze, and respond to data captured by connected clinical devices.
Think inside the box
Imagine if, for every patient with a clinical condition, we could assemble a collection of connected devices to help the clinical team monitor the specific vital signs of the patients they serve. Bluetooth scales would go home with congestive heart failure patients, spirometers with COPD patients, glucose monitors for diabetics. Everybody gets a tablet or a large phone capable of a video consult.
Clinical process models would be transformed from human actions into computational algorithms. Module-oriented programming would execute a care plan in an automated way. Only when the machines receive unexpected data would they reach out to their human counterparts for that human touch.
With that computing power, sophisticated algorithms, and high-speed communication enabling big data transfers, we could get eyes on many more patients. Clinicians would focus their time on patients with immediate needs. Care teams would work at the very top of their license, automating the mundane administrative tasks. Patient outcomes would almost certainly improve, since monitoring should provide early warning of impending crises — and the cost of care would drop for the same reason.
For the most part we have what we need to make this happen today, at least in pilots. If you struggle to figure out how to leverage your clinical teams to care for more of the people in your community, maybe it is time to think inside the box.
