Hey, everyone. Welcome to LabReflex. I'm here with Dr. Aakash. How you doing, man? I'm doing good, Chris. Are you keeping yourself warm? We have a thunderstorm coming up. That's right. Yeah, I was able to avoid yesterday's craziness. Michelle and I were out and about, barely. But today looks like it's about to be something else, too. I'm not looking forward to it. I do have an umbrella that I got for, I think, six dollars. So I'm very confident that it's going to be able to hold up against the store. And that's a real test. And for those who don't know about Houston's weather, yesterday we had a, around this time, we had a heat stroke warning that the weather is going to be super hot. And by the time we got home in the evening, we had a thunderstorm warning. So I'm guessing your umbrella is going to be useful either way. Either way. Yeah, exactly. Keep the sun off. Yeah, my car said 114 degrees when I got to the car yesterday. Now, of course, it's, you know, blacktop and it was on the top of the garage. But I mean, I don't care who you are. 114 degrees is legit. Yeah, I know. Yeah, I don't feel good about it. Well, anyway, so today I wanted to talk about a little bit of a higher level thing. I wanted to discuss what we mean when we talk about market disruption and specifically disruption in the laboratory. A lot of people talk about game changing technologies. This can do this. This can do that. And I thought that it would be useful for us to explore some of these technologies vis-a-vis the laboratory. There's some cool things that are happening. And there really are at a minimum two ways, but often more than two ways to kind of look at these technologies and what it will mean for the laboratory. Yeah. And, you know, it's very convenient, shall I say, to sell a product or sell an idea that, oh, this is going to disrupt the market. And then this is going to be a game changer. Right. Right. Yeah. And with all due respect, we need that thing. Right. For sure. But does it actually make your lab a game changing perspective or not? That's a different thing, right? Yeah. So a lot of things like we say, like, oh, this is going to change the way the lab works. So is it going to enhance? So as we were discussing off the air also, like, there's a thing called as, like, optimizing your available ancillary test or arsenal that you have in your lab versus getting a new one. Right? There are lots of toys available that we can, like, bring in, which can say, tell you that, hey, this is going to be a game changing one. But do we actually need those as a game changing one? Or is there a possibility that what we have to optimize that already that we have been selling in a way that it changes the game for us? Right? Right? Yeah. And this is the right time to discuss about these things. Yeah, you know, the context that one thing I actually, as you were speaking, came to my mind was I was recently reading a book about medical risks. Yes, that's what I do in my spare time. And I was it was talking about relative versus absolute risk for a patient, for example. Yeah. And when we think about relative risks as clinicians, it's obvious what we mean. Yeah. But a lot of patients can get really freaked out. By a relative risk increase. You know, so, for example, you might have a thing that's worth it that'll happen to one in a million people and you might do something. And now the risk to that patient is one in two million people. Yeah. And so the actual relative risk reduction is huge. You just decreased by 50 percent. So now you're way less likely to get the thing. Yeah. But the actual absolute risk reduction is so minisculely small as to be useless. I mean, one in a million went to one in two million. And in the same way, that's kind of what happens here. A lot of times we hear is, oh, this is going to be absolutely game changing for your laboratory. You're going to be able to diagnose XYZ so much faster, 37 percent faster or whatever. And the reality is that it's not nearly that transformative. Yeah. And then, as you know, we all this novelty stuff that we are we will be seeing in coming weeks and months with this AI and all that stuff happening. So novelty itself never always means like makes a technology disruptive. What we need as a disruptive technology, per se, in a lab is like to, you know, we were stuff staffing shortages. Does it does what novelty that we are bringing in a lab? Does it address that? Right. They're doing like we have scarcity of the technical expertise we spoke about in the podcast. We do have openings, but those openings are coming at a stake of we are losing the expertise. Right. And diagnostic delays and all these. So are these are these novelties actually, you know, addressing the underlying economics workflow techniques and like all those results being utility of the results and all that stuff. Is that doing is that happening with that game changing technology technology are we talking about? Maybe if it is, then well, well and good, right? Yeah. Yeah. I mean, every time you move from a manual process to an automated process, it immediately says, you know, the end state is what they generally calculate. So they say, oh, now you won't have to do this manual thing. But of course, there's a lot of setup. There's a lot of validation. There's a lot of this. There's a lot of that. And those things are very challenging to calculate inside of an organization. And so you're totally right that when we think about game changing things, technologies in the lab, in the laboratory, we really need to be thinking in terms of how does it affect the lab as a workflow, right? Whether it's there's a positive ROI or whether there's, you know, delays and diagnosis, manual work, financial pressure, etc. Yeah, totally agree. Totally agree. So one of the one of the things that we have been pretty interested in and we at UTMB have worked with is ABB, the robotics company, the European robotics company, and Roche have announced that they're having this sort of like automated slide architecture. And this is pretty exciting because what it is doing is it's doing a part of the process that's somewhat manual, and they're actually loading some of these slide scanners. So it's a work that is right now manual, but there are some places that don't even do it yet because they're not actually digital. And so I really like this as a game changing technology for the laboratory because it's solving a need that a lot of labs don't yet know that they have. And when they do know they have it, what they'll do is they'll look to an automated solution like ABB as opposed to, you know, necessarily having to hire a highly specialized person. Yeah, and this is what they are calling as a physical AI, like what physical AI, I mean, technically, what's the difference between a physical AI and the AI that the conventional laboratories use? It's like, you know, you have these big giant machines that analyze the data and images and patterns and give you the answers, right? And these answers are relevant to us when we are reporting them. Physical AI is just like, you know, utilizing the bench work at the flight, making the conveyor belt more convenient, shall I say, right? It's like, you know, it uses the artificial intelligence to perceive surrounding actions and like, what tubes do I have? These tubes need to move from which direction to which direction? So I think this ABB robotics and Roshaka are going into that path where like they are not only they know that we have the softwares there which are helping the AI. What about like making that more convenient in physical manners to like, you know, being more productive? And this is what I would assume or I would like to say being a proper disruptive, you know, change is. That is like we have this how instead of like, you know, bringing in a very lucrative, beautiful machine that is like that enters the room with all those shades behind it. Yeah, exactly. What we have, we are going to just make it a little better. Right? Right? Right? That's good. Yeah. No, I totally agree with you. One direction that I think we will as a field will want to move, I think is going to be a more robust AI, physical AI solution. I think that ABB is well positioned to bring about that change. And by that, I mean that you could imagine a world where you have somewhat general purpose robots that are able to do many different things. You change the software and now they do the new thing. Yeah. And maybe you have to validate it, but they're more general use than our current automation lines are. And I think that that is what this is. On top of a somewhat bespoke use case, they're taking general purpose robots and applying them. And so I think that that's going to be a real game changer in the future. And this is kind of the first foray into it. So this is an example of what we would consider disruption. Yeah. And this is like, you know, we haven't spoken about this much yet, but it addresses those laboratory bottlenecks. Yeah, exactly. Yeah. A lot of laboratories right now are at a phase where they are like, unfortunately, barely making ends meet, right? Yes. And for them to do that, bring in this stuff, they need to make sure that they are financially sound with that, as well as whatever they are investing is going to give them a return of investment. And these sort of like minimalistic changes, if that can be brought in, there's a good vibe to that. Yeah, directionally, that makes a lot of sense. Yes, exactly. So there's another technology that we wanted to go over. This is I think it's I think you pronounce it anvil technology. Okay, that they're calling a micro scan. And this is sort of a cell free DNA, sort of panfungal PCR technology. Yeah. And, you know, they're reporting, I think three hour timeframe to result, which of course is a, you know, you're addressing before about expertise. Mycology in general is one of these somewhat bespoke areas, even of microbiology and a lot of highly expert individuals, you know, individuals that are hopefully being aided by this kind of technology. Yeah. And the speed and the breadth of technology or diagnosis is pretty astounding. And so I think this is another example of expanding and simplifying our workflows. Yeah, yeah. And we know all that the invasive fungal infections, we know that they are difficult to treat. They are difficult to culture compared to the rest of them. They frequently occur in patients who are immunocompromised. So the timeframe is of utmost importance to like from diagnosis to treatment. And then they have complex treatments as well. So again, based on the disruption of this testing, what is the potential disruption here is that the most important benefit is not from the number of organisms that it can identify, right? Or like what are included in this panel? The potential disruption as we spoke about, like optimizing what we have, right? It will optimize in a way that it will change the treatment strategy early by diagnosing, by informing the clinicians that this is the organism that we have. Right? We have read it on the scan and all that stuff. And then that's how they are going to treat the patient, especially as we said, like an immunocompromised patient, this becomes of utmost importance. Again, a very good example where we are optimizing something that we already have. Yeah. Yeah. The thing, though, is that I think that you're absolutely right. We are going to expand the and improve. Really, it's really improved the diagnosis process for the clinicians. We're going to get results of them faster. Yeah. But that's the argument that is often made by a lot of our new technologies. That's true. The I think that what's really cool about this is we are now making the job of the tech potentially easier and faster. And so we may be able to either consolidate or at least simplify the bench work that's being done by our fungal teams or mycology teams. Yeah. And that is the part that makes it even more interesting for me. I'm really excited about that part of the tech, that part of this technology. Yes. All right. So we have another one. And this is one where I think is more somewhat a more interesting discussion. So this girl life sciences. I don't know how AUGU REX. I don't know. I'm not. I'm not a linguist. They've got this new spine stat, which is this diagnostic test for axial spondyloarthritis. And I always my mouth is always full of marbles when I try to say that word. And what's great about the test is that it is offering a relatively simple assay and it is making it a definitive diagnosis for patients that have had kind of a diagnostic journey. It's kind of a difficult diagnosis to make. And so it's simplifying clinically what's happening and it's expanding the footprint of the laboratory. And so all of those things are fantastic. They're great. We love it. But what you and I have defined as a disruptive technology for the laboratory, this ain't it. This is one of these tests that expands our footprint. Yes, we do better work. Yes. But all you've done is you've added work to our plate and you haven't made our workflow any easier. Yeah. So this is one of those tests where the lab isn't going to necessarily seek it out and buy it. This is going to be very often driven by the clinicians. Exactly. Because it's technically helping them out. I mean, the others too that we spoke about were helping the clinicians out as well as the patients that we care about the most, but at the expense, not at the expense of lab, right? That's right. And you know what's something interesting? This is also a contestant for 2026 ADLM's Disrupted Award contestants. Yeah, yeah, yeah. How they're defining disruption here is very different. That's a great fact. Yeah, so but see, the thing is, again, a useful essay needs to, I agree, like, you know, sometimes there would be things that you might not feel like are helping the lab, right? But they're helping the clients, right? Yeah, yeah, yeah, exactly. That's fine, right? But then it's not a disruptive, it's not a seller to a person who is negotiable losing the stuff while bringing and keeping them, the other parties happy, right? If you're selling it to me, then selling over to the disruptive technology, but sorry, it's disruptive to your clients, not to you, but you got to bear the burden of it. That's right. I would be like, ah, okay, it's a hard sell, but okay, I might consider it. This is the kind of technology that is precisely the hardest to sell to health systems, because the people who are going to want to buy it are the clinicians. There's going to have to be a ROI, a positive ROI for the hospital. Yeah, you've got to, you've got to get like a positive return on investment for the hospital. The clinician has to be committed to the results that will improve patient outcomes. And the laboratory has to say, yes, I'm going to do that and put it into my workflow. And that's where these kinds of technologies are really a much harder sell to an institution. And I just had a thought about like, you know, if the ones that we are actually thinking of disruptive, to me, they should also be like, you know, providing room for breather to all the parties. And yeah, I think one of the good examples that we can utilize here is the other institution that we have used at the ICAC thing. Yeah, yeah, yeah, yeah. So we, you know better about that. Yeah, yeah, we implemented a change. So Dr. Peter McCaffrey really sold this notion of using and he's our head of AI and a bunch of other things. He brought in a company that had an AI tool that looked at coronary artery calcifications on CT scans and assessed the patient. And these are patients that were coming in for other reasons. And so it was kind of like an additional scan. And, you know, it costs us money to bring it in, but it was a pretty easy sell because it was kind of like on top of a test modality that we're already using. And it found a bunch of patients. And then Brad and I, Dr. Brad Grant and I sent out notifications based on those lab results, or the radiology results and the AI results, and then found a bunch of patients that were needed to go see cardiology, basically. And so what we've done is brought in a bunch of patients and we've had interventions that are going to affect their lives, whether that's therapeutic or, you know, physically interventional. And it's been a fantastic boon. I think that it's been huge for these patients, not a ton of patients, but I mean, everyone that doesn't have a heart attack because we found this is a huge impact on them. The good thing is like you worked on the byproducts, right? This was really cool. Like it's already being done. What do we do with that result? So that can be disruptive, right? It's a big sell. Now, it was a heavy lift, especially on his part, because there was a lot of adoptive, there's a lot of, you know, computer integration and a bunch of other things. So it was pretty, it was a pretty tough sell. But once it got sold, it was a big win. And that could be this case here for the spine stat. It's just a much heavier lift than the other two technologies we were talking about, because there's kind of an internal, you know, stakeholder. We have another one that I think is pretty interesting. And it kind of hearkens back to some of the couple that we talked about with ABB. Roche has got another partnership. Actually, it's really just internal to Roche where they're trying to automate mass spec. And this is historically just, you know, kind of a highly technical area of expertise, especially from our technology standpoint. And so I think that that is another example of potentially very high ROI, because a lot of labs steer clear of mass spec. They also require a lot of very specialized individuals who can run the machines. And the revenue that can be very high, because you could just have a column, it's very low expense. And so per unit costs can be very low. And so I love the fact that we're trying to move into the automation space for mass spec. I'm a little skeptical because I think that a lot of companies have tried to do this in the past. But I'm excited that Roche is taking it on. And that would represent, I think, a disruptive technology. Exactly. And this is not the important innovation is not here, the invention of mass spectrometry. It's the attempt to convert the mass spectrometry into a standardized integrated routine testing. What it's going to do is this approach is going to expand beyond the specialist laboratories. It can reduce your manual preparations, standardize reagents and calibrations and support more routine staffing. So basically, again, addressing the bottleneck. Yeah, exactly. Yeah, exactly. Yeah. And I think that that kind of goes back to what we said before. What bottleneck is removed? Or does this actually increase work for the laboratory? So those are sort of opposite sides of the same coin. Does this improve clinical decision making or does it reduce decision making or does it improve or speed up the decision making? You know, who's going to pay for it? Who's capturing that value? A lot of times it's the lab that pays and then the value is captured by the clinician. Right. So that dynamic matters quite a bit. And then will it work reliably as sort of an ordinary lab test or is it going to be one of these things that are bespoke? OK. So I think that that's really a kind of a critical kind of framework for us to think about market disruption. Yeah. So the bottom line for today was like the relevant question is therefore not simply whether a new instrument or essay performs an impressive technical task. Rather, it's the question is whether it removes more complexity than it actually creates. Yeah. Right. Does it bring us as laboratory or laboratory personnel a better breather? Yeah, I don't want to go home realizing I have got like 500K worth of machine that's just going to like get my work a little bit more complex, providing providing the answers. That's right. I would like to have what I already have and spend like a little bit more on it and just make it more easier to do. Right? Exactly. Yeah. I think that in general, ideally, the technology would solve a problem to the person that you're selling it to. And if you can do that, it's so much easier to sell to a health system. Oftentimes, however, they solve problems and they sell it to a different person. Like I said, with the spine set example, you're selling it to the lab, but you're solving a problem for the clinician. A very good example was like just while you were talking about a television that we had. Yeah. Right. Yeah. Yeah. I mean, it's money, but it wasn't a very expensive, like one million dollar budgeted thing. You're like five hundred thousand dollars. But what it helped because we had an institute with expanding arms with all the satellite places that we have and that we cannot be everywhere. Right. What it did is like here. What we already have there. Here is it. Here's a small screen that can be connected to everywhere and that it now is sold to you. Easy peasy. Right? Exactly. Yeah. Totally. Well, hey, man, it was great talking to you today. Same here, sir. Looking forward to next week. So with that, I hope everyone has a wonderful weekend and we'll talk soon. Is there any weekend that we any no, I don't think there's any holidays that are coming up that we have. Yeah? I know we have a birthday at the house. My middle daughter's birthday is coming up, but that's not a universal celebration that we can miss. So you have to miss it. OK. Absolutely. You have to wish everybody. That's right. Yeah. Two weeks late. That's right. Have a good one, y'all. Thank you very much.
