Barry Russo Explains How Artificial Intelligence Programs Can Simplify Cancer Care

An artificial intelligence system can digest what would take a person 29 hours to read in about 30 seconds, so everything is right in front of providers at the point of care, said Barry Russo, chief executive officer of The Center for Cancer and Blood Disorders.

Transcript

Are there any technology changes that excite you about how they can be used in cancer care?

That fits right into my talk [at Advancing Quality Oncology Care in the Evolving Value-Based Care Landscape] because I’m going to be talking about artificial intelligence tonight. And so, that’s one of the major technology changes from the standpoint of just administratively and processing things through our practice that I’m really excited about. Our electronic medical records historically are just repositories. It’s just where data goes in and you get a little bit out if you’re lucky. In an artificial intelligence system, if it’s applied to your EMR or in other parts of what you’re doing inside the practice, the machine actually has the ability to ingest and digest so many variables of what goes on in your practice.

So for example, if your electronic medical record is in an artificial intelligence based system, it can digest all of your clinical pathways. It understands the clinical scenario of the patient and can then project the appropriate pathway for the patient to you. In addition, it can digest all of the molecular profiling that should potentially apply to that patient based on age, sex, and stage of the patient and disease side of the patient. Put all that in front of you. It could also digest or ingest all of the information from your local payers about what drugs are covered.

In the world of biosimilars today we’re finding that payers are jumping on the biosimilar wagon the minute it is approved. We haven’t even learned about it yet when a payer’s demanding that we use it, and the payer policies are getting so restrictive. Artificial intelligence system can ingest the payer policy, so at the point of care, where the physician’s making the decision about what therapy and supportive drugs to give the patient, the artificial intelligence system can put that specific patient’s payer policy in front of them.

In a market like us, where we have 6 ACOs [accountable care organizations], all 6 of those ACOs have networks that are specific to them. It is impossible to understand when one of our physicians is trying to make a referral to a cardiologist, to a [gastroenterology] doctor, to a pulmonologist, whether that’s in-network for ACO number 1 or is it in network for ACO number 2 or 3 or 4 or which ACO is this patient even in? Again, artificial intelligence can ingest all of that information and put it right at the point-of-care based on the information that it gleams about the patient. That’s all stuff that, right now, we’re having to try and figure out in the backend, retrospectively, in many cases unsuccessfully.

Artificial intelligence system can digest what would take us 29 hours to read in about 30 seconds, so all of that is right in front of you at the point of care. That’s what I’m really excited about. Clinically, technologically we’re looking at really some innovative things coming in radiation, and in relationship to [magnetic resonance imaging]—guided radiation or [positron emission tomography]–guided radiation, all of those are on the horizon, which really hone-in on a much better treatment process for radiation. And, of course, on the medical oncology side it’s all about gene therapy and what’s coming with [chimeric antigen receptor] T [cell therapy] and all of that, and that’s only just begun groundbreaking.

So, there’s so many things out in front of us, so many different payer limitations, so many different molecular things, so many different genetic things, so many different options. What I’m really excited about is an artificial intelligence system that can put it at the point-of-care.