Today on Insights. We go back to a conversation Host Bill Russell had with Dr. Anthony Chang, Founder of AIMed. The topic of discussion was Using AI to Turn Data into Action. And Anthony points to a future where we will have AI machine learning and deep intelligence deciphering datasets to come up with all sorts of new thought processes in terms of how to attack certain disease states.
Hello and welcome to another episode of Insights. My name is Bill Russell. I'm a former CIO for a 16 hospital system 📍 and creator of This Weekin Health IT. A channel dedicated to keeping health IT staff current and engaged. Our hope is that these episodes serve as a resource for the advancement of your career and the continued success of your team. Now onto the 📍 show.
Today on insights. We go back to a conversation host Bill Russell had with Dr. Anthony Chan, Founder of AIMed. The topic of discussion was using AI to turn data into action. And Anthony points to a future where we will have AI machine learning and deep intelligence deciphering data sets to come up with all sorts of new thought processes in terms of how to attack certain disease states.
All right, I'm the CIO, you're the CEO. You're looking at me saying how good is our data, right. And I'm telling you, ah, you know what? Some of these physicians are not great data entry clerks, right? Our data is really all over the place. Not only that, a lot of the information we're getting is from our clinically integrated network and they don't even work for us. And so we have all this data coming in. So I have a data cleanup project. That's one of the things I have to figure out in order to get that data ready for these projects. But there are some datasets that are really good already. Right? Right. So the financial data set is typically relatively clean.
The monitors. The bedside monitors. I mean, do you use that data? I mean, you have a time series data it's a lot more data points.
Right. No, I think that's exactly right. I think the ICU monitor data's fairly straightforward is relatively complete in terms of not having many missing points of data.
There's even publicly available ICU data. And adult ICU is called a mimic three that's available. And you can easily do projects without actually involving your own patients. So they are publicly available databases in healthcare. Just not many, ideally, if you were to add. 20 years from now, what I would like to see. I'd like to see every patient's data imported into the cloud and it'll be universally available for any healthcare stakeholder to look at. That's anonymized perhaps with blockchain or other types of security mechanism. I'm very optimistic it will be tackled within 20 years.
Your belief is that within 20 years, we're going to have AI machine learning, deep intelligence in the ability to point these things at that dataset and come up with all sorts of new thought processes in terms of how to attack certain disease states.
Perhaps like a clinical GPS for the clinicians. So they can actually think perhaps even more creatively than the GPS as you would in a driving situation. You like the GPS, but you may, may not want to adhere to it. And also occasionally, like it happened to me just a week ago, the GPS wasn't working. So you have to now rely on your human intelligence to get you around.
My kids laugh at me cause I get in the car and I put GPS to our home. They're like, you don't know how to get home. And like now I'm just more comfortable with the GPS.
Yeah. And that's how I like physicians to eventually think about AI in medicine, is it's a GPS that you just going to get used to. It's your routine. It's not something that is so esoteric or advanced that you can't understand. It's going to be there quietly as your partner. If you looked at a Microsoft commercial 25 years ago there was actually a segment saying, imagine a future. And I laugh because it says, imagine a future traveling across the country without foldout maps. And people were laughing, they just did not think that was going to be possible. And now with GPS, you don't think twice about driving across the country.
So within 10 or 20 years, as more clinicians are getting educated and aware of AI. I think it's just going to be part of their routine.
And that's what I like to see. It's actually embedded in their clinical routine without disruption, to their workflow, without any distraction from their usual routines and just be the silent partner.
Do you think we need to change how doctors are educated? Do you think we'll start to see AI start to get built into those programs?
Yes, I think I'm starting to see clinicians that are becoming more seriously interested in data science and the younger generation considering dual education in both clinical medicine, data scientists.
So again, I'm playing the healthcare administrator. I'm going to say. This last year, I've had five different vendors come into mind. I mean, how should I be thinking about these vendors? Cause even some vendors from outside of healthcare are coming in and going, Hey, we've done this in other industries.
The complexity of healthcare is such that I'm not, I mean, I'm not saying they can't do it, but I'm just saying the learning curve for them on the healthcare side is pretty high. How should I think about that?
Yeah, I think be careful and be very vigilant for vendors that over promise and under deliver obviously. I think there've been some very big companies that over-hyped and under-delivered, and actually became unsuccessful at those big institutions. So I think those are cautionary tales that I think we need to pay attention to. And I think invest a relatively smaller amount of revenue into sort of the space cautiously and as a way to learn about the limitations of AI in the space.
But I think the promise is tremendous. And I think the future is very bright for this area, but I think don't overspend your resources now and just get to know it. And education is key. I think.
It's is the same thing we did around big data. It's small projects with defined outcomes. And you give those vendors tasks to do, and once they do it, you can then expand it and grow it. Is there going to be a problem scaling AI?
Not at all, because I think the potential is really limitless because of the computational power being so cheap now. And so many algorithms are maturing and I think it's going to be an amazing portfolio of things that are available. And I think if you look at how inefficient and how badly run healthcare is, it's just going to be an amazing transformation the next 10, 20 years.
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