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 Thanks for joining us on this week in Health IT Influence. My name is Bill Russell, former Healthcare CIO for 16 hospital system and creator of this week in Health. IT a channel dedicated to keeping health IT staff current and engaged. Today, John Brownstein joins us. He's an epidemiologist Harvard professor and Chief Innovation Officer for Boston Children's, and we're gonna talk about fighting the pandemic with data and information and just some of the interesting things they have done to get the word out.

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Today we are having a conversation with John Brownstein. Yeah, who? Who is the epi? He's an epidemiologist Harvard professor and Chief Innovation Officer at Boston Children's Hospital. Hey John, welcome to the show. Yeah, it's great to be here. Thanks so much. . I, I see your Dr. Fauci pillow in the background.

It's, uh, yeah, it's, uh, FAU on the couch, always looking over me, making sure that I'm uch on the couch, um, communicating public health messaging in the right way. Yeah. So you're, you're, you are an epi epidemiologist and one of the things, as I was doing research for the show, you do a lot of work with a, B, C, I mean, you're, you're getting the message out.

Is that just in the Boston or in the New England area? No, it's, it's, uh, it's a national effort and I, I spend a lot of time. It's been as part of this pandemic, the epidemiologist. Doing research is, is a big part of it, but also science, communication and really trying to distill the best of science and message that.

And they're clearly, up until recently, there's been a bit of a void. In sort of articulating the science and, and, and really being as evidence-based as possible and to, to, to get the public engaged in these very, you know, challenging times. Yeah. I just did an episode for the Newsday show and we were talking about the vaccine rollout and I, I, I'm personally feel like we're focusing on the wrong things.

I think we'll, we'll, we'll, production will get there. Johnson Johnson will come out with their new vaccine. We'll have more vaccine than we know what to do with in, in the very near future because there's still like . I see different numbers, but it feels like anywhere between 20 and 40% of the population,

That don't want the vaccine, there's a significant education that still needs to happen to get those people to, to want to get it. Yeah. Is is that what you're seeing as well? I think that we have not focused nearly enough on communication and education. We have been so hyper-focused on, of course, the development of this vaccine, which clearly is an amazing scientific achievement we should all be proud of and, and then the secondary focus has been distribution.

But we have not nearly focused on the communication that often gets left behind in public health. And so I think we we're gonna have a hesitancy issue. We're gonna have people that are not believing the science and people that, for good reasons, some to feel like that, get this. Some may be believing some of the things that they read, some of the rumors that Sprout.

And we need a, we need a whole discipline of, of science and communication that's focused on the, the right messaging around this vaccine to get everyone on board. And I, I just, I don't think we've put the resources to match operation warp speed in on communication. Yeah. I am not going to rehash all the things that you're gonna talk about on a, B, c, if people want, wanna see all those things.

It's actually pretty interesting. Just type in a, B, c, John Brownstein, Brownstein in, in, uh, Google, and you'll, you'll see a bunch of clips and you're doing a, a great service to the community. I really wanna, I wanna focus in on data fighting the pandemic from data and information and innovation standpoint.

So. But before we get to that topic, tell us about Boston Children's and your, and your role there. Yeah, so I have, I think, one of the best jobs out there. I get to be the chief innovation officer of a top pediatric hospital where I focus on. Bringing the sort of the digital journey to a healthcare system and work ranging from working from with startups as an accelerator to working with larger companies and thinking about how the, the future of the practice of medicine digital will, will, will be, you know, so commonplace.

And that can range from, of course, innovations on the electronic medical record to telemedicine, which we had ramped up right before. You know, the pandemic. Remote patient monitoring and then more forward things like, how does AI sort of change the, the, the practice of medicine? How do we, um, think about u utilizing the data that we have at our fingertips, make better decisions.

How do we bring in voice and, and biomarkers and machine vision and other new areas of tech? To, to the forefront and, and sort of supplement the clinician, reduce their burnout, and of course, improve patient outcomes through better engagement and and improved experience. How, as the chief innovation officer, how do you determine this is the age old, you know, governance and priority standpoint?

Do you determine what you're focus in? What solutions get? They get mind time. It's, uh, Boston Children's is world class, but it doesn't have an unlimited budget. Right, right. So you have to be very selective. Yeah. I mean, listen, we, we, even in. We have a small mighty team. We've actually been able to accomplish a lot.

But yes, discipline is super important because we can't boil the ocean into all sorts of ideas. We set it, you know, priorities for the year, every year, and, and, and we go off, you know, three or four pillars and those pillars for us right now, or sort of AI and optimization of of, of care delivery, their behavioral health, their innovations in primary care.

You know, physician burnout, we set sort of priority areas and we, we go after them, uncover where there's opportunities to partner with companies and when there's gaps, especially when that's pediatrics, we have a team that can help build some of these solutions. So, you know, we've, we've, we've had a long series of partnerships from companies like Amazon and Google and Nuance to like spin out companies that we've developed ourselves where we found opportunities.

Yeah, so small mighty team, what does that look like? We have teams like Providence had 200 people sitting in the downtown Seattle. I, I, I don't think it's that big anymore, but, but it was a pretty big team. Small, mighty. What does that look like? Well, it's grown. It used to be much more small and mighty. I think we've taken on some of the larger digital health efforts of the hospital, like telemedicine and the patient portal, and so that team has grown to probably more like 60, 70 people, but

A lot of it is in sort of the large scale implementations that are required. 'cause we have this sort of principle of sort of source launch scale. We have a team that is focused on identifying companies or efforts or new IP that can help solve some of those priorities. And then we launch those efforts and we pilot in different service lines.

And then the opera operationalizing some of these bigger topics requires a larger team. Right? How do we, how do we go after? You know, you know, use major nuanced deployment that requires a lot of people. Or how do we stand up telemedicine in a pandemic? That's, there's a lot of people involved in doing that.

Absolutely. All right. I, I sort of wanna look at the pandemic from a data and information standpoint and, and just sort of tap your, your expertise in this. So the, the, the data journey on the pandemic has been, what's the best way to.

I guess nicet.

Just talk about what did it look like? What information did we have, what were we missing and what did we need to fill in pretty quickly in order to start to address some of the challenges that the pandemic would, would deal us? Yeah, I mean, the pandemic has exposed major data gaps when it comes to pandemics from the very, uh, initial, uh, stages to even today.

At the beginning stages, we focused a lot on early detection of outbreaks. So my other hat other than Chief Innovation Officer, is I'm a professor at Harvard Medical School run a lab that has been focused on public health technologies for for many years. And so we've been involved in early identification of a range of disease events like H one N one and Zika.

And in this case, we definitely identified . At the end of December, something that was going on in Wuhan at December 30th, we sent the first alert of something unusual happening, but clearly something was brewing for weeks, if not months ahead of that. And we didn't have the right technologies either at a global scale, but probably not at a local scale either to identify that aberration in symptoms or emergency department visits that sort of delayed our ability to, to track this virus.

And whether that's because we were limited by access to these kinds of, whether it's . Individual level data and symptoms, or because we don't have the right kinds of diagnostics in place, we are challenged. So we already, out of the gate, we were already delayed. Then from there, like being able to track this virus as it spread around the world.

I mean, we built this international network of volunteers that were contributing, uh, data from various parts of the globe to, to sort of pull together our, our sort of a global understanding of what was happening with with Covid. Again, this was a network of volunteers because there was no sort of real global body that was really focused on pulling these data together and giving that sort of global picture.

I. And then of course when, when the virus hit the shores of the us you know, we were, we were pretty data blind. We didn't have good surveillance systems around symptoms or cases. Of course, we didn't have the diagnostics. We didn't have understanding. We, we ended up launching, uh, a platform called Covid Near You, funded by Google, which ultimately allowed us to fully

Uncover some of the, the emergence events of covid in communities because we had no ability to test. Now, eventually systems caught up more testing. We started getting feeds of hospitalization data, mortality data. Eventually all those things became. More commonplace. And we, but again, like we go to like the Johns Hopkins websites or, uh, the Covid Tracking Project or Covid Act now, like these are all efforts that were, are de novo and they aren't really government efforts.

And there's, so there's a big data gap in what government agencies are providing today in terms of a real time visibility. We're challenged in this country because of the way that public health is set up. And so it, it makes sense that it's, it's hard to keep a national picture in real time. I've, I haven't thought about this.

The Johns Hopkins model, what's, what's feeding that? Is that CDC data? What, what is feeding that They're scraping, like, you know what we've done for our Health map platform very similarly. Like they're scraping public health websites. And aggregating that data. Right? So they've found different parts of the web that are, that are putting up daily data about cases, and they're scraping that information at the US level.

It's not a, it's not ACDC website. They're, they're going to like local public health, uh, department websites and, and grabbing that data. Wow. All right. So, wow. You've given a.

Participatory surveillance. Talk about what that is and, and what the value of it's so be. Because we are limited in widespread testing and we actually, we're still limited today. I mean, we're not doing nearly enough testing. One of the areas of surveillance that can come become really handy is the sort of area of syndromic surveillance, understanding symptoms in populations, to give you an insight of how bad the epidemic is.

And we've been crowdsourcing symptoms for actually many years. We actually were doing it for flu beforehand in a system called Flu Near You. And immediately we recognized that we did not understand how this virus was spreading in communities across the country. We're fully data blind. And so we built this crowdsourcing tool where people can report in their symptoms and get text me text message reminders.

And we've got millions of people in the system that are telling us. A few times a week how they're feeling, getting data about their demographics, their, their, their testing status. And now we'll get data about their vaccination status. So it can give you incredibly granular information at the demographics level, the behavior levels, I mean, we just published a paper a couple days ago.

In Lancet that we could use this data to understand the value of masking in the community because we could see sort of what cases were popping up in a particular location, and then look at mask wearing behavior and show that sort of increase in mask wearing behavior. Led to the ability to, the higher probability to control the, the pandemic in that location, not earth shattering, but even to this day in this pandemic publishing, that paper generated a huge amount of controversy that people still don't believe in mass work.

So you'd think at this point we, we, we'd be sort of over the mask debate, uh, that rages on even to this day.

You talk about something like covid near you. It just struck me that, I mean, and we've known this for years, right? So a Google search, there's a lot of people that are out there, symptoms, they're searching for this and that and everything else. I mean, Google would probably create just in, in and of their own search data.

Mm-Hmm. , let alone, uh, you know, and Amazon with purchase and searches and, and purchases for different types of things. They could probably, there's probably enough data out there. Build some, some model. What's on in the country isn't there? It's hard to get all that data together, I would imagine. Exactly. We, we actually were part of the team that helped initially build Google Flu trends and have been working with search query data for a lot of years.

Absolutely. It's, it's, we've always found that it's about integrating various data streams and pulling them together to give the best sort of insight of what's happening in the ground. Not relying on one data source, but how do you pull various streams together and create sort of a, a better sort of situational awareness picture.

I I do wanna ask you if we're collecting the right information at the point of care, but before I get there, I wanna talk about models a little bit because the early on we did this, I, I, I guess we all assume that this thing would spread like it did in New York and it would spread across the country, and we would've sort of this, this massive thing.

But that's, that's not what happened. It spread in New York, it spread in New Orleans, uh, Seattle, la and it spread in pockets. And then, then it sort of subsided. Then it sort of spread again. I mean it, it has, it has a weird pattern. Is there a defined pattern? It would be one of my questions. And then the second would be just around building models.

'cause one of the things is we shut down procedures. You know, these hospitals, I just attended the JP Morgan conference and these hospitals have this big donut from, you know, March to to May in terms of their revenue. And some of it was, was not real warranted. I mean, they were, during that time, some of 'em only had like five Covid patients, but they, they literally had people sitting around doing anything.

So models sort of.

You know, is there any pattern and have we or, or were, were we just guessing early on for the most part? Well, I think, you know, clearly we were, we were caught flatfooted. There was, there was, there was this expectation that we weren't gonna have the pandemic and there were, there was a lot of mixed messaging.

And unfortunately, New York got hit very early on when. We didn't have a lot of understanding of this virus. Obviously New York is very large city in that there's and densely populated and there are a lot of you, so demographic factors for why it was sort of a breeding ground for the initial sort of wave, obviously, because there's inbound tra, you know, transportation in there as well.

And then of course we started applying control strategies. Part of the issue, and, and why it's hard to predict is that we've had very uneven control efforts in different parts of the country. We changed mobility patterns, we did social distancing, mask wearing, but it was uneven. We didn't have sort of a national strategy, or there was no national, uh, a, you know, advocacy for one sort of common approach.

So. Some communities, you were able to manage things and others weren't. And then of course, pandemic fatigue sets in and people's change their behavior and mobility goes up, uh, gather indoor gatherings go up and that's where, where you see, so the problems arise. And of course as temperatures drop and people start to move inside, we see those rises.

Again, New York City just had, you know, really bad time because again, like that's when we sort of were still living life in this sort of normal way. Clearly we made a major pivot right after that and got the pandemic in under control. But slowly but surely, it started merging itself and especially in parts of the, of the country that were probably more reluctant to make a decision on disease control over the economy.

I. Yeah. So let, let's talk about data we're collecting at the point of care and the, and it's, uh, it's interesting because that data sort of changed as the pandemic went along and where you were, we're gonna report, it changed and then changed back. So are we collecting the right information even today at the point of care with regard to, I guess there's a lot different perspectives, right?

Utilization of.

Uh, but also the, the spread and, and, and those kind of things. Are we, are we collecting the right information? Are we collecting too much information? I, what's, where, what's the status? Yeah. I mean, listen, I think we, uh, have decent visibility on the pandemic now. I think we, I. We miss, you know, when you start to aggregate at geographies, you start to miss certain details in terms of race and ethnicity and disparities and, and, you know, granular differences that make a difference in this pandemic, right?

We know that certain, you know, communities of color have gone hit much harder. Rural communities have gone hit much harder, so oftentimes the data that gets presented isn't . At the level of detail or doesn't have the attributes that allows us to fully understand, and we know that, that those kind of data elements are super important.

I mean, we know that with, with that kind of knowledge, you can, you can properly intervene or set up testing or. Improve communications, of course. Now with a vaccine, like how do you target vaccination clinics to the places that need it the most? We're not necessarily using, either we don't have all the attributes or we're not using those attributes in the best possible way to inform the response.

So that's been a problem from the beginning. I think it's definitely got better. But you know, again, we're not the best in as much as we like to talk a big game, but using data to drive decisions, whether it's healthcare or public health, I don't think sort of that is necessarily, especially when it comes to real time response.

I don't think we do a good enough job in that space. So I wanna talk about public health, but also maybe from this perspective, let's fast forward five years from now. I don't know if we're in the middle, but we're still smack dab in the, in the, in the pandemic and, and getting to the other side of it. But five years from now, what would it, what will it look like if we take these lessons and apply them?

Well, and you can talk about any, any of the different areas in terms of surveillance, in terms of public health or that kind stuff. What will it look like in five years if, if we learn the right lessons? Well, listen, I think that there we have to do a better job of investing in the public health workforce.

Clearly, we have seen major gaps in sort of. Of talent and people that can respond that public health departments are severely underfunded and resource under-resourced. I mean, we're expecting public health departments right now to maintain surveillance and efforts, but while at the same time now roll out a vaccine, it it's, it just doesn't work well.

I mean, I, we, I understand the need to have distributed and local based public health, but this level of distributed effort, it creates so much dysfunction. An unevenness of, of resourcing in terms of public health. So hopefully some of the, the new support that's coming at the federal level will help to even the playing field.

I think that, of course, we need to strengthen our ability to respond to, to global threats. I. Over the last several years, we've had significant underfunding of of efforts. I was part of a project funded by U-S-A-I-D to look for Novo novel Coronaviruses in populations. And that project was defunded last summer, so bad timing to defund a novel coronavirus surveillance project right before a pandemic.

But that happened. And so hopefully some of these larger efforts that are involved in sort of field-based surveillance to identify new viruses or . Efforts that are about strengthening global public health surveillance. And then I think at, at a, a federal level, there's this hope and a push right now to invest in sort of a national disease forecasting center, which applies some of the principles from weather where you're try, you know, you, where you're both now casting, but also forecasting.

And in this case it would be diseases. How, what is, you know, the outlook and how do we bring the discipline of disease, surveillance and modeling and bring that to a federal level where we, we have full visibility on what is happening across the wide spectrum of pathogens. I mean, the likelihood that we're gonna see another pandemic is significant.

Who knows what the timing will be for that, but hopefully core sort of underlying resources will come to sort of make sure that we are ready. And then again, then the last thing I'll just mention is diagnostics. We have not done a good enough job to fund and develop diag at home, rapid connected diagnostics that can give us that quick view of what's happening at a population level.

Those are things that we should have been implementing years ago and . I think there's, there's real technology that that could be put out is, is education is always a silver bullet in a lot of this, and we talked about that earlier, how important education is. Do, do you see, I mean, as a result of the pandemic, so many things are gonna change how we approach our doctors and how we look Tele telehealth.

Work from home or the nature of work, commercial real estate, the makeup of hospitals, and I mean, there's a lot of things that have probably changed forever. Education. Do you think we will start to introduce different things, maybe even as, as early on as in grade school, so that when we're having conversations about, we say, well, we're having conversations about science.

Don't have a chemistry and a biology background, or definitely an epidemiology background, and so they rely on the, their sources of information for, to get, to get that. What kind of things can we put into, into an education program to make the next generation just more aware of, of what's facing it? Yeah, I mean, I think that basic health education is something that isn't part of generally.

I mean, obviously. There's components of, of understanding biology that come into early education, but then we don't really talk about human health and risk factors, and we don't talk about, of course, emerging infectious diseases, but also chronic diseases and sort of the general population level impacts of.

Of major illnesses and populations. There's, there's room for that, I would say. But, you know, of course I'm an epidemiologist, so I would say, I would definitely say that. And, but there's a high, there's a lot of focus on climate change and that's a great opportunity to talk about the intersection of human health and climate.

So yeah, I think. It's amazing to me right now as an epidemiologist numbers of people that have understand basic epidemiological concepts. Ilea, I mean, some clearly don't fully grasp them, but you know, have friends and colleagues talk about are not, and understanding mortality or infection rates and case fatality rates.

So it's amazing that some of these basic epi concepts are now mainstream. So. I, I would love to see some of these things be sort of becoming mainstreaming. The first time I got any education around epidemiology and public health was because I forced my way into a grad school class while I was an undergrad.

Otherwise, you'd have no access to this kind of knowledge till well after your undergraduate education. How, how has the pandemic shaped what you're doing at Boston Children's? Yeah. I think it's permanently changed how we think about digital, which is great because we've been preparing it for this for a long time.

But we went from having a, a, a very small sort of telemedicine program to like being at one point doing the bulk of our visits virtually, and now we're in a steady state with which still a lot of visits are being done virtually. And what. Lo and behold, our physicians loved it. Patients loved it.

Satisfaction through the roof saved people trips. So I think you know, that, again, that has changed our ability to, to deliver care in ways that, again, in some ways we expected, but we never thought it'd be this dramatic. It's forced us to think about how we, we get content, we get check-ins from, from, from patients.

It's, it's accelerated our pace in, in remote patient monitoring. It's, it's, it's made everyone aware of that digital is not sort of this like lesser experience or sort of a dumbed down version of the, of the in-person. It's, it's an augmentation. And so I. It's, it's, it's been really transformational. I mean, obviously we focus so much more on the patient portal and, and text-based communications and all sorts of things.

So yeah, it's, it's, it's been incredibly meaningful to our sort of trajectory, I think where we always expected we'd go. It just shortened that timeline. So, talk, talk, talk to me a little bit about your work with, with a, B, C. How did that come about, I guess, and your health system must prioritize it. And we've been, actually, when I was A-C-I-O-I, we, we, we talked a lot about this, uh, developing this new muscle of interacting with the community at a different level.

Uh, we started putting doctors in the grocery stores and the doctors were there for consultation because most, a lot of bad health decisions are made in that grocery store. Yeah, yeah. Yeah. So having, having a doctor there was, was.

Because they, we, we thought, you know, who's gonna ask them a question? Well, it turns out they were fairly busy. Yeah, yeah. People had had a lot of questions. So, so clearly you guys have prioritized this. How did it come about? And, and, and how's, how's it going? Yeah, I mean, I, we did some segments early on in the pandemic.

I mean, I've always been . A big proponent of translating knowledge to, to broader meaning. I mean, I publish a lot in paper and journals that colleagues will read, but nobody, they don't get the same sort of visibility. You know, the, the sort of, the direct impact is incremental. You don't get to, I always thought that getting visibility on some of the research can have a bigger impact and so.

You know, I'd done some initial work with a, B, C actually, my sister, uh, was on Good Morning America, and so I had some connections there and so, you know, it, it sort of grew from there. And I, I've done did some live clips and then it's just been sort of a, a constant sort of channel. And I think for me it's been a good sort of growth area because I've learned to sort of communicate in different ways.

And of course, doing live televisions, it just, yeah, as you said, is totally different muscle. But it's, I think it's, it's, it's, it's been helpful and, and I think it, it plays a role that I think a lot of my colleagues have also played. I mean, I think, I can't turn on the, on CNN or M-S-N-B-C without seeing someone I know talking about the pandemic.

So I think there's been a lot of people that have sort of been called in and have been willing to . Help, uh, help explain sort of complicated areas, uh, of epidemiology and reinforce science and, and also be willing to say we just don't know yet and there's still not enough data, and not be necessarily be so sure of ourselves when the data isn't clear.

And, but you know, of course my hope is now we have a lot of new sort of people in the administration that are, are gonna talk about, they're gonna, they're gonna be able to be put out in front to talk about science. So maybe the roles of some of the academics and hospital people may not be as, as needed.

But, you know, I think it, I think it's good to have all these perspectives and people that have like some, some background or education to be able to, to have those platforms to talk, talk it through the American people. I. Any, any stories come out of it or any major learnings as you're doing live television?

I I, I would imagine at some point you mess up, I dunno. Oh, for sure. I mean, I'm hypercritical of myself. Like I've definitely, uh, unfortunately I did have one situation where, where my, my, the internet. Kicked out in the middle of a, of a live show, and that was, that was pretty traumatizing, where like, holy crap, in the middle of a live te uh, segment, you know, I lose.

So I've been very cautious about my internet speed right before, and there's things, things, tech stuff that goes wrong and that's, that's, that's challenging in the world of doing this all via Zoom. It's not the normal way someone would do television is from your office, but . Yeah, you gotta roll with the punches.

So we had, we had Daniel Ren on the show during the Covid series, and he talked about what they were doing, but he has since moved on. Yeah. So, so you are, you are the only CIO there, innovation? No, no. We have, we, we have an interim CIO, we also have an other CIO, which is the Chief investment Officer. So we have other CIOs can never have enough CIOs.

Yeah, you always, always add more CIOs. Yeah, no, Dan was great. He was a long-term partner of mine. We worked really closely together. I pushed him hard to go fast. He explained to me some of the reasons why my expectations were, uh, were two grand and I think we, we've, we've met each other in the middle and we had a gr a great time.

So I, I learned a lot from him. I definitely miss Meison to Main Medical Center doing some great things there. Yeah, you, you, that tension that exists. I, I was lucky enough to have the innovation and information officer. Oh. But I had team members under me that were in the innovation group and team members that were in the operational side.

Mm-Hmm. . And I felt like I had to have the conversation you just described. I had to have in my own head with , you can only move so fast on certain things. 'cause there's operational realities, there's training, there's, there's technology and stuff. John, thank you for your time. I really appreciate it. Yeah, no, it's great to be here.

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