The arrival of artificial intelligence and its ilk — cognitive computing, deep machine learning — has felt like a vague distant future state for so long that it’s tempting to think it’s still decades away from practicable implementation at the point of care.
And while many use cases today are admittedly still the exception rather than the norm, some examples are emerging to make major healthcare providers take note.
Regenstrief Institute and Indiana University School of Informatics and Computing, for instance, recently examined open source algorithms and machine learning tools in public health reporting: The tools bested human reviewers in detecting cancer using pathology reports and did so faster than people.
Indeed, more and more leading health systems are looking at ways to harness the power of AI, cognitive computing and machine learning.
“Our initial application of deep learning convinced me that these methods have great value to healthcare,” said Andy Schuetz, a senior data scientist at Sutter Health’s Research Development and Dissemination Group. “Development will be driven by our acute need to gain efficiency.”
Schuetz and his colleagues are not alone. By as soon as 2018, some 30 percent of healthcare systems will be running cognitive analytics against patient data and real-world evidence to personalize treatment regiments, according to industry analysts IDC.
What’s more, IDC projects that during the same year physicians will tap cognitive solutions for nearly half of cancer patients and, as a result, will reduce costs and mortality rates by 10 percent.
Race is heating up
IBM’s Watson is the big dog in cognitive computing for healthcare, but the race is on and the track is growing increasingly crowded.
IBM rivals Dell and Hewlett-Packard are readying systems to challenge Watson, while a range of companies including Apple and Hitachi Data Systems are each taking their own tack toward AI, cognitive computing and machine learning.
A report from Deloitte in 2015 rattled off a list of other competitors, including: Luminoso, Alchemy API, Digital Reasoning, Highspot, Lumiata, Sentient Technologies, Enterra, IPSoft and Next IT.
And late last month Google and Microsoft battled it out when Google unwrapped its Cloud Machine Learning and Microsoft shot back that same week with big data analytics of its own and the new phrase “conversational intelligence” to describe its new offerings.
So don’t expect Watson to be the only “thinking machine” option moving forward.
Among the obstacles facing healthcare organizations and the intrepid technology vendors trekking to AI, cognitive computing and machine learning will have to high-step to overcome: data.
Data is always going to be an issue for both healthcare providers and technology vendors. Collecting it, storing it, normalizing it, tracing its lineage and the critical – if not particularly sexy – matter of governance, are all necessary so providers can harness cutting-edge software and hardware innovations to glean insights that enhance patient care.
“Translating data into action — that is the hard part, isn’t it?” said Sarah Mihalik, vice president of provider solutions at IBM Watson Health.
Achieving the transformative potential for AI, she added, is also going to require a mindset and practice shift in how providers embrace technologies and acquire talent.
The right data is essential to solving many of today’s problems but the information itself does not a lasting strategy make.
“Analytics is just one part of an overall data strategy,” said Nicholas Marko, MD, chief data officer at Geisinger Health System.
Other key pieces include: business intelligence, enterprise data warehouse, infrastructure, privacy and security.
“If you’re not focusing on how these pieces are all in motion then invariably you’re going to hit some kind of bottleneck,” Marko said. “The strategy has to be a dynamic living thing, not something you just put down on paper. There is not some secret sauce that allows you to lay down an analytics strategy. It’s a lot of hard work. Nobody has the magic solution.”
Not even technology titans.
AI, cognitive computing and deep machine learning are still nascent technologies but consultancies are suggesting that healthcare organizations begin working these technologies now rather than waiting.
“The risk of investing too late in smart machines is likely greater than the risk of investing too soon,” according to a report from Gartner Group.
There’s little arguing that the degree of complexity around big data in healthcare is exactly why clinicians, physicians and, indeed, the industry at large need these emerging technologies, which have felt so far away for so long.
“I have no doubt that sophisticated learning and AI algorithms will find a place in healthcare over the coming years,” Sutter’s Schuetz said. “I don’t know if it’s two years or ten — but it’s coming.”