Home RedHat’s Ben Cushing on Leveraging AI for the Clinical Care Pathway

RedHat’s Ben Cushing on Leveraging AI for the Clinical Care Pathway

by Ant Sh
RedHat's Ben Cushing on Leveraging AI for the Clinical Care Pathway

Ben Cushing, Chief Architect, Federal Health & Life Sciences at RedHat, recently discussed the core issues at the heart of current digital transformation initiatives. The video was filmed in conjunction with FedTalks, produced by FedScoop, on August 24, 2022, in Washington, D.C. In particular, Benjamin answers the following questions:

  1. Where do you see the greatest opportunities for AI/ML and automation to improve citizen services?
  2. How do you see federal agencies shifting their IT investment priorities heading into the new fiscal year, based on your recent meetings with agency executives?

Here is Ben’s answer to the first question mentioned above:

“I actually believe that AI and automation have to go hand in hand to actually be successful. Most of the AI programs that I’ve witnessed have failed if they didn’t have some sort of automation. And that’s because AI is a big part of workflow. You actually have to assert AI at some point within a workflow for it to actually matter. Otherwise, it’s just empty information that nobody actually uses. So, the area where I’m most excited is at the bedside. We’ve had this concept in healthcare for a long time of clinical decision support. Clinical decision support has traditionally been heuristics and rules, and which are technically inclusive inside the AI bubble. But now, as we get into the machine learning world, we can apply better algorithms to offer clinical decision support to clinicians at the time they need it.”

“The challenge to be successful is to supply those predictions, recommendations, and probabilities within their current workflow. And automation plays a really important role there. If you can automate the clinical care pathway that the doctor is on or the nurses on, then you know when to assert AI predictions, probabilities, and recommendations. And you can actually create stop gaps to allow those insights to be served up properly. And in that way you provide contextual recommendations. That’s the other thing too, like having an AI telling you about a patient who has emergent diabetes is not helpful at all if the patient’s not in front of you. You need that when you’re having the patient encounter. So for me the most exciting space is that area of better diagnosis and then also the impact to population health, so the roll-up of that activity to better care for large cohorts of patients.”

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