AI Pharmacy Robotics in the Home | Charles Gellman, HiDO Health

This transcript was generated automatically. Its accuracy may vary.

[00:00:12] Mike: Charles, for those that haven't come across you online, introduce yourself and tell our listeners what we're talking about today.

[00:00:20] Charles Gellman: My name is Charles Gellman. I'm the CEO and co founder of Heidel Health. And what we're going to speak about today is the future of care and the convenience of your home that's going to alter our future.

[00:00:34] Mike: When people talk about. Technology and care or artificial intelligence and care. I think that some other companies. But try to make it sound like those two don't go together. You need the warm touch of a person and all this kind of stuff. But technology, of course, has kept us all alive with heart machines and all that kind of stuff.

So it's all technology 

[00:01:00] Charles: there are moments in time that can alter the trajectory of Our history. And what I mean about those moments in time, you have to think about the technological advances of the car versus the horse and buggy, the world wide web versus people manually communicating through physical mail to now expanding in a world of different levels of artificial intelligence.

And I think a lot of folks are thinking that AI is going to take their jobs or it's going to replace folks at what they're doing. And that's not in fact. Correct. Where I see AI is AI enhancing people's ability to help others. So for instance in care, you have vast amounts of data sets and it's not realistic for providers, physicians, nurses, pharmacists to be able to understand and be able to pull that information when they're treating patients and all of the different variabilities that exist from a technological standpoint, a labor standpoint.

You have people with different chronic conditions presenting different disease states and having that information accessible at the point of care to then enhance that level of care to that individual and unique. person is the capacity of AI assisting or enhancing folks right now. So I think it just depends upon people's understanding of AI also, as well as the definition and where they think they can use it or leverage this particular technology.

[00:02:36] Mike: And I think there's different definitions of AI, because some people now, as soon as they see this. Take off, they're gonna slap AI on their products or whatever, or company. If there's any hint of something that could be AI or their definition, it's gonna be thrown on there. How would you define AI to actually mean something and not just be fluff with anybody that wants to stamp that on it.

[00:03:10] Charles: That's a great question. I think, again, it's The audience that's interpreting the messenger of the definition of what artificial intelligence is.

AI is how they're going to interpret that message. And some people will say, you know what, that AI is enabling self - driving automobiles to safely go down the road. And that's a particular level of interest with those large data sets where you're able to manipulate and you're able to redefine industries.

And other areas where it might be, chatbots where you're trying to replace somebody in customer service. And they're not getting the same level of service people are accustomed to. That might be limited in the ability of AI to actually help people navigate. And a lot of people are not so keen on listening to the RoboBots when you're calling customer service.

So I think it really depends upon the application, the industry, and then the type of application that it's serving in order to help people navigate through that potential problem.

[00:04:12] Mike: The way I heard it described to me is that AI really is that circular pattern of the computer that sees what the user's doing, and then it.

Refining itself. , can't just have programmers who are programming stuff in like years past and call that artificial intelligence. Is that how you would might define it 

[00:04:37] Charles: yeah, I think when you start thinking about AI, again, you're talking about neural networks and machine learning and generative AI. There's a lot of different applications and algorithms that can enhance the ability of the solution to perform specific tasks or specific activities. You're not going to think about AI, reproducing a human's abilities to do a vast array.

Different types of tests. So if we're segmenting to a specific area, and this is, we'll use a use case in the hospital, right? So a surgeon's in the hospital, they're performing surgery, they're trying to understand or they're navigating specific arteries, and maybe they're using AI to look at the imaging.

If they're trying to remove a tumor, they can now navigate better and it's able to superimpose what it sees based upon all of the different types of scans and images. And that's a very specific use case where you can leverage it where it's able to complement or enhance one's ability to perform a specific activity.

[00:05:38] Mike: That's what I heard. Instead of just one computer being programmed to look for something on the x-ray, it's going to take. All those that are red and, for lack of a better word, find the average and always look for that. And if something else comes in there that might have been an outlier, it's still going to pick that up then from some use case, some hospital halfway around the world and keep putting that in to use that computer to keep teaching itself round and round.

[00:06:10] Charles: And you think about it, The amount of data information that we produce each day is astronomical and it's not going down. It's increasing exponentially year over year. The amount of pictures, the amount of videos that are being taken, the amount of information that's shared throughout the world.

Now you need to... Be able to refine that so people can make intelligent decisions based upon the application of the AI to then perform a specific action. Now, refining and dialing it down is no easy way for people to digest. And then alter the activity based upon the information that's presented to themselves.

And that is something that I believe that we're getting our hands around what AI could be and molding it into a way where it's digestible for the public. And they've seen it through chat, GBT, and other applications to other use cases where people's lives are on the line. 

[00:07:06] Mike: I heard it one time too. I'm not even sure if they use the word AI, but it was about the self driving cars 

Let's say self -driving cars, the knowledge is the same in all of them. And then tomorrow morning Mrs.

Smith, who's 76 years old living in Kentucky, backs out of her driveway and the driveway is just slanted just a little bit so that it just threw off something that maybe the computer hadn't seen before. Then tomorrow, like. Billions of cars have learned from that and they say well, if you're in that situation that Mrs.

Smith was in, this is what the car is going to do now. I know maybe not to that speed, but it just keeps learning and learning. it's going to show up in how the car treats you instead of in some just deep file somewhere. It's gotta be useful.

[00:07:59] Charles: And it's also the application of the technology of being able to review those large data sets in order to intelligently alter the future of those self-driving vehicles to make them safer or maybe more efficient or productive. So I'll give you a story. I have friends that I was playing pickleball with and pickleball is a pretty popular sport these days, but they were crossing the street.

And they're crossing the street in San Francisco, and it was a self-driving car, and the self-driving car made it across, and they were about to cross, but it didn't hit them, but it was within inches of hitting them. It was the self-driving car, in fact, driving safely, but it, because technically it didn't hit the pedestrians, but should have crossed, or should have slowed down, knowing that pedestrians were, Recognized on the sidewalk about to cross over and it could cause an accident.

So I think that there's so many different fringe cases and use cases that we have to start thinking about, far and wide in order to refine it down to a level of safety that can be publicly used. And that requires a whole lot of training, a whole lot of technology, and I still, strongly believe in the power of AI.

And the refinement of it and the applications, but we have to continue to excel at the safety and the applications of using the technology.

[00:09:20] Mike: A.I. took a big leap in the last year and saw what the chat GPT and Bard and, Claude and all these different ones that really put it right in front of people. Did other technology come along? Two in the last, let's say six months, or was there some really cool AI stuff being done like four years ago, let's say for robotics and driving and all that, but we just saw the language models happen like six months ago.

Or was there a big shift six months ago in almost all AI?

[00:09:55] Charles: Yeah, that's a great question. I think that folks have been working on AI machine learning for many years. And my background in data science and specifically clinical informatics, which focus on data science within the medical community and the applications within the hospital and the clinic and outside the hospital.

 We've been speaking about big data where it first started and machine learning and algorithms. And then artificial intelligence and neural networks for quite some time. Now I believe the public and the media have really been exposed to it as of late because of the applications to the wider public.

But a lot of the research and the data and the type of scientific applications has been going on for many years. And I don't see that stopping. I actually see the acceleration. of the development of these technologies and applications available to the public, increasing at an exponential rate.

[00:10:48] Mike: I looked back at my Amazon purchases and it's hard to explain it to the kids and it's hard to even believe it ourselves. Like I remember in one year, I'm going to say, I don't know when the hell it was, I'm going to say, mid nineties somewhere, I might've bought like six things from Amazon.

Now you buy six things a day. It becomes a part of us, you, for example, Charles, let's say you say the word ai a hundred times a day or something like that, five years ago, was that part of your vernacular very much? Even though we saw the large language models come out six months ago, when was it a huge part of your vocabulary every day?

Actually, the term AI, when did you start saying it more?

[00:11:32] Charles: I believe that in the last several years, artificial intelligence and AI was more part of my vernacular. Before, it was really the focus was around big data and data science, and then the applications of machine learning and neural networks, and then it slowly migrated to artificial intelligence, and defining down all the different levels of artificial intelligence, and then the type of applications and delivery methods.

[00:11:57] Mike: You've put your money where your mouth is because you actually do AI you're part of the AI industry with your little robot machine.

We're going to talk about the nuts and bolts and the intelligence of that. 

[00:12:14] Charles: Yeah, so my background is, really I came across an element within the marketplace because of what happened to me with a life event. So I was misdiagnosed. I had a ruptured appendix, spent a couple weeks in the ICU. I was in my early 20s and I wasn't that sophisticated in regards to navigating the healthcare market.

Place at that time so it really started to open my mind . Maybe instead of going down the road that I was interested in, which was finance, I was really enamored by health care because of what happened to me. I shouldn't be around. I'm lucky to be alive and I'm thankful for all the Nurses and doctors and people that cared for me to put me in a position, but then I started asking myself how can I help serve others based upon my experience?

It doesn't happen to others And you quickly realize that there's ways that we can improve things. And my experience led me down a path of working with pharmaceutical companies, medical device companies, as well as medical software. And I was able to oversee the entire parts of the country.

And when you start seeing different parts of the country, you see how things... dramatically from one geography to another and also the ability to care for others. And so my mind started to think about, is there a better way to care for people outside of the sterile walls of a clinic, outside of a hospital?

Maybe people could care for themselves at home if they had the assistance to care for themselves. And then I went down that rabbit hole. What if we can create a robotic device and this is a hybrid between a cure coffee machine and a ring that can help people with chronic diseases care for themselves by taking their medications.

As prescribed, and the most complicated thing that doctors and patients struggle with right now, they have a limited amount of time to talk to each other and understand the information. The patient struggles giving the precise information based upon what they understand in that short window of time, and the doctor's pressed for time, and they're trying to give complex information to the patient.

So you almost get this scenario of this loss in translation that takes place. So what if we can fix that?

[00:14:36] Mike: Charles, you're not kidding about the Keurig and the ring, the doorbell. Cause I watched your machine online and for our listeners, it's about the size of a Keurig. And bottles are. In at the medicine and they're timed and they come out into a cup and then the ring part of it, camera doorbell part of it is this Keurig size machine is actually taking a video of the person putting this up to their mouth and taking it, I suppose 

you could put it on your tongue or something, but people who are using it are trying to do good. It's really cool. I haven't seen anything like that. It's a really cool contraption.

[00:15:18] Charles: Yeah, thank you. Yeah, I never had any intent to create a medical device. Nor am I an engineer. The reason why we created this device is because we were looking into data science . I want to create a blueprint for people to live healthy lives without having to worry about the care and the complexity of their medications.

So that struggle, that blueprint, caregiver burden is very real. About 40 million caregivers are trying to help family, friends and loved ones right now. And there's 6 billion prescriptions going out each year. So let me repeat, 6 billion prescriptions amongst people that are taking five plus medications.

That's over 20 percent of our population. The United States has some types of chronic condition taking. 5 or more meds. And that is what's keeping them out of the hospital emergency room. And when they deviate from that regimen, that's when things fall apart. And a lot of people have life issues happen. Or health issues.

Or they can't remember. Or they're pulled for other reasons. And it's very challenging for a lot of folks. And that's how we treat 90 percent of the patients with medications. That's very real.

[00:16:33] Mike: I'll send somebody home with three meds or nine meds or something like that, and. I know when they're in their home, it might be just an issue if they're in there, lazy boys. And this bottle's three inches further away than it should be because the cat hit it.

And now they don't take it for four or five days, just for some reason like that. Or the reason I don't take it is just laziness or something. And So the first thing that comes to my mind with the Heido is, getting the medicine to them at the right time, and then explaining that part about the camera.

What is that doing? Obviously the person's not a picture of themselves just for the hell of it. I imagine something's going on with that picture. And what is that?

[00:17:18] Charles: So my goal is to create the blueprint for health so people can get the best outcomes possible. So based upon age, race, gender, disease, the cocktails of medications they take. Also, with Leveraging AI and Face ID, which is what we do with the HIDO device, we have all of the data points of the face. So now, what I want you to think about is, what if you can measure through the different changes in the face, disease regression, progression, stabilization.

I want to help people as much as possible through them doing... The right habits, following the orders of the physicians, and let's just say that we're getting better outcomes in a different area. We will know that. We can share that information with health plans and pharmacists and doctors so they can use that information to care for people better than what they're currently doing.

That's called real world evidence and real world data. And the HIDO device is built to capture the information that we don't know right now. We don't know what's happening in the home. We don't have a record. We document dose by dose to get the right medication to the right person, the right time, the right regimen, and then we have a video consumption recording to show that they're consuming the medication and it's no longer a guessing game when the patient and the doctor or nurse talk to each other.

They know definitively that they're taking... The medications are prescribed, and if they don't work, that's fantastic because you can make a change. 

[00:19:03] Mike: Did I hear you right? That is the picture it's taking of the person that.

It might be even looking at things like how swollen their face is and their eyes and things like that. And then does that self teach itself to look for disease states or did I hear that wrong?

[00:19:24] Charles: So we take video recordings, you utilize the device to do Face ID to unlock it. There's applications that conceivably measure different changes within the face.

My goal is to understand disease regression, progression, and stabilization based upon the patient's behaviors and the regimen of the cocktails of medications given.

And we can measure that in a variety of ways. One, we have all the data points. Two. We track the blood work in the medication so you can see the variabilities. So let's say it's a diabetes medication, you can start looking at glucose levels in A1Cs. If it's somebody with heart failures, you look at their BNPs.

And then ultimately, were we able to reduce hospitalizations, ER visits, and total cost of care with these specific patients? And what you have is now you have a circle of data. You have a flywheel effect that occurs, and now... We can start improving the outcomes of patients in the convenience of their home versus a sterile environment where they have all of those resources.

And all those resources are usually allocated 0. 1 percent of someone's life or a lot less. 99. 9 percent of your time is in the home. So let's help people care for themselves outside of the hospital so they can help themselves versus being dependent on others.

[00:20:53] Mike: Do I have that right Charles that this is where maybe the AI comes in where let's say that your camera takes pictures of them and let's say it picks up swelling around this part of the face or something like that. You may not know what that means, but later on, we might find out that a certain percent of people are taking this medicine, and all of a sudden the computer starts churning this stuff. And it says, Oh, those same people had this kind of swelling here. And it sees a million more of them. And pretty soon it says, this is the predictor of a seizure or.

This is a predictor of high blood pressure or 

We don't even know what we're looking for, but you take those data sets and the AI churns it and it comes up with something smarter than we humans are looking for.

[00:21:41] Charles: Maybe smarter than what we're looking for, or maybe we're unaware because those changes are so subtle you can't pick them up. Also, what's interesting is that the number of contraindicated or adverse events that are actually reported based upon the different cocktails and medications are drastically reduced or underreported.

So now imagine if we had real time reports of clinical efficacy, safety, Adverse events in addition to potential, contraindicated medications for people that have these other underlying conditions that we were unaware of because we couldn't pick that up. So there's a lot of elements that could be with this type of technology.

We're just scratching the surface right now. We've done research at Stanford. We've done research at Rush University, which I believe is very newsworthy because We are able to use the HIDO device, a medication dispensing device, with dementia patients with no assistance. They can walk up to the device, they can set it up, and they can take medications unassisted.

There's over 6 million people with dementia and Alzheimer's right now that can take medications, potentially by themselves and not being reliant on other people. The applications of this technology could... Monumentally shift our ability to care for folks.

[00:23:08] Mike: sPeaking about not knowing what the hell you're looking for. Years ago. I went to a pharmacy convention that someone else from my pharmacy had also gone to, and I got to the hotel later in the evening and the next morning I woke up and there's a buffet.

And so it's 15, 18 pieces of bacon. I don't get bacon all the time, come back to the room, take a nap, have just this terrible headache all day, and wake up the next day. I just drove home. I never even made it to the damn convention. I didn't know for about two years that it was the nitrites in this cured meat that was giving me headaches because it was like Six to eight hours later that these would come on.

 Six hours. I eat a lot of different stuff, Charles. And so it's hard to nail that down, but it's just stuff like that. If you had a billion people that were having bacon and getting headaches that day, this stuff is stuff. You don't even know what you're looking for.

 Now, in this case, it probably can't see a headache, but maybe it can see your eyes, doing something or whatever.

[00:24:17] Charles: Yeah, I think there's a variety of different technologies right now that utilize them, they could look at your eyes for instance. And diabetic patients, they can tell whether or not there's going to be some issues and there's technologies behind that to determine. And there's some cutting edge technologies where they have you wearing, the wearable contact lens where they're taking those different deviations of...

Maybe moisture levels or other different types of contracting of those blood vessels. I think the applications based upon those different things could be far and wide for folks. Now, your specific example of bacon, there's millions of people that love bacon, right? But maybe it's a specific type of bacon that you'd be okay with versus the off the shelf, particular bacon that you ate that day.

[00:25:00] Mike: Exactly. Charles. What is something that you wish were true? Just wish this group would understand this, or I wish technology could do this, , what's something that is the bottleneck in your mind? I know it's always going to be improving, but what's a wish of yours to have more.

Success slash acceptance slash benefits and so on.

[00:25:27] Charles: I think the biggest shift that people have been... struggling with for so many years, and this is going to sound very intriguing for a lot of folks, is that the incentives that the government has for health care based upon reimbursement pushes specific services. And what I'd like to see from the Center of Medicare and Medicare Services and Innovation is a transition from mostly acute setting level of care to putting funding in the convenience of the home for patients so they can live good lives in the comfort of their home without having to frequent maybe a clinic or a hospital, but bring the care to the home like it used to be. Because doctors and nurses used to go to the home. They used to know people personally one on one. And we basically have turned this into an environment where there's a very small window of time to interact. And I believe if we're able to focus more of the money in the home, not only we can lower the total cost of care for folks, but I think we can provide improved outcomes and better quality of care if there's that shift 

[00:26:42] Mike: yeah. And it seems to me that most people up until now have devices like Heido and maybe some phone stuff or whatever up until now. I think these companies want data. And they figure that they can get better data from a nurse in the room, checking off something in the hospital and taking the blood pressure and stuff. Then they can be at home, where, every three days the granddaughter comes over and helps grandma with this or that. But I think with your system and others like it with your system, those. I don't think that HEIDO would catch up to what the nurses are doing. I think you would say it's going to be a lot better than that data of the nurses because it's automatic, it's the cameras and in the hospital nurses are busy and I'm just saying nurses as an example, any hospital worker, everybody's busy doing their thing.

And I see quickly Those slopes crossing each other and the in-home data being more beneficial, just like anything. The daily reading on your. Car tire pressure is better than going every six months to the car shop, to the dealer and getting your air pumped or something.

It seems like it's going to cross and do it in a fantastic way. 

[00:28:05] Charles: there are some incentives and reimbursement around remote monitoring right now. And I believe what you're discussing is basically there's a huge difference between acute care settings where there's an episode of care at that moment in time versus continuous monitoring. So if most of the time for patients is spent.

away from the hospital, away from the clinic, you'll be able to find those deviations. What if somebody has a heart issue? If you're being monitored actively... The chances of catching that is much more likely than if you're on, EKG, within the hospital and you are there for a few days versus many months of continuous monitoring because you're wearing a watch that has HRV analysis or you're checking your blood pressure on a regular basis or you're on a scale that's checking that information.

So really the goal behind this is to create a home health ecosystem, which is the paradigm. for people's wellness and health because without your health, we really won't have a whole lot. Let's optimize our health, live a good life, eat well, exercise, and if you struggle with those two and you have to take medications, let's at least optimize your medication so you can smooth out your disease state.

[00:29:23] Mike: I think the readings , in let's say a doctor's office. They're probably either worse than normal because people go there once they're feeling a heart flutter or they get pain or they get more swelling in their legs or something like that.

They're either worse than normal or like in my case when I know I've got like a, like a. Blood What do they call those like a blood test, you know for the fats and stuff in the blood I'll try to control myself for a few days I don't know how much I can shift it maybe with the You know the glucose stuff you watch what you eat for a day or two before the blood test But my point is you can either have stuff that looks worse than normal or better than normal But you really want to look at the day to day stuff

[00:30:06] Charles: It's interesting when patients go or, your friends, your family go to the doctor or go to the nurse, oftentimes people are nervous or they're scared or they have anxiety. So maybe those readings aren't 100 percent accurate because they're in a state that they normally wouldn't be in.

But in the home, and I'm not sure how much time you spent, in different people's homes, in different patients' homes, but... Everyone's home is set up differently. And people's comfort level in their home is usually much more at peace and ease than when they're in a doctor's or clinical setting.

And also being able to recognize what's happening in the home I believe can help produce different ways to treat people based upon what you're seeing. So sending caregivers to the home or home health agents or now, meals on wheels. You observe exactly what's going on with these patients and what they're struggling with and then you can address them versus the unknown.

And the unknown is when people go to the hospital, the ER, you just don't know what's driving them to that place in the first place.

[00:31:12] Mike: The cool thing about your machine is that as pharmacists, for example, we have a lot of touch points with patients. We're talking to some people. Twice a week, just because they're lonely and stuff like that. We can kind of get a feel of some of that and we can look at the, computer, see what they're doing 

but how do we relate that to the doctor or how do we get in the patient to make a change or something? We know what's going on. I think we're inadequate for what we can do for someone who's in their home most of the time. But that's where those data points come in on this stuff, where you can say, sure, the pharmacist maybe felt the person was depressed or.

Little bit more confused than before things like that. How do you relate that though to other caregivers? We're something like this. It really has those data points and of course the artificial intelligence you don't even know What you're looking for as we talked about earlier, but it's doing something with that information

[00:32:11] Charles: I believe that pharmacists can practice at the top of their licenses with the HIDO device. I believe that pharmacists can do a lot more than what they're doing right now. And I believe the future of pharmacy will evolve to a much more highly utilized specialty for the care of populations. And the reason why I feel so strongly about this is because when I first started in the business, I was in clinical surveillance technologies, in the hospital, bringing all the different algorithms and data.

to the point of care for people to make decisions. A lot of it's based upon the vitals and the medications and the information within the EMR system. We can recreate that same environment in the home. And by recreating that same environment in the home, you alter care completely because now people are stabilized based upon a level of data that we've never seen.

This has been a black hole. Nobody's ever known whether or not people are taking their medications as intended. And by a pharmacist knowing if a patient's missed or taken their medications, you can now intervene at the point of care at their home. And that's where I think pharmacy can completely revolutionize the future of pharmacy and medications.

[00:33:31] Mike: I tell people that are well, I really talked to many people afraid of AI, but you see it in the paper and stuff they're afraid of the AI and things like that. And like, all right If patients were all living to a hundred if there was no heart disease and no cancer And all that then maybe what do we humans do now for a job, but it's like there's so much We can do by getting rid of all the stuff that the language models and all that AI can do, and we can set stronger goals of extended life, quality of life, things like that, and really that slope that without the help of the AI in the trenches, we may never get there.

So my point is, don't worry about AI. Let's set higher goals.

[00:34:21] Charles: Yeah, I think, we started to talk about precision medicine quite a bit over the number of years, and it was all about precision medicine. Now, machine learning, big data AI. Where I see things is that when you start looking at these technologies holistically, it's a culmination, an aggregation of All of this information comes together and it really comes down to one fundamental thing, is enabling people to make better decisions, whether it's in the hospital, in the clinic, or at home.

If you can have people make better decisions based upon the data that's presented to them, you get less mistakes, you get better outcomes, and you're able to lower the total cost of care because the information is available in an easy, simple format for people to make better decisions. 

[00:35:06] Mike: I know they've got blood glucose machines, , that, Give you a certain amount of insulin automatically and so on and I don't imagine your systems there But as things keep improving I could even see like oral doses changing based on what you're seeing from the machine, if you can tell you know Swelling is down and such Maybe you can decrease the NSAID from, three times that day down to one. Give the stomach a break. Things like that.

[00:35:37] Charles: If you have a patient that has CHF and they're on Lasix, and that they're not retaining as much water, maybe you're able to remotely titrate down.

 The ability for pharmacy. To alter care by changing titrations for patients with CHF that aren't retaining water. Titrating down Lasix, for example. The ability for pharmacies to completely change the delivery of care and alter it within a confine or data struct that doesn't currently exist is there.

It's there now. The ability of pharmacy to really transform the practice.

[00:36:15] Mike: Charles, I don't want to do it myself, though. I just want to be watching the boob tube. I want the AI to... Bring the dose down itself. If we're going to do this, let's really do it. 

[00:36:26] Charles: That's a great observation. So what if AI could present specific patients for de-prescribing or de-escalation or different titrations, bubble that up so you can virtually smart round and then make adjustments. So now you have one pharmacist looking after several hundred patients because you're only seeing the patients that need you.

versus you going through line by line so we can make you more effective, you can deliver better care, and patients are being taken care of that need to be taken care of. Not just all patients, but the right patients.

[00:37:07] Mike: Charles, in your dreams. Let's say that Haido, instead of being there, could move around. What if there were drones and things kind of like the Jetsons thing? What kind of stuff could you imagine maybe in the future with technology that's not quite there yet?

[00:37:25] Charles: So I wouldn't say that this is much of a dream because we have a patent pending for this technology. So it's a great question. What I foresee to happen is being able to send a pill bottle to the home that knows the medication, dosage, frequency, and count, that'll be auto recognized by a HIDO device or a white labeled version of HIDO, so we can eliminate medication errors from happening at home.

And that's very real, and once that's fully integrated within the pharmacy system, think of every single medication coming home, and patients are no longer required to put in a plastic pill box Monday through Friday, a. m. p. m., but it's auto recognized and auto dispensed based upon how it's prescribed. 

So the amount of errors that occur within the home, which is another unknown.

Can be alleviated and people that have issues with memory or if they have family issues or they're struggling with mental health or they have other issues, all of that's done for them. That's the future I want now, and that's something that we can actually do.

We have patent pending technology right now within the pill cap itself, and Dr. Torres is our clinical pharmacist, and this has been designed by pharmacists, for pharmacists, to help patients at home, and it's around the six rights of medication safety. Each pill cap has an embedded RFID chip, knows medication, name, dosage, frequency, count.

And it's automated with the device, so just like a Keurig coffee machine, you drop the cap in, it auto recognizes everything. And the goal behind this is to eliminate medication errors from happening at home. And that technology I can see being universal as we push out the pill bottles to everyone. And if you can think about it, you have a smartphone, you have a smart watch, you have a smart scale, you have everything smart, but we're still using a plastic pill box of AM PM for something that's Absolute mission critical to keeping people alive, which is insane.

[00:39:35] Mike: When the data is there, pharmacists and healthcare people, use us where it's and not just repetitive stuff, where it's needed. Really where it's needed. Don't even talk to every person about how to remember their medicine and that kind of stuff. there's one patient, I don't know how the hell you get paid for this, maybe one patient who's afraid to, doesn't want to take something because of PTSD, whatever.

 let health professionals really focus on something that needs human interaction and the rest of it, those aren't the ones that need that attention.

[00:40:17] Charles: Yeah. I think there's, ability to isolate the ones that need to have the care versus. Nice to have. And right now, the problem is that there's a limited number of providers, nurses, pharmacists, doctors to help serve all. But we don't need to serve all. We need to serve the ones that actually need the care.

And once you can... Specify who needs the care and then care for them. You reduce the burden on the entire system, but you gotta be able to identify those specific subsets versus blanketing care all over the place.

[00:40:55] Mike: pharmacists really gave the same attention to every person, you'd have some people that would never want to step in your store again. just a different ball game with a 20 year old and a 40 year old and an 80 year old.

It's a different conversation across that board.

[00:41:11] Charles: Yeah, I think when you start thinking about folks with chronic diseases that are polypharmacy, that are in the age range of maybe it's late 30s to 50s or 60s, they might be more open to that conversation because those meds Medications are life altering and changing medications to stabilize how they're living right now.

And if people deviate those medications, especially if people with cardiovascular disease, they may not be around for tomorrow. 

So So understanding how mission critical these specific medications are versus a 20 year old that has acute sinusitis and has taken a short course antibiotic, it's just a completely different ballgame where they may not need that full consult from a pharmacist, whereas those other folks, again, it's mission critical life saving information.

[00:41:58] Mike: Charles, let's talk about your business. Ultimately, as the business keeps being successful, more of your visions can come true. How many employees and do you have a headquarters and where are these things made?

[00:42:13] Charles: And so we'll give a high-level overview. So we have about 15 folks on the team. It's a small startup. We're a little bit outside of Sacramento and everything's made in the United States. We started out of the garage. So two car garages. We started with plastic foldable chairs and soldering tables and putting boards and circuits together.

My co founder, Brandon Woolsey is a biomedical engineer, graduate from UC Davis, exceptionally talented. I met him at a previous startup from a Stanford founder where I was actually an advisor at SIRTEX MED, which is a Stanford based accelerator. So I was able to meet him, exceptionally talented.

He was able to make my vision become a reality. And this vision is a big vision because a lot of folks have failed trying to get this technology in the hands of patients. And what's different about Heido versus everything else that's been seen in the market, We've been 100 percent focused on the patients, the entire development, everything about HIDO is based upon patient feedback, patient questionnaires and patient usage.

So the camera utilization, the face ID, the automated embedded RFID and the pill cap, the actual recording that takes place, everything is around the patient and it's to minimize the burden. So they don't have to remember all of these complicated things. Polypharmacy regimens, and they can just go about their daily life versus having to remember or not remember.

[00:43:42] Mike: So Charles, this starts in the garage. I imagine you put together some things that kind of look like trash cans, probably the first iterations of those, and then you keep testing them. And test them some more and keep refining it.

Doing that kind of stuff. , is that about what's happening then?

[00:44:00] Charles: Yeah, we're on version 4 right now. So the first version was rough, and you have to understand that this is a dream. This started with pen and paper. My handwriting is terrible. It looks like hieroglyphics. So to have an engineer take somebody that, you know, my artwork, it looks like a stick figure, and put it together and put it into, CAD to make it become reality, and then iterate based upon something that no one done before, and then get it to the level we're at right now.

You know it is very significant. So I'm very proud of the team I'm very proud of where we're at and I'm also very proud of the type of validated results that we have so I'm gonna throw this at you and I'm gonna ask you a question of what do you think would be possible by utilizing the Heido device with a patient population of congestive heart failure folks that are ages range 50 to 85.

What type of reduction in hospitalizations do you think could be achieved by implementing a device such as this?

[00:45:03] Mike: I'm going to guess wrong. So you got to fill me in, Charles.

[00:45:06] Charles: 80 percent reduction was achieved with a Medicare Advantage plan in Northern California and we had a 67 percent decrease in the cost of care for these individuals by implementing the HIDO device.

[00:45:19] Mike: Two thoughts on that. One is, talk about what you could use the camera for, Maybe now or as it improves, you can see all kinds of stuff going on with congestive heart failure, probably in the neck and the face and things like that, but that brings me to the question of, this sounds like something that eventually the health systems are going to want to get Into the homes, you want to show the health and all that stuff, but when they then attach that to dollars that's what they have to do once they attach that to dollars, then it's we better get these into the homes.

That seems to be your customer almost.

[00:46:00] Charles: 1. 2 trillion dollars is spent right now for hospitalizations. 1. 2 trillion dollars. Imagine what it would mean to patients' lives and the cost for health plan premiums if we can drastically reduce the utilization of patients that are frequent flyers to hospitals and emergency rooms.

This is a very significant technology to the impact of COVID 19 .

Our loved ones, our friends, our family, in addition to trying to reduce the total cost of care versus doing the same thing over and over again. 

[00:46:38] Mike: Charles, who is the paying customer then? Is it the patient? Is it going to be a lot of the daughters of the patient? Is it going to be the health organization, insurance companies? Who's the end purchaser of these typically?

[00:46:50] Charles: Yeah, so the folks that are funding this are the health plans. The health plans pay for the hospitalizations, the emergency room visits, but they also pay for the medications that aren't taken as intended. And the benefit is to the patient. The patient comes out of pocket for zero dollars because the ROI is so significant to the health plan.

It's in the best interest of not just the plan. All of the other members and all the premiums that spread, you can potentially reduce the total cost of care, reduce the premiums for all the members, and take better care of people at home. So all of the health plans that are self funded or they're part of an accountable care organization or they have risk, this is a significant lever that they can pull that they've never had access to before.

[00:47:43] Mike: I imagine as at least I see it on TV, but I imagine when you're bringing this stuff to the boardroom of certain places , there's a lot of guys and gals, they think they're one step up. Smart from somebody else and they want to lean back in their chair and they want to say but how are you going to do this?

What are some of those comments you get from the suits that they think they're smarter than you on? And then what is the response to that?

[00:48:16] Charles: Every situation is a little bit different. So most of the conversations that we have are well received. They would not have those conversations with me if they weren't open to these new technologies and knowing the type of impact it can have on these patients at home. Now, also, be mindful of the fact that they're also trying to balance a variety of different initiatives based upon the priorities of the organizations because each organization is set up differently.

So some of them may be at full risk. Some of them may be part of an accountable care organization. Or maybe they have other different partners. So it really depends upon how the organization is set up. is their strategic direction, but the folks that we're looking to partner with are the ones that want to be in the home so they can lower the total cost of care and they can improve patient outcomes.

And our mission is to create health equity and lower premiums for everybody that's in the plan. And that is achievable right now.

[00:49:17] Mike: 

where

Is the largest point of human error that you have to overcome? Let's say it's sight or...

People forgetting where the machine is, or people unplugging it and thinking it's a vacuum cleaner. What are you dealing with some of these people that you have to overcome that human nature?

[00:49:39] Charles: Our biggest pain point with deploying the HIDO device is something that I think that every technician and anybody that is a caregiver or a son or a daughter that's a little bit younger is setting up the Wi Fi and seeing if people know their password. The biggest hurdle to setting up this device is whether or not they remember their password.

And this is something that's common, I think, amongst many millions of companies. Password. Forgot my password. Resetting password. So these are very simple steps. That is it. Everything outside of step one is not much of a pain point from our side. Our next major hurdle is.

They don't want to give up the device. So we've had multiple test patients where they're validating stuff and they like it so much because the bar is so low. They don't want to go back to a plastic pill box with AM, PM where they don't know whether or not they're taking the medication.

So the great thing about the HIDO device and the Curse is that when people have it, they don't want to give it up. So I liken it to a microwave or refrigerator. It's an appliance in the house. Once you have a microwave or refrigerator, You don't want to go back. What are you going to do to go back to using a chest and an icebox and going down?

Nobody wants to do that anymore. It's a blessing and a curse, but my goal is to get as many of these out to as many patients as possible to improve patient outcomes and create a state where we understand real world evidence, real world data, and we're able to level the playing field for a lot of folks that, can't comprehend the complexity of medications and the importance and value it is to their overall health and well being.

[00:51:19] Mike: Charles, I think yours is probably a good example of a company that probably needed money. It's hard to grassroot something like that because you can't sell just a portion of it or a sample or, start off small. You need this right away. How does that work out as far as your need for investors? And do you then aim for a certain level?

And then what are your plans? How is that overall structure set up when you have to, it seems, bring money in?

[00:51:53] Charles: First off, for the investors that have invested in HIDO, we're exceptionally grateful and thankful for them supporting this mission and being impact driven. We've had a variety of different levels of funding. We are fortunate enough to have won two NIH Awards, one NIH funded reward or grant was with Stanford collaboration.

The other one is with Rush University focused on dementia patients. We won those two awards and we're applying for two phase two awards. At this moment, we have some excellent data that I'd love to share publicly, but we cannot yet until the NIH knows about these reports. But the patient and the data results far outpaced anything I thought was possible.

And that is going to have a monumental impact on patients in the future. So I can't wait to share with you in the near future. In addition to that, yes, we have we've raised over a million and a half dollars in um, Angel funding, and we are oversubscribed for our next round of funding right now, so we're very excited and supportive of our future and our mission, and what we're trying to do is we're trying to get these patients, the devices, as quickly as we can in the Northern California area, and then we will spread more units in Southern California and other geographic areas based upon the specific partners that we have.

[00:53:11] Mike: The goal then is to expand out and have this, let's say, cover the world. which actually ties in quite nicely to AI, because the more units you have, the more they teach each other. That is the next step.

Am I missing any step beyond this that your company would take? Once we do this, Mike, we're going to do this. It sounds more like you don't have those two steps because the first step is such a beautiful mission.

[00:53:41] Charles: I think there's a couple of different applications based upon the data sets where things become very intriguing. So once there's 10, 000 plus units out there, you can start thinking about clinical trials and fast tracking drug development because you can isolate different Patient recruiting methodologies, because patients typically are on a cocktail of medications when they go through study, but studies typically involve placebo versus a specific molecule that's studied, but we know, in fact, that there's different cocktails of medications elicit different outcomes, so if you send a medication out to a specific group, geographic area, demographics, age, race, income, education, and the chronic disease as well as the different cocktails and medications, you definitively not only have 100 percent data integrity, that monitor, that data is real time.

So potentially you think about accelerating drug development and getting post market surveillance and that information is available to those researchers so they can make decisions quicker and they can also get those products to the market faster because the FDA. Has those high standards.

That's the type of area that I want HIDO to be in so we can do a stronger unbiased clinical study that will serve more folks. We're talking a little bit about the United States, but there's other portions of the world that we could cover like Japan and the EU and the Middle East.

There's many other different geographic regions where I believe that you could have these devices set up and then you can actually study those disease states amongst different geographic regions. regions within the world. And that would be very fascinating from a research 

perspective. 

[00:55:27] Mike: Was there any point in the development of this or in the structure of the business where you had to Decide this way or that way and what were some of those times?

[00:55:41] Charles: So I equate the startup world to being on a roller coaster ride. So you have those ups and downs, but now remove the track.

And we're building the track as we're about to go down the hill. And then we're, you're throwing it up and trying to move as quickly as you can. So

You learn a lot of the unknowns because we're entering into a world of something that's never been done before.

And by entering that world, you have to explore markets or verticals that may or may not work out, or those dots will be connected in the future and they will work out. And the lessons that you learn or the information you gather will Push us in a different direction that we wouldn't have gone into if we never went down that direction in the first place.

They call that pivoting within the startup world, but the way I see it is that we are open to discussions and being educated on different areas within healthcare verticals to help patients. This is all about the focus about patients and improving their outcomes and lowering the cost of care. And whatever path we have to go down to make that happen is the path that we'll go down.

Every single path that we went down was mindful. Now whether or not it worked out now, one could make an argument that, yeah, maybe you spent too much time in this area, but that's not how I see it. I see everything as an educational opportunity to refine and to get better. And, could we spend more time with more patience and get more questions answered?

Yes. But if we didn't spend the time in the first place to get the answers and the questions, then the product wouldn't evolve to what it is

now. And the same thing can be said about entering different marketplaces. You have to be mindful of the amount of time and energy you're spending, and if it's not going the way that you're supposed to be going, you have to make an executive decision to deviate from the course.

If you don't deviate the course, then, to your point, I would say that's

a problem. 

[00:57:34] Mike: the problem. Yeah. Charles, what's your worst hour of the week? What task whether it's mundane or challenging, is there any task during the week that is not your favorite?

[00:57:47] Charles: every single day for me changes. There is no standard day for me. And I like the idea of the whack a mole theory.

You don't really know what's in front of you, you just go with it. But what I also enjoy is not being able to predict my day to day. And, that worst hour of the day would probably be me not doing anything.

And that's very rare. So what I tend to do is no matter what I'm doing, I fill it up with activities. So I'm never sitting idle. So it might be, I'm about to go to a conference over the next couple of days and we're doing other events, talking to team members and investors and planning out the future and delivering devices.

There's always something to do. My worst hour would be not having anything to do and not being able to see this vision become a reality. That would be a problem.

[00:58:41] Mike: I know you don't have a lot of extra time, but if someone said to you, here's an extra hour, Charles, during the week, what would you do with that hour?

[00:58:51] Charles: I try to spend as much time as I can with my family. So my family has been very supportive of my efforts. And the first few years, my wife wasn't so supportive because she was questioning my level of craziness, going down this path. But now, she has been much more supportive in this venture.

 I spend time with my family. My son and I do swimming, and he also enjoys pickleball. So it's nice just to go out there and spend time. And my joy is really from the smiles of my son and my daughter and my wife. Any time that I could spend with them, if it's an extra hour. They would say that I need to spend more hours, but I would

give it to 

them.

[00:59:28] Mike: Charles, golly. Thanks for your time. Cool information. We don't get a lot of inventors here on robots and so that's a cool thing.

 And the mission of helping people doing it from home. It's a very cool concept and I'm really looking forward to seeing where this all goes. So congratulations.

Thanks again for your time and best wishes in the future.

[00:59:53] Charles: Thank you, Mike. I appreciate you having me on the show.