Exploring AI Innovations in Healthcare

Practical Uses of Artificial Intelligence in Medical Laboratories and Healthcare

Estimated read time: 1:20

Summary

In a recent webinar hosted by Lighthouse Lab Services, experts gathered to discuss the transformative role of artificial intelligence (AI) in medical laboratories and healthcare. The session highlighted how AI is poised to enhance laboratory workflows, improve diagnostic accuracy, and facilitate personalized medicine. With AI's potential to reduce costs and increase accessibility, there was also a focus on the challenges and ethical considerations of its implementation. Participants explored both the promise and the challenges of integrating AI, including potential impacts on staffing and regulatory landscapes.

Highlights

Panelists discussed AI's role in enhancing laboratory processes and improving test accuracy. 🤖
The conversation covered ethical considerations such as bias in AI algorithms and the need for transparent operations. 🤔
Experts pointed out AI's potential to streamline data management and enhance pre-analytical workflows. 📈
Discussion highlighted AI's role in potentially reducing healthcare costs and increasing patient accessibility. 💡
The webinar addressed the training and workforce impact, emphasizing AI as a supportive tool rather than a replacement. 🧑‍🔬

Key Takeaways

AI is revolutionizing healthcare by improving lab workflows and diagnostic accuracy. 🚀
Experts stress the importance of AI as a tool to augment, not replace, human capabilities in labs. 🤝
The integration of AI into healthcare poses ethical and regulatory challenges that need careful management. ⚖️
AI can streamline pre-analytical processes, making data entry and validation more efficient. 📊
Personalized medicine may become more accessible as AI helps decipher complex data patterns. 🔍

Overview

Artificial Intelligence is making waves in healthcare, promising to revolutionize how we approach diagnostics and laboratory processes. Pioneers like Abhi Bhosale of CrelioHealth and Vitali Khvatkov of Lighthouse Lab Services highlight AI's potential to streamline lab workflows and improve the accuracy of diagnostic results. Vitali elaborates on how AI algorithms could act like an always-on assistant, catching errors in real-time and ensuring test results are reliable and precise.

AI is not just about improving accuracy; it's about reshaping the entire healthcare landscape, especially in clinical labs. Jihoon Baek from Dendi talks about the potential of AI to redefine traditional reference ranges and make smarter healthcare decisions based on comprehensive population data. The discussion acknowledged the exciting prospect of AI as a tool that doesn't replace humans but augments their capabilities, allowing lab professionals to focus more on complex problem-solving tasks.

However, with great power comes great responsibility. The experts addressed the ethical challenges AI presents, such as bias in training data and lack of transparency in algorithmic decisions. They also touched on regulatory hurdles and the need for updated frameworks that can keep pace with technological advancements. The consensus was clear: AI's potential is vast, but its integration into healthcare systems must be carefully managed to ensure patient safety and data integrity.

Chapters

00:00 - 00:30: Introduction of Guest Speakers The chapter introduces guest speakers who bring diverse perspectives, experience, and knowledge to the conversation. It highlights Abhi Bhosale, the co-founder and CEO of CrelioHealth. He has a strong background in engineering, particularly from pic pun, and is a computer science graduate who turned into a Healthcare Tech leader. With over a decade of experience, he has played a key role in growing CrelioHealth globally.
00:30 - 01:30: CrelioHealth and Lighthouse CIO Introduction CrelioHealth provides services to over 1,500 medical laboratories worldwide, handling millions of medical records daily. The chapter also introduces Vitalii Khvatkov, the CIO at Lighthouse, who brings over 20 years of experience in technology innovation, consulting, and managing large IT projects. His recent work focuses on designing and developing predictive analytical technologies that enhance lab payment processes using big data.
01:30 - 02:30: Jihoon Baek and Dendi Introduction The chapter introduces Jihoon Baek and Dendi, focusing on their work in analytics and computer intelligence to predict payment risk by insurance payers. This technology was also applied at Bio Reference Laboratory. Jihoon Baek works at Lighthouse on a product called RCM Spotlight utilizing this technology. Previously, Vitali served as CEO of a software company that developed cloud-based cancer diagnostic applications with deep learning analytics. Vitali also has experience managing large-scale consulting projects for Fortune 500 companies like Enture, specializing in similar fields.
02:30 - 03:30: Housekeeping and Webinar Recording Details This chapter introduces two key individuals involved in business process transformation and the development of specialized software solutions. Vital is highlighted for his extensive experience in implementing SAP systems and creating 3D analysis solutions for NASA, with academic qualifications including a Master's in Computer Engineering and an MBA from Rice University. Meanwhile, Jihoon Baek is recognized as the founder and CEO of Dendi, a company aiming to be the comprehensive operating system for Diagnostic Reference Labs. Jihoon is noted for his diverse background in software development.
03:30 - 04:30: Buffering and Q&A Details The chapter discusses the application of AI and machine learning in financial analytics within the healthcare sector. It highlights the contributions of Jiun, who started Dendi in 2020, and has since assisted 60 reference labs in enhancing their operations. A housekeeping note suggests closing bandwidth-heavy applications to minimize buffering and confirms that the webinar will be recorded and accessible without needing to request it.
04:30 - 05:30: About Lighthouse Lab Services Chapter Title: About Lighthouse Lab Services This section explains the logistics of a webinar hosted by Lighthouse Lab Services. Participants who registered for the webinar will automatically receive a link to it via email. Audience members are encouraged to submit questions at any time using the Q&A section on the right-hand side of their screen. The organizers will attempt to address some questions during the presentation if they are pertinent, and the rest towards the end.
05:30 - 06:30: Introduction to AI in Healthcare Discussion The chapter introduces Lighthouse Lab Services, a company involved in medical laboratory consulting and recruiting. It mentions the use of polls during the webinar to gather real-time feedback from participants. Lighthouse Lab Services is noted for building over 50 CLE labs and providing revenue cycle management services.
06:30 - 08:00: AI Affecting All Aspects of Laboratories The chapter discusses the role of AI in impacting various aspects of laboratories, particularly in supporting clear Labs across the country. It emphasizes the assistance provided by AI in validating laboratory-developed tests (LDTs), gaining market access, and resolving issues related to payment by commercial payers or network integration. The chapter encourages lab professionals to seek assistance if needed, indicating that AI can offer solutions or point towards one.
08:00 - 09:00: AI-Generated Questions on AI and Labs The chapter discusses the integration of AI in the healthcare sector, specifically in laboratory settings. The speaker notes that while discussions about AI are quite common, the topic's significance can't be overlooked. Therefore, the focus is on understanding the impact of AI on healthcare labs, with insights from experts possessing substantial laboratory knowledge.
09:00 - 10:30: AI Improving Accuracy of Lab Results The chapter discusses the impact of AI on the accuracy of lab results, indicating that its influence will be more extensive than initially expected. It suggests that AI will affect all aspects of a laboratory, not just the technology team. There is ongoing discourse on this topic, with major headlines often presenting polarized views. Some claim AI will revolutionize healthcare, while others argue it might have detrimental effects.
10:30 - 12:00: AI as an Assistant Quality Manager The chapter explores the role of AI as an assistant quality manager, delving into a variety of perspectives on how AI can impact different laboratory areas. The discussion kicks off with addressing several questions about AI's implications, specifically focusing on questions generated by the AI tool, ChatGPT.
12:00 - 13:30: Correlations and Regional Data in AI This chapter titled 'Correlations and Regional Data in AI' delves into the impact of AI on laboratories and healthcare. It begins with a discussion on AI's potential to enhance the accuracy of laboratory results, highlighting the importance of appropriate dialogue regarding AI in these sectors. The speaker introduces the topic by posing a question about AI's role in improving laboratory result accuracy, kicking off with expert insights from participant AB.
13:30 - 14:30: AI in Laboratory Workflows The chapter 'AI in Laboratory Workflows' discusses the impact of artificial intelligence on laboratory operations, particularly focusing on changes in lab workflows and reporting. It addresses how AI can influence the accuracy of medical records or test results and explores the extent of this impact. The narrative emphasizes improvements in these areas attributed to AI advancements.
14:30 - 17:00: Human-AI Interaction in Labs In the chapter titled 'Human-AI Interaction in Labs', the narrative explores the role of AI as an ever-present assistant quality manager within laboratory environments. The technology functions as a continuous layer of quality control, diligently monitoring instruments and correlating this data with lab results. Through this integration, the AI facilitates a deeper understanding of individual test accuracies and outcomes, ensuring a more efficient and error-reduced lab operation.
17:00 - 18:30: AI and AI-Generated Content The chapter discusses the role of AI in identifying abnormal or unexpected results within data, particularly in contexts where instruments or systems may not behave as anticipated. Artificial Intelligence can learn to recognize and identify instances when an instrument goes beyond its linear range, acting as a layer of error detection. This capability emphasizes AI's broader potential in enhancing system accuracy and reliability.
18:30 - 21:00: Pre-Analytical and Post-Analytical AI Applications The chapter discusses the role of AI in pre-analytical and post-analytical processes. AI can identify and understand human errors, such as typing mistakes, and detect when results are altered from what the instrument reported. The chapter explores how AI can assist in contextualizing and correlating test results to improve accuracy and reliability in these stages.
21:00 - 22:30: AI Technology Adoption and Challenges The chapter discusses the adoption and challenges associated with AI technology, particularly in the medical field. It highlights how AI can assist in identifying discrepancies in results, such as a metabolic panel showing a certain set of results that correlate differently with other tests. This technology can enhance systems or workflow solutions used by medical directors, suggesting the potential for significant improvements in medical diagnostics and decision-making. The emphasis is on AI's role in augmenting human capabilities rather than replacing them.
22:30 - 24:00: AI in Training and Workflow Optimization This chapter discusses the utilization of AI in enhancing training and optimizing workflow processes. It emphasizes the role of AI systems in assisting individuals to make better decisions. The excitement surrounding various use cases is evident, as there are numerous scenarios that the speakers are eager to explore and test. Additionally, it is noted that customer-driven requests are a significant aspect of addressing use cases in AI deployment.
24:00 - 25:00: Patient Impact and Personalized Medicine This chapter discusses the use of regional or specific geographical data in personalized medicine, particularly in analyzing microbiomes. It highlights how these localized results can contribute to understanding patient impacts more accurately. The focus is on using specific regional characteristics to enhance the precision of medical tests and treatments, acknowledging that certain tests might yield unusual results depending on the regional biological factors.
25:00 - 26:30: AI's Role in Data Utilization The chapter titled 'AI's Role in Data Utilization' explores the significant role that artificial intelligence plays in managing and analyzing large datasets. It highlights the human propensity for error and suggests that AI can serve as a safety net to catch anomalies or outliers in data processing. The chapter underscores the potential for AI to improve data interpretation and reliability, enhancing overall accuracy and trust in data-driven insights.
26:30 - 28:10: Regulation and Ethical Considerations This chapter focuses on the role of AI in improving laboratory workflows and touches on the importance of reference ranges and criteria for determining test results.
28:10 - 29:30: Pre-Analytical AI Applications in Depth Chapter Title: Pre-Analytical AI Applications in Depth This chapter discusses the potential of AI to redefine traditional scientific reference ranges by utilizing demographic and population data. It highlights the arbitrariness of current reference ranges and suggests that AI can prompt a reevaluation of these standards. AI's ability to interpret vast amounts of data may uncover new insights and raise important questions about existing scientific practices.
29:30 - 30:00: Q&A and Practical AI Applications The chapter titled 'Q&A and Practical AI Applications' discusses the influence of AI on lab work processes. It explores the notion that outcomes like test results being 'within range' or 'out of range' are often arbitrary. AI may prompt us to rethink these traditional approaches and improve the systems currently in place. Although the speaker isn't a clinician, they emphasize AI's potential to bring about a shift in how we've been operating in laboratory environments. This discussion underscores the questioning of conventional methods and encourages embracing AI-driven solutions.

Practical Uses of Artificial Intelligence in Medical Laboratories and Healthcare Transcription

00:00 - 00:30 all right so I'm going to go ahead and I'm going to do some intros to some of the guest speakers we have today we've got a great lineup of people that bring uh different perspectives and some great experience and knowledge uh to the conversation so we have Abhi Bhosale, he's the co-founder and CEO of CrelioHealth. He pioneered streamlining new healthc care Diagnostics through technology through his engineering Roots at pic pun and computer science graduate turned Healthcare Tech leader with over a decade of experience growing CrelioHealth globally. Today,
00:30 - 01:00 CrelioHealth serves more than 1,500 medical labs globally and transacts to million medical records every day. We also have Vitali Khvatkov who is our CIO here at Lighthouse and he is a technology expert an entrepreneur a computer scientist with over 20 years of experience managing Technology Innovation Consulting large scale IT projects his recent experience includes design and development of predictive analytical technology for better lab payments for RCM uh that utilizes big data
01:00 - 01:30 analytics and computer intelligence to predict payment Risk by Insurance payers uh he did this for Bio Reference Laboratory as well he has a product here at Lighthouse called RCM Spotlight um that utilizes that technology uh previously Vitali served as a CEO of a software company developing cloud-based cancer diagnostic applications with deep learning analytics his prior experience also includes managing large scale Consulting projects for Fortune 500 companies like enture specializing
01:30 - 02:00 in business process transformation and implementing of SAP systems as well as developing 3D analysis solutions for NASA space show program uh vital received his Masters in Computer Engineering and an MBA from Rice University. We also have Jihoon Baek, he's the founder and CEO of Dendi. Jihoon uh started the company with the mission of being an all-in-one operating system for Diagnostic reference Labs he has a diverse background in software develop Vel M product
02:00 - 02:30 management applications of AI machine learning and the financial analytics of healthcare sectors jiun started dendi in 2020 and has uh since helped 60 reference labs in the country grow their operations so thank you guys all for being part of the conversation and being with us um a little bit of housekeeping here to reduce your chances of buffering please close any heavy bandwidth using apps that you might have running or things in the background uh most common question we get is will this webinar be recorded can you get a copy of it yes is the answer do you need to ask for it no uh
02:30 - 03:00 so don't worry about that it'll automatically be mailed out to you through email if you've registered for this webinar which you wouldn't be here if you hadn't so you'll get a link don't need to do anything um you don't need to wait till the end to ask questions so if you have questions we're going to be using the Q&A um section which you'll find on the right hand column of your screen you can uh push questions there and we will try to field those towards the end of the webinar we may uh May grab a few of them as we go as well if they're relevant to what we're presenting at
03:00 - 03:30 the time we will run some polls during this webinar we encourage you to participate that's our feedback system to understand what uh you guys think too so we'll push those out and you guys uh have the ability to respond and real time and see what the peers and other people on this webinar think all right so Lighthouse Lab Services just real quick plug for what we do because that's how we keep paying the bills and keep the lights on we are a medical laboratory Consulting and recruiting company we do re revenue cycle management U we build Clea laabs we build 50 plus CLE Labs every
03:30 - 04:00 year uh from scratch and uh we provide the medical directors for a lot of clear Labs through the country on a monthly basis we help validate ldts um get Market access if you're having a hard time getting paid um by commercial payers or getting a network so if there's a issue you're up against in a c clinical lab good chance we have a solution or could point you towards one so keep us in mind for that and we'll give you a chance to ask for assistance if that's something you want to
04:00 - 04:30 connect with us about but we're going to jump in today to the conversation around Ai and healthc care this webinar is probably overdue um everybody was doing talks on AI so I didn't think it was too exciting but it's just uh too big to ignore as well so I wanted to really try to focus it in on uh how AI is going to affect Healthcare but in the laboratory specific so all of our guests have really deep lab knowledge today and so they're going to be able to tie it back and um I think
04:30 - 05:00 it's going to be a little broader than maybe you think you might think that it might affect your technology team I think it's going to affect um probably all all aspects of the laboratory in your company so this is some of the conversation that's happening out there um right now and as you can see as you read through these headlines from you know major major sources um day-to-day you'll read a headline about how AI is going to be a wonderful thing that's going to fix Healthcare and the next headline will be how it's going to destroy health care and um cost you your
05:00 - 05:30 job potentially so a lot of lot of different viewpoints on it maybe all of them could be correct you know there could be a little bit of all these things so we're going to jump into that today and here different perspectives on how AI is going to affect different areas of the laboratory so uh we're going to start with tackling some questions I've got maybe 12 15 different questions um these questions were all generated by uh chat GPT so these are all AI generated questions when
05:30 - 06:00 I asked them how we should talk about uh AI affecting laboratory and Healthcare this is what uh AI came up with so I thought that would be appropriate for the the day so we're gonna we're going to go with that and we're gonna start with our first question is how is AI gonna improve the accuracy of laboratory results and I'm going to direct that one to AB thanks Sean uh so thanks for the introduction and so um you know yeah we uh at K basically kind of Tinker with some of
06:00 - 06:30 these questions and uh think about how um how is that going to change like lab workflows and how is that going to change like reporting and a decent part of that is actually uh can we impact um accuracy of medical records or test results and to what extent so you know um some of things that we um we see really um getting better with AI is I think first and foremost like it it it's
06:30 - 07:00 essentially a um like an assistant quality manager always available for you so and um so it acts like a layer that is always present for um checking qu control of your instruments as well as correlating that with uh lab results and and ultimately kind of you get to understand if uh a particular test
07:00 - 07:30 result is um like abnormal or uh not expected to be uh in the linear range of the instrument so if the instrument kind of doesn't behave correctly um the a systems can learn to identify that and I think in overall it kind of extends to like a greater goal of like being like a layer of error
07:30 - 08:00 Management in some sort so uh it acts like um like it can it can understand um human errors it can understand when results get sort of changed from what the instrument kind of reported versus maybe there's a typing mistake so these sort of things kind of get uh you know you can train AI pretty well um we also believe that it can help you contextualize and correlate test results um so if
08:00 - 08:30 a if a metabolic panel is um having certain set of results and there are other tests which would kind of correlate very differently uh so the AI can really kind of assist or suggest that there is a discrepancy or there is a difference in these two results I think there's a lot of um augmentation that could be done on uh what medical directors use systems or workflow Solutions like ours or you
08:30 - 09:00 know others uh to you know really augmented to a level where the systems ultimately help them make these decisions better and uh we see a tremendous amount of um use cases that I think um uh we are pretty excited to kind of go out and uh test out um you also there's one other use case that that I would like to talk about I think there are the requests we get from some of our customers to
09:00 - 09:30 kind of use these results to add um correlations uh based on Regional or specific geographical uh results of patient so um if um for example how um like you can you can actually look at microbiomes in a particular region and then use that to see if you know this particular test has a very odd
09:30 - 10:00 microbiome detector so um yeah yeah that's all yeah so many exciting parts of that and to think about being able to digest some of those massive amounts of data and find some of that correlation I think the safety net mentality also is something that everybody could get behind that um you know we are prone to making errors as humans and if we can put a safety net behind that to to catch those out liers that seems like something everyone should be able to get get behind um AB
10:00 - 10:30 touched on workflows a little bit uh Jun I wanted to kick over to you what are your thoughts on how AI can help improve workflows in the laboratory yeah uh yeah thanks for having me uh John and uh I actually want to add to what AB was saying as well with regards to test results um if you look at things like reference ranges and uh look at results in general what's determined as a positive versus negative result um those things are all determined with a lot of stats and a lot of
10:30 - 11:00 science uh and the background um but even things like reference ranges I mean at the end of the day and in terms of scientifically if you've chosen for example um demographically you know valid um numbers as as a kind of like guide posts um to determine reference ranges but something like AI um could really help redefine those things based on population data that we have or or even uh things that you know these things are arbitrary uh an AI May kind of resurface uh some questions as
11:00 - 11:30 to how we determine a a test result is a certain outcome uh and so if if you know out of range within range like those are those are things that are arbitrary at the end of the day and so maybe we'll find something different uh with the data that's coming out from AI but um might it make us question why we do what we do a little bit too yeah exactly exactly um in terms of how AI can improve the lab work Flows In general um so I'm not a clinician so you know I I just talk
11:30 - 12:00 touched a little bit about how they can improve uh test resulting uh to some degree um but there's actually a lot of uh I think loow hanging fruit when it comes to especially the clinical lab um pre analytical workflows um that's something that I see a lot of value in for example one of the biggest challenges for all clinical Labs U especially independent reference Labs is getting paid uh and so there's a lot of of uh tools that you know blow hanging fruit that that can be
12:00 - 12:30 solved or or at least improved with AI workflows uh for example pre accessioning uh you have tools like that already exist for example uh like optical character recognition OCR for scanning in insurance cards requisition forms um things like that uh you can do AI for demographics validation um you can do you can use AI for essentially anything that has um that uses statistics uh to to determine an outcome that you can use AI for uh and I think a big part of it really the low
12:30 - 13:00 low hanging fruit here is is uh pre-analytical workflows um getting the requisition or order into into a system like an Lis or getting it getting it out from an EMR um there's a lot of we we see a lot of data issues um for labs uh you know e either you have missing patient demographic data or or or you know Mal malformed patient demographic data or or you have you know you're missing a lot of information that that allows uh the lab to make money and you know improve
13:00 - 13:30 revenues um there's a lot of that going on um and and at the other end the post analytical too um you have a lot of uh tools that are available for post an like revenue cycle for example there's a lot of opportunities to use AI there um so so yeah overall I I think there's just a lot of lwh hang fruit right now uh in terms of workflows it can be done and even in the in the actual lab itself um
13:30 - 14:00 probably there's I think Abby mentioned on it taking QC data U using AI to run QC data from an aggregate perspective that's an opportunity uh a lot of opportunities there as well but um really what I see is the lowest hanging fruits are pre-analytical workflows okay yeah that's interesting smarter more accurate makes makes a lot of sense getting it right on the front end um say we're sold on okay AI is gonna help we should bring it into our workflows we we want to do it uh we signed we signed up with someone to to bring it in houseal what's the challenge of implementing
14:00 - 14:30 it we want to we want to use AI to improve our workflows what what are the obstacles to making that happen right so all the things that you just said about great about AI they true all the terrible things that you've said also true it really depends whether or not we can utilize it right so the way I look at it is AI is like a jet engine right it it can moves your business at the speed of light it can get you to the point 8 be very fast but if you put your engine in your
14:30 - 15:00 lab without appropriate framework for that it's like putting a jet engine on a horse bug you're not going to go anywhere fast you probably will not go anywhere at all right so that's the whole that's the whole thing so um when we when we look at it then sort of can we utilize AI I mean that's a wonderful thing but can you really take advantage of it so from that standpoint the
15:00 - 15:30 framework the other processes the automation that the lab needs to be in place is if we still doing copying and pasting from Excel spre spreadsheets if we still doing manual entries like of a patient data as you know been mentioned before and we're making human mistakes doing that if we downloading and saving files in five different places and trying to reconcile them if we emailing each other you know on on on on the results and patient data then there's real use from that wonderful jet
15:30 - 16:00 engine that we can we can offer and advantages that it it offers so um at lighthouse we serve hundreds of customers we build labs for people right that's what we do and my job uh Frankly Speaking is to make sure that we build the lab we have the infrastructure so we can put the jent engine and we already doing it for some aspects like you know predicting being paid or you know where it can be paid uh but there is much more extension to it uh but first thing first we've
16:00 - 16:30 got to make sure we have an appropriate sort of vehicle to put this jet engine on so we can we can really Propel the business that's basically in a in a in a nutshell that's the problem you have to have you have to put the basics in kind of framework in place to use the AI and take advantage of it Mak since um as we start to think about implementing this I think that there's a natural human tendency towards fear of what does this mean for me is it going to take my job that
16:30 - 17:00 you're talking about AI doing some of these things that I do does that mean that I'm being replaced um how should scientists in the lab maybe even Pathologists think about uh think about that is AI the boogeyman coming for their their role or uh is it going to help them to to you know be able to do their job better anybody want to grab that I'd say it depends it's it's both right if you're
17:00 - 17:30 doing your job well you can really take advantage of a especially like pathologist is shiny prime example uh this if if pathologist I mean they have a tremendously uh challenging job using the incredible cognitive skills you know tens of years of training to find out you know the the malignant patterns but then they have to spend a lot of time quing their reports and you know working diagnoses things that have nothing to do with with their Prime specialty so think about the we're already
17:30 - 18:00 using AI to extract and code the pathology reports it's a tremendous savings but you have to be kind of skilled in understanding how it can how to use it um and so on and so forth though it's a it's a kind of an infinite brain for you that can bring you any cases any patterns all the knowledge in the world but you got to be able to use that knowledge so from that standpoint if you're not capable if you're not keeping up you very well may be impacted negatively by by this development
18:00 - 18:30 however if you if you keep up with your job and kind of stay current with it it's a tremendous help it's it's it's enormous tremendous help so that's my take on that yeah like f said uh go go oh like please so I agree actually what he said like absolutely agree there's tremendous value in terms of how people can use it for day-to-day basis uh everything from like reducing your
18:30 - 19:00 day-to-day work especially the Redundant pieces of your work uh to kind of U helping summarize it maybe create Trends better or create like reports uh like internal lab quality reports or internal lab like um uh end of the day job reports these things could be very well automated by yeah exactly and uh yeah to what everyone was saying I agree with what everyone was
19:00 - 19:30 saying um every time there's been a huge technological shift the internet mobile devices you know all this stuff coming out in the past 20 30 years um if you want to go really far back the Industrial Revolution um and uh anytime there's a huge paradigm shift in technology human beings have been pretty quick to adapt obviously there's people that don't adapt and those people get left behind oftentimes but um you know I think with this um AI is a tool U we don't have generalized AI at all we're not even close to it at this moment
19:30 - 20:00 um I think some people might argue that we are getting closer and closer but um today we're not that close to generalized Ai and so until that happens and uh they take over the world um I think I think we should look at AI as a tool uh in your toolkit and I think it just um it helps to further abstract your job uh so you don't have to do the boring stuff um you can do stuff that's meaningful uh so I think in overall I don't think it poses that much danger uh I mean
20:00 - 20:30 right now there's been a lot of effort made to um made to for example like in other Industries like full self Shing cars for example right um' been working on that for years and years and years but we're not even close to it yet uh and so same thing here in the medical field um it's much more regulated here uh and so I think yeah we're still years and years away from it replacing anyone's job in so far as there's um physical labor needed to perform the job itself yeah I think I think
20:30 - 21:00 you're right I think we're ways out you bring up an interesting point on the the self-driving cars too I was thinking about um this the other day that just the nature of being inside of Health Care has such a high bar and such a low margin for risk tolerance um that I think it's going to be one of the more challenging places for AI to be accepted um whether it's capable or not I think
21:00 - 21:30 self-driving cars probably see that same thing and there's there's also this perception that even if the AI was more accurate and less prone to error than a human I think there's a there's still a resistance to um you know the errors made by a a computer versus by a human and I think we're GNA face that same challenge inside of healthcare when you have people's lives on the other side of these results that even if it's 99.99 uh accurate we're going to focus in on maybe those those few cases
21:30 - 22:00 when something slips through um even though as humans we also have this and so that's H that's a challenge and we'll have to see what what Society deems acceptable in terms of error risk because I don't think anything's ever error free but let's move on to this uh this next slide here um vital I'm going to direct this first question to you how will AI power Technologies impact cost and accessibility for Diagnostic testing for patients everything going to be cheaper if so who benefits
22:00 - 22:30 did the labs pocket the extra margin insurance companies the patient um you know is it gonna get cheaper and if so all of right all the way above right so very dear to my heart this question I think about it every day in what I do um so really the impact that's tremendous different for different type of types of lab though for clinical lab it's a process Improvement the for clinical lab the technology to make to to actually do the clinical test is pretty
22:30 - 23:00 amazing nowadays those tools those instrument as chemistry analyzes they they they fantastic they can do test in a very very quick and economical way so what makes them long and expensive it's all the processes around them that's all the inefficiencies around you know entering patient data validating results QC uh what not all those things so if you strip all that away then you can deliver diagnostic test for clinical lab probably for pennies or dollars on a test no
23:00 - 23:30 problem so the rest of it comes from all the other processes so that's where I can help tremendously to reduce this cost now who will pocket that cost a much bigger discussion we probably need another webinar on that so in the current in the current day um in the current day it seems like it seems like maybe everybody or nobody body but who should who should really pocket the cost is a
23:30 - 24:00 patient in my opinion so the system needs to move to a different models that really really allow the patient to be benefiting from smart decisions about Healthcare as I said very different very different but that's when maybe I can help maybe I can help patients to be smart about their health decisions and they test and so and so on so forth so that's for clinical lab for Pathology Lab it's very different but still pretty tremendous impact so for pathologist the problem is that the great
24:00 - 24:30 at what we do we have shortage of them we have shortage of good Pathologists in a country in in the world for that matter so that's why I can help a free out pathologist from some things that not productive for them like you know coding and and working with reports and and those type of things but also create this extended brain with the world knowledge of pathology that can that that they can use at the fingertips and actually really really improve the quality and ability of pathology Diagnostics around the world much kind of higher sort of road to travel uh
24:30 - 25:00 again because the the medical or says do not harm so that's the kind of the whole medical mentality hinges around that uh so that's really has to be proven that does not harm first before anything else uh but still um the fundament the impact is tremendous of mainly from accessibility standpoint for pathologist because the human still has to be involved it's a full medical do so I wouldn't say the cost should be a major factor but quality and accessibility should for pathology that's that's
25:00 - 25:30 my take on that yeah I think um I read a quote by Gary Kasparov who would play chess against deep blue and there was always this conversation about what will be smarter who will win the computer or the human and what he said which I thought was interesting is you know it might go back and forth whether a human will beat a computer computer beat a human but um a human working with a computer
25:30 - 26:00 will win every time right so when when AI is used to augment and paired with a human brain um really big things happen and uh that's probably where the the rocket fuel is when you mix those two things so that that's a good Bridge into our next question for Jun um how will it help staff like it will it help with the training we've got this shortage going on we don't have enough scientis well it helps you know people be more productive they can get to more um what are your thoughts on how AI might augment or assist with laboratory Staffing shortage or just the lab tech themselves
26:00 - 26:30 yeah I think it's as far as the the lab job of a lab tech I don't know if it changes all that much because even if a lab lab tech is actually working in the lab with the AI tools um it's so abstracted at that point um so oftentimes you may not even know that you're using AI in a lot of cases uh but yeah it's very common in in I get we call it human in the loop that's what we call it um where in Industries where it's a high risk or have a lot of liability uh you use AI like
26:30 - 27:00 John said you pair it with the human that has expertise in that space uh and then you get the best of both worlds uh right now so that's already going on with pathology um you're seeing some softwares out there that that are I think procia is doing that um with with some ai ai power pathology tools um the market but um yeah so in terms of um how can labs more better implement uh AI I think really it comes down to understanding uh what AI can and can't
27:00 - 27:30 do um and so I think there's a lot of confusion when I talk to folks about what it is and isn't um especially what it isn't uh and so what AI is right now is is a probabilistic model it's not a it's not some kind of deterministic algorithm that that tells you exactly what to do every single time uh it just it just gives you Based on data uh a probabilistic uh outcome that's really what it gives you and so you can't just kind of uh there there's a phrase when people say when
27:30 - 28:00 all you have is a hammer everything looks like a nail and so you really really want to avoid that I think um and so it's not some kind of Panacea and so I think you as as a lab Lab person or lab owner executive you have to have a really clear understanding of value um it's like what are you trying to do with the AI Tool uh and what are the most important use cases that we're trying to get done and so I think those are the questions that you have to ask yourself um when you're trying to
28:00 - 28:30 implement any kind of uh tool for training staff or optimizing workflows it's like what what are the outcomes you're looking for um and then when you find a tool that that because there's G to be a lot of tools out there that come out in the next couple years uh with you AI tools that come out um understanding okay like maybe we shouldn't throw the whole kitchen sink at this thing um maybe we shouldn't maybe we should iterate one step at a time and implement and um take small steps towards
28:30 - 29:00 adopting AI uh I think that's probably the right approach uh and so for example even today um you know we talk a lot about Ai and the implications of AI um but I don't know how many people on this webinar today have actually implemented or at least used chat GPT once um and so I think taking baby steps and hey just chat try chat GPT once and see how you like it and see how that affects your potentially affects your workflows I think you start with that and then you can take other baby
29:00 - 29:30 steps or maybe integrate something other other workflow or I don't know use it for marketing purposes or something like that and then kind of get your feet wet that way um I think trying to come up with a grand like corporate uh what's your AI strategy uh kind of a master plan is not the best uh plan for kind of implementing the AI tools that are available yeah I think you're right taking it in bite-sized pieces I'm G to bring us to the results of a poll that we ran um how close
29:30 - 30:00 do you think your lab is to implementing AI tools and we had uh 24% of people said a long way years um 31% said we're close maybe months um another 30% said we're already using it and um 15% said no plans at this time not sure so mixed bag but there's it's out there we got at least a third of people already using it or third of labs rather and another third um on Deck so um not not too far out mons we're saying um AB I'm going to go to you with this next question how does it affect the
30:00 - 30:30 patient we've talked about the business side of the lab how it's going to affect the scientists how it might affect our profitability as a lab owner um what about the patient personalized medicine does uh does medicine get better I think right now Us's Health Care outcomes are pretty pitiful especially for what we spend are we going to start closing that Gap and uh really seeing the patient benefit or what are your thoughts on that yeah I think you know we all have been
30:30 - 31:00 like um like trying to take like steps towards uh having like personalized Healthcare and I think that has been in discussion for like a decade now uh but I think AI is that technology that really brings us closer uh without having to make it too complicated um um I I see a lot of use cases on um like um helping patients like really understand the results uh better uh and also you know getting
31:00 - 31:30 a sort of um like a virtual consultation on their results um maybe extending it to like helping them decide what sort of tests they should do on a routine basis uh but you know that really depends on what state and you know what sort of uh insurance you have we also do see um like big
31:30 - 32:00 relevance of like uh personalization happening uh on the uh bailing side where uh patients can not only understand uh their qualifications of their insurance better but also you know um understand what what sort of test or what sort of costs they would be incurring and you know just get a general sense of of um you know what what is it going to look like from a uh like an xense or Healthcare
32:00 - 32:30 cost time Point um so there are there are a couple of areas that I think the most exciting area that we find for personalized medicine really is like helping patient understand those tests better and like kind of elaborating more on these areas uh but you know there are these other set of like scenarios that you know if it works out it'll be it'll be crazy sure yeah I think um there's
32:30 - 33:00 so much data out there and maybe some of these correlations might be a big part of that um kind of brings us into our next question and I've evolved in my thinking over this over the last couple years um I've always been an advocate for the value of labs inside of medicine you know everybody knows that quote that's probably overly quoted uh 75% of medical decisions are uh based on laboratory data I thought great AI uses tons of data it needs data that's the gas
33:00 - 33:30 and the fuel for AI we produce it as the lab this is our moment we get to step into the spotlight here and really see the value we drive because uh laboratory currently only uses I think it's like 2.6% of healthc care spend goes to the lab and we're producing 75% of the data behind the decisions I thought this is this is it this is AI is going to be the thing that really um puts us uh puts us in the Showcase light um more recently I've started to think that that data that we've
33:30 - 34:00 produced May uh may just get digested used by others I don't know I can't see a clear path to how the laboratory benefits maybe the type of data isn't the type that um really drives AI so I'm gonna kick it over to Jun because we had a little bit of conversation about this that I thought was enlightening on the difference between maybe structured data and unstructured and some of the more valuable types of data for um for AI how how does the lab benefit or does
34:00 - 34:30 it um from ai's appetite for data yeah so um as a lot of people know uh I think there's tremendous demand for dat like diagnostic data that comes out of the lab uh you know a lot of biofarma companies purchase that um they can't seem to get enough of it uh and you have entire companies uh for example flat iron Health uh they have the the enle EMR and they had a huge data play on the oncology
34:30 - 35:00 space uh and they sold the data to biopharma and whatnot and um and so there's we do know there's a huge demand for that and and that labs are the source of that data so that theoretically theoretically you should be able to get uh some kind of monetary value from the data itself um as it pertains to a use cases in Ai and whatnot um so far it hasn't really materialized the the way that I think a lot of people expected it to um even the lab is the source of the data and the lab is the one driving so much decisions um by the way John I asked my wife about that that
35:00 - 35:30 75% thing she thinks it's more like 90% of decis she's a physician so she's telling me maybe it's more like 90% because I I I look at quantitative data all the time uh but yeah so anyways um uh yeah there's there definitely an appetite for it but so far it hasn't really materialized because you see a lot of the software the data resellers and people that aggregate data um they're getting the data from the EMR uh the patient chart which then combines the lab data with the all the other
35:30 - 36:00 uh encounter data from The Physician and so they're selling all those all the uh kind of combined data to the biopharma companies out there that want the data and so from a kind of the perspective of the lab um it's been a little limited but I'm not I don't think it's necessarily tapped out in terms of opportunity because the lab has information uh diagnostic information that perhaps the the emrs don't have U maybe it has you know assay related information that
36:00 - 36:30 matters U maybe there's QC data that matters maybe there's more uh maybe the lab is actually a better aggregator of overall um patient uh data overall like uh qualitative and nonquant qualitative um for example like as we get into genomic sequencing um right now like people have talked about okay how do we integrate genomics testing into the EMR right now it's really hard to um so maybe there's
36:30 - 37:00 applications in you know NGS that pertains to uh you know being able to sell I mean and it's very sensitive topic because obviously genetic data is uh very sensitive but um there could be opportunities there for the lab uh when they get into more and more esoteric or high complexity testing uh but certainly I think there there is still value that that we not may not be seeing right now uh to be able to capture value from the data that that the lab produce every day and I
37:00 - 37:30 think it was helpful for me when I spoke to you about the fact that like a a lab value if that's the data um maybe AI is not really needed in order to run a formula right like we've been able to do that with spreadsheets or whatever for a very very long time and so um just having numbers um probably isn't the type of data that's really going to be monetized it needs to be whether it's imagery or it's non-structured or it's um different types of data being bundled together through the EMR I think that's where really the value starts to get unlocked and uh good
37:30 - 38:00 for us to think about I know I talk to lab owners regularly that have a a data play where they're going to sell their data and it normally means I have a bunch of lab values with decimal points and I'm hoping somebody pays me for those um and I don't I don't know if that paycheck's coming um so starting to think about ways to u to put your data in a a format that's going to be more valuable by pairing it with something else or maybe it's some of that imagery data for those groups that are doing AP um I think 23 and me was the one that everybody was pointing to as they they had
38:00 - 38:30 made this big data play and it's not not playing out real well for them recently if anybody's been following that in the news so um okay we're gonna move over to our last slide just in the respect of time um and the first question is going to go to Abby uh what steps are being taken to ensure that the AI algorithms used in diagnostic testing are transparent unbiased clinical validated how do you make sure that there's not this uh you hear people talk about it this you know bias that's built into
38:30 - 39:00 the training set that carries over and has maybe bad uh outcomes for patients yeah joh um this is a question that I think keeps all of us up at night and I think you know that really ensures that whatever these Solutions get buil uh you kind of make them uh as unbiased as possible and as validated as possible um so one of the ways I think um you know really make sense is one to have
39:00 - 39:30 a feedback loop in place so uh you have um like any sort of AI suggestion or an action kind of has a sort of um like a thumbs up thumbs down at the least which kind of factors in a minimal feedback loop at at least on top of that um you could offer like flagging Solutions where people can really
39:30 - 40:00 flag unexpected uh scenarios and ultimately I think um the more relevant training data that you can actually feed into um the the more like closer to expectation the AI system would be so um I think we really believe that um at the stage it is today uh AI solution should be sort
40:00 - 40:30 of suggesting or sort of aiding or guiding and not really performing the action itself uh so I think that would be the right sweet spot where you kind of still get the benefit of um like a more quicker workflow but um still are cautious on deciding when um to let AI do it automatically yeah
40:30 - 41:00 absolutely um that those ethical questions lead us into kind of regulatory um the groups that are going to be responsible for that um I'm going to direct this question to Vitali what do Regulators whether it's uh CMS or FDA who knows who it's going to be these days what do they need to think about as they try to write the rules for this I know we're already working with several uh liquid biops companies that have ai algorithms built into how they're um reporting out their test results a
41:00 - 41:30 little bit of the Wild West right now certainly wasn't contemplated in 1988 when the most recent set of clear regulations was written um what are the what are the themes and topics that Regulators should be thinking about as they try to draft the rules of the game for using AI in the lab yeah that's a billion dollar question right um so the thing is that Healthcare Market is La Market is not Market in a true sense it's not driven by market forces it's driven by regulations uh makes
41:30 - 42:00 it special for better for worse um and um those Regulators in my humble opinion are hopelessly outdated hopelessly outdated by decades um and they driven by best intentions patient safety right kind of do not harm that's the that's the rule number one but but they have to come up with the ways to actually catch up with the uh with the world and with the current current state of
42:00 - 42:30 affairs so regulations that put in place they mean well they want to protect the patients but they provide tremendous hindrance to the development to to the progress so um how that should change it's really up to them but that's where so basically the regulations need to change to be conducive to a um kind of quick development without compromising safety it's I know it's a to order
42:30 - 43:00 but um if we do not make progress there we will be lagging behind and um probably even more so and that actually affects patient safety and patients outcome uh to start with so that's a that's a very very big question that The Regulators have to be facing nowadays yeah if anything I think that the risks are are pretty well flagged in terms of uh antennas are up people are concerned you know
43:00 - 43:30 what how how might this negatively impact things maybe even to the too much so so that we we lose out right I was listening exactly you kind of throwing in the baby with a buff up water you know that's right there's there's like um some therapy tools that are AI based like um for behavioral therapy like what you would see with counseling that um you know completely I AI back and could provide you know services to people that can't afford to go speak to a therapist
43:30 - 44:00 and this is just like analogy or kind of like a parallel type offering but I could see that inside of healthcare too I heard of optometrist recently where there was a AI back tool that could do the job almost as good as an optometrist and the optometrist themselves said well it's it's about like 97% as good as the human um so they didn't see real value in it but it could be used to reach groups that could never go I doctor right and so how do we understand those risks um
44:00 - 44:30 but not also be so scared of them that we we just allow people yeah and also and also those outdated processes make the make the health care and lab specifically very more way more expensive than it could be it's another thing it adds the cost yes sometimes tremendous cost Jun your thoughts potential limitations and risks associated with this what how should we be thinking about it should we be thinking about backing down kind of the standard that we hold it to so we can reach
44:30 - 45:00 more people for the greater good um holding the line make tightening it up what are your thoughts in terms of how we should be thinking about Ai and Healthcare yeah uh as Vali said uh Regulators just by nature of their job uh are going to be hopelessly outdated in terms of how they think about AI um they're already somewhat outdated in terms of how they think about Cloud Technologies uh and modern software in general or Hardware in general um but again it's like Bal said it's not it's not because they're stupid it's because they have good intentions that that
45:00 - 45:30 they're conservative about it um I would say that some of the things that are inherent to AI make it really uh risky uh in terms of use cases and clinical Diagnostics uh the biggest one is that AI tends to be a black box uh meaning that uh you get you put in one input it's not always going to give you the same output um and so so what that means is that when something goes wrong in AI
45:30 - 46:00 applications um now you don't really know why it goes wrong just like if you have a self-checking car takes a left turn out of out of the blue you don't really know why until you do a postmortem uh and so that's one of the the limitations and risk associated relying heavily on AI um obviously like like I said earlier um when all you have is a AI Hammer uh everything might look like a nail uh so you have to be careful with that um but I think the as long as folks adopt AI tools um where it's
46:00 - 46:30 less risky um you know like you don't want to be messing around with AI uh all the time for uh test resulting and whatnot um but if you're doing it for something a little less little little bit less risky I think um it it der risks the entire thing a little bit um and obviously right now um there's a lot of uh Innovation happening in in the field uh right right now and so a lot of the limitations
46:30 - 47:00 that are current U might go away in a couple years uh and so I think it's it's really important to continue iterating uh on how our understanding of AI goes and um yeah I think right now though that there is risk in there's risk in kind of understanding having to understand that the AI is a black box and when when it goes wrong you don't know why yeah yeah as we clinically validate tests in the lab ldts in particular we're we're running you know uh large batches of samples to make sure
47:00 - 47:30 that we get the outcome that we expect to get consistently under different standards the the sample's been sitting on the shelf for x amount of time whatever it is um do we consistently get the result that we're planning and I think that um as you implement AI into your laboratory workflows especially the clinical diagnostic side at a minimum it should be part of that validation pipeline right and we might might not fully understand why we're not getting the
47:30 - 48:00 result but at least we can we can test that um with a a significant end to to say do we as we put things in one end to the pipe do they come out as expected on the other end um is a is a minimal level of uh of validation that I think we need to do inside the cleab environment um so moving this is just kind of looking ahead um clinical lab versus anatomic lab um where do we think that it's going to have greater impact anyone want to opine on which of these is going to change
48:00 - 48:30 first right the answer well obviously answer is both right as we talked about answer is answer is both in different ways and there's a different kind of hurdles to cross for the lab for clinical lab I I think that's more attainable actually uh because it's in the lab's hand I mean really if lbs catch up with the technology progress in and kind of improve the processes they can really even today they can they can take advantage of of of latest
48:30 - 49:00 AI um so by simplifying simplifying the process making them more efficient and all all those good things getting better paid um but for pathology it's a big big promise uh but it's a longer uh Longer Road uh to success because that's where it needs to be cly approved and and accepted and uh weaved into the pathologist training and kind of uh workflows and those people don't change very
49:00 - 49:30 easily I mean I worked with Pathologists I mean they've been trained for 10 20 years and you know they they stick to the ways um so that will be a much kind of Longer Road um to to to to to the benefits but again potential potentially tremendous tremendous U tremendous benefits for both of those sure um yeah I think the in my mind at least the the imagery associated with ap seems
49:30 - 50:00 to be something that we previously hadn't been able to really use use our systems to to benefit and the idea of being able to look at millions of images um be smar because of but that's why it's also kind of coming back to that risks AI has an inheritant problem very inheritant problem it's trained on existing data so it can provide the data for you it can come up with with even
50:00 - 50:30 generative right based on existing data but by definition it's not going to give you answers if the answers have been known before so that just it's just the way and we have to understand it so that's a that's a major risk right if that's I'm using a simple example if you are um if you're are kind of training a child and AI is basically child without content if you're training Ai and
50:30 - 51:00 saying this is a picture of a dog this is picture of a cat this is picture of a dog this is picture of a cat child will learn that very well but then you show a picture of a tiger and child will say what it's a cat because it hasn't it hasn't seen tiger before so that's so that something we have to understand about fundamentally right that's what that's that's where we have to we have to be very very careful about it uh in terms of getting getting the benefits yeah as a lighthouse we do
51:00 - 51:30 a lot of recruiting and I think believe it was Google you guys can fact check me if I'm wrong but um produced a resume screening tool that would replace a recruiter right it's going to look and it's going to tell who's qualified and it's going to be able to just utilize uh AI to source resumés and make sure that people qualified and then as they deployed it they realized that it was fairly sexist and racist because it felt like Executives should be maybe white males set
51:30 - 52:00 it had looked at was that was what was out there right that was was in the roles and so as it tried to find lookalikes um there was that bias and so that they caught it they unplugged it they didn't roll it out as they saw it but a great example of maybe it doesn't never seen cat or tiger before and so it doesn't know that exactly it is so um I'm G to move us over to the the Q&A from uh participants to see if we can get a couple of those in we're coming up on the hour so we get about seven minutes left I'd love to tackle a couple questions I see multiple in here that are
52:00 - 52:30 intrigued by the pre-analytical looks like you know that that comment jiun about how how do we use AI to maybe uh help in the pre-analytical processes is something that people are finding intriguing can you talk about that anymore or give an application of what that might look like um to use AI on pre yeah yeah uh so I think uh one of the easy kind of uh there's a lot of lwh hanging fruit is is what I would say because we always say at least in the revenue cycle space
52:30 - 53:00 people say garbage in garbage out uh and so a lot of the bad data frankly comes from Lis uh in the lab space and so uh if you can optimize the data coming in correct and and correct for the missing data or incorrect data um that can really help and so what I mean by that is is um there's multiple sources of of data coming in to to the laboratory via Lis um whether it's EMR whether it's a portal of some sort whether it's a manual requisitions um there's the kind of obvious
53:00 - 53:30 uh applications for AI or or are um demographics demographics cleanup um and documentation uh and and when you have documentation you can when you scan in let's say a requisition form or scan in like an insurance card um there's tools out there nowadays where you can scan the insurance card and then it will take the text uh from the insurance card and then transcribe it into form and then it saves data um I I've asked around and and almost nobody's adopted this in in in actuality um just
53:30 - 54:00 because people don't know about it and uh when people are told about it they pretend like it's black magic uh and I'm like well that's that's AI for you right there um this we call it OCR or optical character recognition um there's also you know things like uh demographics checking for example that's that's not when you get that when you have a person's name and it's misspelled um sometimes you could run an AI algorithm or something like that to maybe spell it correctly or
54:00 - 54:30 autofill addresses or autofill information about the person that may not be possible by human being um there's also on the other side of things uh post analytical side of things too there's a lot of applications there but yeah I think I think the ones I just mentioned are probably the most obvious use cases for AI um and that's applicable today uh not 5 years from now you can do it today uh and so those are the ones I'll I'll bring up excellent um jump to another question um here's
54:30 - 55:00 kind of a specific case but maybe we can broaden it to um to have more application um Brian conl is saying hey when we use our T can systems common problem is either the tip can't reach the liquid because the liquid's too low or goes too far down and hits the bottom of the plate can AI help is this a is this a application for AI to be able to come in and determine how far the tip is
55:00 - 55:30 going into aload out of a sample can it be add Dynamic ability to come of some of our existing systems I think the answer is I mean in some cases it can but I think in this this problem that you have you might want to talk to the tan person get that result um no I think it is it is representative of something that we kind of work with systems that tend to be pretty well
55:30 - 56:00 coded in a finite way static way like this is what it does every time and there probably is a an evolution into it being more Dynamic at some point to be able to look at what it's dealing with and adapt probably better than older system have been but probably probably a littleit ways out um going through here think about billing applications uh how practical affordable are these tools for like a smaller lab right it seems like AI That's great might help Lab Core Quest or
56:00 - 56:30 a big Health System but what about what about my small lab is this going to be something that's in the nor possibilities for me to reach yeah I can I can definitely speak to that I mean we able to pack even today we able to package it as a kind of plug andplay solution for everybody La big or small we basically what we do we can we can tap into the current flow of your normal document that you exchange with the payers so that's actually one area that's standardized as a federal Hippa law that requires everybody to send they claim insurance information and payment information
56:30 - 57:00 exactly the same way which makes it very very efficient for us we can just kind of tap into that tap into that flow and it's really Plug and Play Big Small doesn't matter there is like pretty much zero uh zero effort to do it and here we go you can you can have ai question is can you take advantage of do you have a work process to sort of pick up up and and you know follow up with it and actually uh do something with it so that being actionable that's the question the fact that
57:00 - 57:30 we can do it today absolutely yes for everybody that's not that's not a sort of technical issue today thank you uh I will um go ahead and close us out here I want to thank ABI Vali Jun appreciate your time your thoughts uh your inputs on this thank you all for attending um we we really appreciate it we will have an upcoming webinar our next one I think we're going to do sort of an ad hoc one whenever the FDA ldt announcement drops we're going to be
57:30 - 58:00 uh following that a day or two later digest that information as quick as possible and bring you some of the best legal Minds in the business on what that means for your lab so we're watching that closely keep an eye out for um notices on that but thank you guys all for attending uh thank you to our our guest speakers really appreciate it thank you very much John