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Dr Ray Page Explains How Artificial Intelligence Programs Can Help Identify High-Risk Patients

As clinicians learn how to manage patients most efficiently under a value-based care of delivery, they need to identify patients and risk-stratify those patients, said Ray Page, DO, PhD, president and director of research at The Center for Cancer and Blood Disorders.


As clinicians learn how to manage patients most efficiently under a value-based care of delivery, they need to identify patients and risk-stratify those patients, said Ray Page, DO, PhD, president and director of research at The Center for Cancer and Blood Disorders.

Transcript

What is the artificial intelligence program that Center for Cancer and Blood Disorders is piloting?

This is a very exciting program that we’re piloting and we’re working with an artificial intelligence company called Jvion. The reason that’s important is that as we’re learning how to manage our patients most efficiently under a value-based care of delivery, we really get to a point that we need to identify patients and risk-stratify those patients. If we can identify difficult or problem patients early then maybe we can have interventions that can help with those folks.

We have worked with a company, Jvion, to where we share all of our clinical and practice information but they also have a vast amount of data and information that’s out there, to where for any individual patient that we have, they can explore over 4000 data points around that patient and be able to use those data points to help characterize that patient and potentially identify certain risk factors with those patients. As part of what we’re developed with them is we’ve looked at 7 different vectors and these are just things that we think can negatively impact patients. Working with Jvion, with the artificial intelligence tool that we have, 1 of those vectors is can we predict or identify which of our patients may are going to die within the next 30 days, because if we can identify those patients then maybe there’s interventions that we can plug into those patients to actually prevent that event from occurring or maybe it’s in agreement that those patients indeed are in a state where they are likely to die within the next 30 days and so it allows us to have assurances that we’re appropriately talking to them about palliative care and end-of-life. And so, if we agree with some of the data that Jvion has identifying those patients, then we can incorporate out palliative care team and get those appropriate care plan visits with those patients to help direct their therapy in that way.

There’s many other vectors that we’re looking at so if we can identify people that are at risk of having depression and they’re going to need to be on anti-depressants we can get those people plugged into our psychotherapists if we can identify people that are predicted to have increasing debility, where in the coming months that they might end up in the emergency room or the hospital, then we can incorporate those people into our rehab program or our dietician program in order to optimize their functional capacity in their health and we can look at certain vectors like that in order to help risk-stratify our patients and identify patients that would otherwise we may not have any idea that there’s something underlying that’s going on with them.

Our early data that we have has shown that we have positive impact on those patients and we can utilize, again, the information that we have to get our case managers involved and to get those people appropriately resourced to our ancillary services such as psychotherapy, palliative care, rehab, dietician, integrated therapy, all those things.

 
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