IoT and Agentic AI Power Tomorrow’s Smart Hospitals

Hospitals don’t run on hope—they run on data.

Today’s healthcare systems are under pressure to do more with less. From equipment shortages to long wait times, inefficiencies are everywhere. But what if AI agents could fix them in real time?

In this episode, we explore how a new class of IoT-driven intelligence is transforming hospital operations from the inside out. You’ll hear how real-time data and agentic AI are reshaping healthcare by:

• Turning physical spaces into connected, trackable ecosystems
• Automating maintenance, cleaning, and resource allocation
• Reducing wait times through dynamic workflow updates
• Enhancing patient safety with predictive monitoring
• Enabling cross-hospital benchmarking with live KPIs

Tune in to see what happens when hospitals start operating like production lines—with smarter outcomes.

Transcript

Intro:
You are listening to IoT Leaders, a podcast from Eseye that shares real IoT stories from the field about digital transformation, lessons learned, success stories, and innovation strategies that work.

Nick Earle: So, this week we are talking healthcare, and not just healthcare, we're talking the how to turn the operations of, in this case, a hospital - in fact, several hospitals into a production line with a level of automation that assembles an automotive production line or an Amazon warehouse. And if that sounds crazy, with all of our personal experience of the healthcare system, no matter what country you're in, then I would say keep listening.

My guest today is Dr. Burak Barak. He's the CTO of a company based in Türkiye. They operate globally called Borda, B-O-R-D-A Technology. And they have he's got a deep IoT background, but they've taken IoT for healthcare and have turned it into a system which resembles, and you'll hear us talk about this, the world's biggest Sims game where you're showing the real time collaboration between assets in the healthcare and the staff in a hospital and people i.e. the patients. And then putting agentic AI on top to define the processes, optimize the processes, change the processes, and give comparative KPIs across different hospitals within a country or within a healthcare group. It's a very interesting story. It really links IoT with AI, which is a big subject right now and gives some great examples of what agentic AI can do. Without further ado let's get going with my interview with Burak Bardak, who's the CTO of Borda Technology.

Enjoy.

So Burak, good morning and welcome to the IoT Leaders Podcast. Now this is a very interesting podcast because of healthcare is a really important area and we're going to get into the whole healthcare vertical. But before we do that, you've also got a very deep and long experience in the field.

And one of the themes of the IoT Leaders podcast, especially the recent ones we've been doing, is the increasingly their IoT and AI, and I think this one in particular really is a blend of those two. So, to start us off can you just give me an overview of your background and the different areas you've worked in before you arrived at Borda Technology as the CTO.

Dr. Burak Bardak: Yeah, sure. Nick, thanks for inviting me again. I'm originally from Cyprus and completed my undergrad studies in electronics and communications in Istanbul, Türkiye, where I worked with AI for wireless communication during these studies. And this was back in 2007 actually later that I moved to England for my Masters in Computer Engineering.

Again, I focused on AI algorithm or imagery construction and enhancement back in 2008. And then my path led me to location technologies, which is both indoor and outdoor positioning and navigation, which was my PhD research was focused on. I did my PhD in London at University of Westminster, worked on a project, called Inside Project.

It was a collaboration between four different universities and industry and my Research focus on optimization system integration. And then for my postgraduate studies as a Post Doc, I work on edge process for cameras, develop power processing, parallel processing algorithms and compiler implementations to run AI at the edge, which is within the cameras, up to 600 core processors.

Everything is within low, very low latency. And after nearly a decade in R&D, I transitioned into the private sector working in highly regulated industrials like banking, finance, healthcare, and payment systems.

Nick Earle: Yeah, very impressive. I should say also very relevant. Essentially that's 18 years of experience in the areas and from a time, well before it was called IoT, it was machine learning but also, 600 processes in a camera. I believe from when we spoke originally that was in military settings. We can't go into that.

But that's incredible power edge processing. That's a great example of edge processing and AI image enhancement. And I know that you, that was in the biological field, you're looking at human cells.

Dr. Burak Bardak: I worked on mainstream construction for microscopes. We use different phase information to enhance the image, and in order to do that, we train the AI in the background, which is an in the old days, we called them the neural networks. Now the name is there, right? Yeah. Neural.

Nick Earle: That's right. They were net self-healing neural networks. Yeah. Yeah. Microsoft used to brand them.

Dr. Burak Bardak: Yeah. And we enhanced the images get the 3D we toggled the 2D images to 3D ones with the help of the AI. And then basically these things build up. Small things they build up, and it came the AI that we know today and everyone.

Nick Earle: Yeah. Changing weekly as we find on this podcast.

So, you're now at Borda Technology and you're based in Türkiye. And most people who listen to this I think I'm right, probably won't know anything about Borda Technology, although we do have viewers in 95 countries, so there will be some people in Turkey who know all about you and the other countries in which you operate, but for everybody else, can you just give me a high level overview of Borda Technology and what they do and its relation to the background, the deep background you've got in IoT and AI.

Dr. Burak Bardak: About six years ago I joined Borda Technology where I lead work on Next Generation IoT location technologies. In simpler terms, we build the next generation technology for indoor tracking. So, we track entities in enclosed environments in during outdoor environment actually, and we build the technology to be able to do this in a very high precise and low latency wave. So basically, we can achieve up to 10 centimeters precision with battery powered tags.

Nick Earle: Okay, so let's go into the tech a bit. We're going to talk a lot about business processes and get into Agentic AI. But on our journey, let's start with the bottom rung of the tech.

Asset tracking, indoor and outdoor. Specific capabilities, which is very relevant to your background of being able to identify an asset within 10 centimeters. I think you said, which is not very much at all. That's pretty industry leading. And it uses BLE technology from a sensor?

Dr. Burak Bardak: So, it's multi-technology we use ultra-wide band, we use BLE as our base, and we use BLE angle of arrival. So, for ultra-wide band, you can achieve to the 10 centimeters. With the BLE angle of arrival, you can achieve to the submeter level aggressive, which is 40/50 centimeters. And then with the RSI, you get a kind of a zone level accuracy.

Nick Earle: So, you are using some form of triangulation. We're going to talk, yeah. So, if it was in a hospital, you're putting sensors, battery-powered sensors on devices using triangulation to identify them. And the battery life of one of these sensors?

Dr. Burak Bardak: So, we call them tags, actually.

And these tags, they have five years of better life usually is around. Three to five years, depending on the usage and our gateways, which we call them locators, and they're used to calculate the angle of the transmitter signal from the tags. They're called locators. And power through the ethernet, power over ethernet, we install these on the ceiling.

And then we have these tags. This is a sample of one that has five years of battery life. We can track the stuff or persons who wants to be tracked and for various reasons, can be tracked for less than sub-meter level, let's say.

Nick Earle: Again, to help people unpack this.

You held something up there, look like a credit card but most people are probably listening yeah. The taxonomy here is, assets are people and patients now in the healthcare environment, a hospital for example. Yeah. And we'll talk about a case study, but if we just so first of all, the assets are the tags that get physically attached to the assets.

Dr. Burak Bardak: Yeah, the asset techs are very small tags that we have. Again, I'm showing it, but it's a very small three by five centimeters tag, which has five years of battery life, and we can track the asset’s location along. They also have some inertial sensors.

It can sense the vibrations, they can sense the temperature, they can sense movement.

Nick Earle: So, you build up a picture of all the assets in a hospital. And we've had a couple of healthcare examples on the pod, and tracking assets in hospitals is a major issue because people tend to pick them up if it's a trolley or something. They roll it down a corridor, they take it somewhere else, and nobody ever knows where it is. Yes, so tracking the assets now, the dot, which looked like, for those listening, you were holding up something that looked like, I would say like a very thin domino.

That was about the size of it. Then you said about the staff. Now the staff was the credit card one. So, we've tracking assets and triangulation. The staff are the nurses and the doctors, I assume.

Dr. Burak Bardak: Nurses, doctors, healthcare workers.

Nick Earle: And this is the credit card size device. And I think you mentioned the word safety when you said that. Yeah. So, what's the use case for the staff? And then from there we'll get into the patients.

Dr. Burak Bardak: Sure. So basically, unfortunately, there can be attacks on healthcare workers in hospitals, and our job is to prevent these attacks and for stress purposes, staff stress, we call our proper protocol stress prevention.

So, whenever a healthcare worker is basically being attacked or someone is basically detention is increasing in that particular situation. They double click. Or maybe it can be programmed, or they long present on our staff deck. And this basically triggers an alarm in the background with the exact position of that particular staff.

And we can even connect to CCTV commerce and get some screenshots and send that them to security, right? Then security will know which stuff is being under attack, by whom and where. And then they can divert the security there to prevent this attack being to the healthcare worker.

Nick Earle: So primary use is the security of stuff, and it is a big thing. It's a big thing. There's a lot of people who drink a lot who tend to end up in A&E, with mental health issues. But of course, a side effect, is you're also getting the location of the healthcare worker as well.

So now you've got the location of the assets, you've got the location and whether they're moving or not, or vibration. You've got a location of all the healthcare workers and their identity. So, you know where they are. Now let's look at the third aspect in this triumvirate, which is patients.

So, talk me through what happens. If I'm going to a hospital in Ankara, in Türkiye the patient, how does this system track me? What happens as what happens when I check in as a patient?

Dr. Burak Bardak: Okay. After you check in as a patient, basically we get the admission information through the integration through the EHR to our system, and the nurse knows which patient needs to be tagged.

And they go next to the patient, and they tag the patient. Tagging the patient means something like a smartwatch a very small tag. I'll show it again.

Nick Earle: A risk tag or something. Oh, they go up something like a risk tag.

Dr. Burak Bardak: Yeah. And we do have response, which is our tags are reusable. They attach this tag to the hand of the patient, and we can start tracking the patient's location.

Nick Earle: So again, for those of you not on video because Burak has been holding up several devices on video. What I would say is if you've been to a Coldplay concert you've been issued with a multi a wristband that changes colour. It looks like that.

Dr. Burak Bardak: It's smaller though.

Nick Earle: We're not using the video for the kiss cam. That’s another story.

But it's smaller than that. But essentially what we've got here is we know the location of all the assets and they're moving. And what assets they are. We know the location of all the healthcare staff and who they are, and the patients know who the patients are, and I know in Türkiye and across the Middle East and I'm very jealous.

I've operated out of the Middle East, and I know this, but I'm very jealous as someone who's in the UK. You also have the systems, the data that the patient's healthcare records can be integrated as well from the GPs, the doctors nationally and across the region. So that really helps because then you've got all the patient's history in the data.

So, what you've got is like this model with the patient history and then above it, you've got assets staff, and you've got patients, and now you've got the data. And again, in my mind, it's like a big Sims game. It's a very complicated Sims game because it's a hospital and everything's moving.

The staff are moving, the assets are moving, the patients are moving, and a lot of information about everything. Okay, so that's the basis.

Dr. Burak Bardak: Yes. and obviously it's a very big Sims game and it's 24/7 operation. The operation doesn't stop, and you have to be on the move all the time, and you have to collect all the information that you can get from the sensors, from the IT network or from the HR and feed in one big system and start working on your magic to optimize it. Optimize it, make it more efficient, making track turnaround times, make them faster, more efficient. You have to manage your capacity, so it goes on. It's like a very big system, very complex system, and you need all of the information you can get to feed in that model that you created and start on the optimization.

Nick Earle: So visually you encourage people to think of it like a stack of bricks. You've got the bottom level. You got the basic data from the patient's health records, the GPs around Türkiye, or even visitors who are unfortunate to end it up in a hospital. You've got you've got the assets; you've got the big IoT scheme of the assets, the people and the staff and the patients.

And now you're collecting a lot of KPIs, which is what you just talked about. You're collecting, I would imagine tens of, if not hundreds of thousands, I mean over time, millions of data points KPIs into a system, which I guess comes to now what Borda Technology do. And it's going to lead us into the AI piece.

Because now you have a solution now for not just reporting the KPIs, but actually. Looking at the processes and the process optimization. Is that right?

Dr. Burak Bardak: Yeah, that's a hundred percent correct actually. So, we, for of these reasons, we track staff and patients, and the assets are a very big part of the operation inside and hospital.

Because, obviously medical devices, they're very expensive and it's very hard for hospital management to measure the usage of these assets. And they somehow invest and procure more asset than they need because they don't have any way to measure the utilization of these assets.

Our system provides some visibility to reorganize the assets to the right places. And that in with doing that, they can reduce the asset. They can increase asset utilization, they can reduce the investment they need to make for the assets for the hospital, and basically, they over purchase by 20-30% because they don't know where their equipment is.

They don't know how it’s being used. They don't know how or if they are maintained. If they're up and running, if they're calibrated. So, our system basically solves that problems and they can reduce the investment they have to put in by 30%.

Nick Earle: 30%. Just basic information, like finding things. Sometimes it's the simplest, it's the simple stuff, which has the biggest payment.

We did a podcast recently which is also IoT and AI with the guy who runs IoT and AI for Volvo in 140 factories. And he's tracking millions of components, but he said one of the biggest paybacks is when they make a truck and they put it out in the field, they're all white initially before they get painted.

And they do it in stages and they have to call them back into the factory for the next stage of assembly. And they can't actually, they don't know which truck is which. So, they lose trucks. They lose trucks in huge fields because they're all white. And they have a team of people who run around zapping barcodes on windshields.

To find a particular truck that needs a certain extra added to it because of the configuration. But this is similar concept, is you are finding portable x-ray machines ultrasounds, important bits of…

Dr. Burak Bardak: IV pumps, patient monitors, wheelchairs, beds - whatever that hospital uses within the hospital, we track it.

Nick Earle: And you say it's down the corridor in the room on the left.

Now that's KPIs. Now what's interesting, certainly when I spoke to you previously, is that a lot of systems that we see stop at the KPI, they say, here's an asset tracking system. Look at the data, look at the KPIs. You can get an efficiency by finding assets quicker, but what I know you are doing is you have a series of AI tools because you are now taking it up the next layer of the stack, which is the agentic, using the KPIs, using the data. Because AI is only as good as the data. You feed it. So, you're collecting step one, collect all the data from everything.

And then you are feeding it up into an LLM and you are using Agentic AI.

So, let's break that down for the listeners.

Dr. Burak Bardak: It's a very interesting subject. So, we are building agents in the background. So, we have the system to collect the data, which is the most precise data you can get from, location wise and sensor wise, we obviously have the ability to collect them.

We collect them, we store them, and we feed them to an AI agent in the background, such as proactive asset management. It is an AI agent in the background that monitor equipment usage in real time. Instead of staff checking manually. This AI agent can check and predict, and device are underutilized, and it commend reallocation.

This will basically increase the utilization and prevent new purchases, and also operational workload as well. Obviously, on top of that, every asset within the healthcare sector, actually within any industry almost requires annual maintenance or maybe weekly or monthly maintenance, calibration cleaning and storage.

And these are all operational costs. And these are couple of hundred dollars per device per year, let's say. And this can be prevented by using an AI agent in the background, which is offloading the operational workload from staff to the AI agents that work with real data.

Again, workflow optimization we do have in the background. We basically follow the workflows. It can be patient or staff movement; it can be interactions between staff and an asset, or it can be interaction between staff and a patient, or an asset and a patient, which is quite common. We can tell which asset is used with which patient.

The data in this interaction is very rich actually. We can extract the information. If this asset needs to be maintained, needs to be cleaned, or disinfected before used to another patient. We can put in a work order for cleaning to the biomedical team, you can prevent using this particular asset.

If it is not calibrated, it's not good for use. You can prevent that, and you can flag all the bottlenecks in the background. You can put in beds if a certain patient is waiting for a room to be cleaned. You can basically, in the background, you can optimize this workflow. You can put in a cleaning order.

And you can reduce the wait time. Everything is in the data actually.

Nick Earle: Excuse me. because you're saying some really interesting stuff and I just want to make sure, because most people aren't doing this so you're living it, you're living it and breathing it. So, I want to really make sure people are understanding this because the agentic AI which is fed by the data and that's a series of as you said, workflow optimization capabilities that run in the background. And so, the idea of a patient has finished with a procedure and needs to, I don't know, be put in a room for a period of time, but the room is not clean. And the person who normally would be wheeling the patient on the trolley down the corridor doesn't know.

The room's not clean, gets to the room but it’s not clean. So, then we've got to find into the room, people end up in corridors. Lots of examples. I can think of a personal example. Occasionally I've ended up in A&E probably when I've done something stupid or something's happened to me and I'm thinking, why does it take seven hours?

And I'm talking from a UK perspective, but I think it's pretty bad all over the world. And if you actually look at, why it takes seven hours and sometimes it takes 17 hours. Most of that time is you are waiting for the next step in the process. You're sitting on a plastic chair in a room with a terrible TV and a coffee machine that doesn't work, and you are waiting to get triaged.

And then you wait, and you try and work out who's ahead of you in the line. And then you see the doctor. Then the doctor wants you to see somebody else, and then they do some tests, and then they have to wait for the results and, yada.

And then at the end they say, okay, this is what it is. You just go back to your chair because we need to get your medications. Don't leave until you get your medications, but then you wait again, and you wait again.

And so, there's all these process inefficiencies and that makes up the bulk of the seven hours.

I think it probably makes six of the seven hours. What you are saying is that the, I think and keep me honest is that as a result of collecting the data, you're not only getting the KPIs, but what the agents are doing because they're reacting  in real time and they are changing.

The agents are dynamic. It's not like SAP workflow or any of these ERP systems where you define the processes and the processes never change. What you're saying is that the data is dynamic. The agents, they could be a procurement agent, a behaviour agent, or a support agent to take support from reactive to proactive.

Let's do an update because we know it's going to need an update and that'll all be automated. Lots of different agents, these agents are actually then able to map out the dynamic picture of the process for this operation, this procedure. It could be a cataract operation; it could be what happens in A&E.

It could be a kidney investigation, a kidney operation. So, you are now getting optimized process information for what a hospital really does, which is execute collaboratively with people and assets and patients across a series of processes. And so, you've now got this, the Sims game is now much more than assets.

It's a series of processes that are interacting with a big dashboard showing inefficiencies.

Dr. Burak Bardak: Actually, a series of very complex processes and AI agents learn from this process, and they interact between each other. They update. And asset agent can update the workflow agents, or the workflow agent can create another action.

Everything will be connected to each other. Everything will talk to each other, and then the adaptive AI will solve the problems where you need as an actual human controlling the actual healthcare worker. It can be a nurse, or a doctor needs to be, it frees them up to solve the problem as opposed to doing the admin.

Nick Earle: It gets rid the admin and some of the bureaucracy. And if I'm a general manager or a manager of a hospital, this dashboard is incredibly important to me because it can save me money as you point out. Even the basic, save 30%, but it also enhances the patient experience.

Dr. Burak Bardak: And the quality of the healthcare service that you are getting as a patient.

That's very important. Wait time. The quality of the asset that is being used with you is well maintained, is up to date. It's calibrated. Imagine an IV pump, which is not calibrated, and instead of pushing one litre per hour, it pushes you three litres per hour and it's a disaster.

Nick Earle: So, they get a better healthcare experience. Ultimately what it's all about. But the other question I've got is. There's more than what you have a lot of hospitals as customers.

In Türkiye for example, Canada, the US.

Dr. Burak Bardak: Yes, we do have some customers in Europe as well. It's all around the world.

Nick Earle: All around the world. And if I'm where I was going, me is if I'm a CEO of a hospital. Or the CFO or whatever management team of the hospital can I see how well my hospital is doing compared to other hospitals?

Dr. Burak Bardak: Yes, you can compare them. We do have customers that big hospital groups, they own multiple hospitals. They manage, they track the processes with our system, and we can actually compare two different hospital buildings managed by two different management teams, and we can compare the processes, their asset utilization rates. Even we can compare their performance on the operations or rooms, the room’s utilization rate. We can compare the length of the durations, the duration of the operations for different hospitals, different doctors for the same procedure for different patients. Obviously, we can compare with the was verdict there, why it took two times longer than the other one.

And it usually is an operational failure. The operation didn't start on time because the disinfection team didn't arrive, they couldn't clean it, or maybe the asset was missing. They waited for the asset. Since we tracked the asset, we noted asset has been delayed and they waited that time for the particular assets.

Nick Earle: Yeah, so there's even an HR advantage, and if you run six hospitals, you're part of a hospital group, you can do performance measurement across the hospitals. So, it's a very interesting area, and as I said agentic AI is something which in our experience people are just realizing what it actually is.

It's not ChatGPT only that. You're using multiple, you're not just betting everything on one model, are you use, can you just maybe describe at a high level which model?

Dr. Burak Bardak: Obviously healthcare is a very highly regulated environment. You can’t just get the information about staff, location or asset calibration data and put in a public LLM. They have compliance, GDPR etc in the background. So, we do have our own data sets. We do have our own LLM in the background and we use some public LLM as well.

Open AI is one of the favourites that we have. But for interactions with the end users, which can be a little bit more challenging because interactions can be, nurse can be from a different country, they can use different words. We use public LLMs, but that interactions that require the data access to that particular hospital's data, we use on-prem or maybe in cloud private LLMs in background, private agents.

Nick Earle: Okay. A series of different ones, which is consistent. People say it's not one AI; you use different tools. And of course, the humans are still interacting, but they're looking at the data, they're interpreting it, they're checking it with their knowledge.

Dr. Burak Bardak: And they have the ability to correct the workflow, actually. So, we allow them to give feedback to their particular AI agents so we can improve it.

Nick Earle: So, it can be improved, which is how they do the accelerated learning. Yes. So, the robots are not yet in control, but let's finish because I think it is a very interesting area. And as you said it very briefly as you said, this is healthcare, but actually there's been a lot of people listening to this saying, that's very applicable to the industry I'm in.

But where do you think, because you, going back to your beginning, you've been in research, you've been 18 years in what we now know as AI that you've done a lot of different things.

I should have mentioned because you have a PhD, you're a doctor in the field. Where is it going? Because it seems like you are defining the whole operationalization, in this case, the healthcare. Is it's almost like it's catching up with what would be normal on a very advanced manufacturing line?

Say an automotive? Or an Amazon warehouse. Would that be right?

Dr. Burak Bardak: Yeah, actually, yes. It can be an automation production line or Amazon warehouse. Let's say if a tool or part goes missing in a production line, the production stops. It's the same as in a warehouse. And if you don't know where the items are, you end up searching them.

And everything is delayed. Your processing time gets really bad, and the hospital faced the same problem actually instead of lost packages or the delayed cars. It's patients the care is that the waiting are gets longer. You end up waiting in ER six hours for a small procedure.

Our tags and the IoT brings the same precision and efficiency into healthcare. And may we make sure that the staff equipment and patients are always in the right place at the right time, in the right order of time, order of workflows.

Nick Earle: And as you were speaking, I could actually make the case that it's more complex than a production line because you're taking the philosophy of the just in time production the production line which is well known in manufacturing or the robots working in the factory for Amazon with the humans just looking at screens and being told what to do next.

But you don't have members of the public wandering around the production line of your factory. You don't have people wandering around picking their own boxes off the shelf at the Amazon warehouse.

You've got thousands, tens of thousands of patients. It's the patient component. It's what I call the triumvirate.

Dr. Burak Bardak: And the other thing is also you have the time component there with the wait time goes long. The patient's situation can change that, increase the risk of workloads to be disturbed or has to be changed.

And you need something in the background that adapts to the workloads, adapt to the changing situations because the problem that you're going to. You are trying to solve, keeps changing, and you have to keep track and update yourself. You can just say, okay, this is the predefined workflow we have to follow with.

No, the situation can change.

Nick Earle: And then agent modifies and it can actually say, okay, based on new data I've got now, this is the new workflow process.

Okay. Listen, let's finish. We could talk for a long time about this. Let's finish here. I want to come back to my, I talked about the Coldplay analogy.

I think you told me you've seen Coldplay recently.

Dr. Burak Bardak: Yes. A couple of weeks ago.

Nick Earle: Okay. I've seen them as well. Amazing concept. It was the patient gets the wrist the wrist bracelet, now when you leave the Coldplay concert as you come down the stairs at Wembley, or I assume it was Wembley that you there's people there collecting them, please hand them back.

Is it the same system for you do you collect them back off the patients and what percentage of patients hand them in?

Dr. Burak Bardak: So basically, the process is if a patient's going to be discharged, the nurse should go there and take the bracelet out and discharge the patient.

If they don't do that, they can't be discharged. If there's an alarm because we also monitor the patient for their own safety.

Nick Earle: Yeah, like taking the cannular out of the arm.

Dr. Burak Bardak: Yeah. And maybe the patient shouldn’t be leaving that particular room. Maybe it's an infectious patient and it has to be quarantined.

We also monitor the bracelet condition, and we get a warning in the background if they're trying to tamper the tech, so we don't lose the tag. Obviously like maybe one or 2% is being lost or they're being stolen, but we don't use the tags because losing the tags means losing the patient.

We don't lose the patients as well. We make sure the tags are attached to the patient from admission to discharge.

Nick Earle: Okay. And they're taken off. And they’re thrown away. And because they need to be sanitized and, yes. So, listen I think we should stop it here. It's a very interesting area just to recap your company is called Borda, B-O-R-D-A Technology. And I know you've got on your website quite a few case studies of hospitals healthcare institutions that are using the system. And I also know that the opportunity in this particular area is huge. Healthcare is the single thing every government spends, healthcare and defence.

But in is where they most of the money. And it and it's just accelerating as we get older and more demand for treatments. But it, I do believe also it is actually, it's not healthcare specific. The concept of what we talked about of the IoT, the data in your words the assets, the staff and the people or the customers is actually generic across many industries and the operationalization of a huge complex entity like a hospital.

And operationalizing through technology a hospital where the staff are following the instructions. Like the Amazon warehouse where on the screen it says “do this next” is a very interesting idea and has huge ROI. I want to thank you Burak for being on the pod.

I think it's a particularly interesting one. And for anyone listening please visit Borda B-O-R-D-A Borda Technology and you'll get a wealth of information there in the case studies and as I say we do have listeners all around the world and I think every country where we have listeners wants to solve problems like this.

Thanks again for being on. Good to talk.

Dr. Burak Bardak: Thanks Nick. Yeah, for inviting me.

Nick Earle: You're welcome. It was very welcome. It was very lovely talking to you. Yeah. And hopefully the listeners will get the same. I hope so. One day, and I know we don't want to talk about the NHS in the UK.

One day, I really hope that we have a British case study that actually talks about this.

Dr. Burak Bardak: Yeah, we are in, we are looking for that actually. Hopefully we'll do a very good use case with the NHS in the future.

Nick Earle: Anyway, thank you Burak.

I really appreciate it and thanks for being my guest on the IoT Leaders Podcast.

Dr. Burak Bardak: Thank you. Thank you for your time.

Outro:
You’ve been listening to IoT Leaders, featuring top digitization leadership on the frontlines of IoT. We hope today’s episode has sparked new ideas and inspired your IoT and digital transformation plans. Thanks for listening. Until next time!