2841: Leading the Leap in Healthcare Efficiency Through AI

[00:00:00] Have you ever wondered how technology can streamline healthcare operations, making hospitals

[00:00:07] more efficient and ultimately improve patient care? Well today I want to venture into the

[00:00:13] world of AI and lean principles in healthcare with the founder and CEO of a company called

[00:00:21] LeanTas. His name is Mauer and today he's going to show his journey from creating the

[00:00:27] Lean Service Operations Practice, talk about his time at McKinsey & Company to revolutionising

[00:00:33] healthcare operations with LeanTas and also provide a fascinating insight into the marriage

[00:00:39] of technology and healthcare. I also want to learn more about how LeanTas is standing

[00:00:44] at the forefront of this integration, offering AI-powered and SaaS-based solutions that

[00:00:50] significantly can enhance capacity management, staffing and patient flow across numerous health

[00:00:57] systems. I will also of course delve into Mohan's story and the innovative solutions

[00:01:01] that LeanTas provides. But before we get today's guest on, it's time for a quick shout out to

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[00:01:43] held as the most secure MFT on the market. So once again, kiteworks.com and now let's get

[00:01:50] today's guest on. So buckle up and hold on tight as I beam your ears all the way to the US

[00:01:56] where we're going to explore the impact of AI and healthcare operations, the challenges overcome

[00:02:01] and the future of efficient care delivery. So a massive warm welcome to the show. Can you tell

[00:02:09] if I've only seen a little about who you are and what you do? I'm Elias Gregg to be here.

[00:02:15] Mohan Girdardas, I'm the founder and CEO of LeanTas. LeanTas is a predictive and prescriptive

[00:02:21] analytics company. We use crazy math data science, AI and machine learning to unlock capacity

[00:02:30] in the most important assets in a health system operating rooms in patient beds, infusion

[00:02:36] chairs, etc. Prior to starting LeanTas, I spent 18 years at McKinsey and I ran McKinsey's

[00:02:43] Lean manufacturing and Lean service operations practices both in the US and in the Asia Pacific

[00:02:49] region. And the whole vision around LeanTas was Lean transformation as a service. Imagine delivering

[00:02:57] operational excellence but rather than using excel-level math and consultants on the ground using

[00:03:04] sophisticated AI and ML and a software as a service platform to deliver it. And so that's how

[00:03:11] LeanTas was born. The incredible story, there's so much to unpack there and as you said,

[00:03:17] LeanTas has been at the forefront of transforming healthcare operations through AI and lean

[00:03:22] print supports for a long time now but I also like to dig a little bit deeper on the origin

[00:03:27] story and the story of my guests there and the journey that put them on this path. You mentioned

[00:03:33] about McKinsey and your former life so can you share any insights on maybe a pivotal moment or

[00:03:39] challenge that just spot the inception of LeanTas and how your journey at McKinsey maybe

[00:03:46] influenced your approach to solving some of those inefficiencies in healthcare. I suppose what

[00:03:51] I'm trying to say, there's got to be a story there right now there is the many stories so

[00:03:56] I got to work in the most sophisticated service industries driving Lean transformation during

[00:04:03] my McKinsey days. So for instance, I spent five years inside Delta Airlines doing everything from

[00:04:09] engine maintenance to procurement to fuel to route planning to yield management etc so it

[00:04:17] worked on a bunch of things. Worked with UPS, wore the brown uniform, rode the truck, saw how they

[00:04:24] run their sorting hubs in the middle of the night so these are very sophisticated asset-intensive

[00:04:30] service businesses and there's a lot to unpack there so let me take a crack at it.

[00:04:36] In a manufacturing business you can hide operational inefficiencies so for instance if I'm making

[00:04:43] widgets and running a manufacturing plant out some way far away and you place an order for a

[00:04:50] thousand parts I could be running the worst production environment in the world but I can meet

[00:04:55] my shipment by just pulling a thousand pieces from inventory and shipping it to you so you don't

[00:05:00] know that I'm running a bad manufacturing plant because every time you place some order you get

[00:05:05] your shipment. In a service business, you cannot do that because there's nothing to inventory are

[00:05:12] you going to inventory the doctor or the patient which one's going to go in the freezer neither can

[00:05:16] so in a service business you cannot hide inefficiency as easily as you could in a manufacturing

[00:05:22] business or in a supply chain business and so this was the aha for me on saying there is a

[00:05:29] way to think about it and so I stepped back and said what were the problems in delivering a

[00:05:37] consulting service to do lean transformations and it boiled down to two. They invariably use

[00:05:43] Excel level math. Excel math is middle school math if you use middle school math you'll get middle

[00:05:49] school answers and operational problems are very sophisticated then non-linear they're hard to predict

[00:05:56] things like that and the second was the reliant on consultancy, reliance on consultants whether

[00:06:02] they are in house or outside actually ends up you know backtracking on everything you did so for

[00:06:11] instance consultants don't have a day job they have a project job and so during the project they

[00:06:16] work on the flock on Gantt charts and lists and analytics and to-dos and follow ups and syndication

[00:06:23] etc at some point they go away the internal project team rolls away or the external team goes away

[00:06:29] and the senior management team doesn't have the time or the bandwidth to do all of that

[00:06:33] and so the vision around lean task was imagine if we could deliver a lean transformation

[00:06:39] replace Excel math with sophisticated math and replace the wise advice of a consultant with a

[00:06:45] recommendation engine if Amazon can whisper the right product for you to buy and Netflix can tell you

[00:06:51] the right movie to see why can't we offer up the right advice to the front line and so that was

[00:06:57] the vision of lean task I was so confident I walked away from a senior partner job at McKinsey

[00:07:03] to go started when up and down sandhill met with 20 venture capitalists none of them would fund it

[00:07:10] because they just they just didn't fund it they all took a first meeting they said it was great

[00:07:14] they wouldn't fund it I also convinced I said fine I'll fund it myself and so I set aside a little

[00:07:21] bit of money and funded the first six months on my own but I called them back and said you guys

[00:07:26] why didn't you fund it and they said well we think you'll end up building a tech enabled consulting

[00:07:31] business and that's not fundable we don't think you'll build a product business

[00:07:37] said fine but anyway I got and started I then got friends and family to invest I got 23 friends

[00:07:45] to put in anywhere from $50,000 to $100,000 so raised about a million and a half they're all

[00:07:53] very happy by the way because on the last funding round they all got to exit and between 25 and 40

[00:07:59] times their money back so I've got a lot of very good friends who are very pleased with their seed

[00:08:04] investment and then we we launched an interestingly meal our initial launch was not in any one industry

[00:08:13] we were in 10 different industries applying this concept so Google was a client flexronics was a

[00:08:17] client Clorox is a client Home Depot was a client across the board what we built in a common way

[00:08:24] was absorb a large chunk of data do intelligent operational algorithms on it and deliver the

[00:08:32] solution through a web based app and make the code increasingly common across these use cases

[00:08:39] so we had 90% code based commonality across wildly diverse problem sets so that was the

[00:08:48] origin story wow what an incredible story now love how you got your friends and family and for

[00:08:54] what are they must have felt like a risk you move at the time if it weathered the other way then

[00:08:59] you could have lost all your friends and family but everyone is so happy now with that at 10-20

[00:09:04] time exit fee of course it's funny I did think about that a lot and a funny story on as a side

[00:09:14] I once had a friend who was running an investment boutique kind of a thing and one of his funds

[00:09:22] went belly up and so a lot of people lost money and he would always cringe when people saw him

[00:09:30] out at a restaurant or something because he knew he'd lost people's money and yet he's out

[00:09:35] you know eating at a restaurant living his life basically yes so when I got my friends and family

[00:09:40] to put money I wanted that to never happen so I laid down two ground rules I didn't have a

[00:09:47] business plan when they were putting in their money so the two ground rules I said were

[00:09:52] I'll do this full time for at least three years so I don't take your money and then say oops

[00:09:55] I've got a job somewhere and I'm gone and two until we have revenue I won't take a salary

[00:10:01] because I didn't want people to say I've taken their money and I'm out eating at a restaurant with

[00:10:06] their money so I'm not taking a salary on three revenues which means if I'm eating or I'm eating

[00:10:12] on my own savings so no one can be any before that all right we've lost two ground rules we got

[00:10:18] we raised 1.35 million in two weeks amazing that worked out pretty well and first forward to 2024

[00:10:25] and Lane Tass has been recognised for you it's excellence in capacity optimization management

[00:10:32] I've got to ask everyone's going crazy about AI at the moment but how do you see AI

[00:10:36] further evolving to address some of the complex challenges in healthcare specifically as well

[00:10:41] what are the next frontiers in AI that you believe could significantly enhance patient care and

[00:10:47] operational efficiency because we hear about the height but it's solving real world problems

[00:10:51] that's the exciting stuff right it is so in our word we clearly distinguish between

[00:10:56] clinical AI and operational AI right so clinical AI is where you're using AI to make clinical

[00:11:03] judgments it could be as common as we see reading a radiology scan and identifying patterns or

[00:11:11] analysing blood work or you know doing those sorts of things but it could be all the way to

[00:11:15] diagnosis and you know recommended treatment paths and so on that's what we don't do so we're

[00:11:21] right here we don't do clinical AI because if you go down the clinical AI path you'd better have

[00:11:26] a team of trained clinicians who can you know work through this right we focus on operational AI

[00:11:34] which means make better operational decisions and that we feel is probably

[00:11:41] closer in than true clinical AI so true clinical AI is a bit of further out in our mind

[00:11:50] right on operation AI even then you have to be very very careful because you can't afford to get

[00:11:56] anything wrong right the and the very nature of AI is learn learn from mistakes well nobody wants

[00:12:04] to be the mistake right so I'm acutely conscious in a self-driving car that I don't want to be the

[00:12:11] mistake that the algorithm learns from because I'll be wrapped around the tree while the algorithm got

[00:12:15] better for the next guy so so we're very conscious about being thoughtful about how we use AI

[00:12:23] so in our mind we we have lots of analytics we have lots of recommendation engines a way to put a

[00:12:29] patient which surgery block to take away from which surgeon which other surgeon should you offer

[00:12:34] it to etc so we think about AI in sort of three modes one is the lean forward mode where someone's

[00:12:42] got an idea and says hey tell me about which surgeon could afford to give up time tomorrow or

[00:12:49] on where on when's this right that's a lean forward you've got a specific question you just don't know

[00:12:55] how to dig through all the various EHR records or all the various analytic dashboards to assemble an

[00:13:01] intelligent point of view but you're leaning forward because you have a very specific question

[00:13:06] that you want the AI to answer for you so that's kind of one mode the other mode is lean back

[00:13:12] I don't know what I don't know tell me a problem I should know about

[00:13:17] for instance next Wednesday between 12 and three unit three is going to be understaffed by three

[00:13:22] nurses you didn't know that I'm telling you that now so you can get up and do something about it

[00:13:28] right so that's the lean back think of that as the cobalan of you go to bed at night your alarm system

[00:13:34] will tell you somebody popped a window broke open a door right but you go to sleep saying I'm not

[00:13:40] going to wake up every five minutes and check all my doors and windows at some point something pops

[00:13:45] I'll know about but you've got the security of leaning back and saying yeah I'll get to it when

[00:13:49] someone tells me what to do so that's the second mode AI can be very helpful it just it's continuously

[00:13:56] scanning the surface of what needs to be addressed and bringing to you what is interesting to you

[00:14:03] relevant to your role relevant to what you've indicated interested and the third mode is kind of in

[00:14:09] the moment problem solving something's gone wrong what should I do right what's the next best answer

[00:14:16] so I'm facing a traffic jam what's the new route I should take we are used to ways telling us that

[00:14:22] we used to google maps telling us that we suddenly hit a wall of traffic on the freeway

[00:14:27] we don't know where we are but the nav system without batting an eye it tells you get off your do this

[00:14:32] do that and you and you're back on track so just like we can accept problem solving from an inanimate

[00:14:39] object in the car we can accept problem solving for a real-life healthcare operational issue so

[00:14:44] that's kind of how we think about the modes in which AI can work. Before you came out of the

[00:14:52] podcast today I was doing a little research on your guys I was reading about your recent launch

[00:14:57] of the perioptive transformation as a service which also seems to mark a significant milestone in

[00:15:03] guaranteeing hospital revenue and profitability solving real problems again for the healthcare

[00:15:08] industry so can you tell me a bit more about what makes this service a little bit different maybe

[00:15:13] stand out in the market and what are some of the key factors that in should all its success and

[00:15:19] reliability for hospitals because I'm very tight ship said aren't you have us you're

[00:15:25] um transformation is a service has always been in our DNA right we name the company

[00:15:30] as so lean transformation is a service we will always delivering it quietly but we just stepped up

[00:15:37] how we are actually talking about it because here's the reality if you throw a software over

[00:15:44] the wall at someone the chances are they won't get the full value out of it right I mean

[00:15:49] we all use 5% of Microsoft word and probably 10% of PowerPoint I mean there's a whole bunch of

[00:15:54] capabilities you never use and so that's the reality you cannot just throw software over the wall

[00:15:59] yeah you have to put into it the change management so that people start to use it well people start

[00:16:06] to see the benefit of what they're doing so with every one of our deployments we dedicate a team

[00:16:13] to each customer and say this is your team this is your go forward team and they will be there side by

[00:16:20] side with you at governance meetings at you know allocations at whenever you've got decisions

[00:16:25] to make you can lean on the steep we don't charge separately for it it's it's an all you can eat

[00:16:29] salad bar our services is built in into ensuring you get success out of it and so early on

[00:16:37] we said this is new and different for health systems we need to make it easy for them so we

[00:16:43] guaranteed it from the very inception we guaranteed our products where we said for six months after

[00:16:49] you sign up if you don't feel you're getting the value just tell us we'll go away and we'll refund

[00:16:54] every nickel you pay us and after that we don't confuse customers with hostages so you're not

[00:17:01] locked into a three-year contract with no exit ramp you can opt out whenever you want okay

[00:17:08] and so that's kind of how we we set it up in the beginning it was helpful for us we didn't have

[00:17:13] that many customers it was a useful kind of show of support and a show of confidence today we

[00:17:19] actually don't need to offer that because most health system IT companies don't offer that

[00:17:25] but what we found is it's actually a great thing to continue to offer even though we've got

[00:17:29] 200 plus customers and we can say hey our stuff works go talk to your peers I find it's a very

[00:17:35] focusing device for our teams because our teams wake up every morning knowing we serve at the

[00:17:40] pleasure of our healthcare customers if we don't deliver value they will opt up so this morning

[00:17:48] 13500 infusion chairs woke up nobody cancelled 5600 hours woke up nobody cancelled 20 000 beds woke

[00:17:54] up nobody cancelled and you know what they won't cancel tomorrow either yeah because we our aspiration

[00:18:01] is to be like electricity to the home you don't wake up saying I'm going to cut off my electric

[00:18:05] service tomorrow because you know you're not going to go back to rubbing stones to creating a fire

[00:18:09] so you do need your electric service we want to be that electric service and so long after we don't

[00:18:15] need it we continue to offer it because it's reassuring to customers that our teams understand

[00:18:23] products had better work our interactions had better be responsive we had better offer an ad value

[00:18:30] and we'd better have you know good positive results to show for it if we don't have those they'll

[00:18:36] move to their feet and so that's kind of how we think about transformations and service

[00:18:42] and speaking of positive results I was also reading that late test experience remarkable growth

[00:18:47] like you're serving more than 1000 hospitals now so I've got to ask what have been the most

[00:18:53] challenging aspects of scaling your solutions across such a diverse range of healthcare settings and

[00:18:59] what opportunities have you maybe uncovered during this expansion because I suspect there's a few

[00:19:05] appeared whilst expanding too right right um healthcare is challenging healthcare is conservative

[00:19:13] and by the way I love that they're conservative if I'm going to get operated on I'm happy that

[00:19:19] the surgeon isn't using a scalpel that he found on Amazon last night right so I'm happy that

[00:19:24] they are careful about selecting vendors and uh and going through a rigorous wedding process

[00:19:30] they also rely on their peers and counterparts across because unlike other businesses

[00:19:36] healthcare is a hyper local business they don't compete with each other the same way I mean if I

[00:19:40] got sick I'm going to adopt in California yeah your Sanford's not competing with Sloan Ketrick they both

[00:19:46] excellent cancer institutions but if someone's going to this not that and therefore they collaborate a

[00:19:52] lot more they trust each other a lot more they they share across executives a lot and so you've

[00:19:57] got to make sure uh that you have a good experience your customers have a good experience with you

[00:20:03] everywhere because they talk to each other all the time right so that's that's a pretty uh clear

[00:20:08] so in scaling because they're conservative the selling cycle is very long it's six months nine

[00:20:13] months to sell so part of the challenge in scaling uh is to to have enough discussions going knowing

[00:20:21] that it's gonna take a long time for a decision that's kind of one second when you've seen one health

[00:20:27] care system you've seen one health care system so your deployments are not identical it's not a

[00:20:33] cookie cutter you know what you can deploy here you can deploy there so you have to build deployment

[00:20:38] teams that can scale now we don't want custom code because if you if we modified our software to

[00:20:45] be custom in each place it's impossible to maintain because then only one engineer knows how

[00:20:51] health system acts as doing something so our software solutions are massively configurable they're

[00:20:58] not customized and there's a big difference in that it's one body of code running in the cloud

[00:21:05] now during the deployment process we may flip a thousand switches that this is where the robots are

[00:21:10] this is how many surgeries they can do this is their policy this is what they do and all those

[00:21:14] configurations switches are set during the deployment process but there's one body of code

[00:21:20] the simplest way to think about this is there's one body of code in google running search

[00:21:25] they don't have a different version for Portugal or Spain it's wildly different languages wildly

[00:21:30] different search methods but it's one body of code all that's figured out in how they tokenize

[00:21:35] at the front end etc etc right so the same way one body of code so the deployment had to become

[00:21:42] scalable and then the ongoing customer success has to be scalable as well we have to talk to

[00:21:48] customers every week every two weeks every four weeks depending on their where they are in the

[00:21:52] journey as they become expert they need us less and less it's Stanford deployed our infusion

[00:21:58] solution in 2013 it's 11 years later we still talk to them very regularly they don't need to talk

[00:22:03] to us every week because they know how to use the stuff as well as our team does but they still have

[00:22:09] occasional problems with it you know memorialstone catering deployed across all their cancer center

[00:22:14] in 2016 so it's eight years now and we still talk to them every few weeks so

[00:22:19] that's the the more than which we've managed to scale and along the way we've grown the organization

[00:22:27] do you know nearly 400 people now across the board fantastic and of course the integration of

[00:22:33] technology into healthcare operations requires not just technological adaptation but also cultural

[00:22:41] and procedural ships and not drifting back to the hey we've always done things this way so how do

[00:22:46] you lean test work alongside healthcare providers to better foster these changes and are there any

[00:22:52] examples where collaboration led to significant improvements in patient outcomes or operational

[00:22:58] efficiency I appreciate you probably can't share too much with that so I won't even mention

[00:23:02] any names but any examples bring to mind on that collaboration with healthcare providers

[00:23:07] and improving the culture so this is exactly what I said about our transformation as a service

[00:23:14] because we put our team side by side to work their throat now change management is a big word

[00:23:19] and I think the change management challenge is bigger when you try to persuade something

[00:23:25] persuade someone to do something that they inherently don't want to do right if you design your

[00:23:33] solutions so that they easy to use they're intuitive and it's in the enlightened self-interest

[00:23:40] off that person to do it because they get better outcomes with less effort then the change management

[00:23:46] burden comes up it becomes more of a sure to them they get the aha and then guess what they're

[00:23:52] going to do it that way because it's quicker it's cheaper it's more intuitive it's faster and it

[00:23:56] gives them better answers and it saves them time right think about this no one from Uber has come to

[00:24:01] my house to change manage me into using the Uber app it's just a better way of going from point

[00:24:08] I don't need to look up the yellow pages call a yellow cab explain where I am explain where I'm

[00:24:12] headed check that he'd take a credit card I don't need to worry about all of that to click some

[00:24:17] my phone and I've got a card at my doorstep so this will change management required it's just a

[00:24:22] better way of doing things so our solutions are meant to do that we've got plenty of examples right

[00:24:27] Baptist Health Jacksonville partner with us they've seen a 16% improvement in primetime utilization

[00:24:34] at nearly 10% increase the number of case minutes that they do the number of abandoned blocks has

[00:24:40] gone down by 20 plus percent no one health partner with us in the middle of the pandemic on both

[00:24:46] the water and infusion they figured out they don't even need a waiting room for infusion because

[00:24:51] the flow has become so much better and they've we've got on our website we've got dozens of case

[00:24:57] studies where we share the results so that that becomes pretty straightforward for us

[00:25:02] and on the flip side of everything we're talking about here as AI plays that increase in crucial

[00:25:08] role in healthcare the next thing is data privacy and ethical considerations they quickly come to

[00:25:15] the forefront so how do you lead us address some of these concerns especially when dealing with

[00:25:20] sensitive patient data and making predictive analysis that could affect an impact patient

[00:25:27] decisions and if you could share that true data privacy is these are company destroying risks

[00:25:35] you take right because if you are cavalier about it something bad is going to happen

[00:25:41] so we are obsessed about data security and privacy so we just go to extraordinary length so

[00:25:46] we're we're hip-assertified you know high-tech certified software type to comply with all of those

[00:25:53] standards we've cleared we then encapsulate so every customer's data is quarantined from every

[00:26:01] other customer's data so we don't just make one big data pool and put everyone's data into it

[00:26:06] so it's completely quarantined it is encrypted it's encrypted addresses encrypted in motion

[00:26:12] uhum and it's quarantined it's role-based so by the eye can't in despite my role I can't look at

[00:26:19] patient data because the engineers have said I don't need to see it so uh so we have very tight

[00:26:26] controls on who can see what and we have massive audit logging and trails so we know if data is

[00:26:33] being moved in ways it shouldn't be moved and so we've been at this a long time and we are very

[00:26:40] very secure on how we do it we also have a a minimalist attitude we don't just suck in data for the

[00:26:46] welling in data we just get exactly what we need to do exactly what we're trying to do

[00:26:53] and if we want to look ahead into the future I was incredibly difficult at the moment with

[00:26:57] the pace of technological change but we'll see your vision for the future of healthcare operations how

[00:27:02] do you foresee lean tasks and similar technologies helping shape this landscape of healthcare delivery

[00:27:08] patient experience and indeed overall system sustainability over the next decade as I said

[00:27:14] the time moves so quickly the pace of change moves so quickly but how do you see this evolving

[00:27:20] so very simply if I to bet two things you know think about the clinical advancements

[00:27:28] in this country over the last 30 years nothing short of magical robotic surgery precision medicine

[00:27:35] genomics uh just it's unbelievable what has been better over the same 30 years

[00:27:41] look at the advancement in operational sophistication of healthcare it's virtually non-existent

[00:27:47] paper records were digitized that's a mauree people still make appointments by looking at the calendar

[00:27:53] and saying Neil Wednesday at 10 o'clock oh ran demands to cast ignore us who ran constraint based

[00:27:59] optimization algorithms to say Neil Wednesday at 10 o'clock was the right answer because the staff

[00:28:05] they cookment the facilities the rooms were all ready with the right skills at the right time to deal

[00:28:10] with Neil at on Wednesday at 8 o'clock nobody did any of that they just look to the calendar well

[00:28:15] that's the way you may your appointments in the stone ages right so when we think about the upside

[00:28:23] of operational improvement if you look at asset intensive service businesses healthcare is by far

[00:28:30] the laggard in efficiencies compared to other asset intensive service businesses now we have

[00:28:36] to acknowledge that healthcare has some fundamental structural disadvantages meaning it is at

[00:28:42] best a 12 hour operation 7 a m to 7 p m okay airlines don't work that really fly the the only time

[00:28:48] an airline makes money is when the plane is in the air flight all the other times are just getting

[00:28:53] ready to put the airline aircraft up in the air and flight right therefore they run 24 7 you can

[00:29:00] expect surgeries to happen at two in the morning so by definition perfect performance from healthcare

[00:29:06] would be 50% utilization half the day right 12 hours yeah even within that they're the laggard on it

[00:29:14] if you could move the efficiency by 10 points which is not that hard to do it unlocks 200 billion

[00:29:20] dollars of value a year on healthcare costs a lot we are by far years ahead of anyone else and how

[00:29:29] to think about operational sophistication in healthcare operations right and using a i and m l and

[00:29:37] so that's our north star and what we're going after nothing that's a beautiful moment to end

[00:29:44] on but we start at the podcast today talking about your journey at McKinsey the and how I

[00:29:49] influence your approach to solving and efficiencies in healthcare as we come full circle I'm going

[00:29:54] to ask you to look back now and share the funniest almost interesting story that has happened in

[00:30:00] your career because I suspect you picked up a few over my over the years right yeah let me stay

[00:30:05] within the lean task context and not not go back to the McKinsey world for the moment but yeah as

[00:30:11] I told you at the beginning when lean test started we were across many different industries right

[00:30:16] and so we were happily serving Google Flexronics etc. and we had actually started serving

[00:30:23] Stanford healthcare in 2000 and hand or 2011 itself doing a simple patient satisfaction analytic

[00:30:31] uh you know suite of analytics around it and this was May of 2013 uh somewhere around then I

[00:30:39] want to say I was sitting in the office of Dr. Shishardry who ran all of Stanford's cancer

[00:30:45] programs back then in fact he still runs all of cancer programs right now um and he was telling me

[00:30:52] about his infusion center they they called it the infusion treatment area ITA and he said I've got

[00:30:58] a problem there so I said really what's your problem and he said well it's empty early morning it's

[00:31:03] empty late in the afternoon but in the middle of the day it's like a train station I've got patients

[00:31:07] waiting nurses frazzled and I can't seem to get them through this feels to me and these are operations

[00:31:13] guys an XGE guy and he said this feels like a classic lean line balancing kind of a problem

[00:31:20] you guys know lean you guys know math you guys know software surely you can work on this right

[00:31:26] so at that point in time I would not have recognized an infusion chair if I tripped over one I'd never

[00:31:32] seen an infusion chair in chemotherapy so we started a white board with him and we were talking

[00:31:38] about how could we level load the chair balancing and stuff and we said you know let's try it why

[00:31:43] don't we do a pilot project with you so he said show we had a hew casady who achieved data scientist

[00:31:50] just joined the company was literally a week or so in so hew Sophia and I started working with

[00:31:58] with Dr. Sheshadri and his team it took us six months to get the algorithm right it took us six more

[00:32:04] months for hew to build his million element matrices and constraint based theory around it

[00:32:09] but then it was matching the wake times went down by 40 percent the capacity went up by 20

[00:32:14] and infusion was just one other for 20 customers in 10 different industries at that time

[00:32:20] but what he started was suddenly something that was scalable a unique way of solving a hard problem

[00:32:27] that every infusion center in the world needs and that was 2013 one infusion center 50 chairs

[00:32:34] today it's 700 infusion centers nearly 14 thousand chairs and so if there's a pivotal moment that

[00:32:40] started it it was white boarding with Dr. Sheshadri in his office back in 2013 wow what an

[00:32:47] inspirational story and we could talk for another hour on this stuff so many great stories

[00:32:52] and the great achievements that you've made there but before I let you go for anyone listening

[00:32:57] just want to find out more information about anything we talked about maybe even connect with you

[00:33:02] ask your team a question what is the best starting point for all things lean tasks

[00:33:06] and just drop an email to uh as supported lean test.com or just you know do me mind.g.ed lean

[00:33:15] test.com and we will get you out in well I will also include links to your LinkedIn and the

[00:33:23] lean task website to the show notes so people can find you nice and easy we covered so much

[00:33:29] the story behind lead tests. How it became this market leading in or the market leading

[00:33:35] in providing AI powered SaaS based capacity management staffing, patient,

[00:33:40] flow care software for house, systems and so much more and of course we didn't even get to talk

[00:33:44] about the fact that you guys are unicorn as well recently surpassing the $1 billion valuation

[00:33:49] within the recent acquisition of hospital IQ we'll have to get you back on in the future to

[00:33:55] carry on this conversation but more than anything just thank you for sharing the story behind

[00:33:59] me all today. Thank you very many a lot. Pleasure as always. I think navigating the complexities of

[00:34:05] healthcare operations really just require innovative approaches and technologies

[00:34:11] and moment today and lean tasks exemplify how the power of AI and lean principles can transform

[00:34:18] healthcare systems ensuring that they are more efficient responsive and capable of delivering

[00:34:24] care that these patients need when they need it and as we conclude today's discussion I think

[00:34:30] it's clear that the journey of lean tasks is not just about optimizing healthcare operations but

[00:34:34] also setting a new standard for how technology can enhance patient care and system efficiency.

[00:34:43] And as we reflect on today's conversation consider the potential of AI in your professional

[00:34:48] landscape how can AI driven solutions revolutionize your field, your industry.

[00:34:55] Share your thoughts with me by emailing me tech blog writer outrope.com twitter link to

[00:35:00] an Instagram just at me you'll see here's unless continue this dialogue on leveraging technology

[00:35:07] to solve some of these real world challenges but that's it for today so thank you for listening

[00:35:13] and until next time. Don't be a stranger