Brad Womble, Senior Director of Strategy, Marketing and Mergers & Acquisitions, at Jabil Healthcare, sat down with me in this episode of Healthcare Goes Digital. Among many other things, we explored how insights from “digital twins” can be used to improve patient outcomes, clinical trials, drug formulations, and medical device design.
Here is a sneak peek of our conversation:
Q: One thing that also comes to mind is, I think about the “digital twin” and the advantage of the digital twin. Again, if you are looking at the why behind it, I’m thinking about the complexities as you’ve ascertained earlier around precision medicine. Now, one of the ways we go about doing that is being able to perfect the perfect concoction. Let’s say, for example, you have cancer, and you want to perfect the concoction of a variety of different types of chemical entities. With the digital twin, basically a mathematical algorithm, you could actually fidget and try all of those different types of concoctions of that oncologic medicine without actually ever harming the actual biological form. Any thoughts on that use or version of the digital twin?
A: Exactly. I mean, especially as it relates to moving into nucleic medicines, genetic medicines; where you really need to understand the different genotypes of the patients so that you can help arrive at a drug that’s going to treat them the best. But you know what’s interesting, is that, yes, absolutely, the patient digital twin theoretically would be able to help that type of advancement. But when you think about that digital twin, it really goes across all different industries and all different ecosystems, including devices.
We’re designing devices. Now, there are software systems out there like Ancest and others that literally help you create a virtual digital twin of a device where you can actually run through thousands of different simulations of that device. The whole idea is, “how do we help our customers get quicker to market?” If you could run through a thousand different simulations of different potential error modes, as it relates to a device, and instead of actually having to build a device to run through those thousand different scenarios, you’re able to sort of accelerate that.
The other thing is that we’re able to take that device or twin and now compare it against reality, compared against the actual device when it’s in the field. But, it doesn’t just stop at a device. We think about a manufacturing environment. That’s where your machines become your manufacturing equipment to become learning equipment, how to better, incrementally, learn how to build a better device because you’re creating a digital twin for the performance of that particular device. You could literally create a whole connected ecosystem of digital twins that go from the patient to the device, to the manufacturing environment, to the supply chain, and it all connects together into this beautiful web.
For more of our discussion, you can watch the whole Fireside Chat with Brad Womble, or listen to the podcast version, below.
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