Next week, hundreds of companies and tens of thousands of healthtech pros will descend on Orlando to share the latest technologies and visions of tomorrow’s healthcare experience.
But it’s not all big news and new frontiers. For every breakthrough, there will be flops, also-rans, and never-weres.
Here’s a few stories you should watch for and a few you can probably ignore.
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Watch for… Serious Interoperability Moves
The single biggest thing holding back healthtech is interoperability – the inability of the healthcare ecosystem to talk effectively to itself. It’s killing patient experience. It’s holding up innovation. It’s slowing investment.
There’ve been fits and starts in past years – shared standards like HL7 and FHIR have helped, but adoption has been sluggish and uneven. But with more and more data exchange vendors springing up and bringing new platforms to market, this year that could all change.
But you can ignore… the Amazon hype
With Atul Gawande slated to deliver this year’s opening keynote, all eyes were on Amazon’s burgeoning healthcare ventures… until it came out that Atul was a no-show, backing out at the last minute.
If Amazon had anything earth-splitting to announce, they would have taken that opportunity. There may be some new details trickling out – as there already have been – but give this one another year, folks.
Watch for… the Internet of Healthcare Things comes of age
The Internet of Things (IoT) is nothing new, especially in healthcare. Everything from wearable devices to ingestible sensors have come and gone in past year, and this HIMSS will be no different.
So why the big fanfare? Well, IoT is finally becoming a mature technology. With shared standards and a field of seasoned platforms and vendors, IoT is reaching a point where it can stop reinventing the wheel and start innovating in place.
And that means it’s finally within reach for more than just cutting-edge organizations. This is the year wider adoption really begins.
But you can ignore… Machine Learning prophets
IBM Watson collapsed in on itself. Models have been questioned and scrutinized. There’s even evidence of unsafe recommendations and failed diagnoses. Is machine learning a done deal?
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Of course not. But it’s far from the rosy vision we’d been promised. As we wrote late last year, machine learning has reached tool status – useful when deployed correctly, counterproductive when misused.
In short, you can safely ignore anyone singing that same old tune about Dr. HAL-9000 being just around the corner to solve all your patient woes. Leave that for the sci-fi writers.
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