Webinars

_{Suren Avunjian:} Thank you everyone for taking the time to join us today. We're excited for this round table and we'll also have a recording of this that you can share with your colleagues. 

^{Michael Kalinowski}: A warm welcome to all of our webinar attendees and thanks for joining us today.

^{Michael Kalinowski}: We’re certain that the next hour will be well worth your time. We have a really awesome expert panel that's been put together to discuss the present state of technology in the laboratory industry and then what the future will look like as innovation and emerging technologies take hold. The goal of today's discussion is to help our laboratory colleagues prepare for the future and also move forward with confidence after gaining a better understanding of the tech tools now available and the innovation on the horizon.

^{Michael Kalinowski}: Today you'll hear from a collection of experts with varied backgrounds who all believe that the most successful clinical labs and pathology groups will be driven by technology. These tech enabled labs will enjoy lower operational costs and increase productivity, and the technology will allow them to become market leaders who are better able to attract and also retain more customers and improve net collections.

^{Michael Kalinowski}: Please also note that we've set aside some time at the end of this webinar for a question and answer session. And we welcome your questions and interaction throughout this discussion. Please submit your questions via the chat function during the Zoom call. 

^{Michael Kalinowski}: Now let's introduce our expert panel.

^{Michael Kalinowski}: First, let's introduce Bruce Friedman, Professor Emeritus, University of Michigan Medical School. Stan Schofield, Managing Principal of the Compass Group. Khosrow Shotorbani, President, Executive Director, Project Santa Fe Foundation and Lab 2.0. Dennis Winsten, President, Dennis Winston and Associates Healthcare Systems Consultants.

^{Michael Kalinowski}: Our moderator for today's discussion is Suren Avunjian, LigoLab Co-Founder, and CEO. 

^{Michael Kalinowski}: Let’s start with Suren. He'll have some opening remarks. 

_{Suren Avunjian}: Thank you very much, Michael, for the warm intro. Ladies and gentlemen, esteemed colleagues and fellow professionals in the field of laboratory science, it's a pleasure and an honor to welcome you all to today's discussion with this all-star lineup of industry thought leaders and members of the LigoLab Advisory Board on the current state of technology and its impact on future laboratories. As we gather here today, we stand at a precipice of a new era in laboratory industry, driven by rapid advancements in technologies that promise to revolutionize the way we work, collaborate, and innovate. 

_{Suren Avunjian}: In recent years, we've witnessed the digital transformation across various industries, and the field of laboratory business is no exception. From artificial intelligence to laboratory automation and digital pathology, technology has profoundly shaped the landscape of modern laboratories.

_{Suren Avunjian}: Today, clinical laboratories are experiencing disruption due to various factors such as technological advancements, regulatory changes, and evolving healthcare landscapes. Laboratories of the future will increasingly resemble technology companies as they leverage advanced tools and systems to improve efficiency, accuracy, and collaboration.

_{Suren Avunjian}: To build a solid foundation for this vision, laboratories must consider several key factors. Laboratories need to invest in state-of-the-art equipment, software, and hardware to stay at the forefront of technological advancements. Future laboratories must implement robust data management systems to handle the growing volume and complexity of lab generated data decision making, and to unlock its valuable insights.

_{Suren Avunjian}: To facilitate seamless data exchanging collaboration, laboratories must prioritize interoperability and standardization to accelerate innovation and stay ahead of the curve. Laboratories should actively seek partnership with technology companies and other stakeholders in the industry. The laboratory of the future must be agile and adaptable, ready to embrace new technologies and methodologies new business models as they emerge. This requires a flexible organizational structure and an open-minded culture, and a willingness to transform with novel approaches. By focusing on these key factors, laboratories can establish a strong foundation for their future vision as a technology driven organization, equipped to navigate the rapidly evolving landscape of laboratory technology innovation.

_{Suren Avunjian}: As we delve deeper into our round table discussion today, we'll explore the current state of laboratory technology and examine the most promising emerging trends poised to reshape the future of lab. We'll also discuss the challenges and opportunities that these innovations may present, and the strategies that can help us harness their full potential in pursuit of excellence.

_{Suren Avunjian}: Together let us embark on a journey to envision the laboratory of the future. A space where cutting edge technology, human ingenuity, and a relentless pursuit of knowledge converge to push the boundaries of what is possible. 

_{Suren Avunjian}: Thank you all for joining the round table today, and let us begin our exploration into what we need to do today to be better prepared for tomorrow's exciting world of emerging technologies and their impact on the future laboratory world.

_{Suren Avunjian}: So with that I'd love to kick it off with Stan and find out in terms of market trends, what are the top laboratory issues today and for the next five years as you see it?

_{‍Stan Schofield}: I would say first of all, the lab has just plenty of challenges, but probably the top three and going into five years, it's gonna be around staffing, automation and reimbursement. Today, staffing. There's fewer staff. 

_{‍Stan Schofield}: Many of us are training our own staff as laboratory technicians or machine operators. The world of having clinical laboratory scientists at every workstation is very difficult, if not impossible these days. Cost are very significant around labor pools, and being able to attract staffing.

‍_{Stan Schofield}: It's not just the technical staff, it's the pre-analytic staff, the phlebotomy staff, support staff, lab assistance. Staffing is the biggest issue. The Compass Group, which is 33 health systems and almost 700 hospitals that I work with, we're running about 15 to 17% vacancy in most lab, and it's probably not gonna get a lot better over the next five years.

‍_{Stan Schofield}: People are gonna have to go to more onsite training programs to build their own kind of staff and have it available. If you go to automation, everybody wants nice machines. Automation actually, is fewer human touchpoints. We don't have the people so you've got to try and get the equipment. Even the smaller facilities are looking at more automated solutions and pre-analytic processing lines for specimen processing.

_{‍Stan Schofield}: Another area of automation that's in high demand and high cost is digital pathology. Where does it fit? Everybody would love to have it, but it's very expensive and hard to maintain. And I don't know that the hospitals and all the facilities can afford digital pathology. It's the future, but it's a tough  road financially right now.

‍_{Stan Schofield}: Faster processing's required through automation. Everybody wants all the results yesterday and for free, and that's not going to change. The demand of service and performance of the labs reached a pinnacle during covid, and it hasn't backed off. And then finally, reimbursement. It's hard to run a lab, but it's even harder to get paid these days.

‍_{Stan Schofield}: And the reduction in reimbursement through the various government agencies and the insurance programs. In the last six or seven years, we're down about 55% reimbursement per test than we were back in, 2012, 2013, and 2014. I think that preauthorizations are way up, with more molecular assays requiring greater validation and approval, making it easier for insurance companies to deny payment. So what you need is a very robust laboratory revenue cycle management (RCM) aspect to your operations. In the past you had hospital operations and lab billing and it wasn't very sophisticated. If you're a lab today, you need your own RCM tools and RCM processes. So those are the things that I think are most important today.

_{‍Stan Schofield}: I think another area, and maybe we'll talk on it a little bit, is value and what's the role of the laboratory and value contracts? We’re moving from fee for service to payments for value, and it's getting traction. I think that's something that more and more labs are gonna be facing as far as the reimbursement window.

_{‍Stan Schofield}: So over the next five years, there's plenty of challenges, but the big three I think are staffing, automation and reimbursement or getting paid for the services you provide. 

_{Suren Avunjian}: Thank you, Stan. You brought up really good points and I wanna poise a question to Dennis as far as laboratories utilizing multiple systems and what does it mean to have things interfaced and having one source of truth?

_{Suren Avunjian}: What's your vision on these disparate systems and the integration between them in a siloed lab data exchange world? 

^{Dennis Winsten}: That's a good question, and coordination and the correlation and consistency of laboratory clinical and financial data is really a key factor to better efficiency, quality and improve productivity.

^{Dennis Winsten}: Now, lots of times I hear comments talking about laboratory information systems being integrated, and in fact they're not integrated, they're interfaced. Interfacing is not the same as integration. Interfacing requires the transmission of transactions and messages between the systems, whereas integration is all contained in the same system.

^{‍Dennis Winsten}: And again, they're not the same. Clearly a lot of interfacing is done with HL7 interfacing to instruments, which of course is required. But when you look at some of the challenges associated with interfacing relative to integration, if there's a change made in either of the systems that's interfaced, that's going to require retesting, it may require some downtime.

‍^{Dennis Winsten}: It certainly requires remapping of the system. So there's always this issue. There's also an issue of terminology. Sometimes the two systems that are interfaced don't really describe their data in the same way. So there's some inconsistencies. And another issue that can occur is if one or both of the systems goes down. How do you know which system is current and which system is the actual source of truth? But with regard to integration, there's basically one comprehensive system. All the data's there.

^{‍Dennis Winsten}: You don't have a silo of financial information and a silo of clinical information and clinical lab and anatomic pathology lab for example. You can get real time data access throughout the system. You're assured that the data you're using is consistent and it's unambiguous because again, it's in one system.

^{‍Dennis Winsten}: And another factor, we'll talk about business intelligence and AI later, but one thing about having an integrated system is that your business intelligence, your business analytics, can work across the realm, across the scope of data that's there, across that spectrum of both clinical and financial information.

‍^{Dennis Winsten}: And you don't need to work on reconciling distance, the information between, for example, a lab information system and a revenue cycle management system. So there are differences, and it's important to understand that interfacing is not integration.

_{Suren Avunjian}: Thank you, Dennis. 

_{Suren Avunjian}: Bruce what is your vision on the role of the LIS? Will it be constant even as new technologies become available? 

_{Bruce Friedman}: That's an excellent question, Suren, and relatively easy to answer. I think the first thing we all need to understand is that lab data is the biggest bargain in healthcare today.

_{Bruce Friedman}: Typically, in a large health system, the budget for the labs will be about five or 6%, but yet lab data contributes to something like 70 or 80% of all clinical decision making. And that's because the labs have been automated for several decades. So I consider the LIS a part of this automation of all the labs.

_{‍Bruce Friedman}: Now, what we're seeing is AI creeping into what I would call the subsystems, which I would define as the individual laboratories like chemistry and hematology and microbiology. AI and automation of course took anatomic pathology by storm, but it's now being deployed at the level of individual laboratories.

_{Bruce Friedman}: Now we need some kind of agent, an AI agent that oversees all of the work of all the different laboratories. In the case of most LigoLab customers the question is very easy to solve because LigoLab will provide AI support as we go forward in the future. However, for large health systems, it's a little bit more complicated because essentially for many hospitals or pathology departments, the LIS module is a port, is a portion or individual unit within the overarching overarching EHR, and I don't wanna be too pessimistic about this, but I believe that the EHR companies like Epic are very broad and they have a lot of people to satisfy.

_{Bruce Friedman}: And I suspect that AI will come probably first to some the clinical hospital operations and not to the labs. So I believe there will be room in  the laboratories for some kind of overarching AI presence or agent that would be there to take the data from all the individual laboratories and integrate and interpret.

_{‍Bruce Friedman}: And this has always been the case. We've had rules in the labs for decades. So there are always rules operating at the analyzer level and the overarching total lab operation. So I don't know who's gonna provide this large overarching AI agent that will oversee the work and the rules operating at all the individual laboratories.

‍_{Bruce Friedman}: I think the IVD companies, for large health systems, may provide this solution that would include large companies like Beckman and of course and Roche. So I think I'm very optimistic about the future of the labs in terms of AI. We've been using rules for many decades.

_{‍Bruce Friedman}: We'll continue in that realm and for many of you listening to this broadcast LigoLab will provide that solution for you. That is that overarching agent that will control and interpret the data coming from the individual labs. So I'm optimistic about the future and automation in the labs.

‍_{Suren Avunjian}: That's a fantastic point and sets us up for the topic of AI. But before I dive too deep into that, I think it's really vital to discuss the curation and aggregation of lab data because without that you really can't build proper AI models. Khosrow, what's your vision on how that can improve laboratories operations and finances?

^{Khosrow R. Shotorbani}: I think Stan covered quite nicely the challenges facing lab, not to mention reduction of the payment in the severe fashion. I believe that current business model may have reached a strategic inflection point.

^{‍Khosrow R. Shotorbani}: But I'm gonna borrow something from Bruce, what he said. Optimism. Even though our industry is facing the most dangerous commoditization, I feel quite bold about the role of the clinical lab in the future state of healthcare. If we are aligning ourselves, as Stan talked about, the space of the value, if it's translated into managing clinical risk, I really think that we are moving from the the notion of reactive confirmation of what the diagnosis is to a proactive prediction of what that's going to be. We know the current P and L, and we have to reduce our cost because our payment is reducing significantly. But we had to put a stepping stone into business of the value in the space of managing both clinical and financial risk.

^{‍Khosrow R. Shotorbani}: That payer, including Medicare and Medicare Advantage, is going to require and demand of us. We're not ready to that, and I think the runway is about three to five years. We gotta get up, optimize the current process of the clinical lab. We gotta diversify our top line so we're mitigating the risks of the changes in our reimbursement, but we really gotta start transforming and utilizing the longitudinal data, which AI could help as a stepping stone in the future model.

^{‍Khosrow R. Shotorbani}: This basically allows us to do proactive risk stratification, even at the asymptomatic stage. That's going to be a requirement for value-based care. 

‍_{Suren Avunjian}: We have a question that we received from the chat. In what areas of anatomic pathology workflow can rules and automations replace human resources?

‍_{Suren Avunjian}: Anyone want to answer that? 

_{Bruce Friedman}: I would say in terms of image analysis, which is now starting to hit the market in terms of approved systems starting with prostate cancer. And there's gonna be explosion of these various packages that will interpret the images obtained in the digitization of the image.

_{Suren Avunjian}: Speaking of images, I wanted to note that all the images you're seeing on the slide deck today were generated by AI. They were all using mid journey. So that was a little fun tidbit. 

_{Suren Avunjian}: So with the discussion of AI, Dennis how do you see artificial intelligence revolutionizing clinical laboratory workflows, particularly in the area of data analysis and diagnostics?

‍^{Dennis Winsten}: As everyone knows, artificial intelligence is exploding. Examples of it every day. You just mentioned the images that you're showing. Siri. Alexa, there's auto driving online ads that are specifically focused to you, and I'm sure I've gotten mine already this morning and I'll be getting more for the rest of the day.

^{Dennis Winsten}: But yeah, it's exploding and there are a lot of issues associated with it's exploding, not only in healthcare but across our whole society. But as far as AI in laboratories and in healthcare, there are a number of different application areas, if you will. One Bruce mentioned already, digital pathology, scanning slides and identifying anomalies in the slides with a high degree.

^{Dennis Winsten}: Another one is pattern recognition, which involves looking at large databases, large longitudinal databases of clinical information. Being able to discern patterns that humans would not have the time or necessarily the ability to detect, and being able to point out those trends and those indicators.

^{Dennis Winsten}: Another one is clinical decision support in terms of being able to provide predictive analysis. And that also applies as far as the laboratory is concerned in business management. That is giving laboratory managers the insights and advice they need to operate their laboratories more effectively and efficiently.

^{Dennis Winsten}: And another one that's rarely brought up, and I know we can have some more discussion about this later because it's a big issue, is cybersecurity. That's another area. But I do wanna comment on the analytics side, if I may because if you look at business analytics and business intelligence, you'll look back at kind of what we have now and what we've had in the past, and that's been descriptive data.

‍^{Dennis Winsten}: That is the systems will summarize raw data for interpretation and specifically it will describe what has happened. And it's basic statistics and the reports that you see, the dashboards and the graphs. So what we moved into now with artificial intelligence is a predictive model.

^{Dennis Winsten}: That is, you have enough capability to be able to determine what could happen, what is likely to happen based on the analysis of historical data. And this is using analytical tools including statistical modeling and other algorithms. So that's very nice. Predictive is good, but I think the next step is even more important and that's prescriptive with artificial intelligence and machine learning, where the machine is learning from the data and the new data that it's getting to be able to alter what it suggests.

^{Dennis Winsten}: And prescriptive says it's going to suggest decision options that are the most likely to optimize the outcomes. For example, prescriptive indicates what should happen or the best course of action. So this is a very powerful tool in using mathematical based techniques. 

‍^{Dennis Winsten}: So there's a heck of a lot of things that are happening in AI that are gonna be beneficial to the laboratory, but not without some risks. And we'll talk about those I bet a little bit later. 

_{Suren Avunjian}: Bruce, you wanted to add a comment?

_{‍Bruce Friedman}: I look upon what Dennis just spoke about. I look upon this as reflex testing on steroids. And we've had for two decades, essentially reflex testing on 24 hour cycles. But I think that this is gonna be compressed with AI such that the cycles will be more like four or five hours. Now the labs will have to keep up, but I think that there will be testing to a logical endpoint in the diagnosis within a 24 hour period.

_{Bruce Friedman}: Now, this is not without some risk and some political attention because the clinicians, I think many of them, particularly the older ones, will not allow this. They're positioned to do this, but I think the younger docs are overworked and will be very happy to set reflex testing to a diagnosis within a 24 hour period.

_{‍Bruce Friedman}: And AI will be greatly able to do that. 

^{Dennis Winsten:} They, the old guys won't be around too much longer. 

_{‍Bruce Friedman}: And where does this webinar go with that?

_{Suren Avunjian}: It will be the customer of the process. You mentioned cost outside of what are other current limitations and challenges in implementing AI technologies in clinical laboratories and how can these be addressed? 

‍_{Stan Schofield}: Lemme just say that I think the average laboratory has to depend on technology from a third party because they don't have the intellectual capital, the experience to develop AI kind of tools. So what we would be doing is working with other sophisticated organizations to develop the tools and adapt them to our workflow or our environment. In other words, we'd have to go shopping for the technology, developing it, writing code and training machine learning devices. The average laboratory can't do that.

‍_{Stan Schofield}: It's just is too complex. It's too costly to do that from scratch. So we're gonna have to be end users from the development state, steps from pharmacy developing it, then diagnostic companies will be developing it, and then the laboratory will be the end market user and they'll have to buy it, and they may be able to modify it or customize it, but I don't think very many laboratories outside of one or two of the national commercial labs will have any kind of resources to devote to this kind of technology development in the next five years.

_{Suren Avunjian}: Khosrow, would you like to expand on this? 

^{‍Khosrow R. Shotorbani}: Yeah, I agree that the average lab may not have the sophistication, but average lab's gonna have to learn it. That's basically the rule of thumb. But I'm gonna go back to what Bruce just said. If the LIS system needs to be the central repository of the raw material called data, that's a foundation. AI to me, needs to come in when we are able to aggregate, clean and correlate the foundation of the longitudinal view. And AI basically becomes the intelligence gathering for us. The foundation has to start with “can we truly aggregate and curate the data real time?” Because harnessing the real time of the lab data is the actionability.

^{‍Khosrow R. Shotorbani}: Zero latency of the data is our value proposition. But if it's gonna take six months just to gather the data, we just lost our value proposition. Gathering, curating, correlating the fundamental foundation of the repository of data, then AI comes in the layers on top of it to tell us, okay what does all of this mean?

^{‍Khosrow R. Shotorbani}: And later on, maybe I can share an example of it, but the foundation isn't there yet. We're so fragmented when it comes to data. We can basically connect the dots and say this is the actionable intelligence, but I don't think AI is gonna help in there. I think we need to start curating that repository first where AI wraps around it, in my opinion.

_{Suren Avunjian}: Let's go to Dennis and then Bruce next please. 

^{Dennis Winsten}: I think one of the key things about the AI is gonna be its ease of use. And in addition to the ease of use, the validation. That it’s working properly is going to be another important factor in in its acceptance by the laboratory and by the healthcare community.

_{Suren Avunjian}: Bruce, would you like to dive deeper into that? 

_{Bruce Friedman}: Yes. Just very quickly, I have total confidence that the lab industry will absorb AI almost effortlessly. And in my whole career, I've seen the lab being a driver for technology, for automation and technology. So I have no qualms about this. I feel very confident because by and large laboratory personnel and professionals are very comfortable working with automation and technology, and our industries will provide that for us.

_{Suren Avunjian}: Dennis, please. 

^{Dennis Winsten}: I think clearly one of the issues we have to deal with, and I think we're still having problems dealing with it today, is how do you assure that the data that you have in your longitudinal database is validated?

^{Dennis Winsten}: Because AI is only as good as the data it's going to operate on. And I'm not sure that today, and maybe this is an area where AI can help that, is to be able to look at data that's coming into the system to say, is this data inconsistent in any way? Is this data, does this data not meet, quote, the standards for that type of data element or that type of information.

^{Dennis Winsten}: And I think that as a front end may be very helpful for AI to assure that data coming into databases in the future is validated as good data. The old expression goes back 30 or 40 years, garbage in, garbage out and artificial intelligence is not gonna solve that if it's dealing with garbage.

_{Suren Avunjian}: Bruce, go ahead.

_{‍Bruce Friedman:} I wanna get a little bit even more with the future of science comment here, and it's not gonna happen tomorrow in terms of AI. But predictive analytics in two to three years are gonna look at a patient's variation within a normal range and predict based on large data, what diseases patients will be developing in the future, say 10 years or 15 years.

_{‍Bruce Friedman}: Hence, this is not something we need to worry about now, but predictive analytics is gonna take us in that direction and that's gonna have very powerful social implications. Not tomorrow, but perhaps in five or 10 years. 

_{Suren Avunjian}: Khosrow and Stan, maybe in that order I'd love to get your input. Is it practical to have AI driven personalization of patient reports to improve the clinical decision making process and patient outcomes for the providers and the patient?

‍_{Suren Avunjian:} What do you think about helping patients instead of the patient going and Googling, all of the lab input helping the patient with the summarization and personalizing that report to them? 

‍_{Stan Schofield}: Let me jump in there. I think the potential of that and the functionality have great opportunities, but systems and providers of healthcare want to control information so that they are managing the patient through a process rather than the patient saying, oh, I know what the answer is, and I don't need anybody now.

‍_{Stan Schofield}: And I think physicians, health systems and providers want to stay with the patient stuck to them and needing them and relying on them because that's their role in their function today. Because the sophistication of technology giving the patient the correct answer, what are they gonna do with it?

_{Stan Schofield}: So they're not trained in dealing with it and they may not be sophisticated to deal with it. And those are the challenges that systems and providers have to work with. The technology companies and AI data providers have to come up with some reasonable guardrails there because, in my opinion, giving a patient a full roadmap won't help if they don't know where the first road sign is and what to do with it. 

_{Stan Schofield}: If you wanna get out of town, any road will take you there. But if you wanna get to your vacation, you gotta take the exact route. And usually it takes some training and some mapping, and that's the role of the providers transforming this information into guidelines and a roadmap with the patients rather than leaving them out in the woods by themselves.

_{Stan Schofield}: So I don't think it's wrong. The patients have more information and they will become more sophisticated. The younger generation is certainly technologically adaptable to this. But once again, the continuum of the aging in the population, you know what people at 25 can do with information and data far exceeds what most people at 70 can do.

_{Stan Schofield}: And there has to be some kind of normalization of a process, and I think the providers are gonna be the universal translators and guides in the healthcare journey for many years to come. 

_{Suren Avunjian}: Khosrow?

^{‍Khosrow R. Shotorbani:} Let's face it, AI been around since 1950s. It's the latest shiny objects. And I do agree with Stan.

^{‍Khosrow R. Shotorbani}: This may not be just technological advancement, but it's cultural change The US is actually tracking behind and not leading in this process. In other countries, including the Middle East, the lab report is not a numeric value standalone. In fact, there is a page that shows the trendline and how the delta changes over time and the individual actually tracks it. So my definition of personalized medicine is about how did the individual change compared to their themselves, above and beyond within the normal range. 

^{‍Khosrow R. Shotorbani}: Let's take an example. Creatinine. Often the value has gone up 50% within the normal range. We're not even flagging it yet until it's out of the range.

^{‍Khosrow R. Shotorbani}: That requires change of the pathology. Prescriptively what Dennis said, Hey, this is actionable. Do something about it now. We have to be part of the care to get to that point. That means we have to no longer be passive. We released a result, but we have to assure that there was a diagnosis that someone needed to take in action.

^{‍Khosrow R. Shotorbani}: That's a culture transformation here, not technological transformation. 

_{Suren Avunjian}: That's a great point. Thank you, Khosrow.

_{Suren Avunjian}: Dennis, any closing remarks on this slide before we move on? 

^{Dennis Winsten:} It isn't the artificial intelligence we have now with machine learning. It was based on logic and algorithms in the past, so we have an advantage now in that machine learning will change the algorithms and what it does based on new information that it receives and in terms of personalized medicine. 

^{Dennis Winsten}: I can give you an example.

^{Dennis Winsten}: My son's a psychiatrist and as most of you know the drugs that are given for different symptoms vary and their impact on patients vary significantly to the point of either making them feel better or causing them to commit suicide. So we have a lot of data, and again, assuming the longitudinal data is good, we're gonna have more and more information.

^{Dennis Winsten}: We're gonna have more genetic information, we're gonna have more past history information, which should allow both the lab and clinicians to be able to say, again, this is predictive. This is the best course of action for us to take, whether it's testing or whether it's therapies because of the information that is being analyzed and being presented to the clinicians and to the laboratorians.

_{Suren Avunjian}: I think really to cause a major shift in the way we run laboratories, we're gonna see a larger shift in how we get paid. 

_{Suren Avunjian}: Although there's several good reasons to shift healthcare to value-based model, the transition will require significant changes to healthcare as we know it. So what will it take for value-based care to become the dominant form of care? 

^{Khosrow R. Shotorbani:} Clearly we're actually referring to this as lab initiated care model to elevate the laboratory out of the basements, assume a seat at the table and help design the care model future that is driven by clinical intervention, clinical prevention, and cost avoidance. 

^{Khosrow R. Shotorbani}: If this was all about data, Google would've solved it 10, 15 years ago. The two basically creates a new toolbox that we begin not waiting for the order to arrive. We begin proactive risk, stratifying the data that we're sitting on. We're looking for that needle in the haystack, and we're basically looking for where was the gap in care that was missed.

^{Khosrow R. Shotorbani}: Did we prevent something? And on the financial side, what were the total cost avoidance, such as readmission or hospitalization? And did we adjust the risk? So if I can just conclude with a case that we're about to submit to the National Kidney Foundation, we all know that CKD is a huge prevalence with comorbidity.

^{Khosrow R. Shotorbani}: Roughly about 37 million suffer from that. 95% of these conditions are missed within the primary care. That's what we will actually indicate via our study. And by the time that we, the individual reaches the stage four, the individual is going to be on dialysis. Lifespan of the dialysis is between one to five years.

^{Khosrow R. Shotorbani}: Here's the beauty. What the lab really can do within the first three stages. Asymptomatic stage lab is the essential part, measuring the very basic biomarkers of the lab that are telling us something, but we're not catching it. 

^{Khosrow R. Shotorbani}: How do we get paid for risk in the future? We haven't evolved that yet. 

_{Suren Avunjian}: Thank you, Khosrow. 

_{Suren Avunjian}: We have a question from the attendees and  I'll propose it to Stan. 

_{Suren Avunjian}: What can my lab do today to prepare for this major shift to value-based reimbursement? 

_{Stan Schofield}: Okay. It's a great question and many of us are still struggling with a clean answer, but let me give you my best advice at the moment.

_{Stan Schofield}: Get closer to the patient. Yep. The lab, many years has been relegated to the basement or a commodity. Lab work has drawn results, come back. Nobody ever sees the lab get closer to the patient. What does that mean? It means take an active role and helping drive the patient through the health system and the provider network efficiently and cost effectively and quickly. 

_{Stan Schofield}: The big mantra is length of stay. Every hospital is trying to cut the length of stay, so what the lab needs to do, number one, you have to have the lowest possible cost, okay? Per test. You have to be efficient, you have to be automated. You gotta get paid, so you don't have a lot of risk loss, and you have to have the right staffing combination to keep the cost down.

‍_{Stan Schofield}: The second thing is efficiency. Drive the patient through the system quickly, if that means point of care instrumentation. I've always thought the central lab, it's much cheaper, it's this and that. But Covid changed my mind. Having covid testing at a molecular level at a hospital two and a half hours away was very efficient compared to, half the cost going to the core lab, but 12 to 16 hour delay in care and therapies for the patient and or special infection control, isolation, moon suits and things like that in the emergency department. So I think, embrace it. Make it a good business analytic decision and participate. All the hospitals and the health systems are working towards contracting.

_{Stan Schofield}: Get involved. Get a seat at the table. Work with your data analytic people and your financial people that are doing the contracting. But first of all, you gotta know your costs. You gotta be efficient in your cost per test, but then you have to raise your hand and say, Hey, Let me help you on the lab side and work with the data and the contracting people because they don't understand what the lab is and the value that the lab might bring.

‍_{Stan Schofield}: So those are the things that you can do today and over the next year or two and make a difference. 

_{Suren Avunjian}: Bruce, please, 

_{Bruce Friedman}: Stan is getting closer to the patient. Is that gonna cause friction within the system with a clinician saying, that's our job? That's not the lab's job. 

_{Stan Schofield}: No. I don't mean like standing at the bedside, but the idea of having some of the things working with the chief medical officer and the medical director of, let's say, internal medicine around the example of would be the kidney markers that Khosrow, just talked about.

_{Stan Schofield}: Very easy. To get involved. And it doesn't mean you're gonna be sitting on the end of the bed with the patient waiting for the clinician. What you're gonna be doing is be proactive and pick up some of these markers and pick up these standards of care and help champion them and make sure that the lab is trained and that the lab information system (LIS) flag these things appropriately and bring the attention to the medical officers and the nursing officers of the systems, not just being left in the basement.

_{Stan Schofield}: And once in a while they remember the lab because of covid. You need to be a little more active in that. 

_{Suren Avunjian}: Dennis, you wanted to expand on that? 

^{Dennis Winsten}: Yeah I think, and this goes back a ways, one of the reasons the lab is often unappreciated overall with regard to a health system is basically the lab hasn’t needed a directional link to patient care.

^{Dennis Winsten}: That is the lab takes these tests, does the tests, gets the results, and they send those out. But the lab rarely finds out specifically the outcomes. So here's the cause, you've determined that you've sent out results to the clinicians. Theoretically they take actions and 70% of clinical decisions are based on lab data.

^{Dennis Winsten}: But rarely does the lab ever have any follow through that says, because we provided these results, going back to, Khosrow’s comments about preventing, for example, preventing having doing a dialysis lab. The labs don't get the feedback they need. I think to really be able to establish what benefits actually took place because of the data that the lab provided. It's unidirectional. They don't get that feedback that says, because you came up with these results, these good analyses, the patient was saved or the the length of stay was reduced or the morbidity was reduced, whatever. So the unidirectional nature of the lab, I think has been a problem for a long time.

_{Suren Avunjian}: That's true. Thoughts on that? 

^{Khosrow R. Shotorbani}: Maybe piggyback on what Stan just talked about.

^{Khosrow R. Shotorbani}: Getting out and having a seat with value-based care, we need to advance ourselves to a level that no health system should ever sign a value-based care contract without the labs input in. Often they already have the intelligence in their hand on the outreach, what we call, which we need to retire that phrase and that's that the intelligence should be fed whether the value-based care agreement is to be signed or not. That to me is the most sacred ground that we have got to be in. Number two, I do agree with this, Stan, and regarding the question that Bruce just talked about, I honestly do not see a friction between clinical lab pathology and physicians.

^{Khosrow R. Shotorbani}: Not to mention, especially with primary care lab, is a catalyst to unleash the values of the population held, but it puts the primary care on top of its game, especially now that we're into the telehealth. To me, that's just another reactive mechanism. If we don't put intelligence around it, it's just instead of going to bricks and mortar, you're now zoom calling.

^{Khosrow R. Shotorbani}: If you don't put that environment with this value on the slide we're talking about putting the focus of the physician where to focus and where not to focus. We're gonna put them on top of their game, and the last piece, in my opinion, we need to start talking about just a test and start talking about the change in a test, which is basically that longitudinal data even within the normal range.

^{Khosrow R. Shotorbani}: It really is no longer about a test and we may not get paid for a test in the future anyway. I think we need to embrace the longitudinal view as the holy grail of laboratory medicine.

_{Suren Avunjian}: Bruce, would you add some closing? 

_{Bruce Friedman}: Yeah, I'd like to just slightly extend the remarks about longitudinal database and earlier diagnosis of chronic kidney disease. The logical extension of that is the diagnosis of pre-disease, probably on a community basis. And we're not gonna see that for a decade or more, but what that means is that a lot of our drugs will have to be retested for pre-disease as opposed to clinically manifest disease.

_{Bruce Friedman}: And that's gonna really turn healthcare on ts on its axis. 

_{Suren Avunjian}: What a fascinating discussion. If there aren't any questions, we're getting close to the top of the hour. I wanna make sure we're respecting everyone's time. So yeah, we reached the conclusion of our enlightening round table and discussing the future of clinical laboratories. 

_{Suren Avunjian}: We'd love your feedback on future topics of webinars as well, and I'd like to take a moment to express my deepest gratitude to our esteemed speakers and our attendees for your invaluable contribution and active participation.

_{Suren Avunjian}: Thank you all. We are really looking forward to learning more on how we can help the greater community and the industry with topics that will drive more value, and we're truly fortunate to have this opportunity to learn from you and the wisdom that this roundtable brings to our attendees.

_{Suren Avunjian}: Thank you again for your curiosity and diverse perspectives you brought to this conversation. Your questions and comments foster the stimulating and dynamic exchange of ideas, and as we move forward let us continue to collaborate and innovate harnessing the potential of technology, AI, and personalized medicine to shape the future of clinical laboratories and ultimately improve patient outcomes.

_{Suren Avunjian}: The insights and the connections we've gained through this roundtable will undoubtedly be the contribution to our collective efforts in achieving this goal. But once again, thank you all for being part of this insightful roundtable. We hope to see you at the future events and wish you the best in your endeavors to advance the field of clinical laboratory industry.