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.