>> Welcome to this IBM Watson Health client conversation. And we're probing the dynamics of the relationship between IBM and it's clients. We're going to look back at some of the challenges of 2020 and look forward to, you know, present year's priorities. We'll also touch on the future state of healthcare. My name is Dave Vellante. I'll be your host and I'm from theCUBE. And with me are Doctor Alex Towbin, who's Associate Chief Clinical Operations and Informatics at Cincinnati ChilDoctoren's Hospital and John Chrisman of course from IBM Watson health. Welcome gentlemen, Good to see you. Thanks for coming on. >> Thanks for having us. >> Yeah, thanks for having me. >> Yeah I know from talking to many clients around the world, of course virtually this past year, 11 months or so that relationships with technology partners they've been critical over during the pandemic to really help folks get through that. Not that we're through it yet but, we're still through the year now, there's I'm talking professionally and personally and Doctor Towbin, I wonder if you could please talk about 2020 and what role the IBM partnership played in helping Cincinnati children's, you know press on in the face of incredible challenges? >> Yeah, I think our story of 2020 really starts before the pandemic and we were fortunate to be able to plan a disaster and do disaster drill scenarios. And so, as we were going through those disaster drill scenarios, we were trying to build a solution that would enable us to be able to work if all of our systems were down and we worked with IBM Watson Health to design that solution to implement it, it involves using other solutions from our primary one. And we performed that disaster drill in the late January, early February timeframe of 2020. And while that drill had nothing to do with COVID it got us thinking about how to deal with a disaster, how to prepare for a disaster. And so we've just completed that and COVID was coming on the horizon. I'm starting to hear about it coming into the U.S for the first time. And we took that very seriously on our department. And so, because we had prepared for this this disaster drill had gone through the entire exercise and we built out different scenarios for what could happen with COVID what would be our worst case scenarios and how we would deal with them. And so we were able to then bring that to quickly down to two options on how our department and our hospital would handle COVID and deal with that within the radiology department and like many other sites that becomes options of working from home or working in a isolated way and an and an office scenario like where I'm sitting now and we planned out both scenarios and eventually made the decision. Our decision at that point was to work in our offices. We're fortunate to have private offices where we can retreat to and something like that. And so then our relationship with IBM was helpful and that we needed to secure more pieces of hardware. And so even though IBM is our PACS vendor and our enterprise imaging vendor, they also help us to secure the high resolution monitors that are needed. And we needed a large influx of those during the pandemic and IBM was able to help us to get those. >> Wow! So yeah you were able to sort of test your organization resilience before the pandemic. I mean, John, that's quite an accomplishment for last year. I'm sure there are many others. I wonder if one of you could pick it up from here and bring your perspectives into it and, you know maybe ask any questions that you would like to ask them. >> Yeah, sure, Doctor Towbin, that's great that we were able to help you with the hardware and procure things. So I'm just curious before the pandemic how many of the radiologists ever got to read from home, was that a luxury back then? And then post pandemic, are you guys going to shift to how many are on-site versus remote? >> Yeah, so we have a couple of scenarios. We've had talk about it both from our PACS perspective as well as from our VNA enterprise imaging perspective from PACS perspective we always designed our solution to be able to work from a home machine. Our machines, people would access that through a hospital-based VPN. So they would log in directly to VPN and then access the PACS that way. And that worked well. And many of our radiologists do that particularly when they're on call works best for our neuroradiologist who are on call a little bit more frequently. And so they do read from home in that scenario. With enterprise imaging and are used to the enterprise viewer and iConnect access. We always wanted that solution to work over the internet. And so it's set up securely through the internet but not through the VPN. And we have radiologists use that as a way to view studies from home, even not from home, so it can be over one of their mobile devices, such as an iPad and could be at least reviewing studies then. We, for the most part for our radiologist in the hospital that's why we made the decision to stay in the hospital. At COVID time, we have such a strong teaching mission in our department in such a commitment to the education of our trainees. We think that hospital being in the hospital is our best way to do that, it's so hard. We find to do it over something like zoom or other sharing screen-sharing technology. So we've stayed in and I think we'll continue to stay in. There will be some of those needs from a call perspective for example, reading from home, and that will continue. >> And then what's your success been with this with the technology and the efficiency of reading from home? Do you feel like you're just as efficient when you're at home versus onsite? >> The technology is okay. The, our challenges when we're reading from the PACS which is the preferred way to do it rather than the enterprise archive, the challenge is we have to use the PACS So we have to be connected through VPN which limits our bandwidth and that makes it a little bit slower to read. And also the dictation software is a little bit slower when we're doing it. So moving study to study that rapid turnover doesn't happen but we have other ways to make, to accelerate the workflow. We cashed studies through the worklist. So they're on the machine, they load a little bit more rapidly and that works pretty well. So not quite as fast, but not terrible. >> We appreciate your partnership. I know it's been going on 10 years. I think you guys have a policy that you have to look at the market again every 10 years. So what do you think of how the market's changed and how we've evolved with the VNA and with the zero footprint viewers? A lot of that wasn't available when you initially signed up with Amicas years ago, so. >> Yeah, we signed up so we've been on this platform and then, you know now the IBM family starting in 2010, so it's now now 11 years that we're, we've been on as this version of the PACS and about eight, seven or eight years from the iConnect platform. And through that, we've seen quite an evolution. We were one of the first Amicas clients to be on version six and one of the largest enterprises. And that went from, we had trouble at the launch of that product. We've worked very closely with Amicas then to merge. And now IBM from the development side, as well as the support side to have really what we think is a great product that works very well for us and drives our entire workflow all the operations of our department. And so we've really relished that relationship with now IBM. And it's been a very good one, and it's allowed us to do the things like having disaster drill planning that we talked about earlier as far as where I see the market I think PACS in particular is on the verge of the 3.0 version as a marketplace. So PACSS 1 one was about building the packs, I think, and and having electronic imaging digital imaging, PACS 2.0 is more of web-based technology, getting it out of those private networks within a radiology department. And so giving a little bit more to the masses and 3.0 is going to be more about incorporating machine learning. I really see that as the way the market's going to go and to where I think we're at the infancy of that part of the market now about how do you bring books in for machine learning algorithms to help to drive workflow or to drive some image interpretation or analysis, as far as enterprise imaging, we're on the cusp of a lot there as well. So we've been really driving deep with enterprise imaging leading nationally enterprise imaging and I have a role in the MSAM Enterprise Imaging Community. And through all of that work we've been trying to tackle works well from enterprise imaging point of view the challenges are outside of radiology, outside of cardiology and the places where we're trying to deal with medical photos, the photographs taken with a smart device or a digital camera of another type, and trying to have workflow that makes sense for providers not in those specialty to that don't have tools like a DICOM modality workloads store these giant million-dollar MRI scanners that do all the work for you, but dealing with off the shelf, consumer electronics. So making sure the workflow works for them, trying to tie reports in trying to standardize the language around it, so how do we tag photos correctly so that we can identify relevancy all of those things we're working through and are not yet standard within our, within the industry. And so we're doing a lot there and trying and seeing the products in the marketplace continuing to evolve around that on the viewer side, there's really been a big emergence as you mentioned about the zero footprint viewers or the enterprise viewer, allowing easy access easy viewing of images throughout the enterprise of all types of imaging through obtained in the enterprise and will eventually incorporate video pathology. The market is also trying to figure out if there can be one type of viewer that does them all that and so that type of universal viewer, a viewer that cardiologists can use the same as a radiologist the same as a dermatologist, same as a pathologist we're all I think a long way away from that. But that's the Marcus trying to figure those two things out. >> Yeah, I agree with you. I agree with your assessment. You talked about the non DICOM areas, and I know you've you've partnered with us, with ImageMover and you've got some mobile device capture taking place. And you're looking to expand that more to the enterprise. Are you also starting to use the XDS registry? That's part of the iConnect enterprise archive, or as well as wrapping things in DICOM, or are you going to stick with just wrapping things in DICOM? >> Yeah, so far we've been very bunched pro DICOM and using that throughout the enterprise. And we've always thought, or maybe we've evolved to think that there is going to be a role for XDS are I think our early concerns with XDS are the lack of other institutions using it. And so, even though it's designed for portability if no one else reads it, it's not portable. If no one else is using that. But as we move more and more into other specialties things like dermatology, ophthalmology, some of the labeling that's needed in those images and the uses, the secondary uses of those images for education, for publication, for dermatology workflow or ophthalmology workflow, needs to get back to that native file and the DICOM wrap may not make sense for them. And so we've been actively talking about switching towards XDS for some of the non DICOM, such as dermatology. We've not yet done that though. >> Given the era children's hospital has the impact on your patient load, then similar to what regular adult hospitals are, or have you guys had a pretty steady number of studies over the last year? >> In relay through the pandemic, we've had, it has been decreased, but children fortunately have not been as severely affected as adults. There is definitely disease in children and we see a fair amount of that. There are some unique things that happen in kids but that fortunately rare. So there's this severe inflammatory response that kids can get and can cause them to get very sick but it is quite rare. Our volumes are, I think I'm not I think our volumes are stable and our advanced imaging things like CT, MRI, nuclear medicine, they're really most decreased in radiography. And we see some weird patterns, inpatient volumes are relatively stable. So our single view chest x-rays, for example, have been stable. ER, visits are way down because people are either wearing masks, isolating or not wanting to come to the ER. So they're not getting sick with things like the flu or or even common colds or pneumonias. And so they're not coming into the ER as much. So our two view x-rays have dropped by like 30%. And so we were looking at this just yesterday. If you follow the graphs for the two we saw a dip of both around March, but essentially the one view chest were a straight line and the two view chest were a straight line and in March dropped 30 to 50% and then stayed at that lower level. Other x-rays are on the, stay at that low level side. >> Thanks, I know in 2021 we've got a big upgrade coming with you guys soon and you're going to stay in our standalone mode. I understand what the PACSS and not integrate deeply to the VNA. And so you'll have a couple more layers of storage there but can you talk about your excitement about going to 8.1 and what you're looking forward to based on your testimony. >> Yeah we're actually in, we're upgrading as we're talking which is interesting, but it's a good time for talking. I'm not doing that part of the work. And so our testing has worked well. I think we're, we are excited. We, you know, we've been on the product as I mentioned for over 10 years now. And for many of those years we were among the first, at each version. Now we're way behind. And we want to get back up to the latest and greatest and we want to stay cutting edge. There've been a lot of reasons why we haven't moved up to that level, but we do. We're very careful in our testing and we needed a version that would work for us. And there were things about previous versions that just didn't and as you mentioned, we're staying in that standalone mode. We very much want to be on the integrated mode in our future because enterprise imaging is so important and understanding how the comparisons fit in with the comparison in dermatology or chest wall deformity clinic, or other areas how those fit into the radiology story is important and it helped me as a radiologist be a better radiologist to see all those other pictures. So I want them there but we have to have the workflow, right. And so that's the part that we're still working towards and making sure that that fits so we will get there. It'll probably be in the next year or two to get to that immigrating mode. >> As you, look at the number of vendors you have I think you guys prefer to have less vendor partners than than more I know in the cardiology area you guys do some cardiology work. What has been the history or any, any look to the future of that related to enterprise imaging? Do you look to incorporate more of that into a singular solution? >> Cardiology is entirely part of our enterprise imaging solution. We all the cardiology amendments go to our vendor neutral archive on the iConnect platform. All of them are viewed across the enterprise using our enterprise viewer. They have their unique specialty viewer which is, you know, fine. I'm a believer that specialty, different specialties, deserve to have their specialty viewers to do theirs specialty reads. And at this point I don't think the universal viewer works or makes sense until we have that. And so all the cardiology images are there. They're all of our historical cardiology images are migrated and part of our enterprise solution. So they're part of the entire reference the challenge is they're just not all in PACSS. And so that's where, you know, an example, great example, why we need to get to this to the integrated mode to be able to see those. And the reason we didn't do that is the cardiology archive is so large to add a storage to the PACS archive. Didn't make sense if we knew we were going to be in an integrated mode eventually, and we didn't want to double our PACS storage and then get rid of it a couple of years later. >> So once you're on a new version of merge PACS and you're beyond this, what are your other goals in 2021? Are you looking to bring AI in? Are you using anybody else's AI currently? >> Yeah, we do have AI clinical it's phone age, so it's not not a ton of things but we've been using it clinically, fully integrated, it launches. When I open a study, when I opened a bone age study impacts it launches we have a bone age calculator as well that we've been using for almost two decades now. And so that we have to use that still but launching that automatically includes the patient's sex and birth date, which are keys for determining bone age, and all that information is there automatically. But at the same time, the images are sent to the machine learning algorithm. And in the background the machine determines a bone age that in the background it sends it straight to our dictation system and it's there when we opened the study. And so if I agree with that I signed the report and we're done. If I disagree, I copy it from my calculator and put it in until it takes just a couple of clicks. We are working on expanding. We've done a lot of research in artificial intelligence and the department. And so we've been things are sort of in the middle of translation of moving it from the research pure research realm to the clinical realm, something we're actively working on trying to get them in. Others are a little bit more difficult. >> That's the question on that John, Doctor, when you talk about injecting, you know machine intelligence into the equation. >> Yeah. >> What, how do you sort of value that? Does that give you automation? Does it improve your quality? Does it speed the outcome and maybe it's all of those but how do you sort of evaluate the impact to your organisation? >> I there's a lot of ways you can do it. And you touched on one of my favorite one of my favorite talking points, in a lot of what we've been doing and early machine learning is around image interpretation helping me as a radiologist to see a finding. Unfortunately, most of the things are fairly simple tasks that it's asking us to do. Like, is there a broken bone? Yes or no, I'm not trying to sound self-congratulatory or anything, but I'm really good at finding broken bones. I get, I've been doing it for a long time and, and radio, you know so machines doing that, they're going to perform as well as I can perform, you know, and that's the goal. Maybe they'll perform a little bit better maybe a little bit worse but we're talking tiny increments there they're really to me, not much value of that it's not something I would want. I don't value that at a time where I think machine learning can have real value around more on some of the things that you mentioned. So can it make me more efficient? Can it do the things that are so annoying that and they'd take, they're so tedious that they make me unhappy. A lot of little measurements for example are like that an example. So in a patient with cancer, we measure a little tumors everywhere and that's really important for their care, but it's tedious and so if a machine could do that in an automated way and I checked it that, you know, patient when because he or she can get that good quality care and I have a, you know, a workflow efficiency game. So that one's important. Another one that would be important is if the machine can see things I can't see. So I'm really good at finding fractures. I'm not really good at understanding what all the pixels mean and, you know in that same patient with cancer, oh what do all the pixels mean in that tumor? I know it's a tumor. I can see the tumor, I can say it's a tumor but sometimes those pixels have a lot of information in them and may give us prognosis, you know, say that this patient may, maybe this patient will do well with this specific type of chemotherapy or a specific or has a better prognosis with one with one drug compared to another. Those are things that we can't usually pick out. You know, it's beyond the level of that are I can perceive that one is really the cutting edge of machine learning. We're not there yet and then the other thing are things that, you know just the behind the scenes stuff that I don't necessarily need to be doing, or, you know so it's the non interpretive artificial intelligence. >> Dave: Right. >> And that's what I've been also trying to push. So an example of when the algorithms that we've been developing here we check airways. And this is a little bit historical in our department, but we want to make sure we're not missing a severe airway infection. That can be deadly, it's incredibly rare. Vaccines have made it go away completely but we still check airways. And so what happens is the technologist takes the x-ray. They come in to ask us if it's okay, we are interrupted from what we're doing. We open up the study, say yes or no. Okay, not okay, if it's not okay they go back, take another study. Then come back to us again and say, is it okay or not? And we repeat this a couple of times it takes them time that they don't need to spend and takes us time. And so we have, we've built an algorithm where the machine can check that and their machine is as good or a little bit worse than us, but give can give that feedback. >> Dave: Got it. >> The challenge is getting that feedback to the technologist quickly. And so that's, that's I think part for us to work on stuff. >> Thank you for that. So, John, we've probably got three or four minutes left. I'll let you bring it home and appreciate that Doctor Towbin >> I think one of the biggest impacts probably I knew this last year with the pandemic, Doctor Towbin is this, I know you're a big foodie. So having been to some good restaurants and dinners with the hot nurse in a house how's the pandemic affected you personally. And some of the things you like to do outside of work. >> Everything is shut down. And everything has changed. I have not left the house since March besides come to work and my family hasn't either. And so we're hardcore quarantining and staying you know, staying out and keeping it home. So we've not gone out to dinner or done much else. >> So its DoorDash and Uber Eats or just learned to cook at home. >> It's all cooking at home. We're fortunate, my wife loves to cook. My kids love to cook. I enjoy cooking, but I don't have the time as often. So we've done a lot of different are on our own experimenting. Maybe when the silver lining one of the things I've really relished about all this is all this time I get to spend with my family. And that closeness that we've been able to achieve because of being confined in our house the whole time. And so I've played get to play video games with my kids every night. We'd been on a big Fortnite Keck lately since it's been down making. So we've been playing that every night since we've watched movies a lot. And so as a family, we've, I it's something I'll look back fondly even though it's been a very difficult time but it's been an enjoyable time. >> I agree, I've enjoyed more family time this year as well, but final question is in 2021, beyond the PACS upgrade what are the top other two projects that you want to accomplish with us this year? And how can we help you? >> I think our big one is are the big projects are unexpanded enterprise imaging. And so we want to continue rolling out to other areas that will include eventually incorporating scopes, all the images from the operating room. We need to be able to get into pathology. I think the pathology is really going to be a long game. Unfortunately, I've been saying that already for 10 years and it's still probably another 10 years ago but we need to go. We can start with the gross pathology images all the pictures that we take for tumor boards and get those in before we start talking about whole slide scanning and getting in more of the more of the photographs in the institution. So we have a route ambulatory but we need inpatient and ER. >> All right one last question. What can IBM do to be a better partner for you guys? >> I think it's keep listening keep listening and keep innovating. And don't be afraid to be that innovative partner sort of thinking as the small company that startup, rather than the giant bohemoth that can sometimes happen with large companies, it's harder. It is fear to turn quickly, but being a nimble company and making quick decisions, quick innovations. >> Great, quick question. How would you grade IBM, your a tough grader? >> It depends on what I am a tough grader but it depends on what, you know as the overall corporate partnership? >> Yeah the relationship. >> I'd say it's A minus. >> Its pretty good. >> I think, I mean, I, we get a lot of love from IBM. I'm talking specifically in the imaging space. I not, maybe not, I don't know as much on the hardware side but we, yeah, we have a really good relationship. We feel like we're listened to and we're valued. >> All right, well guys, thanks so much. >> So even if it's not an A plus- >> Go ahead. >> I think there's some more to, you know, from the to keep innovating side there's little things that we just let you know we've been asking for that we don't always get but understand the company has to make business decisions not decisions on what's best for me. >> Of course got to hold that carrot out too. Well thanks guys, really appreciate your time. Great conversation. >> Yeah, thank you. >> All right and thank you for spending some time with us. You're watching client conversations with IBM Watson Health.