Dr John Frownfelter: AI Helps Derive Meaning From Collected Healthcare Data

Artificial intelligence (AI) can help derive meaning from data collected in healthcare to avoid noise and wasted efforts, said John Frownfelter, MD, FACP, chief medical officer of Jvion.

Transcript

With more attention being placed on things like social determinants of health, how can AI and machine learning help to turn that information into actionable data?

The burning phrase for 2019 seems to be “social determinants of health” or “social drivers of health,” if you will, and other phrases around that. The initial reaction or the initial action step people take, then, is to get the data, and what they have then is a whole lot of data and they don’t know what to do with it. AI will help to turn that data and derive meaning from it. Which is very important, because if you don’t have that, you end up wasting energy and effort if you say, “well this whole zip code is in a food desert,” and then you treat everybody the same because they live in that zip code. That’s not the right approach. It’s not producing better patient care. It’s producing more costly patient care. So, AI will help to identify which patients are actually at risk.

We take a unique approach and we understand patients with thousands of variables that we see about them to identify who is at risk from a social standpoint, and then we actually roll those up into regions and zip codes and whatnot, and it creates a sense of an understanding about an area, not based upon what we’ve learned about census data or other socially derived data sources, but rather at the individual level and then we roll it up and we get a sense of what’s happening in that region.

So, it’s powerful when you have knowledge coming from that data. Otherwise, it’s not only noise, but it can actually create a lot of wasted effort.