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      Your Guide to an Autonomous Revenue Cycle
      Plot a course toward forward-thinking innovation that improves efficiency, the patient experience and your bottom line.
       

      Meanwhile Back at the Lab…The Modern Digital Laboratory That Is!

      Healthcare Rethink - Episode 75

      Healthcare Rethink, a FinThrive Podcast hosted by Brian Urban, explores this question with guest David West, CEO of Proscia. This episode explores the transformative impact of digital pathology on healthcare, highlighting Proscia’s role in advancing cancer diagnosis and treatment through innovative technology.



       

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      David West:
      Thanks for having me, Brian. I've been looking forward to this.
       
      Brian Urban:
      This is great because you are set up more than a lot of other guests that we have on our show, and we've had 75 episodes to date. You have a legit microphone. You got a legit retina display camera. You got the good setup, David.
       
      David West:
      I deserve zero credit for it. There's amazing people here that are, they sent me this, they're like, "David, you got to have this microphone." Okay, cool. I like it.
       
      Brian Urban:
      I'm so excited to have this conversation. You've been really having a lot of amazing podcast episodes. Great press, great big announcements we're going to get into as well. But love to have our audience get familiar with our guest right off the top as usual. So let's talk about David West, the person here, the leader before you became CEO of the company that you co-developed Proscia. Who's David West and how did you get into this work? Why into health care?
       
      David West:
      Yeah. My background is, I'm a software guy at heart, a nerd, aka. I was at Johns Hopkins when I met these scientists who were building algorithms to predict cancer outcomes from images of biopsies. And I was just fascinated by this idea that we could use computers to understand cancer in a much deeper way than the standard of care. You go into a normal laboratory, you're still seeing physical glass slides, physical microscopes, kind of like, if you can see my screen here, the 1937 Olympus sitting behind me hasn't really changed in 150 years. We're still using this Victorian era technology.
       
      I think what got me really excited about just the space and the opportunity was, here you have these huge images that pathology is starting to move from. We're moving away from the microscope paradigm to the image-based paradigm, scanning these slides. There's a gigabytes worth of data in every single one of these images. So I started with some of my co-founders to build some software, very humble beginnings at first, and that turned into my full-time job. And for me, my mom is a cancer survivor, so I am kind of personally passionate about the intersection of cancer and health care broadly with technology, especially in data and AI. And I'm also someone who's, I've probably always been an entrepreneur. My first business was tapetodisk.net, and it kind of gives away-
       
      Brian Urban:
      I did not know that.
       
      David West:
      That net gives you a sense, I don't know if anyone's still using net. I'm sure there's some domains out there. Never really took off for a couple of reasons. Basically, people were sending me these VHS tapes and I was converting it to DVDs back at a time when DVD was kind of the main medium. Made a little bit of money for a kid, but not any serious money. But I think it got me excited about building businesses and technology.
       
      Brian Urban:
      I love that because obviously you can tell, just hearing you for a couple of minutes here, entrepreneur by background, rule breaker probably in your background a little bit too, pushing the limits I think through technology, and really helping a very small corner of an ecosystem that has a huge impact across health science advance even further than I think was ever maybe even thought possible, just maybe in terms of just innovation. So it's amazing where you've come from in terms of your work back at Johns Hopkins. This is really where I understand Proscia was born. So tell me about a little bit more details into what you were doing at Hopkins at the time and what now it's meant in terms of impact to diagnostic labs and researchers across pharmaceutical manufacturers and across pharmaceutical science. I really want to hear this kind of journey from end to end.
       
      David West:
      Yeah. So I was really fortunate to work with some amazing mentors at Johns Hopkins. Frankly, I started there as an undergrad, studying biomedical engineering. And frankly, my classmates were all way more academically gifted than I was and were motivated to go to medical school, and so they had to get that 4.0 GPA. It was a kind of competitive environment, and I think that kind of took some of the fun out of learning for me. But I was like, "Oh, I'm at this amazing place with a lot of really smart people who are doing really interesting things." And I kind of just went out and started talking to people whose work I was interested in, knocking on the doors of folks at Hopkins Medicine scientists and doctors who are publishing interesting academic work.
       
      I met a guy by the name of Don Coffey who's no longer with us, but he was a legend at Johns Hopkins, the founder of the cancer center there. He was like an advisor to some US president on cancer. He asked me, "What are you interested in?" Oh shoot, what am I interested in? I think I told him I'm interested in the brain and cognition, and I'm interested in cancer and I'm interested in software. He said, "You've got to meet this guy Bob Veltri," who is his colleague that was building these algorithms. Dr. Veltri's work was in what would've been referred to at the time as quantitative nuclear morphometry. It's a total mouthful, but this was back 10 years ago. In today's terms, kind of the early days of machine learning and AI when new deep learning technologies were becoming available and it was advancing what was possible from a computer vision and machine learning standpoint.
       
      So there was this kind of perfect convergence between new computational capabilities. That's one thing. The other thing is just, this was at a time when there was this explosion in cloud computing and so our storing compute capabilities to handle this kind of data and the types of things that you'd want to do with this data were suddenly becoming possible. It also was at a time when, in this space, there had been these scanners out there on the market for a number of years that would take the glass slides and scan them into gigabyte-sized images, but they were kind of niche research technologies. Had started getting adopted in academic research and pharma, but it had never been approved for diagnostic use. And that's part of the reason why most laboratories still had microscopes rather than image-based pathology, which you look at radiology, that's been image-based for decades even back then. So there was this kind of perfect trend between the right market tailwinds, and it was just before the first scanner was approved by the FDA for diagnostic use, which happened in 2017.
       
      So we were starting the business at, in a sense, the right time. But what we realized was that if you want to get these AI applications to laboratories, to pathologists, they need to be digital first. And there was this huge software infrastructure layer that was required to do that. So the first product that we built was called pathology cloud, and it just made it super easy for pathologists and scientists to deal with this massive image data. I kind of describe it as this combination between Google Drive and Google Maps. It actually uses very similar technology to satellite imagery. You're streaming these massive image files into a web-based, cloud-based environment. We just made it so easy for these people to interact with this data, but we built it on a modern tech stack where you'd be able to mine this data and develop and deliver AI applications in that same ecosystem. So we put it out there and kind of took off. We raised a little bit of money, and we started getting our first paying customers and building a team, and then became my full-time job.
       
      Brian Urban:
      You say it so casually. The work that you've done and the contribution it's had to scientists using very dated or stuck in a particular time period, Victorian period as you put it, has been unbelievable. And I think the really interesting downstream impacts is the influence that you're having toward AI development, that not only looks at imaging from a quality perspective to identify defects or what's an air bubble or what's a pen mark, things like that, but also into earlier detection. And then what that's contributing toward research from an academic perspective, let alone the academics that are affiliated with the medical center, it is mind-blowing, but I love how casual you say that, David. So I'm curious, with the AI perspective, there's a big component of that. So there's a quality control tech that you have taken to market, and that's showing a few things I was alluding to. But I'm curious, can you share a little bit more about the impact on AI in pathology that Proscia has had?
       
      David West:
      We're in the very early days of using AI to impact in this area, in medicine broadly really. What's happening right now is, there's these two trends that are coinciding. One, you have this explosion in new AI capabilities that's gone through these waves. The first wave was when I first got into the space, when deep learning became mainstream technology, and now the type of things that most people have interacted with, like ChatGPT. Large language models have enabled large models in general foundation models including in biology. And then the other side of this is that there's been this explosion in new pathology data. A lot of what we've been focused on first and foremost is making sure that labs can go digital in the first place and take advantage of all of this data. With all that data, we and our partners and our customers are developing these AI models that can help us impact drug discovery, identify new targets, identify new therapies that can help us identify patients that those therapies are going to work for, ensure that we're dealing with high-quality data.
       
      So you talked about one of our own homegrown AI applications, Auto QC, which is an important kind of precursor to downstream AI applications. It's a very time-consuming, laborious part of the laboratory workflow, is to manually QC these slides, looking for artifacts, issues that come up in the staining process. It's a very physical process to prepare this. In the scanning process, there could be blurs, a lot of things can go wrong in there. So we're increasing the quality of that. We're also increasing the efficiency of those laboratories. And we also bring on some AI applications from some of our partners that are helping pathologists identify tumor that might be a needle in a haystack. And this is common in say, prostate cancer or breast cancer where pathologists are required to kind of sift through very large geographies of tissue to find that oftentimes one small little instance. They're also doing in prostate cancer. And this is kind of the origins of approach we were doing, prostate cancer research.
       
      There's this grading thing that happens, Gleason scoring. Actually, there's a really interesting Wall Street Journal article about the challenges with the current diagnostic criteria for grading. Pathologists disagree all the time. There's something like 56% concordance for prostate cancer grading, and the difference in clinical outcomes and treatment actually is huge. If you're on the aggressive side of that, you're talking about a radical prostatectomy, you're removing your prostate. On the less aggressive side of that, which is a very fine line, active surveillance, which is basically watch and do nothing. So the clinical sort of seesaw there hinges on a pathologist making that call, which is a very subjective call.
       
      So there's all of these use cases for AI in pathology. And what's been crazy for me is that over the number of years that we've been building this company, is that we've seen more and more applications come out to market that we did not expect, some of which we've developed that our customers have asked about and told us about, some of which our customers are developing themselves. Already today there are hundreds of AI applications that are being used on our platform to help develop and discover and develop new drugs and biomarkers for those drugs, which is so important in precision medicine. And I think where we're really at a frontier here is that many of those technologies are translating into clinical diagnostic setting. They're getting regulatory approval, and that means that these can be used for diagnostic use.
       
      Brian Urban:
      I love that you gave us those specific examples, and I love you for sharing your mother's cancer battle. Your example, I think, not only speaks to the reason why Proscia is so aggressively putting out impactful technology, but the downstream impact it's having on disease discovery. And the timeliness that's so important for treatment paths, for a clinician to be able to share with a patient, it's lifesaving, or if it's life-threatening, then the AI component is clearly helping as a guiding light to the scientists that are working from a diagnostics perspective on what is happening with an individual that may or may not be experiencing a cancer or aggressive form of cancer.
       
      So I think looking back 20 years, my father passed away from an aggressive form of prostate cancer. He was 52 when he got diagnosed, 55 when he passed away. And at that time, it was very subjective. It was a surveillance approach. And now, man, the lives that are being changed and the science that's being changed is phenomenal. So it just speaks that the challenges that have existed from diagnostics perspective is not going away in vain. It's being taken as lessons learned, how can we improve? So thank goodness for David West, your team, and what you're doing.
       
      Speaking of regulatory approval, got to touch on Concentriq, and tell me if I'm pronouncing that. That seems ... Okay. That was good? All right. This is amazing. This is a digital pathology platform, so it does a lot of data analysis. I look at it and I'm like, okay, clinical research organizations, like Parexel out there that we've spoken to, using this to help advance decision-making, doing a lot of other data analysis and synthesis and sharing it downstream, but there's so many other stakeholders that this appeals to. So tell us a little bit more about the journey here with Concentriq and some of the uses that's really shown a great impact so far.
       
      David West:
      Yeah. Well, first of all, thanks for sharing this story of your father. And actually, you reminded me, I have this painting behind me from an artist, Evin Felix. You can kind of see it. This one's called Guardian, but we have this whole series that she did after her father passed away from prostate cancer, and it was kind of inspired by pathology. We found her art in this book by a pathologist, Dr. Marilyn Bui called The Healing Art of Pathology. And I was just really inspired by Evin Felix' story and what she went through. It has kind of been a reminder of the personal mission. So I appreciate you sharing that. That's what gets me out of bed every morning.
       
      We're very lucky to have recently received FDA 510(k) clearance, 510(k) is like the type of clearance that you need for our type of software, a couple of weeks ago. Really what's cool about that is that we've had this product Concentriq that's been able to impact a lot of research use cases, life sciences organizations over the years. And we got our regulatory clearance to sell for clinical use and diagnostic use in Europe a year and a half ago. Now, this 510(k) clearance, the FDA clearance in the US lets us market this solution into the US for clinical diagnostic use. And really what that is is a bigger step forward in our mission, closer to the patient and closer to the impact that this technology I think really has the potential for.
       
      Right now, today, we have two versions of Concentriq. We've got our research product and our AP or AP-Dx product. AP standing for anatomic pathology. The AP-Dx product is used for diagnostics. And we serve the pharma companies on the research side as well as academic medical centers and diagnostic laboratories, including hospital-based diagnostic laboratories, in their routine diagnostic use. So customers will use our product in supporting everything from early stage to discovery research, looking for novel therapies, novel compounds that can make their way into clinical trials. We also support use cases in preclinical safety assessment where these companies, before they file submissions to the FDA to move into clinical trials, need to test on animal models to understand drug toxicity.
       
      So a big part of our customer base is using our product for those types of studies, very high volume stuff, thousands of slides in these studies. And before our technology, these laboratories, these life sciences organizations, you talk about one clinical research organization, we worked with a bunch of preclinical contract research organizations that do these studies on behalf of their sponsors, they're physically shipping slides from point A to point B, they're sending flying pathologists in order to do this. I mean, it's a huge pain in the butt to do these studies, but when you can use the imaging technology, it makes it a lot easier to conduct these studies. I think over time we'll see how AI can maybe increase the efficiency and the quality of these studies.
       
      And then the other kind of life sciences areas, the clinical trials themselves, that's a big frontier here. And we're lucky to work with life sciences organizations like IQVIA, one of our customers, who's pioneering the use of digital pathology for clinical trials, helping bring pathologists central review for these trials so that you don't have to fly pathologists all over the world just like you would in a safety assessment. They're using these AI-based biomarkers in order to identify patients that are candidates for these clinical trials and maybe even assessing kind of as a secondary endpoint the effect of these therapies. So there's just a couple of examples of the use cases that our customers are leveraging this technology for in that life sciences R&D continuum, from discovery all the way till market. Of course, that brings me to the second part that we've touched on, which is actual routine diagnostic use. And ultimately, what we want our customers to be able to do is bring these new therapies and diagnostics to market and have the platform and customer base with diagnostic laboratories and hospital systems to bring these to patients.
       
      Brian Urban:
      I think that's what's so fascinating. I think a large part of our audience spans across technology in some facet relative to health care, health plans, policy, and everything in between, social care, technologists as well. And if you're not in the pathology, which is traditionally a hard bench science, you don't know the logistics behind the whole diagnostic spectrum. Looking at it, you're sending slides via mail, you're sending pathologists physically via airplane travel, road travel, et cetera. Man, the efficiency not only in terms of time, cost, I would imagine in a few years, if you haven't already started this behind the scenes, it's going to show the economic shift in terms of an impact that Proscia has contributed toward. Is there any insights right now that Proscia has or you're able to share that says, "Hey, we've had this economic impact on the ecosystem and we've seen X, Y, Z"? I mean, there has to be such a dramatic change that add. Have you seen that or have you-
       
      David West:
      Yeah, definitely. Working with some of the customers, these life sciences organizations and laboratories actually don't always go preach it from the mountaintop because they're competitive organizations that want to take every edge they can get in R&D efficiency and in diagnostic efficiency. But the short answer is yes. Some of the early studies in this space for just basic use cases have demonstrated, simply using digital imaging, something like 15 to 20% efficiency increase, which is fairly significant for these organizations that are spending billions of dollars on drug R&D and a lot of very tight logistical operations in high volume diagnostic laboratories. That makes a huge difference to their margins, to their ability to compete and win in what is actually a pretty fragmented market. And that's kind of just the start. Once you start layering on AI applications, and we've done a study around this, you see further increase in that case. The study that we did was on prostate diagnostic efficiency, and that's just in one small example there.
       
      So we're starting to see a lot of this data come in, and it's still early days. I mean, the data on economic impact has come from, if you look at that classic technology adoption curve, it's your innovators and early adopters, those have been the ones that have paved the way and built that blueprint for every other laboratory who now says, "Oh, I see how if I invest in these scanners and I invest in software and I invest in AI, I can reap the rewards of that, and very quickly." Now we're in the early majority phase. We're approaching 15% global adoption in the roughly billion slides on the diagnostic side. We're well-adopted on the research side, almost a hundred percent if you kind of polled all pharma organizations. 
       
      So what that means is that the organizations who are adopting this today, who are on that part of the adoption curve, they're able to use the blueprint from the folks who went before them. Also, we're seeing more economic impact data and financial impact data for these organizations. It's getting to the point, I'll say, where there's a little bit of a compounding effect or a flywheel effect that's happening in the market where when I'm talking to these laboratory leaders, there's a bit of fear of missing out that's happening. You don't want to be last when it comes to adopting this technology. I think we're 18 to 24 months away from a point where laboratories that don't have this technology are going to be falling behind.
       
      Brian Urban:
      I like that you painted that picture, especially for our viewers who aren't familiar with the ecosystem relative to your corner here. The adoption curve, I think that's a very simple example everyone could follow in terms of the market leaders, the changemakers, early adopters. They're the first ones out the gate. They pave the way. They go through the learning, experimenting application of it. They refine it, and then it's an easier path forward for those just behind them.
       
      But the timeframe that you mentioned, just a couple years, if you're not integrating this type of technology into the standard cut of your workflow, you're going to be dramatically left behind. Not only are you going to be economically impacted from a business perspective, but the quality of what you're producing might not be relevant anymore. So it's amazing what your tech has done. It's pulling the market forward. It has to start from an entrepreneurial perspective and looking from the outside in, and in this case, you were already in the inside and being able to create something so unique. That's just amazing. Great example.
       
      So David, I kind of want to look at a few other things relative to where your platform and your tech is going. So I'm curious on alternative data sets. So we have a lot of really cool guests that speak a lot to health economics, outcome research data, real-world evidence data, patient insights, all that type of data. It seems that your tech is going to be ingesting or linking to a lot of those data sets to be able to advance not only clinical research organizations' work, but everyone across the spectrum, even medical device and things like that. My imagination is kind of exploding when I think about all the other data sets that could be incorporated with what your platform is analyzing. Is that something that's on the road ahead for you all, or are you already there now and I am just not seeing it?
       
      David West:
      No, I think the thing with pathology, digitization is that it's a totally new type of data. I mean, the information existed, but in a physical glass and tissue-based medium, sitting in a warehouse, collecting dust, doing nothing for us, and maybe distilled down to some unstructured PDF pathology report with some pathologist dictation of what's going on and maybe a lab text gross description or pathologist assistance gross description. That's all it is. It gets boiled down to a few relatively subjective unstructured descriptions. And now when you go digital, you have a billion pixels in every one of these images and maybe a couple dozen images per case that are telling a story about that patient's cancer or other disease. The interesting thing is, as part of the workflow, just to power the pathologist workflow, like what we have to do for them to be able to read slides digitally on their computer, we're pulling in information from the electronic medical record, from the lab information system really is where it's coming in from, which is sort of a child application of the electronic medical record.
       
      So we're getting all this really rich lab data that's coming in, molecular data, et cetera. And one of the things that we're kind of working towards, especially because we have all of these life sciences organizations that are really hungry to use this data and AI for many use cases like you talked about, we're starting to work with some partners to pull in link to other components of that patient record, outcome data, treatment data, et cetera, to develop these de-identified patient cohorts that can be used to answer those types of questions. I think what's interesting is that you've seen the real-world data and real-world evidence ecosystem emerge over the last few years with, I don't know, like Flatiron Health is a classic example that people talk about where they started with oncology EMR data, which is really interesting. But oncology EMR data, I think there's breadth there, but it's relatively kind of cursory in its clinical description of the patient. Lab data is super, super rich, but it's new and it's going digital for the first time.
       
      So I think people are trying to focus on the research side, and others, including you mentioned medical device companies, we work with some medical device companies that are using it for this, they're starting to figure out new use cases for this type of pathology and lab data. Right now our job as a company is to make really easy to be able to do that, to work with our partners, including the labs that say, "Oh, my gosh, I want to be part of the healthcare research paradigm. I don't want to just write a report and bill for a patient. I want to do some good beyond that and maybe I can play a bigger role there."
       
      Brian Urban:
      I think that's amazing because it's so new and so emerging. It is taking some time, but the integration in terms of a puzzle fit is just so obvious. I do like what you noted before on clinical trials and just now recently to population targeting or population breakdown almost. I think Flatiron was a good example too. We think about the advancement of clinical trials now, FDA announcing that there has to be clinical trial diversity. You see CVS had invested, they pulled back, and Walgreens is still heavily invested into that. They're pulling in pipeline with some of their pharmaceutical manufacturer partners to start to use different data sets they've never used before and that they're trying to acquire or survey on race, ethnicity, language, gender too. And it's amazing. This stuff should have existed forever because we look at clinical trials, more than three quarters are white and white guys too. So it's kind of the standard thing that a lot of our country has grown up in terms of advancing medicine, but now it's finally at a point where we're shifting gears. This has to be really exciting, especially for you from a pathology perspective.
       
      David West:
      Totally. If you're a patient, so much of what determines whether you get on the right clinical trial and also what determines whether a particular demographic is represented in the clinical trial is based on, did they happen to be close to or be able to afford going to a maybe top academic medical center or cancer center? But I think when we have access to more data, we get a better representation of broader patient population. I think there's a huge opportunity to use this data for that, for better clinical trials, and ultimately better patient outcomes.
       
      Brian Urban:
      Yeah, it's so promising, the view ahead, David. I mean, I could talk to you for a whole other hour. I want to get your perspective on the longer road ahead for Proscia. I think all the things, you're already making significant contributions toward or influencing a lot of other players across the ecosystem that otherwise probably wouldn't have changed their R&D perspectives, their targets on what they want to research and develop in their own organizations. But for Proscia, five years out, what does the organization turn into? What are some of the biggest contributions maybe you'll be making as you continue to mature as an organization?
       
      David West:
      Yeah. Right now, our task is to get more labs using digital pathology and help them get a lot of value out of that and help move from this glass-based discipline to a data-driven discipline. The next, I think, 24 to 36 months are really critical in that. I'll be transparent, we want to be the platform for the world's pathology. I think so far we've been really successful at that. We have now the largest digital pathology diagnostic lab operation in the world using our product. We have the joint pathology center, which is currently scanning 55 million images in their archives. That's a federal archive that's been used to identify and treat and research rare diseases, played a huge role in COVID. And with 14 of the top 20 pharma companies.
       
      I think we've been really, really focused as a platform company in getting the distribution and the data and just creating a lot of value for customers at this stage of their journey right now. But we're in the early days of that journey, and when I look at other aspects of health care and how those have gone digital or where totally new data mediums have emerged, we're still just scratching the surface like this as anyone. And I think I could sit here and say, well, here are the things that I'd want to see us doing or where I can imagine we'd be doing. But I think if I actually look back in five years, there would be things that I couldn't imagine right now that come up because there's really smart people working in this space. The community and the use cases that have emerged from that community have continued to surprise me.
       
      But I think at the end of the day, really big picture, we're in an age of precision medicine and we're in an age where we are developing what are literally cures for some patients. The problem with that is that they're cures for some patients, not for every patient. We're moving away from the sort of blunt force instruments that have defined treatment of the past 30+ years to targeted therapies. We're able to leverage, I mean, look at what's happening in my backyard, in Philadelphia, there's a lot of work in cell and gene therapy. There's some really interesting new technologies that are coming out.
       
      But we have to identify more treatments. We also have to develop the technologies to be able to identify the patients that will respond to those treatments when maybe only five or 10% of them will respond, and where it's very diagnostically challenging to identify those patients. And I think that's where AI and digital pathology will play a massive role, both in giving us this sort of deep and comprehensive biological insights that are required to identify and develop these novel therapies as well as the biomarkers that will identify the patients who will respond.
       
      Brian Urban:
      I think what's so interesting about what you just closed on, David, was, to put it simply, we can no longer be a blunt instrument or glass microscope-driven industry. We need to be a data-driven-informed industry, and especially with diagnostics and clinical resource organizations. I love your approach, and I'm a fan. I'm rooting for Proscia. I can't wait to see the impact that's going to be made in the years ahead, but I like your focus now because your focus is, "Hey, well, right now, we're aiming one. Let's stay here. It's great to be imaginary, but we need to stay here to accomplish foundationally where we need to be as an industry." It's just so well put. I loved our conversation today, David, thank you for joining our show.
       
      David West:
      Yeah, I really appreciate you having me. That's been a great discussion.
       
      Brian Urban:
      And I feel a follow-up coming up.
       
      David West:
      All right.
       
      Brian Urban:
      Definitely going to be tracking you guys.
       
      David West:
      I do too.
       
      Brian Urban:
      Yeah, it's been a joy and I really appreciate it. For more exciting insights and excerpts, please visit us at finthrive.com.
       
       

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