Machine learning: Changing everything but healthcare

Yet the industry has more potential for care improvement and ROI than others that have already adopted the technologies.

By Tom Sullivan

October 25, 2016

12:16 PM

BOSTON — Machine learning has proven it can beat traditional human techniques in healthcare for some time now, yet it remains limited in use in the healthcare industry. But that may be about to change.

"Machine learning is changing everything — except maybe healthcare," MIT professor John Guttag said here at the Big Data and Healthcare Analytics Forum on Oct. 24.

While machine learning drives products and services such as Google Maps, many websites' tracking of shopping habits and presenting options, banking, credit card companies and others, healthcare providers have done much less with the existing technologies.

"There’s lots of talk, but very little action, very little progress in healthcare," Guttag said.

That’s despite 40 years of empirical evidence that the technology outperforms traditional methods at predicting what will happen, according to Leonard D’Avolio, as associate professor at Harvard Medical School and CEO of Cyft.

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"The math is done, that game is over," D’Avolio said. "We’re past the point of these technologies needing to be limited to a few users."

Listing artificial intelligence, big data, cognitive computing and natural language processing, D’Avolio said healthcare has a naming problem that inhibits organizations' understanding of the available technologies and how they can be put to work.

He added that there’s more potential for positive impact in healthcare than other industries that have already put machine learning technologies to work.

"Our current approach to analytics will change dramatically in five years," said D'Avolio. "We have data in ways we never had in the past. We simply have not used our data, our collective experience from delivering care to improve the quality of care we deliver."

There are, of course, challenges.

Guttag, an MIT professor, said companies such as Google, IBM, Microsoft and others cannot merely point existing machine learning software at medical data because it is typically not technically big data but, instead, small data that arrives and changes quickly.

Both Guttag and D’Avolio urged attendees to consider the upsides of existing technologies now.

"ROI on big data will be even higher in healthcare than other industries," D’Avolio said. "Big data technologies will be even more important for us than waste management, telecom, even casinos."

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