Great, thank you so much, Tessie, and thank you for allowing me to be here. All right, I have no personal financial disclosures, although I must say Open Pediatrics does receive a lot of in-kind funding from grants and collaborators and foundations. So I'm going to talk about three main concepts today, knowledge, networks, and numbers, and with each of those, go over some of the basic concepts of what we've had, what's been in the past, where we are today, and hopefully a concept of where we are in the future. So just to set the stage, like Matt nicely began, no talk about the future I think is appropriate without a talk of the past and where we've been. So if we think about where we have gone in the last 20 years, it's striking. Facebook came out in 2004, YouTube in 2005, Twitter 2006, and the iPhone came out in 2007. So if you think about where we are and where we've come as a group in a society, there have been incredible changes in that short bit of time. There's been a lot more social media that's been developed since. Matt really nicely went over this new emerging technology of the metaverse, augmented, mixed, and virtual reality. And then we've heard a lot of talks at this session about big data, analytics, artificial intelligence. So a lot of emerging things that are going to transform us in the future. And so to start with knowledge, really the open education movement is one that is near and dear to my heart and started in the early 2000s with MIT putting all of their courses out for free for anyone around the planet and really democratized the concept of education, which was previously siloed behind firewalls of institutions, universities, organizations. And that subsequently has really expanded with edX, Khan Academy being early adopters in this idea. And the Lancet actually did a commission in 2010 and really emphasized the way to move health professionals' education forward in the future is by sharing resources and that information technology was ripe to do so. So this was wonderful to see and this was about the time that I was right in the heat of my residency training and fellowship, so it really resonated well with me. And obviously that's where I chose to make my career. And for us in Peds Critical Care, Barry Markovits was the first big website to really kind of bring together resources, put them out for everyone, try to globally share, try to communicate, collaborate, and bring everyone together on pedccm.org. And this continues today with journal clubs and lots of great resources that are shared. Within Open Pediatrics, we decided to take a slightly different slant because Barry was doing this so wonderfully and decided to work into the content creation realm and think about how we could, as an organization, develop content that was going to be useful to us, but also useful to the rest of the planet that may not have the access to resources like we have here in the U.S., for example, and to put it out there with the same tenets that the open education movement did for free for everyone always. And so we put this out on our platform, and we also put this on YouTube, which is actually the largest driver of our traffic. You know, YouTube is the largest search engine nowadays, so most things come from there. And as a way that this really has improved the knowledge and global knowledge sharing of content, just as an example, our Pediatric Critical Care Medicine curriculum is a longitudinal course. Pre-fellowship curriculum has been used by over 2,000 learners in 91 countries. So we can, this is, like Matt mentioned, not the sole way that people are educated, but the floor and a foundation that people can incorporate and educators can incorporate this into other learning modalities and use this to accent what's already being taught and trained. As one example, during COVID, I think we obviously all have lived this heavily. This was a huge transformation with a lot of workforce shortages, people that needed to come into the hospital and maybe haven't used a ventilator in a long time. We were thinking about how are we going to be recruiting different people to manage this influx of patients. There were a lot of people that were furloughed at home thinking, oh my gosh, what's going to happen if I need to use a ventilator? And so we were able to adapt our existing mechanical ventilation simulator and use the insights that we were seeing in the adult ICUs in Boston and designed some mathematical modeling and figured out how to create patient simulations for the common types of phenotypes we were seeing in the hospital. And in two weeks, we were able to put out a version that included COVID specific considerations. So we released this on April 1st and within the first 72 hours, 28,000 people used this ventilator. And so what this shows is that you can use existing resources, modify those for emerging pandemics and really in a cost effective way, I mean, we had already built this. It cost nothing except for my time sitting in my library when I was sitting at home, you know, basically generating it and building this content out. And so if you think about the amount of cost to scale and to, you know, this ventilator has now been used by 500,000 people, the unit price to train people is really incredible. So quite cost efficient if we can figure out how best to scale these resources. And where are we going? You know, large organizations have historically put together a lot of this content. SCCM has learned ICU. A lot of the groups that I've shown you before have a lot of funding, but now this is meant to be a very overwhelming slide. There are so many opportunities for any one of us to create content on our own in an easy fashion. And so really, it's not going to be dependent on these large organizations anymore, but really the creativity of those that are out there. And one domain that I think is really emerging is podcasts. It's really easy to kind of put a podcast together. You need a little bit of good sound equipment and some knowledge and a great personality to kind of interview people. And you can see here, there's probably several that I'm missing, but these are some of the more prominent podcasts in our field at the moment. And I expect that this is going to continue to emerge as we think about it. And as we've talked about collaboration, you know, the role of social media to augment these kinds of resources as well as others is really to bring the conversation, bring the people into this content. There's been several sessions that have talked about the conversation that comes out from social media and how we can nurture that in the future. And so I think with open education, this global sharing, and really providing platforms for people to use that they've never had before is going to continue to emerge, and I'm excited to see what the next 10 years looks like. So if you move on to networks, historically, networks are groups of people that come together, oftentimes have conferences, share their expertise, their resources. There's some mentoring oftentimes involved, sharing of ideas, development of collaborative research platforms. And now with Zoom and any virtual platforms, we can do this way more easy than we ever had before. You know, and as one collaborative network in our field, the Policing Network comes together. They've, you know, got 91 PICUs that are enrolled in various studies with, I think, 30 active studies right now. They've published hundreds of manuscripts. And so this way of bringing people together and doing these rigorous studies is fantastic. However, we've been also discussing at this meeting a lot of how do we think about different kinds of study designs and how do we accelerate this process when information is rapidly changing? And so I think the PICU COVID-19 International Collaborative is one example of this, both kind of pulling in knowledge. This was originally designed as a webinar series to bring in healthcare professionals from all over the planet that were seeing pediatric COVID and discuss what were the current issues for PICU providers. It was a worldwide participation. We had thousands of people on these calls, including members from the World Health Organization, the NIH, the CDC from both here and Europe, and the editors of New England Journal and JAMA were listening in to hear what were the emerging trends. And so for all of those people that could join for these calls, they were incredibly helpful. We put some of these up out on social media. One of the interesting things that kind of did come out of here was this group got together and said, we've got to figure out a way to rapidly get some information out. And so this point prevalence study was done within several short weeks and was published. And as you can see by the altmetric circle here, was really picked up and shared broadly throughout news. And we've seen a lot of examples of studies that have come out and have not been beneficial because they were too quick and they weren't as rigorously reviewed. So there's obviously a caution as we think about these new study designs, but also the concept of really being able to share things rapidly when we urgently need it. We are rethinking study designs and that's going to be, I think, crucial and important for us to think about. And the technologies that we have to bring people together to have those conversations, to share data and work collaboratively, I think are going to be only evolving even further as we go forward. So then if we bring this into the research realm, thinking about numbers, large database studies and thinking about how do we display this, the idea of collaborative data is not a new one. The vPICU and the ELSO databases have been around for a long time with individuals bringing together and sharing data from their hospitals so that we can generate insights from a larger group rather than our single institution is not new and has been really done well with hundreds of research studies coming out of each of these networks. We've talked about the adaptive platform trial remap cap and how these novel and new ways to design randomized studies can really be instrumental to moving our research avenues forward and thinking about how do we gather this sort of data. But what I will say is a lot of this data is traditional data that we're getting from electronic health records and when we think about big data, it's way more complicated. And so big data typically is thought about with three Vs. It's high volume, large amounts of information coming in. You think about a lot of disparate sources, EHR, imaging. How do we incorporate what's found on the chest x-ray other than the written report? Do we look at the pixels? How do we interpret MRIs and bring this into these large database studies? How do we look at genomic sequencing? What about our patients that are at home on chronic ventilators that are wearing Fitbits? And how do we integrate that data into these research studies or sort of make sense of that? And this data is coming at us with a pace that we've never seen before, the velocity that's coming in. This variable structure, how do we put all of this together in a database that we can interpret and make sense of is really, really hard. And if we think about it, it's not just the numbers, it's not the computing power, it's also the people. So we've got to have the right people in the room that are thinking about it, the data scientists, the clinicians, and by clinicians I mean we've got to have everybody that is there touching the patient, the respiratory therapist, the pharmacist, the nurses, anybody that is there working collaboratively and together. And maybe I'll show you a couple of examples. Maybe we've got to bring in someone outside of science and we've got to bring out lay people and we've got to really think about creative ways to look at our data. I think Chris Hovack spoke about augmented intelligence instead of artificial intelligence and that right now AI doesn't have common sense and we need the common sense from the individuals to bring that together and help us make sense of this until we are better off. I did try to use chatGBT to make my talk today but it didn't produce the slides so I'm hoping in the future that we'll do that. And if we think about big data, there are a lot of different groups that are trying to do this. In Europe, the European Medical Information Framework is trying to create a large data ocean to bring in electronic health records and other data into a database that people can query and conduct studies from. We've talked a lot about the Human Genome Project at this talk. And then thinking about patients, the NIH has been enrolling patients in the All of Us research program, trying to bring on patient-related data. And then, you know, larger companies that are sort of thinking about how do we use technologies that are being used for other industries and bringing them forth. So you know, IBM Watson is one example of that. I wanted to say Dr. Ned Kearney had mentioned in his plenary talk that humans solve problems. So we're talking right now about a lot of data. But one of the things I just wanted to share two stories about how humans are better than computers at solving problems. The first one on the left is a program called Foldit. And basically this is out of the University of Washington. And for 10 years, scientists were trying to figure out the structure of a retrovirus protease enzyme and try to figure out how it was organized so that they could figure out drugs that might help to treat HIV. And they spent 10 years trying to figure out this protein structure. And they decided to make a game of it and employ the help of video game players and put this out there and developed a game that the players would actually have to change the structure and they would figure out different levels of energy. And as they adapted it, it was really pretty sophisticated. And these video gamers solved this problem that had plagued these researchers for 10 years. It took them 10 days to solve this problem. And this program, Foldit, utilizes crowdsourcing and utilizes people's intuition to sort of think about novel ways that we may be in the scientific arena may not think about different ways to approach problems. And this is still going. They have lots of different games. They have lots of different projects that go up in this Foldit platform and it's still active. It started, I think, in early 2010. And then on the right-hand side is a picture of some early Homo sapien cave drawings. And there are a lot of, as you can see, dots and lines and different arrows. This hasn't been able to be solved in, you know, thousands of years. And there was a furniture restorer, Ben Bacon, in the U.K., who went and saw some of these drawings and got kind of hooked on sort of thinking about these. And he described he got a little bit obsessed and really took a step back and said, well, what would early cave people be interested in with these kind of animals? And was able to decipher, you know, parts of the code, not all of it, but that these circles probably represented lunar months. And there are some lines of Ys that are thought to represent life cycles and birth of different animals and thinking about what the early humans might be interested in. And so it was this idea of coming in and sort of being not beholden to the traditional scientific ideas, but maybe some creative solutions and bringing a new idea to that, that crowdsourcing and maybe bringing in others might be helpful. So I'm going to stop here. And thank you all. And I think we'll all be happy to take some questions.

knowledge

networks

open education movement

collaboration

big data