Health Innovators
Health Innovators

Episode 101 · 1 year ago

The Data Biome: Difference Between Valuable Data and Noise


Data is - and always will be - a chaotic neutral (meaning sometimes, it can be incredibly valuable, and at other times, simply noise).

Understanding which healthcare data collected has value and which is noise? That’s where the real work begins.

On this week’s segment, Jeffrey Carlisle, CEO at Pneuma Systems Corporation; Howard Rosen, CEO and Founder of LifeWIRE; and Brent Wright, Associate Dean for Rural Health Innovation at the University of Louisville, join me for a discussion that centers on the data biome and understanding how data value works in healthcare.

From interoperability of centralized data to who should have ownership over healthcare data, there’s a lot to unpack here - and all of it is valuable. Come and listen to this week’s episode!

Here are the show highlights:

Data: is yours valuable or is it just noise? (0:05)

The power of data in post-market surveillance (6:01)

Interoperability and centralized data: what the future might hold (9:58)

Patient data biome - how far are we from that reality? (17:37)

Who should have ownership of patient data and how it’s integrated (25:55)

The role data plays in a patient’s healthcare (31:55 )

Guest Bios

Jeffrey Carlisle is CEO at Pneuma Systems Corporation. He earned his ScB in Applied Math/Biology from Brown University. 

If you’d like to get in touch with Jeffrey after the show, feel free to reach out to him via LinkedIn at Jeffrey Carlisle or via email at

Howard Rosen is CEO and Founder of LifeWIRE Group. He earned his HBBA in Economics and Marketing and MBA in International Finance/Marketing from York University, Schulich School of business.

If you’d like to get in touch with Howard after the show, feel free to reach out to him via LinkedIn at Howard Rosen or via email at

Brent Wright is the Associate Dean for Rural Health Innovation at the University of Louisville. He earned his BS in Human Studies from the University of Kentucky and his Masters in Medical Management from the University of Southern California  

If you’d like to get in touch with Brent after the show, feel free to reach out to him via LinkedIn at Brent Wright or via email at

You're listening to health innovators, a podcast and video show about the leaders, influencers and early adoptors who are shaping the future of healthcare. I'm your host, Dr Roxy Movie. Welcome back health innovators. On today's episode, you're in for a real treat. We are hosting another executive briefing with some brilliant leaders and great colleagues and friends, Jeff Carlyle, the president of Numa Systems Corps, Howard Rosen, CEO and founder of life wire, and rent right, the Associate Dean for Real Health Innovation at the University at Louisville. Welcome back to the show, guys. So today we're talking about data, patient data, medical data, and so I want to just kind of kick off our conversation with what's the difference between good data versus bad data? Well, in New England, of course, we referred to it as Dator. So is that good data or bad day or dat one of the distant the data doesn't really have any value unless it's been converted to information and that conversion requires context. Organization includes, you know, sorting and filtering and that kind of thing, and then analysis. So that bad data is stuff that doesn't ever get to actionable information. It's noise. Good data is, you know, not favorable or unfavorable, it's just real information that's actionable. So if you have information that's not actionable, then it's also noise. But information is actionable, meaning it will change your behavior. That that's that's what I would say is valuable and and the difficulty of that is it depends on who is utilize the data, because you have different groups, as we talked about earlier in the pre show. Is the patient has a certain out to day that's actual valuable for them, versus the clinician, versus a provider anywhere in that ecosystem, which complicates the matter. So what made bad data, using the context of actual for one group, maybe actually valuable and good for another? Well, that's the kind of white why should this be easy? Yes, absolutely, exactly, because why should this be easy? Okay, so what is what's happening like? What do we do? Do we have a lot of data out there? Do we have a lot of information? What's what's happening today, and what are we expecting to happen in the future? I think we're drowning a data. I mean I think we suffer from data day loose syndrome. I mean it's just as we're data calls US anxiety. We have so much of it but we don't know how to put it together, to bring forth information and knowledge, to give us concept and that allow us to act, because that's the reason we had in medic medicine. We want data so we can act on the behalf of the patient. That's what we want to do. But because Datas and disparate sources and we don't have true in or off to this day to allow us to bring a lot of information forward, we just don't have that. So it leadst to a lot of frustration. Really medical when medical errors happen and the quote record from an acute care facility is is brought to court, you know it can be six meet high, massive amounts of the and in many cases they injury it's caused to the patient could have been solved by a three by five card. It. It's...

...not the amount that is our friend. The amount is our enim really, really really need to have kind of problem oriented focus and distillation of data and we don't. That's hard to get. So is this something that you think machine learning in AI is going to help fix because it's going to be able to help us process these gigantic data sets and pull out something that's meaningful. I think it's certainly certainly helped to a certain extent, but there's so many sources of data. You know, a simple one are just patients going on to Dr Google. That's a source of data, to source information. That necessarily AI machine learnings are going to help because they're been going to their clinician or to the provider with that data set get whether it's actual or filtered to anything else. That just adds to the fire hose of additional data that bread was talking about and I think we have to be careful about the noise, you know, that's generated from all of this. I mean we just want clarity. If AI and machine learning can give us clarity, can take a lot of information and bring it to that single point of truth that we're seeking, I think that's wonderful. But if it's just going to generate more, more baggage, more noise within the system, it's very frustrating. I mean, but I think you even you have to back up here as you start talking about, you know, terms such as AI and machine learning, you really got to get the data right, and I still think that we really did. We struggle in medicine to make sure we get, you know, a diabetic known as diabetic, where you can take a thousand, Tenzero, a hundred thousand diabetics, where you can put that into these algorithms and then get true knowledge coming forth. We still have dirty data. We still have a lot of dirty data out there and systems that don't communicate well with one another. So we're still in many ways, while we're talking about such great tools that were mentioned here, such as AI, we still in healthcare, I think, in the trenches just struggle. I think you know, we we joke, a few of US joke about how sometimes how beautiful paper, paper is. You can talk about papers be an archaic, but sometimes there's just a beauty of paper that you can do with a pin. I mean you can make a mark on paper, you can show you can create a quick diagram on paper that you can't do that for patients, you know, within electronic medical records. So there's still there's still a beauty there. You have to balance it, but really getting it right. That's that's where that's where I'm looking for the true innovators to come forth. How do we really not just data science but but bringing that, merging that data science and data graphic work together to really help us as clinicians and patients? And it's saying that there's nothing new under the sun and we always, you know, kind of come back to where we wore. So maybe the future write the twenty thirty date at the thirty data is going to be picked back to paper and we're going to in two thousand and thirty we're going to say, remember the good old days when we had paper, Howard. But but it's a brand's point in from the data and we're talking about it's also what is it collect the in terms of the context? Like, for example, work that we've done with wearables, and I know this is shocking, but a number of the wearables were not very accurate. However, many of them were act inaccurate one hundred percent of the time and exactly the same way. So we were doing is not measuring the data but measuring the change in the data from those wearables, and that actually became valuable information when he's we saw that change. So it's not taking the the main thing we just look at the change in the Delta actually became a very valuable insight of clinicians who we wor work with. So it is back to your point. It's what is it the data can you how do you make an information? Back to Jeff's comment, that's one way we actually took data that was not necessarily strong as it was, but made it information by just taking the measuring the change over time. You know, we touched on a previous call about the FDA. But if you were to understand that the information you collect in the clinical trial is very,...

...very limited, it's artificially constrained. Patient population is is very control but the real power of the data comes in the postmarket surveillance when you've got patients of all kinds of comorbidities and different environments, and I mean just an a massive complexity. Yet if you're got good data, then the clarity becomes is amazing. Right. So that, I think ai is more interesting on a societal analysis then on an individual patient. Yeah, and I doubt if you've ever seen the patient where you said, oh my goodness, I could have treated in statient so much better if I'd only had a computer helping with the data. It just doesn't happen, because that's not the perfectly limiting factor. It's in the aggregate that is, but for an individual patient it's really the thoughtful quality of the data. Yeah, it's that curation than curation absolutely, or the incremental measurement over time to have some predictions about what may potentially happen with that patient. I think the I think we've yet to see the potential of that type of predictive and analytics on these data sets, on this for a world evidence that we're talking about. I think I think what you said there. I think into howards point on Delta. I think that's going to be wonderful to see, is we get people more used to wear and, you know, devices that collect the biometric data. I mean we don't we don't know what it's like to measure someone's heart rate over ten years. We're going to know what that's like in the years and decades ahead to see what what type of subtle variability that exists that may show you, you know, early hypertension, early a fib you know, predictability for a heart attack. I just don't think we we've not had the ability to collect that data and that's, you know, it. Coming back to the AIML point, I think that that's what's really exciting. I do have concern on how we've applied diligence to the data that's before us now. You know it to me we don't handle data well. You know, if you move from local a local hospital to hospital, how are we going to be able to gain inside from these large data sets that we really need? Well, and I think in a lot of ways covid has made that data even more despair it because now we've got virtual care, right, we've got all of these remote patient monitoring all these wearables, all of these different devices and to collect data that's outside of the traditional healthcare ecosystem. So it's even more siload than ever before, and so I think the the challenges that we've had previously with interoperability and creating some kind of centralized data set is maybe even exacerbated by it. Mean it's good for us to finally be able to collect this data of the person in the home instead of just in the treatment center, but then it's it creates another problem, I think, for us to figure out how are we gathering and integrating that data. What do you guys think? There's that part and it's don't talk to aggregate all this data. But on the other side, each individuals different. So Brett, obviously, I'm sure runnings all the time. So a heart rate from one patient is different from heart rate for another, as to the north, as with their norm is. So yes, you're collecting on aggregant this data, but at the same time you need to break it down to to I think called precision medicine, to extent that each person is individual. So again it's understanding their individual as opposed to saying, well, the population does x. So this is the situation. It's breaking it down too much more specific. Again, this is part of this gross fire...

...hose of data. Is Each person's element square can be different from the next person. We can lower complicates it if we can lower the cost of collecting some of the day that, like you know, Paul Suck Cimetar ten years ago was, you know, twenty five hundred and now it's twenty five and you can do that kind of thing. It's very important. You know, somebody comes to the doctrine they have back pain and they go get an x ray and they see spinal abnormality. It's almost impossible not to jump to the conclusion that that pain is caused by that Avermello, the abnormality. They have been around for fifteen years and be completely unrelated to the to the back pain. But getting baseline data and looking at changes is really valuable. So, Brent, earlier you mentioned a term data biome. Tell us what did you create that? Did come up with that on your own? Where that come from? What does it mean? where? You caught me Roxy I did you know, I thought I created it right there on, commented Jeff said, and then you caught me just now, because is that you were finishing up that portion. I was googling. So I saw, you know, went to Google real quick and I see that there's quite a googling on the show. I'm I love it. Yeah, last well, you know, I felt that question was comment, so I wanted to check how someone else would go going. So yourself, I check myself. So, yeah, someone's already got this data biome term out there. But you know, it's not that hard data. You've got a lot of microbiomes, a lot of biomes out there, a lot of study. But if you think it the way I would conceptualize a data biome, it's look at all the inner all the data that we would view like Phi, your standard medical record, and then you look at the biometric data, you look at all the wearables we have down and bring that in as another line of data, and then you look at Genomic data and then you look at consumer data. You know, I had a discussion. Someone reached out to me about rural health and I'm like, you know, really, when you start thinking about people's health, you know it's how you how you exercise. How many times have I said Diet and exercise, Diet and exercise, and how many times of patient's not listed? Listen to that. But when you really think about it, if you could look, if you had some idea of like, okay, this is what you're really eating, these are the receipts. You know, I see that you have like less than one percent of your monthly receipts in fruits and vegetables, in fresh fruits and vegetables, not can and then if you think about getting down to that consumer data and aligning that to give you context of everything that's going on, including social determinance of health. When I'm hearing a lot of people ask for social determined data, you put that all together. Then you start getting this I would I really like the term data buy I on while others can like claim to it, I think we can use it here or approach it, and because that's what we're chasing. You know, we want that comprehensive view of patients so we can treat him. Because you can. You can tell me you have two diabetics. I can have diabetic and patient in room one and two, but they're going to be different based on all of that contextual data around them. The my doctor asked me how many beers a day do you drink, and I said well too. They said, really, how many beers a week do you buy? That is the epitity of the accounting there, between the consumption and the acquisition. That's right, but that but that's it's an exit, a great example of back to roxy. One of your comments was in terms of the data and the context of the data. It's also with all these things. How is the data requested or asked of the individuals a patient generated? Is it? Where will related? Where's it come from? Because the context what's being asked? Just to your point, Jeff, is they maybe two different answers.

So it's again awareness of that becomes what? Why not? That just another factor, but that becomes a factor in at all and will determine whether it is good data or bad data, in which it's actual or not. Yeah, and that passive data, that cannot lie versus, you know, the qualitative data, where it could be that we're lying or we just remember it very differently than the research show out. Yeah, I remember not long ago that I went to a new doctor and they wanted me to fill out a paper record, which was just very difficult for me. Cost a lot of stress in fact, for me to fill out a paper record. And they said you have any surgeries or illness, just report and I said now, a couple and and by the time I finished billing that perform I said can have another sheet of paper. Place had forgotten. All right, you just can't remember. And when we create medical records for people, I've done on previous occasions, it actually takes months, months to get the record to be complete because they're just so much family history, so many other related histories that you cannot you cannot remember on the fly. So it takes multiple iterations and literally months, which you can't compress in two hours. They can't. It can't be done. You gotta take calendar time to do it. So I think a perpetual cumulative patient owned record that can always be audited and corrected but never recreated. And never try to remember your appendectament. All right, it's always there. You can look at it and correct it. But that's really critical for medical record accuracy. If you look at an medical record for a hospitalize patient, it will be filled with uncorrectable errors. Some of them don't cause harm, but it is ridiculous number of errors of omission and commission in any hospital, pit, hospitalize pensions record always yeah, so when we talk about this idea, brnt that I'm going to just give you credit for that. You came up with the patient biome, pasion data biome. You know I mean, it sounds like a dream come true. So how far are we really from that reality? Are Is there in are there any players in the market place that are either moving the needle or best suited to help us get closer to that full holistic aggregation of data? Yeah, I think that. You know, the the term that comes to mind is PHR. It seems like there's an interest around PHRs. I mean there's some failures. Noted failures out there early on, but if you think about how apple came back with the tablet, I mean the people were saying tablet was no more and then here you come with the tablet and people like the IPAD. I think I don't know if I can give specific companies. I know people are working on it and I want to be correct, but I don't know that anyone's out there doing it right or is going to get it. You know, there's some big companies. They all want to do health data. I think the real question is how do we get patients involved? You know how I've always believed that patients are are sometimes the last people thought about in healthcare. And we talked about how important it is to move data. Why doesn't the patient get some credit, you know, for day to used to we would bring glass bottles back to the grocery store and get credit for them. We get like a deposit on them. You know, why don't patients get some type of remuneration for agreeing to be, you know, very liberal in sharing their data, whether that's for research or other other causes? And I think that patients for a lot of years, as long as I've been practicing, and I've been practicing twenty years now. It's hard, hard to believe I'm...

...saying that, but even in the early days when I thought a universal record was coming like within five years, that patients still at that time they thought that that medicine had everything, they thought that healthcare has everything. They're like, you just have it a computer, you just have it there. There's this belief among patients that we have all their data perfectly, much like a credit report, like an experience Trans Unit, Equifax. I think we're going to have to look at that model. Personally, I don't believe in interrupt anymore. I don't think we're going to get interoperability. I think interoperability is going to come through some other mechanism and that mechanism is going to look like the credit reporting bureaus, because actually they're their philosophy is correct. How I think? I think interruperability can come by having the focus of the information, the ownership of it, with the patient. So you're going to you're not trying to get a lot of different caregivers to seamlessly exchange information. What you're doing instead is really allowing a patient, and I don't say this lightly that I know the patient needs help in doing it. That patient can't manage their own medical record anymore than you can manage your own taxes. Right. So it's too complex. Not that it's complex, but it's more complex and specialized than any patient will want to do. But if you have that assistance, that paid for, monetize kind of assistance to help organize your record and it's easily transferable to your next caregiver, whoever it is and wherever it is, that that gives you the kind of interoperability. As long as there's an agreed upon structure. And today structures the continuity of care record and ast and thirty one and all that stuff, that's not really that doesn't really do the job. It too complex. Yet it's too simple. There's, I think, a better structure. But the more important thing isn't the structure of the data as much as it is the ownership of it. In the ownership will drive the apability. To your point, Jeff, with all the patients own their data, the system itself will before us to go, Oh, we want this data, we're going to have to actually create this universal interoperability. So becomes a demand. Push you want to be in business, you have to find a way of actually be able to read that data. Hey, it's Dr Roxy here with a quick break from the conversation. Are you trying to figure out what moves you need to make to survive and thrive in the new covid economy? I want every health innovator to find their most viable and profitable pivot strategy, which is why I created the covid proof your business pivot kit. The pivot kit is a step by step framework that helps you find your best pivot strategy. It walks you through six categories you need to examine for a three hundred and sixty degree view of your business. I call them the six critical pivot lenses. As you make your way through this comprehensive kit, you'll be armed with the tools, tips and strategies you need to make sure you can pivot with speed without missing out on critical details and opportunities. Learn more at legacy DNACOM backslash kit. I want to go back to something that you said, Brent, about the credit bureau. Talk a little bit more about that and that comparison that you're making. Okay, so I think it's really interesting if you look at. So you look at banks and and you look at sharing information about what's like loans to transunion. Experience Equifox, you upload that information. They do that for free. They do that and why do they do that? Why do they push that information to those bureaus? Is because that they know that they have safer lending universally throughout the financial system...

...because that is shared through those three bureaus. Those are the main ones. So why not in healthcare? Why don't we have that sharing? Because it's better if we share the information about patients and about knowledge about research. The demand for clinical trials is I understand it. I'm not a primary researcher, but those that I know who do do research and promote clinical trials shake their heads. I mean even at larger universities it's just expensive to enroll, it's expensive to conduct. It's very challenging for for universities. Just think what we have now, the data collection tools at our disposal, whether it's a biometric device or our phone. And what you know? If if patients on their data and say look, if you got, imagine getting a text message that you qualify for this study, you know you can roll in the study for a hundred dollars? Would you release your mate? Would you release you your healthcare information from this APP? You know, click release your healthcare information. All of a sudden, boom you're in roll, boom you're in Rowe. Boom your in Rowe. We've got to wet that the information is there and we're healthcare has an Amnesia when it comes to patient data because at every point of carry it's you Redo that work all the time. So annoying. Were Two thousand and twenty one and we're still filling out the paper chart every time. You don't want to cause harm with the data, and one of the things that causes harm is the request for re entry of data, which is just an opportunity for a transcription error. You never want to re enter data. You want to confirm it, edited, check it off, sign it, initial it, whatever you want to do to review it, but you really should never allowed data to be re entered. The other data point is that you should avoid excess specificity. All right, we wanted from a billing system ICDNINE CODES TO ICD ten, and there was never really even a theory as to how that could help anybody. It just it forced people to go to computerized billing. So it helped that industry but no one else, because they the artificial specificity actually caused harm, and I think just can't argue with that. If it's no, it's like how com I. Well, yeah, it's and that's part of the problem is it's this forced innovation. Well, we got to show we're innovatd with making changes. But sometimes to get to point, if there's no basis for that except change for the sake of change, you can create more arm or more confusion or again, in particularly to transfer information between the two systems. It's hard to see the good. Just change the sake of change isn't necessarily good change. If you imagine if someone decided we were going to have a hundred commandments instead of ten. Well, and you know, you guys touched on, you know, the patient being the person that owns the data and then being the ones that are kind of determining how it gets integrated and who it gets integrated with, and you know, kind of like the tax prep and you know what comes to mind is really almost that role being of the caregiver. Now, maybe there's not always an official care giver involved, but you know, a lot of times we're talking about really sick people that aren't going to necessarily be that tax prep person. But a lot of times the care giver, it just in the whole effort of being an advocate for their loved ones, ends up becoming kind of that text tax prep person that has to that needs to be responsible for aggregating all of that data and in determining who gets it. That and that...

...becomes tricky, because we went through that with my in laws as they're getting older. Is you become that advocate. But you know what, what prep of I had? But education, if I had, you know, to become that, or my wife and I become that advocate. We're sure of put in that position. And so you're making a SOS. A lot of these decisions being made, yes, as a patient ownership, and it comes down to okay, well, what do you know about what should be transferred or what what elements of this are value or not valuable? So I have told people that by education of an MBA, and I said m now stands for medical just because of all these things I had to do and forced to be it. Now I don't think it's a credit anywhere, but I kept them trying, but but we're forced to and as we get, you know, more of these sandwich generations and we're, you know, caring for parents. Is that example that we're becoming those caregivers. We don't have the education, but all we do is, you go online. So we did talk to a few people. Now is all we can do. So again, because it goes back to the context. But, alas, data. What's the proper context and what's the valuable context that we have that can actually make those decisions? Think it's certainly not a black and white scenario. Yeah, I think you've got aggregating and then you've got navigating, you know where the caregiver comes in, and then I think you've got decision support, and I think that's where autonomy comes in, because in health care we still have to abide by patient autonomy and I think as people have more tools at their disposal, I think if we can show them how their day they can give them greater opportunity around the knowledge of what's going on with them, around the potential consequences of their disease or the outcomes or the treatment algorithms. I think that that people can make use of those, but they they're going to have to have the user experience correct and again they will not work well if you don't have good data going into them, and I think rather than the health healthcare systems be worried about that, I think they're going to have to use those with patients, because I think this whole thing around data and data blocking and not sharing data, I think health care systems see that is the ultimate advantage. But that all that's going to go away. When it goes away, I'm not sure, but it is going to go away as patients have more control over their data and I think you're going to find a patients still need their local healthcare systems as much as they always have and trying to come go, get past that fear is going to be, I think, a big leap for data integration. Right another a baby step where today, if you go to quest or Lab Corp, you know you'll get your your blood analysis and all the outlying values will be highlighted in red, and that's very, very helpful. Right. It's extremely helpful because it is what you also had a color coding or some other indication of how it shifted, even if it's in in in limits, within limits, or out how it shifted over time. Right. So you've got the patient became the normal value set and you're looking at exceptions. So you can look at clinical exceptions, obviously for all mammals or whatever you or to look at, but in addition get in the benefit of trend for that individual patient. Yeah, I think. I think those tools are going to be great in getting people a customed I think if you look at some basic bio metrics like walking, like step counters, things like that, just I think it really is. I mean, I think that's where I think this is where the counterintuitive play comes in for success and healthcare innovation. I think you've got to have the science and I think you had to have the artist come together, because people are going to engage as they have a very rich infographic experience. If you show me my data and it excel spreadsheet, that's boring. If you show me, you know,... data with color and maybe bubble charts or something that really give me more of a more of an insight and more of an engagement with my healthcare data, I'm the patients wrinkled in, then maybe you know, look, you, you know, you're doing great. Oh, by the way, humanagers sent you a note. Anthem just sent you a note and said you're doing so great that they're going to you know, you're going to get this. I mean maybe you're going to get a water bottle or or a baseball CAP, or maybe you're going to get five dollars off your premium or something. I mean we all there's there's different tools out there, but I think people, I think to your point earlier, Jeff, you talked about the money. How you install systems based on the money, and I think that's where healthcare systems are. We talked about treating patients in the virtue of treating patients, but to the end of the day you're usually we are leading based on the economics and then the humanistic aspect of the patient care and the physician care and the promoter care are sometime secondary, based on how quickly you can get the economic installation in. So I think we have to I think we have to bridge that and think about more how we engage to get better outcomes. So who's responsible for educating consumers about the role that data plays in their health care and their family and the the rights that they have to ownership and even potential compensation. Well, I think, and add an element to that and not that as I have an answer to your question, which is a very value important question, is the day we've been talking about the most parts sort of leaning bit more to Biologics, but you also have the mental health side of things until the s Dh as well, when you look at data relating to financial records, to buying habits, to where you driving, like all sorts of things that tie into the mental health side, which is a whole other set of data which is considerably more complex, but deals on the mental health side. And so to your question, Roxy How do you educate somebody when I would argue, Brent, and hopefully can tell me I'm wrong, we really don't have a handle on all that yet. We just realizing. You have all these data points, but I if there's necessary real handle on the effect all these people, all these things have even on the on the healthcare system, let alone the individual for making those decisions. HMM. Well, and you think about the amount of data, just consumer data, that we provide to let's say facebook or instagram or even just google, based upon our Google search right? Or are Google maps or apple maps right? There's just so much data that these other entities are collecting that can be that can be used against us in harmful ways, whether we realize it are not, and we have a very different conversation about keeping that information private or disclosing that information allowing these tools to have full access to it, versus our healthcare data that, you know, in some ways is even more critical for it to be managed well. I think all the patient privacy stuff is, I think, according to most patients, a little bit overblown, because they're very few medical anomalies that you have that aren't obvious to people anyway. I mean there I guess there are some people walking out seemingly healthy but you know, secretly. More with the obese or seemingly healthy but with the broken leg. A lot of it is not secret, right. A lot of pretty obvious. They may not know exactly what you have, but they know what here you're sick or not. But it's what I said, maction privacy is certainly an impediment to innovation in healthcare management. I think in your question, rocksy,... asked you know, who educates consumers, and I think you know calling them, using the term consumers is very important here because it is I've gone along in my career, I I've opened up my view of health care and I've come to the realization now that health care is everyone's responsibility. Whether you have a health illness, healthcare is still going to affect you in some form of fashion, and I would anyone who can tell me how it's not. I would love to know, because whether that's in your whether that's in your investment in count whether it's in your taxes, whether it's in your family, I mean you're going to be affected by health care and how health care is utilized and how people treat their health and we've got a we've got a long way to go if you think about where we are right now with yeah, we do and them it and happen, how people are looking at health and healthcare and science and the lack of scientific literacy that we're seeing around, how we're just dealing with one issue and it's not like we it's not like we've we cured cancer and obesity and cardiovascular disease and all of that and now we're just we have covin on our plate. We've sort of forgotten about all of the chronic diseases as we deal with with this most most immediate threat that we're seeing through covid. So I think everyone hasn't hasn't obligation in educating consumers and I would like to see more, you know, whether it's retail space or whether it's a large online retailer or an in story retailer, to, you know, to work innovatively, to think about how they can interact with patients health because again, you know, go back to diet and exercise. You know, I think we need to rethink those. I think just, you know, maybe a hackathon on those would be really good. How can we rethink how to inform people on what they're doing? So I'm going to call out all the billionaires out there, Jeff Bezos, the trillionaire out there. Trillion are before Covid, I don't even know what you call it. Now, after Covid, you know that needs to grab ahold of this mission and invest some dollars in this, because I agree with you. I think that every person in the world can benefit from this type of education and greater literacy. So we talked a lot of we talked about a lot of different layers around data and patient data. So we think about the purpose of this show in the audience of the you know our listenership and viewership. How does this conversation that we've had today affect those innovators that are in the market place today, that are bringing innovations to market, or maybe even those people who, you know, are playing some role in the innovation ecosystem? I think everything, every device or every invention has got some element of information to it. I'm not perhaps not everything, but when in the most part it does, and you just have to be thinking about your invention in the context of a much, much bigger ecosystem. You know, it's no longer okay to think about segregated data, and so I think that's the big thing. You could get away with it, you know, ten years ago, but today you've got to figure out how you play, you know, in the internet or things and Internet of medical information. So I think that's the bigger thing. Think more broadly about where the information is coming from, where does it have to go and how do you fit into holistic picture? Yeah, I think the use within the ecosystem is really important because said there, as we talked about earlier, there's many, many elements in that explosystem and the different data points to be a different value. Along the waist it's going to be really aware of all that and the fact it's going to be used and if it's collective's going to be potentially used and could be misused, not nessy intentionally,...

...but just misused. Got To be aware to make sure it it's contextualized and processed in a manner that is usable understood along along the way. You know, I I've come to this and looking at date and what it means and I think it all bowls down to prediction. I think it every data point, you know, insight, knowledge, however you want to frame that. I think ultimately, people who are really playing the big data game or looking towards prediction everything that do. How did that predict your next site, your next Click, your next movement? You know, in in medicine it's your next heart attack or your next visit or I think that really thinking about how you use data for predicted capabilities, because I think that that is one of the core areas. I think that's the reason I believe it to be important is because I think the quest for prediction drives value, incredible value. Yeah, so I you know, have a different guests on the show all the time and there's a couple that come to mind when I hear you describe that, Brent. So one of them in particular is doing some predicted prediction or around bone density and the propensity to fall and so kind of looking at early interventions of people, you know, the cost of the healthcare system once they fall, the cost to that family and that quality of life, and so that's that would be just one really you know example that comes to mind really quickly of how this is, you know, data being used in a real positive way, in a real valuable way, not just for the sake of data, in that type of prediction. Or you think about heart attacks, right, you know all of the different indicators that could potentially predict where when someone's going to have a heart attack versus heartburn, and you know, what are some interventions that could be at least presented having that greater awareness? Yeah, the the I'm about to engage in some clinical studies for a new infusion pump, and this the world today is very, very, very different than it was ten years ago. So the FDA is looking for USABILITY studies, but they're still thinking about a clipboard and somebody watching you know a dozen nurses use their product. But what we're building in are very sophisticated counters of behavior to find out, you know, how many millisecond delay is that between this act and that act, and look at the big data aspects of it to find out where are people possibly confused? You know, where are they hunting around? Where did they make a mistake or you know, where did you let them make a mistake? So it's a very different world in terms of information management. That was even even ten years ago and I thought, yeah, that goes to the point. It just the so much data blood. We don't even know what kind of data that we really have. Like we know sort of specific points of data, just and examples. We had data as a communication platform. A nailment of data that we realized again in the mental health site. He is not just the date, time step of when someone responded to a message, but with, you know, measuring how quick someone does respond to a message, whether it's email, text or whatever the case may be, and back to the change in the speed of which they change, where it takes some longer and longer longer. Again we've realized that becomes an indication of a change of behavior and the working the condition with that change is but that was a data point that resulted as a because of collecting data points, so I said. So within all that data there's new data points of collection to explore that we invaluable for management in the decision support. So many closing words as we wrap up here. We kind of wrap up this conversation around data. Data. The big name is, you know,...

...patient centered data. That's so much more holistic than we had ever imagined a short time ago. It's all the stuff that Brent talked about the biome, and it it be. It's becoming easier and easier to do it. So I'm actually hopeful that that kind of data will will be available because the speed with which we can do things now and all our financial transactions is instantanins and there's really no technical reason why we can't be doing the same thing and healthcare it Moil and come. As we're looking at the patient and data is we talked about earlier, look at the patient as a consumer. So in terms as a perspective, I think that helps understanding the data, of the value the data and the need for the data of the patient side onward. But I think until you and we're recognize you here. Ain't more and more, but I think until the entire ecosystem recognizes it, you're can still going to have something stop gaps. Once it's all recognized, then okay, at least you're moving towards solution to managing all this. I think is as this new patient consumer moves forward and healthcare and moves forward within the entire ecosystem, whether it's healthcare or commerce. I think that what we really see need to happen is ubiquity. We really want this data ubiquity around around individuals so we can get the we can get the best from our life. Man. I think think that sounds somewhat clasche but that's really I mean a lot of healthcare is oftentime seen is just being a moment of trial, a moment of sickness or illness. But I think that we're going to have to change the paradigm around how we view health and health data to make sure we are adapting every day to our best wellness and and that that's going to be an exciting time and that's where some great tools are going to be able to be used in implemented. But ubiquity for me, that's where we want to drive around data and that's where we're going to see some exciting things advance. So I'm going to leave us a close us out on this word that I think every innovator out there should go and poach a data scientist from a consumer goods company. Procter and gamble, watch out for your data scientists, right. These are the folks that have been dealing with consumer data and understand how to leverage it in and out and and we could use some of those folks and healthcare and having, you know, as we as innovators, whether we are corporate innovator or we're a startup or academic innovator, you know, thinking about our team and the teams that were building and planning how that data scientist and when they fit into that role, just like you would having a CEO, a president of Business Development Person, a marketing person, thinking about the role that data science plays on really, really early on in that business model. Well, thanks, guys. Thanks again for joining me. Is another great episode on the health innovator show. Really appreciate your wisdom today. Thank you, thank you, Roxy. Thank you so much for listening. I know you're busy working to bring your life changing innovation to market and I value your time and attention. To get the latest episodes on your mobile device, automatically subscribe to the show on your favorite podcast APP like apple podcast, spotify and stitcher. Thank you for listening and I appreciate everyone who shared the show with friends and colleagues. See You on the next episode of Health Innovators.

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