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The Value Of Pairing Machine Learning With EMRs

Posted on January 5, 2017 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

According to Leonard D’Avolio, the healthcare industry has tools at its disposal, known variously as AI, big data, machine learning, data mining and cognitive computing, which can turn the EMR into a platform which supports next-gen value-based care.

Until we drop the fuzzy rhetoric around these tools – which have offered superior predictive performance for two decades, he notes – it’s unlikely we’ll generate full value from using them. But if we take a hard, cold look at the strengths and weaknesses of such approaches, we’ll get further, says D’Avolio, who wrote on this topic recently for The Health Care Blog.

D’Avolio, a PhD who serves as assistant professor at Harvard Medical School, is also CEO and co-founder of AI vendor Cyft, and clearly has a dog in this fight. Still, my instinct is that his points on the pros and cons of machine learning/AI/whatever are reasonable and add to the discussion of EMRs’ future.

According to D’Avolio, some of the benefits of machine learning technologies include:

  • The ability to consider many more data points than traditional risk scoring or rules-based models
  • The fact that machine learning-related approaches don’t require that data be properly formatted or standardized (a big deal given how varied such data inflows are these days)
  • The fact that if you combine machine learning with natural language processing, you can mine free text created by clinicians or case managers to predict which patients may need attention

On the flip side, he notes, this family of technologies comes with a major limitation as well. To date, he points out, such platforms have only been accessible to experts, as interfaces are typically designed for use by specially trained data scientists. As a result, the results of machine learning processes have traditionally been delivered as recommendations, rather than datasets or modules which can be shared around an organization.

While D’Avolio doesn’t say this himself, my guess is that the new world he heralds – in which machine learning, natural language processing and other cutting-edge technologies are common – won’t be arriving for quite some time.

Of course, for healthcare organizations with enough resources, the future is now, and cases like the predictive analytics efforts going on within Paris public hospitals and Geisinger Health System make the point nicely. Clearly, there’s much to be gained in performing advanced, liquidly-flowing analyses of EMR data and related resources. (Geisinger has already seen multiple benefits from its investments, though its data analytics rollout is relatively new.)

On the other hand, independent medical practices, smaller and rural hospitals and ancillary providers may not see much direct impact from these projects for quite a while. So while D’Avolio’s enthusiasm for marrying EMRs and machine learning makes sense, the game is just getting started.

Artificial Intelligence Can Improve Healthcare

Posted on July 20, 2016 I Written By

Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare.

In recent times, there has been a lot of discussion of artificial intelligence in public forums, some generated by thought leaders like Bill Gates and Stephen Hawking. Late last year Hawking actually argued that artificial intelligence “could spell the end of the human race.”

But most scientists and researchers don’t seem to be as worried as Gates and Hawking. They contend that while machines and software may do an increasingly better job of imitating human intelligence, there’s no foreseeable way in which they could become a self-conscious threat to humanity.

In fact, it seems far more likely that AI will work to serve human needs, including healthcare improvement. Here’s five examples of how AI could help bring us smarter medicine (courtesy of Fast Company):

  1. Diagnosing disease:

Want to improve diagnostic accuracy? Companies like Enlitic may help. Enlitic is studying massive numbers of medical images to help radiologists pick up small details like tiny fractures and tumors.

  1. Medication management

Here’s a twist on traditional med management strategies. The AiCure app is leveraging a smartphone webcam, in tandem with AI technology, to learn whether patients are adhering to their prescription regimen.

  1. Virtual clinicians

Though it may sound daring, a few healthcare leaders are considering giving no-humans-involved health advice a try. Some are turning to startup Sense.ly, which offers a virtual nurse, Molly. The Sense.ly interface uses machine learning to help care for chronically-ill patients between doctor’s visits.

  1. Drug creation:

AI may soon speed up the development of pharmaceutical drugs. Vendors in this field include Atomwise, whose technology leverages supercomputers to dig up therapies for database of molecular structures, and Berg Health, which studies data on why some people survive diseases.

  1. Precision medicine:

Working as part of a broader effort seeking targeted diagnoses and treatments for individuals, startup Deep Genomics is wrangling huge data sets of genetic information in an effort to find mutations and linkages to disease.

In addition to all of these clinically-oriented efforts, which seem quite promising in and of themselves, it seems clear that there are endless ways in which computing firepower, big data and AI could come together to help healthcare business operations.

Just to name the first applications that popped into my head, consider the impact AI could have on patient scheduling, particularly in high-volume hostile environments. What about using such technology to do a better job of predicting what approaches work best for collecting patient balances, and even to execute those efforts is sophisticated way?

And of course, there are countless other ways in which AI could help providers leverage clinical data in real time. Sure, EMR vendors are already rolling out technology attempting to help hospitals target emergent conditions (such as sepsis), but what if AI logic could go beyond condition-specific modules to proactively predicting a much broader range of problems?

The truth is, I don’t claim to have a specific expertise in AI, so my guesses on what applications makes sense are no better than any other observer’s. On the other hand, though, if anyone reading this has cool stories to tell about what they’re doing with AI technology I’d love to hear them.