Data Covid-19 This Week in Health IT
April 5, 2020

 – Episode #

Guest Information

Share this clip:

Share on linkedin
Share on twitter
Share on facebook
Share on email

April 1, 2020: The role of data in fighting the COVID-19 outbreak cannot be underestimated and there is no one better to talk about healthcare data than Health Catalyst CTO, Dale Sanders. In our chat with Dale today we look at his professional history and how this informs his approach and perspective on the current pandemic before breaking down the important areas of the healthcare system that need to be addressed. We discuss personal protective equipment, accurate registries and screening criteria as Dale unpacks what is most pressing at this moment. He also shares some of the important lessons that the data has taught us thus far, although the picture is not yet clear. Dale talks about surprises that have occurred in this time and how these have led to the difficulty of treating COVID-19. We look at the response from the US and the disappointing lack of coordination between states within the country. The last part of our conversation offers listeners some thoughts on how things could have been different, how they might be next time and the current steps that Health Catalyst is taking for their patients.

Key Points From This Episode:

  • Dale’s background in the military and the disaster preparation that it entailed.   
  • The three important patient categories that healthcare systems need to pay attention to.
  • Challenges around numbers for PPE; the value of accurate estimates. 
  • The installation of functional registries across the market. 
  • Screening criteria for testing and the ongoing validation of this process. 
  • Lessons we have learned from Wuhan, China about patients displaying symptoms. 
  • The surprising variety of severity of COVID-19 and the opportunity this provides.
  • Inability to nationally organize in the US and the lack of training that has been conducted.
  • Steps that could have and should have been taken in preparation for this pandemic. 
  • Using the data that is collected from early warning stages; taking cues from the military. 
  • Measures that Health Catalyst is currently taking for the patients.  

Data vs The Pandemic with Health Catalyst

Want to tune in on your favorite listening platform? Don't forget to subscribe!

Thank You to Our Show Sponsors

Related Content

Amplify great thinking to propel healthcare forward and raise up the next generation of health leaders.

© Copyright 2021 Health Lyrics All rights reserved

Data vs The Pandemic with Dale Sanders of Health Catalyst

Episode 217: Transcript – April 1, 2020

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

[0:00:04.8] BR: Welcome to This Week in Health IT Influence, where we talk to the people who are influencing health IT.

My name is Bill Russell, healthcare, CIO, coach 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. I want to do a special shout-out to our show sponsor, Sirius Healthcare. Sirius stepped up to sponsor the shows we’ve been producing in an effort to capture and share the experience, stories and wisdom of the industry during this pandemic. I am extremely grateful for them and I’m grateful for their commitment to the mission of This Week in Health IT to develop the next generation of health IT leaders.

Today’s show is about data. When I think about data, I think of Star Trek first. The second thing I think of is health catalyst. Then further on, I think of Dale Sanders. If you’re following Dale on social media, you know that he has been extremely busy lately. I’m excited to have gotten the opportunity to sit down with him this past week and I’m looking forward to sharing that show with you right now.

 

[0:01:04.8] BR: Today’s conversation is with Dale Sanders, Health Catalyst CTO. Good afternoon, Dale. Welcome back to the show.

 

[0:01:11.2] DS: Hi, Bill. Thanks. Good to be here.

 

[0:01:13.5] BR: Well, thanks for taking some time to meet me. You really have a great background for this. You have a military background, you have an IT background and you work with one of the premier data companies in healthcare, so I’m really looking forward to the conversation.

I don’t want to minimize the military background, because I’ve seen a bunch of back and forths with you. I had a conversation today with [inaudible] forward. The military really spends a lot of time preparing for these kinds of situations, don’t they?

 

[0:01:43.0] DS: Yeah. I mean, that’s the nature of the military, especially as an officer, you’re constantly preparing for disaster. That’s what you do for battlefield conditions and hoping that you never actually have to gauge, but they are – the exercises are non-stop. The training is very formal, both the procedural, as well as the mental training for these things. Yeah, a couple of times I’ve reflected the last couple of weeks about this really is an interesting convergence of my military background with data and decision support and battlefield management and then healthcare data and epidemiology.

 

In a morbid sense, it’s intellectually interesting and fascinating for me at this time in history, but it’s sad too. I certainly feel bad for our country and I feel especially bad for the hotspots, like New York City.

 

[0:02:47.7] BR: Yeah. Yeah, I’ve been talking to some of the organizations in these affected areas and they have a lot of questions. I’m hoping that we can delve into some of these things. I’m trying to think if there’s anyone more prolific on LinkedIn than myself and you would clearly be way above me. You’re somebody who I strive for. If people aren’t following you on LinkedIn, you’re constantly bringing articles that I normally would not read to the forefront and I’m just learning a ton about public health, about data, about organizations that have been really focused in on elevating the game of analytics and data science around this, so it’s been good.

 

Let’s just get right to the questions. Again, I sourced some of these questions. Dr. [inaudible 0:03:35.9] was kind enough to send me some questions. Charles Boise was kind enough. You know both those gentlemen. The questions are going to be a little harder than they usually are, because I cheated a little bit. Let’s just start with now what are three analytic insights that all or nearly all health systems can access today with the capabilities that they have in place, that could help them to allocate the limited resources that we have more effectively?

 

[0:04:04.1] DS: Oh, my gosh. Across the board. What could they access? Fundamentally, there are two issues right now that the entire country is struggling to answer. That is the consumption rate of PPE and wartime environment essentially. Then predicting the actual infection rate and not just the infection rate, but the severity of illness rate in their catchment areas.

 

This analytic environment is actually quite complicated right now, because we don’t have a clear picture of the three identities of patient types that we should be focused on right now. Those three identities are the super high-risk members of the community, who if they are infected, will likely suffer a fatality. Initially, they came out that hypertensive patients, patients over 60 and patients with COPD were clearly at risk.

 

Now that’s starting to get a little uncertain too. The age and the demographics are smearing a bit. There’s some debate about whether immunocompromised patients are at risk or not. Some physicians feel that the immunocompromised patients are actually at lower risk, because the virus is essentially stimulating an over response from the immune system and that’s what’s leading the [inaudible] and things. That’s one identity that we should all be trying to access.

 

Who’s at risk and are they practicing social isolation? If they do present to the hospital, do we have the capacity to escalate their care? Then the second identity that’s fundamental are those that are suspected with symptoms, like flu and RSV that might have COVID, or were not quite sure and we’re not ready to test yet, because we don’t have enough tests to go around, but you need to start watching those folks. If they present to the facility, you need to prepare two tests. Then the third category of course, is the confirmed COVID patients.

 

Those are the three identities that every healthcare system in their analytic environment need to start identifying and managing. The CDC has been – well, I’ll just flat out say, the CDC has been largely ineffective in this response in my opinion. I try to give them as many graces as I can. I know this is confusing and chaotic environment, but they’ve been slow to publish the national standards that we should all be adhering to in those three registries that I just mentioned.

 

We published them from health catalysts. We’ve done our best to synthesize the evidence in the industry, as well as that from CDC and we published those registries out to our clients and we’d also make those available. I published those on LinkedIn. Virtually, all of your analytic use cases start with a consistent and precise definition of those three patient types; their capacity planning, your testing strategy. When I say capacity planning, I’m talking about PPE, ICUs, vents, beds, staffing, all of that.

 

Let me mention one other thing too before I forget. One of the challenges that I see everyone’s struggling with right now besides capacity planning based on in to rates in their catchment areas is the management of PPE. Let me just encourage everyone that’s struggling with this right now, to first as an organization, because we don’t have any national guidance on this, define what you should be doing in terms of consumption on a per COVID patient per day basis. In other words, how many items of PPE; hoods, glasses, gowns, bonnets, bunny suits, shoe covers, how many items of that PPE are you consuming, or do you think you should consume on a per COVID, per patient day basis? Does that make sense?

 

[0:08:50.7] BR: Yup.

 

[0:08:51.3] DS: Let me just pause on that for a second. This is basic supply chain 101, right? Model what you think is realistic from a caregiver safety perspective, what you think you should be consuming, realizing that we’re in wartime, so we don’t have unlimited resources. They’re probably going to have to issue guidance that extends the utilization of PPE out longer than it normally would be, but determine what your clinical guidance and best judgment indicates for the protection of your fairgoers, and also of course transmission to other members of the hospital, the direct caregivers.

 

Then model that according to the inventory that you have. Then look at what your burn rate is going to be and when you plan on running out of that and then that will drive supply chain and distribution upstream. It’s an unfortunate situation right now. Across the country and all of the leading systems that I’ve talked to, I don’t see anyone managing the supply chain of PPE from that perspective.

 

What they’re looking at is a consumption of PPE and then they’re adjusting the utilization guidance according to their inventory, not according to what is appropriate for caregiver safety. If we never get to a guidance-based consumption model around that supply chain, we’ll never be able to effectively protect our caregivers.

 

We’re essentially letting the PPE that we have drive our consumption guidance, as opposed to what we think is safest for our caregivers, then pushing those requirements back upstream, because that’s the good news. There is some progress being made on the production of PPE. Of course, there’s a lack of a national strategy about how to allocate that. If there’s no models and there’s no data to support the allocation, then it’s going to be hard if we ever do get our arms around the distribution of PPE to know where it should go.

 

[0:11:08.3] BR: Yeah. I think was yesterday or the day before CMS came out and actually started to collect that information, or want to collect that information from all the health systems, what you have and what’s available, so that they can start to allocate that effectively. I want to I want to drive into the systems a little bit, because to a certain extent, you’re saying that the inventory is driving the use of PPE, but to another extent were –

 

I think there’s two breakdowns of systems here, right? We’re talking about registries across an entire community. A lot of the time, we have registries within health systems. We don’t have registries across an entire community. Then the second thing is around supply chain management systems. If I remember correctly, some of the supply chain management things treat things like masks as consumables. You look at it as a quantity of a box. A box could have whatever, 25 in them. We never really managed those that closely and I’m not sure the supply chain systems —

 

I mean, is there something that kicks in from a crisis standpoint that you say, “All right. These systems aren’t going to work. We have to figure out how to do something else.” What would you do around the registry? Let’s focus on that one, so that’s probably the most important one. How do you all of a sudden, if it wasn’t in place, get registries going across your market, in your region across multiple entities?

 

[0:12:46.0] DS: Yeah. Well, you’re not going to be able to do it in a community, like a traditional registry. That’s for sure. What should have happened and what still can happen to catch up to this is take the guidelines that are emerging in the industry and again, I’ll post those on LinkedIn if folks don’t have access to that yet. Put those registries into your analytics environment and then make sure that your EHR data collection is lining up with that, realizing that the EHR data collection right now is one of the last things that people really want to do.

 

You’ve got to build your registries out and the attributes of that registry within the reality of the chaotic environment we have. No one has time to click through a hundred attributes to precisely define a patient type, which by the way if we follow the CDC PUI form, there’s a snowball’s chance that that form is going to be filled out right. There is a snowball’s chance. It’s a paper-based form. It’s cumbersome. It doesn’t reflect the workflow, or the patient. Some of the data is available on the presentation of the patient. Some of the data won’t be available for a couple of months; you won’t know the outcome.

 

That CDC PUI form is essentially useless right now. It’s absolutely a public health perspective of this disease. What we need is situational awareness, which goes back to crisis management and military. The concept that I’m going to opine on later writings and things is called intelligence preparation of the battlefield. It’s a complement concept and practice in the military to think deliberately how you’re going to lay down the data environment in a battlefield to give the commanding officers the information they need to adjust and adapt. That’s what we don’t have right now.

 

Get those. Those registries have to be in place with a minimum viable data set, minimum MVDS. That’s another thing that CDC should have done and I would love to see and maybe I should try to do this. Actually, I’ve been thinking about it more. Someone needs to convene the EHR vendors, the major EHR vendors, the four big names and consolidate an MVDS strategy, make sure that that’s all being collected as quickly as we can out in the field and then feed that into the analytics platform to drive all these use cases. Don’t go down the CDC PUI form.

 

[0:15:23.8] BR: Right. You simplify that pretty significantly. Let me move on here. Let’s talk about the screening criteria. Have the current screening criteria for who should be sent to COVID-19 testing been validated in and community use? What are the opportunities to continually revalidate and adjust the screening criteria?

 

[0:15:48.3] DS: If by screening criteria, you mean patients that are high-risk, or patients that are presenting mild symptoms?

 

[0:15:53.7] BR: Right.

 

[0:15:55.8] DS: Yeah. I think the screening criteria for preliminary symptoms is pretty well-established right now, right? With the fever, some GI issues, but it’s primarily fever, body aches, dry cough, some GI issues, sense of smell and hearing loss, or taste rather, not hearing. There’s decent screening criteria. If you pass those criteria, then there’s a pretty good chance right now that you have the disease.

 

The question is what do you do with those folks, right? The lessons that we learned and are learning from Wuhan and other places is sending those folks back home to the family ended up being one of the primary points of transmissibility, right? We set those mildly symptomatic folks in China back home and they infected their family. That in combination with the infections with the caregivers is what caused the major outbreak.

 

By the way, I might mention before I forget it, I’m fortunate enough to have acquaintances and conversations with my equivalent in China. Baidu Cloud is the name of the health catalyst version of China. I’m talking now with those folks and their analytics teams. They’re three to four months ahead of us in terms of data analysis and pattern recognition and things like that. That’s been an interesting learning experience to chat with them. I’m spinning up similar conversations with friends and colleagues in Singapore and would be happy to report back on all of that one when it starts to become more clear.

 

[0:17:44.1] BR: That would be awesome. What’s the most surprising insight that you’ve learned in the last couple of months as you’ve been tracking this?

 

[0:17:55.9] DS: I think one of the most surprising things to me, Bill, is the wide variety of severity of illness. It’s a perplexing disease, right? To have a large portion of the population completely asymptomatic, compared to a portion of the population who progresses to fatality in a matter of days. That has been one of the most surprising and perplexing things for me. I hope that what it does is it gives us a large population to understand the immunology and the genomics of response in those folks that are asymptomatic. Maybe it will accelerate some treatment.

 

I also would caution, we’ve spent lots and lots of money on vaccines for SARS and MERS coronaviruses and nothing’s been effective. In fact, there was almost no money going into that research anymore, until the COVID-19 situation because there was no progress being made in a vaccine. Even the pharmaceutical treatments were largely ineffective.

 

It’ll be really interesting to see whether or not we can make progress with the coronavirus that we’ve never made before. That’s one surprising thing. I think the other surprising thing is at the macro-level, I’m just completely surprised at our country’s inability to organize a national effort around this. It’s really come down to every municipality and every state on their own. By the way, some folks might interpret that as being politically motivated. I’ve been a political independent since I was five-years-old. That has nothing to do with politics. I’m in conversations with senior officials. I had a phone call this morning with a very senior official in the federal government, a Republican. He’s completely stunned at how disorganized the national effort has been.

 

Then literally, his advice was every state and every municipality needs to take care of itself. That’s one thing that surprised me. I think the other thing that surprised me too is the – This is not a big surprise, but it confirmed what I probably knew. In all the conversations I’ve had with my physician friends and clients, I’ve purposely asked them, have you ever had any training, even remotely close to this that would prepare you for this disaster and battle management? The consistent answer is no.

 

They might have had a class in epidemiology. They might have had a class in public health, but our physicians have never had formal training in this thing for the most part. I know that there are some exceptions to that. I hope as a consequence of this event that we step back as a country and provide that training. Not just the training, but also literally, the lessons from the military about how to manage these situations and the organizational structures and the roles that are played. It’s not rocket science. It’s been solved in other sectors before.

 

[0:21:36.4] BR: Yeah, I’m going to ask you to talk about laying out the data thing going – the data mechanism going forward. If we could rewind the clock a year and a half, two years and we’re going to lay out the fundamental principles for data, I’d love to hear what your thoughts are on that. The thing I would say in terms of the response, it’s really – we lack imagination.

 

If you were to go into a health system and say, we need to prepare for this. They would look at you like, “Come on. I mean, is this going to be on the test?” It’s that people are like, “Well, why didn’t we lock down New York earlier?” I’m living here in Florida. People from New York are and you see them with the New York license plates on their car. They’re acting like there’s no issue, whatsoever. They’re out at Home Depot. They’re just driving around.

 

To a certain extent, certain communities, certain cultures, it’s hard to just walk in there and say, “We’re going to lock you down. We’re going to do these kinds of things.” If there isn’t a frame of reference, we lack imagination to say, “Hey, this could be worse than we think.” It’s going to be. In China, you can just walk in and say, “This is what we’re going to do.” Everyone goes, “Yeah, this is what we’re going to do.” 

 

In the US, it becomes a little harder because there’s just cultural challenges. I’m not making excuses. I’m just saying it is a multi-faceted complex problem. Let’s go to the data aspect of this. If we could rewind two years and we had the imagination to imagine this was coming, we saw SARS, we saw swine flu, Ebola. We took those markers seriously and we said, “All right, we got to prepare for the next one.” What should from a data standpoint, data and analytics standpoint, what should we have put in place?

 

[0:23:36.4] DS: Well, it’s battlefield surveillance, right? You’re looking for signals, unusual signals in the field that indicate a new risk is emerging. We knew that and this is a good part about the CDC, actually. The field units in CDC and other organizations are routinely looking at these novel viruses crossing over.

 

If you’re going to follow the military metaphor, those human intelligence sources that are out in the field looking at this could start sending signals, collecting intelligence, reporting that back up to a structure that’s nationally organizing all of those signals and looking around and saying, “Okay. Are we starting to see for instance, an unusual spike in flu-like symptoms that we can’t quite explain?”

 

Interestingly, a lot of folks and a lot of physician friends of mine have all said, we had unusual symptoms of flu in December, January, maybe even as early as November, that now everyone’s wondering if that was actually COVID, in mild to asymptomatic patients. Then some of those of course, progressing into ARDS, but were they just I think largely diagnosed as flu cases. We’re actually looking at our data right now to see if there were unusual spikes and flu-like symptoms in November or December that would have signaled that something was happening.

 

[0:25:18.7] BR: That’s the early warning system that you want to put in place. All right, so let’s assume we see the early warning. Now where do we go with the data?

 

[0:25:28.3] DS: Well, with any early warning, you have to decide are we seeing false positives, or false negatives, right? In healthcare, we tend to lean towards false positives, right? In other words, as we dial up sensitivity and specificity and we’re finding a balance between those things, generally speaking what we do in healthcare is we dial up sensitivity and we prefer to err on the side of false positives.

 

In these situations, you can’t do that. Because if you assume that everyone has the disease, you’re going to over-treat and you’re going to consume all of your supplies. It’s like scrambling your fighters every time your radar picks up a crow. That’s literally the military metaphor. You cannot scramble fighters every time if you dial up the sensitivity of your radar, every time a crow flies by and you scramble fighters, you’re going to soon run out of fighters and pilots and fuel.

 

What we should have done looking for those symptoms, unusual collection out in the field, listening to what clinicians are saying. This has to be an ongoing thing. In peace time, the military has an intelligence network that’s constantly mining for threats that you don’t know about. You don’t want to spin up this network of threat intelligence when you think there’s a threat, because by that time, it’s probably too late. You have to have it constantly in the field, monitoring, looking for patterns that might indicate a threat, then you have to forward that up to a national center that’s sifting through that to look for patterns across the world and across the country to make sense of it.

 

One of the first things that we should have done is reacted to the early warning signs in Wuhan. There were plenty of epidemiologists who kept warning us, as soon as this emerged in Wuhan that this could be a significant problem and we did not listen to that. I put myself in that. I made a couple of posts on social media saying, let’s not forget that there’s 18,000 deaths from normal influenza this year, so let’s not overreact to this.

 

We should have paid more attention. We should have started ramping up registries at that time. We should have ramped up the minimum viable data set to collect in the EHR, not paid much attention to the CDC PUI form. We should have started looking at what this might mean to PPE and the progression of the disease towards ventilation and ARDS. Lots of 20-20 things that we could have done. I hope as we go forward especially with the stimulus package, there has to be a new investment in pandemic information surveillance. I’m not seeing any evidence to that, actually in the stimulus, Bill. Hopefully, there’ll be another round of that. I can’t imagine that as a country, we wouldn’t invest in that now.

 

[0:28:41.2] BR: Yeah. I really appreciate your time. There’s part of me – actually, you know what? You were kind enough to give me your time and Health Catalyst is kind enough to give me your time. I really want to close this out with what is Health Catalyst doing for your clients right now? Are there any new approaches, or things that you’re doing around this that you can share with us?

 

[0:29:11.3] DS: Yeah. We’re doing a number of things, of course. Our teams typically have analytics engineers that are associated with our technology at client sites. Those folks, the analytics engineers spun up very quickly. One of the early applications that we deployed was a patient tracking application, so that we could watch the progression of a COVID patient through the healthcare system, both by location, as well as by the people that they were touching and interacting with. We got a sense for who had been exposed at our client sites that might not have had PPE involved.

 

That was one of the first things we did. Of course, there was all sorts of ad hoc analytics all around the management and supply of ICUs and ventilators. We’ve got a capacity planning tool and modeling tool that we leverage based upon the epidemiology work at UPenn. They were great. They put an open source Github repository out there for their model. We borrowed that model. We enhanced it and we added a few features. We contributed our code back to them. That capacity planning model is out there now and I encourage – I can send a link out to you for that by the way, Bill.

 

[0:30:33.8] BR: That would be great.

 

[0:30:35.4] DS: We’ve got a number of different dashboards. Frankly, I’m a little disappointed in our ability to provide a dashboard sooner to this. We got a little spun up with a couple of projects that had a longer lead time. They got away from my influence. By the time they were down the runway, I didn’t have time to redirect it.

 

Anyway, that distracted us from the core COVID management dashboard that has all the things you would expect to see on there right about informed by the registry, all about logistics and supply chain because that’s where battles are won or lost. It’s all around the logistics and supply chain.

 

Then longer term, the next couple of weeks we have a – we have a patient safety and syndromic surveillance application that we will deploy specifically to COVID. We have an existing patient safety surveillance system for inpatient monitoring. We’re adjusting that to address COVID management and we’re also adjusting it to incorporate signals in the community. We’ll release that in the next couple of weeks. Those are the big things. I don’t think I’m forgetting anything right at the top my head. Of course, the registries, I mentioned that. That’s been pushed out to all of our clients.

 

[0:31:58.8] BR: I’m sorry. Last question, I promise. Is there anything we’re doing? Patients who recover and there’s been weird – it’s really hard from where we sit to determine what’s really happening, but they’re saying hey, there’s risk of people actually getting the virus again, or getting sick again. Are we are we tracking those patients once they leave the health systems, or are we just waiting for them to represent at this point?

 

[0:32:28.6] DS: I don’t know of anyone that’s tracking them proactively, Bill. Of course, we can track them when they come back to the system. I don’t know of a healthcare system, like Singapore, South Korea, China, they were definitely tracking those folks through the social media apps. I’m not aware that we’re doing anything like that.

 

By the way, let me comment really quick on one item of testing that I still am stunned that we did not achieve as a country. You can break the US into 10 different, essentially CDC regions and health and human services regions. We should have spun up random sampling of the populations in those 10 regions right away to get an idea of who was infected and then implemented social isolation around who was infected in the geography and the context of that.

 

Now, our only option is rationing tests, or mass testing of the entire population and rationing tests according to who presents isn’t going to give you a good sample, and mass testing isn’t going to be realistic. I’m still surprised and this common sense has been confirmed by a lot of my PhD, epidemiology friends, they still don’t understand why we haven’t implemented a national sampling testing strategy to really understand who’s asymptomatic, who’s mildly symptomatic and what that means to hospital capacity management.

 

[0:33:57.1] BR: Absolutely. Well Dale, thank you very much for taking the time. As this progresses, I would love to have you come back on, because we are learning so much almost by the hour at this point, or by the day at least. I think in two weeks, we’ll have had just a ton more information to talk about.

 

[0:34:18.3] DS: Yeah. If any of your followers have questions and things, Bill, they can reach out to me, or that you can reach out to me if they contact you first. I’ll try to chase down answers. The situation is still pretty fluid. There’s not a lot of answers right now, but I’m happy to help where I can. Also one other thing, I’m working on some national committees and things and work groups. If I can use that venue to bring forward issues, again happy to do that.

 

[0:34:55.3] BR: Yeah. Let’s get those links. We’ll get that done. Again, thank you very much for your time. I really appreciate it. Yeah, I look forward to catching up again.

 

[0:35:05.2] DS: Okay. Thanks, Bill.

 

[END OF EPISODE]

 

[0:35:06.3] BR: That’s all for this show. Special thanks to our channel sponsors VMware, Starbridge Advisors, Galen Healthcare, Health Lyrics and Pro Talent Advisors for choosing to invest in developing the next generation of health leaders.

 

If you want to support the fastest-growing podcast in the health IT space, the best way to do that is to share it with a peer. Send an e-mail, DM, whatever you do. You could also follow us on social media, subscribe to our YouTube channel. There’s lot of different ways you can support us, but sharing it with peers is the best.

 

Please get back often as we will be dropping many more shows until we flatten the curve across the country. Thanks for listening. That’s all for now.

 

[END]

 

Play Video