← Becker’s Healthcare Podcast
Becker’s Healthcare Podcast
Dr. Don Casey, Associate Professor of Internal Medicine at Rush Medical College
00:23:19
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- [00:02]
A
@ Athenahealth, we know your ambulatory practice wants healthier a healthier business, healthier care teams and healthier patients. But the complexities of modern healthcare tech make it hard for you and your care teams to focus on what matters most. That's where athenahealth can help our AI native all in one solutions reduce administrative burdens, streamline billing and payments, and deliver critical insights when clinicians need it most. That means fewer clicks, more time for patients, and stronger bottom Practicing medicine is complex, but running a practice can be that Much simpler with Athenahealth. See how simpler is healthier at athenahealth.com.
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B
Hi everyone, this is Scott King with Becker's Healthcare Podcast. Thrilled today to be joined by Donald Casey, Associate professor of Internal Medicine at Rush Medical College. Donald, how are you doing today? Thanks so much for joining us.
- [00:56]
C
I'm good, Scott. Please call me. Don't.
- [00:58]
B
You got it. Don Will do appreciate you joining us. We're gonna have a great conversation today, a lot of kind of big topics and trends in health care to discuss. But before we get there, I just wonder if you could just please tell us a little bit about your background.
- [01:12]
C
Sure thing, Scott. First of all, thanks for the invitation. It's been a pleasure to be working with Scott Becker's team over the years on a lot of great events and a lot of great insights. So I appreciate the opportunity to connect with you. I am by by training a general internist. I trained at Rush in Chicago here for first half of my career and then later went into management and then ultimately where I am now, I'm sort of a hybrid of everything I've done in my career. I am no longer practice internal medicine, nor am I in a healthcare delivery setting. I'm really focused mainly on the on the medical side as faculty at Rush, where I'm an associate professor of internal medicine, but also at the Thomas Jefferson College of Population Health where I'm an adjunct professor of quality, patient safety and also population health. And then I've also for the past 11 years been on the faculty of the University of Minnesota Institute for Healthcare Informatics up in Minneapolis. And that's been quite interesting in my career to be hanging out with all these healthcare data scientists. So, and then I'm a subject matter expert in a number of, a number of areas. I was just in Boston this past Friday with a group called icer, you may know about them, Institute for Clinical and Economic Review. We're really focused on, as one example of where I work on rational pricing for new drugs that are coming on the market, especially those that are very expensive. So it's, it's a hybrid of a lot of things at once and it's doing me well at my point in time.
- [02:57]
B
Now you certainly are doing a lot of things. I appreciate the background info and the specifics there. John, the first topic I kind of wanted to get to is nearly half of medical practices reported using AI in some capacity last year and it remains a key topic for health IT leaders. From your perspective, what are the use cases that are making a difference right now and how are you leveraging them in your organization?
- [03:24]
C
Yeah, I appreciate that question. It's one that I see often and again with a disclaimer, I'm no longer in medical practice, but I am a patient and I'm involved with number of health systems. It's interesting that we talk about nearly half are quote unquote, using AI. And I would venture to say, since AI is not a new topic, we've been using AI, so to speak, for years in many, many capac. Now in particular, I think, as borne out by the recent event that I attended at Becker's, certainly medical practices from a business standpoint are beginning to use a lot more strategies and information systems, for example, that deploy AI techniques. And those are very vast in terms of the issues. For example, the big ones, reducing workflow burden. That's a big one. You know, using natural language processing so that physicians and other clinicians don't have to spend time standing in front of the electronic health record as much. We hope so that reducing that burden is certainly front and center in terms of the applications that are coming up, not just with the large electronic healthcare records vendors, but also with, you know, other groups that are trying to solve for specific problems and specific specialties. Obviously revenue cycle is a big deal too. You know, you got to keep the lights on. And revenue cycle is, is a, it's, it's, it's a big game in terms of, and I'll use my, that's my term. But obviously with keeping up with all the rules, regulations and requirements of submitting claims and then getting paid for them is becoming, I think, a big deal in terms of just making it easier, more accurate, better, obviously avoiding any sort of problems with fraud and abuse and cybersecurity. And then I think the biggest part that's exciting for me from patient care is the access to clinical information in many fronts. Some of it is good and some of it isn't so good. For example, the alerts that come up in the electronic health record, we're Trying to minimize those that matter so that they don't burden people. And yet, on the other hand, the amount of information that's much more readily available through the use of, let's say, copilot, which is what I use, but there are others compared to just using search strategies, I, I sort of think search is, for the most part, unless you're trying to find a restaurant, pretty much out of, out of favor. And that the ability to put more rational and useful information together on the informatics side is becoming very exciting for me. This obviously translates into other issues such as clinical decision support, but also, and we're going to get into this a bit more, Scott, is patients are using it immensely and showing up in the healthcare environments with lots of interesting and useful information. And that's requiring the clinicians at the front end to handle that with respect, but also to be sure that we're not going down the wrong pathway with, let's say, a hallucination about some strange drug that is used, you know, in the woods, so to speak. So we'll leave it at that. I think the, the bottom line is it's an exciting and particularly growing challenge and opportunity.
- [07:06]
B
Don, that's really interesting because we, we talk obviously with a lot of systems about using AI, but you mentioned patients coming in and you know, kind of making the appointment or whatever, therefore easier and more efficient because they have used AI and are prepared for that meeting. What uses are you seeing from patients specifically? What are, what are they doing?
- [07:27]
C
Well, I would say let's put it together. I mean, we have the doctor slash clinician relationship that includes other, other practitioners who may or may not have Dr. In their name. But you know, you can look around, Scott, and let's just say this has been going on for a while. I can name, I wrote down a few specialties for which the amount of information requires sort of these data science, machine learning applications. I don't like to use the term AI because it, it's too hyped up. And I think it sounds to a lot of people who aren't familiar with this like it's something new. I mean, it actually first was coined almost about 70 years ago at my alma mater at Dartmouth, at a, at a. The first. That was when the first term artificial intelligence appeared. But if you look at, let's, let's just tick off some specialties. All right, Radiology, huge opportunities here to look at, for example, reading by a non human on top of what radiology experts are using to enhance, not to, not to substitute, better insights Pathology, you know, that's a huge area where the, the, for example, the tissues that are being analyzed are subject to tremendous amounts of computing power in terms of learning. You know, just pick the cancer cells. For example, dermatology. Right. That's another area that is, is really being used on a daily basis so patients can walk into their dermatologist and have, let's say, a problem with risk of melanoma and be subject to cameras that will be used using artificial intelligence to help guide not only the presence of melanoma, but perhaps the risk of it. Oncology is a huge issue, something that I've been focused on a lot, cardiac electrophysiology. I mean, if someone goes in for atrial fibrillation, for example, the amount of data in these electrophysiologic mappings when ablation is done is huge. You know, there's biosensing in, in the world of cardio cardiovascular medicine all over the place, ophthalmology, you know, getting your eyes checked with a camera that then can automatically, without even seeing a doctor, be subject to an analysis that tells you if you're at risk for, let's say, early macular degeneration or diabetic eye disease. I mean, these things are in practice now. Scott. So what I'm, what I'm trying to say is that there are a growing, growing large number of use cases, so to speak, from the standpoint of patient care, certainly on the business side, we talk about it a lot. But from the standpoint of what matters in healthcare, that to me is where the new evolution and discovery are very, very exciting.
- [10:25]
B
Absolutely. And Don, as virtual care expands from AI enabled tools and remote monitoring to broader digital health platforms, introducing new technology brings new challenges. So what advice do you have for leaders navigating everything from governance to patient engagement? And can you share an example from your experience that's kind of balanced innovation with operational constraints?
- [10:53]
C
Well, obviously there's the monetary aspect of this. I mean, you can't, you, you don't have limited unlimited budgets like some large health systems do. I mean, Mayo Clinic's got a whole office building with IBM across, across the way from it. But that's obviously a business strategy that is also enhancing Mayo's, you know, supportive, excellent care. That's an extreme example, but from day to day, the average health system doesn't have that type of access, so to speak. I'd say let's back out of that question for a minute, if I might. I mentioned sensing, I mean, sensing devices that patients are wearing and I don't like the term wearables because not all sensing devices are wearable. But I'm working, for example, on a device that can measure your blood pressure, every beat. And if your heart rate is 60 per minute, that's 3,600. Blood pressure is taken in an hour. So whether we like it or not, and these are, this is one of many, dozens of different types of devices around the world. And part of it is we don't know, you know, what's real and what isn't the ability to validate these devices. You know, we're just getting started on it, but certainly you're going to need, let's say that we use the term artificial intelligence to sort out what these new devices are actually telling us. It isn't just, you know, my blood pressure was 140 anymore. So I think that patients are already using more and more AI in their daily lives. And you know, how do we decide what's good and what's bad? And that's an interface that health systems have to really start working much more hard on. This is a new game. Certainly in the inpatient side hospitals are using, you know, machine learning and predictive modeling and all these other things for people with really sick, who are really sick in let's say intensive care units, trying to find out who's at risk for sepsis, et cetera. I mean there's a, lots, lots going on in that regard that apply to the daily practice and with that comes responsibility again to be sure that we're not causing harm. And my colleague Constantine Alferis, who's the head of ihi, the Institute for Healthcare Informatics at University of Minnesota has a book out, about 450 pages saying the, it's, it's called the, the Strengths, so to speak, and the Perils of Artificial Intelligence. And it's, it's a book I recommend to everyone it, it's available. I'm not pitching the book, I'm just telling you that's 450 pages. So we have a lot, a lot, a lot of thinking to do about how we go down this road. I also think that in the health system, to your point, I think that leaders can't just shoot from the hip and delegate and say, wow, here's something with AI that we're doing. I think they have to get down in the weeds every day and continuously get hands on with what's happening because clinicians, as even some of the specialties I mentioned that are relevant to health systems are already doing this. And so the other, the other lens is governance can no longer be passive. They have to show up and be ready to support the health system as well. So it's a hybrid of looking at the organizational structure from the standpoint of who exactly is accountable. And I think the leaders have to hold themselves accountable as well. I mean, hospitals as we know, will more and more become what I call centers for advanced sick care. That's without question not going to change. But as we know, you know, there are new areas where, you know, patients are using different aspects of this, including hospital at home. That's one paradigm. It isn't the only paradigm to get out of the hospital and not be in there. So I think that from my standpoint, the best thing they can do also is even within their health system is engage the clinical experts in the specialties. As one example that I spoke about with the use cases that show, and this is the important word, Scott, impact on population health. Not just wow, it did X, Y and Z and someone likes us. I mean that's important. But on the other hand, there's a lot of this that's embedded in the technology and will become heavier and heavier as we go along. I do think it requires leaders to in essence get out of the house and learn as much as they can and as I said, read books like Dr. Olafaris and find out what the risks are, not just the benefits. Because there are risks.
- [15:53]
B
Absolutely. It's certainly very timely topic that everyone does need to kind of stay current on education with. Of course. How are you seeing recent legislation, both state and federal, affect healthcare organizations and healthcare it specifically. Have you adjusted strategies in response?
- [16:14]
C
Well, I, I think that the question. There's a lot in there. I, I do know because I watch C Span a lot that certainly if you just look at the Commerce Department, there's a whole lot centered in that one area of the government that's focused on this large scale discussion of not just AI, but the advancement of, again, I don't even like the term digital technology around the world. I mean, there are all sorts of issues with cybersecurity that we can't even imagine right now. Right. As one issue. But you know, that's important to pay attention to from a legislative standpoint, to not just wait for a bill to come out, but to listen to these discussions that are occurring. I find quite frankly, people get upset about politics. If you listen to the Commerce Committee in the House, there are a lot of smart people there who actually have like minds about some of these issues such as cybersecurity. So I think it's if you're just going to learn about it watching the news or reading the New York Times, forget about it. I do do spend a lot of time specifically on the regulatory side. The fda, the Food and Drug Administration has something which we nicknamed Cheddar CDRH center for Devices and Radiologic Health is really evolving its regulatory approach not just to the devices themselves, but something called, and this is their term, software. As a medical device, you can imagine, let's say you've got an app on your phone that will tell you if you have signs of depression and that you should seek help. Those things have to be regulated from the standpoint of being sure that these aren't hooting and hollering about nothing and just sort of wowie zawi stuff. I mean we're talking about people's health and people's lives now and they're just beginning to look at, they've been at this for several years. I would bet you. Most healthcare leaders are not even aware of what CDRH is or does. But I think they're at least looking at things like AI enabled device software functions which includes how manufacturers should handle updates, monitoring and transparency. You know, then these things are always subject to lots of change on a regular basis given the evalu, the evolution of the software, but also the evolution of information that comes out of the work. So that, you know, if we're just talking about, let's say algorithms, how are we learning and improving over time and then the amount of data that we're generating is enormous. It's beyond belief. And the use of real, what we call real world evidence is also coming about so that these devices for example, that are occurring everywhere contain information. We have to analyze those and, and use, you know, them to not just evaluate the device itself from a post market surveillance standpoint, but for effectiveness evaluation. Right, that's really what we're, what we're looking for. And you know, here in Chicago, if I can put a pitch in Scott, we have an exciting time because, and this is a little bit off message but I do think it's going to be applicable to health care. We have a giant effort in quantum computing down on the lake that's begin just beginning to start up. I'm sure you've heard about this and it's amazing to me to see that University of Illinois, University of Chicago, Northwestern Illinois Tech, which used to be IIT and some other local universities here are going together out to Silicon Valley to raise money for this. There's also IBM, Google, you know, you can you can throw all the big names into that. And quantum computing is going to obviously take a lot of energy, but it's also going to increase the speed by which we are now using the so called AI enabled devices here, which means that we have a long way to go in terms of learning as much as we can about what works. And so that, to me, is kind of exciting for the future and an example of why Chicago is still a pretty exciting place to be.
- [20:38]
B
It certainly is. Don, you shared so many great thoughts on where technology is at in healthcare and so many great practical use cases. What I want to ask for your last question is what's your top piece of advice for healthcare leaders as they prepare for further advancements in technology and rising demands for care?
- [20:57]
C
Okay, well, now you, now you've got me right in my sweet spot. My number one, number two, number three, number 10, big dot is high blood pressure. It's not anything that anyone pays attention to in the C suite or in the governance room, but to me, it's the largest problem in the world. And I'm wearing a device, I wore it last night, but I'm wearing a device that measures my blood pressure every beat. I told you about that. And I think that I, I would advise each of these leaders to get something that's validated and learn more about their blood pressure. Not. I went to the doctor's office, you know, in April, and it was 138 over 70. I mean, that's like saying the temperature in Chicago In April was 62, and therefore the temperature in Chicago the whole year is 62. It's just, just not correct. Right. So I, I would say learn, learn some of these. Get, get to. Get to learn some of these things. Get down in the weeds. Go, go into the radiology department and see how the radiologists are using this. Talk to the cardiac electrophysiologist. I'm dealing with specialists here, but obviously I think the frontline clinicians in general, internal medicine and family medicine, can easily tell you and show you how they're beginning to use this type of information. Certainly we want to worry about revenue cycle too, so maybe they can have a conversation about documentation and coding as well. But I think it's just get out of the house and get in the weeds.
- [22:30]
B
Don, thanks so much for joining us. It was a great conversation. I look forward to working with you again soon.
- [22:34]
C
Thank you, sir.
- [22:37]
A
At athenahealth, we know your ambulatory practice wants healthier, a healthier business, healthier care teams and healthier patients. But the complexities of modern healthcare tech make it hard for you and your care teams to focus on what matters most. That's where athenahealth can help our AI native all in one solutions reduce administrative burdens, streamline billing and payments, and deliver critical insights when clinicians need it most. That means fewer clicks, more time for patients, and stronger bottom lines. Practicing medicine is complex, but running a practice can be that much simpler with Athenahealth. See how simpler is healthier@athenahealth.com.