This transcription is provided by artificial intelligence. We believe in technology but understand that even the most intelligent robots can sometimes get speech recognition wrong.

 Welcome to this Week in Health It where we amplify Great thinking to Propel Healthcare forward today we have Dr. Anthony Chang with us to talk all things ai. Dr. Chang is the founder of AI Med. He's also the Chief Intelligence and innovation officer for CHOC Children's in Southern California. So we're gonna give.

AI grade during the pandemic. We're gonna take a look at where AI is gonna go moving into next year, so looking forward to that. My name is Bill Russell, former Healthcare, C-I-O-C-I-O, coach, consultant, and creator of this week in health. It a set of podcast videos and collaboration events dedicated to developing the next generation of health leaders.

So if you're listening to this show saying. Man, I wish, I wish our company were getting our message out on this week in health It. This is your opportunity. We've opened up those sponsorships for next year. If you want to get a copy of our offerings, go ahead and send me a note bill at this weekend, health it.com.

I will respond back, give you our package, and you can take a look at it. We would love to partner with you to get your message out to the community. Alright now. Onto this discussion about ai. All right, so today we are going to talk about ai, AI during the pandemic, AI and startups AI and use, uh, and where, wherever else we happen to, to stumble upon.

So people have, have, have gotten a lot outta that and, and I appreciate you coming on. So chief Intelligence and Innovation officer for chalk, but it's more than that, right? Well, the found founder and director of the Medical Intelligence and Innovation Institute, or MI three for short. And it was from a generous grant from the mic, uh, Michelle Lung Foundation or the Sharon Disney Lung Foundation that is supportive of the artificial intelligence agenda for not just children, but for adults and medical care in general.

Wow. Yeah. So you, you have, you've really immersed yourself in this AI space. You, you, you started AI Med. You're, you're, you're still a practicing physician. Did, let's start with the AI med. How, how's the work at AI Med going? Well, we had to make a, a big pivot 'cause we were, as you recall, live event and, but we had already had pivoted to some virtual events and webinars and network formations for people interested in a certain sub area under AI in medicine.

And, and my book finally came out, I think we were talking about it last year. So it's so far I think, uh, well received because I think clinicians really didn't have a textbook that spoke to them in this space. Some of the other books are more geared for data scientists and AI experts, but I really wanted to create something that was ma more for the non-data scientists and non-AI experts.

So I think that's going to really. Create, uh, a sense of urgency as well as some validation for this area. Yeah. So what, what was your, what, what was the, the core of the book? What, what's your message to physicians at this point? That it's a very robust resource that we should take advantage of in healthcare as other sectors in society have done so.

But with, with the clinician being a partner, rather than being delegated to in terms of using AI tools in healthcare. Yeah. And that's what we talked about before is, is, is AI as a partner. It's, it's, it's not, the AI is not the, not the clinician, but it's a partner to go and gather up the information, process that information, present some potential

Uh, I don't know, points along the way to help the clinician, uh, to maybe operate a little faster, to, uh, process a little bit more information to handle the, the burden of the, of the daily interaction with computers. I, I mean, that's what I remember I was talking about is, is are those primarily what you talked about in the book?

Yes. That's the consistent take home message is let's use data science and what it's capable of doing. Uh, advantage that it offers and in working in partnership and in synergy with clinicians not against. So I think what's been nice is I'm no longer seeing a lot of papers that compare computers with clinicians.

I always thought that was not helpful. And what I'm seeing more and more now is clinicians with data science versus computers without. Clinicians without that help, and I think it's somewhat analogous to you driving, you can easily drive without AGPS, especially if it's somewhere close and you are very familiar, but most patients are complicated and it does help a great deal to have the support of data science.

It's like driving with AG Ps to a place that's.

And, and I use the GPS to go everywhere and it's, it just, for me, it's taking away part of the cognitive load. Right. So it's, I I, I could easily get on autopilot myself as I'm driving around and forget to make a turn. But if the autopilot's on, I'm not gonna forget to make that turn. 'cause it's gonna, it's gonna prompt me to make that turn.

Right. No, that's a, that's a good insight too. So, but you still have to have human in the loop because. Just went with my GPS and, and automated driving. Uh, for instance, as you recall, Newport Beach, there's, uh, a bunch of islands around here and it would've directed me right off the cliff into the ocean because if, forgot that the car had to take, get on a ferry to go to.

Some of the islands, Balboa Islands. So you have to have human in the loop. And that was a good reminder for all of us when I was seeing my GPS heading the car right into the ocean because it really didn't realize that it needed to connect me with a ferry. And I think that's the same in clinical decision, right?

So, or or medical image interpretation. You don't wanna have the human entirely outta the loop. And humans really need to be in the loop. Yeah, absolutely. Alright, so, so let's, pandemic is top of mind. Let's talk about that a little bit. Has, has the pandemic impacted positively or negatively the, the progress of AI in medicine right now?

I think both. Uh, I think if I were to give AI a grade for, during this pandemic, it would be like AB minus C plus. And it's not the really the AI's fault , it's basically a manifestation of how badly we need much better data and IT infrastructure and healthcare. I think when you don't have those things in place as well as a public health strategy in place, then the best AI that, that this country has is not going to help enough.

So on the diagnostic side, I think we've really fallen short because the testing wasn't, uh, readily available. Still isn't, and, but using AI for instance, you can apply machine learning to the pool testing concept to increase the odds of finding a positive person with the virus. Although in some states, if the positivity rate is so high, it doesn't even matter anymore.

But I think you can use AI even on the diagnostic side. So on the therapy side, it's been really exciting because now we can predict protein structure with just the genomic sequence using deep learning. We can also do vaccine design and strategy with deep learning. So all of these things are available now to help us with the therapy side, but it would've been nice to have the data and IT infrastructure.

Good enough so that the AI can really take place. As a concrete example, it took us two or three months to realize that proning patients and who need, uh, higher oxygen levels in their blood was just as good, if not better than mechanical ventilation that should have been discovered within days and weeks of this pandemic.

If we. Real time analytics built in to data and IT infrastructure in hospitals around the world. We should have just known that within days and weeks, but it took months and it took months for intensives of us to text each other to, to learn that strategy. So the AI consumes data and specifically clean data data that is ready to be consumed by ai.

But I, I would think that the, the, the example you just gave. I mean, we're getting telemetry data, we're getting, all that stuff is relatively clean data. Why weren't we able to, to, to do stuff with that quickly? Well, maybe, maybe clean data, but the data wasn't shared. So if you don't share data amongst the hospitals, then you're not necessarily gonna have information and knowledge from that data.

So we need very badly, we need a international consortium of hospitals to pull their data so that. When the next pandemic hits, then we're gonna be much faster at coming up with insights about therapy. And this goes to not just, this goes to medical therapy with re rem, dvir and all the other agents antibody agents, so that you really need to pull the patient experience.

So that the next patient will truly benefit from the cumulative knowledge that you built from the prior patients. Yeah. And, and we're from a public health standpoint, those, that data and that data architecture is almost not even at the starting gate for AI at this point. Yeah. Particularly in the us We should be embarrassed public.

A few second, third world countries probably arguably have as good if not better public health infrastructure as we have now. We have great expertise in global health and virology and epidemiology, but when you don't have the infrastructure, just like you can have a great AI expert, but without the data infrastructure, you still are disadvantaged.

So I think one of the things I've. Having good public health strategy coupled with good AI strategy is like having protective gear as well as weapons. When you fight a war, in this case it's the virus, so you kind of have to have both, right? You have to have both good public health and data and IT infrastructure with great AI technology, and that will be a very synergistic.

And this is somebody who, who worked in that business for the better part of 30 years, and they just said that it, it just, it'll take, it'll take them all of this year just to develop it, and it'll take them most of next year to, uh, test it out. But, but it looks like we now have two candidates, Moderna and, and Pfizer.

We, we could start seeing, uh, some general use in, in December and, and you a lot of that, or, or a bunch of that is, is due to the use of ai, do you think? Well, I, I don't know. I'm curious to find out after they released the vaccine, they probably don't wanna say too much prior to the release, but I'm betting that they were using, uh, analytics to some degree to, to really accelerate the design of the.

I, I think that's, it's hard to imagine that humans alone were involved in that whole process. Yeah. There's so many things you have to rule out. You have to get rid of non-effective possibilities pretty quickly. And the whole excitement about drug repurposing, I think we talked about this last time, is just using machine learning to, to find new ways of using drugs that have already been approved for a different purpose.

And because. Draw the, the, uh, parallels as quickly as computers can. Well, I, the, the interesting thing about these two drugs is they're both genetic drugs vaccines. Genetic vaccines as opposed to, or the more traditional vaccines. Uh, all right. We'll get back to our show in just a minute. I want to give you an update on the CliffNotes referral Program for those of you who don't know what CliffNotes is.

What we have done is we create a summary of what goes on on each show, and we get that into your inbox 24 hours after the show airs. So what you get is a brief summary of the show. You get bullet points as to what the high points were with timestamps, and then you get one to four video clips. So if you just wanna see that the highlights, the key moments of the show, you can just click.

Click on those, watch those and stay current. We did that in response to some of the things you guys were telling us. Made it easier, easier for you to share the content of the show with your peers, with your staff, with, with others. And it's a great way for you to stay current, uh, especially when you're busy and you can't listen to every episode.

So we wanted to make sure that you have an easy way to stay up to date as to what's going on in the show. as a result, because you told us this is, has been such a great service. We created a referral program 'cause we wanna get this in the hands of as many people as possible, and we can only do that through you.

And so what we did is we said, all right, let's, let's do a competition. Everyone likes a competition. So we, we came up with some prizes and some ways for you. . To refer this out to your friends, to your staff, to people within your organization. The prize are pretty straightforward. Everybody who does a single referral will be entered into a drawing to win a a, uh, work from home kit from this week in health.

It, uh, if you get up to 10 referrals, you get a black moleskin notebook delivered to you. Has this week in health it logo embossed on it. Uh, I love the Moskin notebooks. I use 'em all the time. And then finally, the person who gets the most referrals will be given the opportunity to come on the show, uh, to do a Tuesday Newsday episode with me.

That's the episodes that I usually do with Drexel Ford, and you are gonna get invited to come on and talk about the news with me. It's really straightforward. A lot of fun. It's really conversational. We'll have a great time doing it and I hope that you are excited about this. As excited as I am to have people put your name in for a referral right now, if you go out onto our website, hit subscribe.

There's a, a new box in there that says referred by. If they put in your email address, you'll get credit for each referral that they put in there. We already have people that are . Approaching the, uh, moleskin, uh, notebook level. So, uh, keep pushing through. And we have, we've had just a ton of referrals to this point, so we really appreciate all that you guys are doing, and we hope that the content is helping you to stay current and helping you to do your jobs more effectively.

So, with that in mind, let's get back to the show here. I, I want you to project a little bit so. Let's project out. Let, let's, let's make it a long time, 20 years from now before the next pandemic hits, what, how could we experience the pandemic better? And, and what things would you have in place in order for us to utilize data and utilize the technology in order to just create a, a, a better way of addressing and handling the, the pandemic, say 20 years from now?

Well, I wish I could say that the next pandemic will be 20 years from now, because Yeah, I, they're, they're coming, they're coming too quick these days, so I don't, I'm hoping it could easily be next year, so we need to, we don't have a 20 year, um, timeline to come up with a better. Data and IT infrastructure.

So, and one of the dividends perhaps from this pandemic bill is that I think we realized how inadequate we were with the best, some of the best medicine that, uh, we have. And yet we were paralyzed with lack of data and, and information from the healthcare system. So I think that's going to accelerate that development to build better data, sharing data, um, analytics across hospitals.

And I'm already seeing that with some hospitals sharing medical image data to be interpreted there because it's basically human versus virus right now. And nothing bans people together more than an external threat, which this is as a crisis. So I think humans are banning together the traditional silos of academic centers are not nearly as as high.

So, uh, I see collaborative efforts. As editor in chief of a journal called Intelligence Based Medicine, which is essentially AI clinical projects. I'm seeing collaborative efforts far more than even earlier this year. So I, I think that's an encouraging sign. So we talk about collaboration between clinicians and data scientists, but I'm also hoping that collaboration will be among hospitals and health systems as well to really put ai, you know, permanently on the, on the map for, uh, improving healthcare.

So, so let's talk about some of those projects. I mean, med AI was doing some shark tanks. What, what kind of projects are you seeing? What kind of interesting things are you seeing? What kind of startups are being formed out of the, out of the AI work that, that you're a part of? Yeah. AI Med was sponsoring pitch events, and what I'm seeing more and more is.

Well, the medical image interpretation was very popular as a topic just looking at cts and chest x-Ray to diagnose covid. I don't think that particularly will make huge impact, even though that's been a data scientist stream to do that project. I. I think the, the much more practical aspects of delivering Covid and post covid care is where the action has been to some extent.

And I'm encouraged by that. So by that I mean using machine learning to prioritize patients that need to be seen. Uh, now that we are potentially in a position to get ready for reopening of a lot of clinics and centers, how do you prioritize those?

Using machine learning to, to put patients on a priority list would be one very good way to use machine learning. 'cause humans are just not very good at remembering all the details about all of your patients. And I have up to 10,000 patients I'm following. There's just no way I can remember Everyone's sort of priority sort of standing in terms of getting seen.

So I think that's gonna help. Workflow issues can be helped by . Some sort of automation. So I think, uh, we're gonna discover ways of using machine learning in a. Sort of traditional way that we've been talking about in the last couple years. So mainly getting machine learning into workflow rather than sort of more academic, you know, image projects.

Yeah, so the, it's interesting, I, I had a conversation with Lean Toss earlier this year and they were talking about using ai, uh, around the, the, the flow of of patients into the OR and those kind of things. And they were increasing the efficiency of the, or obviously that's where money is. But now you go into a pandemic and they're actually applying that same logic across some, some other, some other areas to increase the overall throughput and, and, and efficiency.

All of AI has made some significant, uh, moves in the RPA space. Those are all forms of, of different types of ai. Actually, can we go back? 'cause you, you did a, a great short segment in our last interview, but it was almost two years ago. What, what are the different types of, of AI that, that we're seeing being used in healthcare?

Well, I think you can. Describe AI and healthcare into three kind of categories. One is assisted, so that's like your I Roomba wrap vacuum cleaner. So there are robots in healthcare that can take a blood sample and do all the testing without humans involved, and then automate the process and then send the results into the electronic director.

So that's assisted is a repetitive task. That doesn't require a human to intervene or give, you know, oversight over, that's just a very kind of a robotic process. The middle category is augmented AI in healthcare, so that's where you hear about Watson Health. Getting involved with Memorial Sloan Kettering, even though didn't deliver as as big a dividend as everyone had hope, but a lot of the analytics work that's being done with.

Are in that augmented category where humans have to be providing some insight and experience and labeling for the computer data science to benefit from that, uh, knowledge to make predictions basically make better predictions. And then you have on the other end of the spectrum what's called automated AI in healthcare, and that's auto autonomous.

It's also called. So that's where a algorithm can really make its own decision without a human in the loop. Now, it sounds intimidating, but there are times that you really don't necessarily have access to healthcare. So for instance, the I-D-X-D-R software that is approved for autonomous AI in healthcare can interpret a fundus photograph for diabetic uh, retinopathy.

And because that's based on a lot of ophthalmologists experience, so, but I still think with autonomous ai, the medical world is not quite ready to let these go entirely without oversight. And I think it's good for a while to have oversight. No different than, you know, any modern technology. You just want to have enough human experience with the technology to feel more and more comfortable.

But the human acceptance overall. AI is changing. I remember five years ago, even when I used to do an audience poll about who would, you know, ride an autonomous driven vehicle, virtually no hands will go up and now more than half the hands will go up. So I think we're sort of increasing our, our accommodation of these technologies.

Yeah, it's, it's interesting. I I, I wanna go in two directions with you. One, is there, there's, there's big debate around. Ethics and bias, right? Because AI essentially is, is code behind it that's being written by people. And people have bias. People have, yeah. I mean, and, and some prejudice and whatnot, which can, can enter into the, to the algorithms that are, that are utilized.

Where's that conversation going? And are, are we, I assume we're having that conversation. It. It permeate some organizations. I've actually heard of some organizations setting up groups that actually look at how they're bringing AI into their healthcare system and reviewing the algorithms for things like bias and, and those kind of things.

Well, a couple of thoughts. One is a lot of times the bias is not intentional. It's just that it was. Yeah, absolutely not. I I, I didn't mean to imply that, I'm just saying it's, it's, it's just part of who we are, I guess. Yeah. I think a lot of it's unintentional, but perhaps bias that we weren't realizing and sometimes it's, it is just appreciating certain assumptions.

Were not entirely correct when you look at the data. So I think one of the best things about AI in healthcare is that as clinicians, we learn a lot about ourselves by and how we think. By applying ai. So it's a reflection on how we have practiced in the past just as much as discovering new things with machine learning.

So point number one is we need to be partners with being in terms of being accountable for bias. It's not like the algorithm is biased, so that's the algorithm's fault. Where're, we should be 50 50 partnership with algorithms in terms of being accountable for bias. And the other thing is. As you have alluded to, if we have bias, even if it's unintentional, it's gonna be just automated and perpetuated.

So we need to be very careful that we don't do that as much as we can. So we kind of have to look at the entire workflow of these projects from how we label data, right? Because you can have bias just labeling data. From the labeling of data, curation of data through the algorithm process, and then outcomes, the prediction.

And we still have to look at the prediction because maybe that's bias because somewhere along the line during the workload, things became biased. And so I think it's, it's a tough problem, but I don't think it's a deal breaker when it comes to applying AI in healthcare. We just have to be very cognizant of potential biases.

And use our real world experiences to to, to make sure that we have as little bias as possible. It may be impossible to get rid of bias entirely because humans are involved in trying to figure this out. We may need the, the AI to, to figure out the bias in ai. Ironically. Yeah. I, I, the, the thing that's encouraging me is I am seeing health systems stand up governance around bringing AI into the organization, which means that it is, that it's, first of all, they've, they've involved clinicians, there's, there's physicians who are part of that process times.

The clinicians and, and nurses to be part of the, of the technology projects, and they're heavily engaged. They, they see the value and I, and I just think the fact that they're standing those up is, is a, it's a good sign, but, and we'll see where that goes. One of the things I did want to touch on with you is AI Med is global.

You were, you were doing conferences in Asia, you were doing 'EM in Australia. Were you in Europe or Yes. We're, we're usually in Europe at least once in a year. So, so you were all over the place. I'm, I'm curious, as you sort of look at this is where, where are the places where you're seeing, uh, the most innovation in ai?

Is it, is it the us Is it Asia? Is it, is it other parts of the world? Well, I think based on emails and interest from different regions in the world, I. Probably UK and parts of Europe are still very engaged. China for sure, Singapore and other Asian countries, Japan and Korea are also, and US obviously, but only not the entire country obviously, but certain pockets.

And then Canada, Israel, so. About a dozen places that are particularly interested and engaged. We just launched the American Board of Artificial Intelligence and Medicine, or A-B-A-I-M for certification and knowledge starting next year, and I'm already getting calls from the uk, Australia, and China about bringing that process to their country.

So that's hugely exciting for us. I didn't expect us to be international so early. But we offer a two day review course, uh, throughout the year, every month. And then we have a certification process that you can kind of assess your, your level of knowledge in the space and with everyone aiming to improve their knowledge, of course.

So the course is basically based on the textbook. The textbook is a little bit daunting if, even though I think it's relatively straightforward reading for any clinician. So we kind of divided the book into 50, um, educational modules that we review for the review course. Wow. What's, what's the pandemic been like for you?

I just, this is my curiosity thing here. 'cause I, I don't think I knew of anyone who was more busy than you prior. Prior to, uh, the pandemic and the pandemic slowed us all down. Right? I mean, there was, there, we, we weren't traveling as much. I think I've only been on the plane, uh, plane twice this year. And it would normally be about.

40 or 50 times by this point of the year and, and you would've amassed a ton of miles. What did, what did you do with that time? 'cause you're not one to sit around much, so, but what, what does it, what did it look like for you? Well, of course it's a great time for self-reflection, right? Because we have, are forced to have a lot of time alone or with your family.

First of all, the most important dividend for me during this pandemic on the. As a data scientist, I calculated out the increase in percentage wise, the number of interactive hours I have with my two girls. As you, as you recall. Um, they were little and well, they're bigger now. They're five and seven now, bill, so, so it's really a great time to spend, you know, time with them.

It's almost fourfold increase in the interactive time, number of hours, and that's been just amazing for me that. In the middle of this pandemic, we need to look for positive dividends and one positive dividend is we've gotten closer than ever before. And just the sheer volume of time that we're together 24 7, literally the, the, that experience has been great.

And then because of traveling less and having less traveling just locally even, right, because I spent an hour and a half probably going back and forth to the hospital. Gain the time gain. We've launched the American Board, which no one thought was possible in six months, but we did. We have the book got launched and then I'm following that up with an AI and cardiology book.

'cause I'm a cardiologist, so that's launched. We have a video series and production, so it's not like, because I have a lot of time now, I can do all that, but it just. You direct time to projects that would've been harder when you're more active traveling. So, so at, so at at cha you talked about not commuting in as much.

Are you guys using in-hospital telehealth solutions to do rounds and those kind of things? Yes. Uh, we're, I'm seeing my patients virtually now and miss them terribly. A pediatric cardiologist, you, your patients, and. Now there's um, not that. So, so I see patients virtually and to my surprise, I think a lot of patients and families are actually quite happy with that arrangement during, especially during a pandemic.

'cause they don't like the risk of going in either. Um, not just myself, but so, but it's not the same. But I think in the meantime it also taught me that perhaps a third of my visits could easily be done through telehealth and. Inconvenience the families to come in, even without a pandemic because a lot of my patients are handicapped and they during wheelchairs, and it is a big deal for them to get in the van and come over and see me, so to the clinic and see me.

So I've learned to to, it's a different practice of medicine as you can imagine. You have to trust your eyes even more than before. So I think that's worked out, I think just fine for, uh, almost all the patients and families. Yeah, I, I mean, telehealth will be one of the things we look at and we'll have fundamentally changed.

Uh, I remember sitting across from some, some physicians who, who are look at me and say, I'm never gonna do telehealth. You can't all this other stuff. And those same physicians are, I'm sure using telehealth just like you. Probably a third of their. We're, we're also exploring a very exciting area, which is using AI and wearable technology.

Yeah. So all the wearable devices are not very helpful unless you can either embed or use AI for the data that's gonna generate. So, because humans is not gonna able to keep up with that streaming of data. So in a way, the pandemic also gave us an opportunity to explore that area. Uh, so what, what's next for AI Med?

As you recall last two, two years ago, we just started going into clinical subspecialties, like cardiology and radiology. But we're gonna expand, you know, into 10 or 12 more. We're also going to have a more active ecosystem and subsystem. So we launched something called AI Connect. As a way to help clinicians and companies and hospital systems and academic centers to connect in a certain category of ai.

So that's been very helpful and successful. So just getting all the stakeholders together to talk about the problems in AI and healthcare has been tremendous. So we're gonna be pretty active and and busy next year. So you're, you're expanding the community even as you're not getting together. The community's growing and, and the connection is growing if people wanna be involved in the stuff you're doing.

You guys, you guys do some podcasts as well, right? We do quite a few webinars, yes. Yeah. Oh, you do webinars. Okay. So if people want, if people want more information, they want to get connected, what's the best way for them to, to do that? They can just go on our website, which is AI med io. Has all the events and activities.

There's also now the American Board ABA im.org, that's for the American board, and they can sign up for the courses as well as the certification process that they wanna get their group certified starting next year. That's a good way to get certified and have a crash course in ai. If you don't wanna go back to school like I did for four years.

A two day course really kind of gets you up to speed, um, very quickly. And then we have a medical intelligence society, or MIS that's mainly for clinicians, but it's open to everybody, just like everything I do. And that's where the clinicians get together and talk about their clinical projects from an academic perspective.

And we talk about journals and journal articles. So it's particularly good for clinicians, but it's open to everybody, so. We have a lot going on trying to keep all these things, um, going during pandemic, but I'm grateful to all the teams are, are behind all of these major initiatives. Yeah, no, it's fantastic.

You've put together quite the community. If anyone's listening to this and they want to hear the really cool story of how you got into AI and going back to school, what they should do is go back and listen to the archive. Because that, that really was, I enjoyed that episode. I enjoyed sitting up there.

Yeah, hopefully. Um. In the middle of another pandemic. So, but I, I think there's a sense of urgency for us to really get our act together as humans, or particularly caught off guard with the magnitude of the virus and the capabilities. And you can learn from viruses too, even though they're technically not even, uh, living bill, right?

They're just.

The DNA sequence, they're not technically living, but you know what? They're very well behaved. They work as a team. They don't have a centralized leadership that can get in the way. They just all function, uh, with one goal in mind, in fact, and kill, kill humans. So and duplicate. So, and you're saying, and you're saying We could do that same thing.

I think we can learn from their, what's good about the virus, which is. Technically a very well functioning, complex adaptive system. They adapt to changes in environment. They work in unison as a, as a team, and I think humans can learn from that and hopefully we will, we will be able to learn from this virus.

Absolutely. Anthony, thank you again for your time. I, I really appreciate it and it's always good to see that Southern California sun behind you, . Yes. And we very, very much looking forward to Bill having another. In person. So thank you very much for the opportunity. Absolutely. That's all for this week.

Special thanks to our channel sponsors VMware Starbridge Advisors, Galen Healthcare Health lyrics, serious Healthcare Pro talent advisors, HealthNEXT and McAfee, for choosing to invest in developing the next generation of health leaders. We really appreciate their support. Don't forget to sign up for clip notes.

Send an email, hit the website. We wanna make you and your system more productive. This shows the production of this week in Health It. For more great content, check out the website this week, health.com. Check out our YouTube channel. We continue to modify that and make that better and easier to find things that you are looking for there.

Please check back. Every Tuesday we do Newsday. Every Wednesday we try to do a solution showcase. Every Friday we do interviews with industry influencers and we will continue to do that through the end of the year. And then we have some interesting things lined up for the new year and I can't wait to share those with you, and we will start sharing those with you here shortly.

So thanks for listening. That's all for now.