Health Innovators
Health Innovators

Episode 101 · 3 months 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, apodcast and video show about the leaders, influencers and early adoptors who are shapingthe future of healthcare. I'm your host, Dr Roxy Movie. Welcome back healthinnovators. On today's episode, you're in for a real treat. Weare hosting another executive briefing with some brilliant leaders and great colleagues and friends,Jeff Carlyle, the president of Numa Systems Corps, Howard Rosen, CEO andfounder of life wire, and rent right, the Associate Dean for Real Health Innovationat the University at Louisville. Welcome back to the show, guys.So today we're talking about data, patient data, medical data, and soI want to just kind of kick off our conversation with what's the difference betweengood data versus bad data? Well, in New England, of course,we referred to it as Dator. So is that good data or bad dayor dat one of the distant the data doesn't really have any value unless it'sbeen converted to information and that conversion requires context. Organization includes, you know, sorting and filtering and that kind of thing, and then analysis. Sothat 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 justreal information that's actionable. So if you have information that's not actionable, thenit'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 ofthat is it depends on who is utilize the data, because you have differentgroups, as we talked about earlier in the pre show. Is the patienthas 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 whatmade bad data, using the context of actual for one group, maybeactually valuable and good for another? Well, that's the kind of white why shouldthis be easy? Yes, absolutely, exactly, because why should this beeasy? Okay, so what is what's happening like? What do wedo? Do we have a lot of data out there? Do we havea lot of information? What's what's happening today, and what are we expectingto happen in the future? I think we're drowning a data. I meanI think we suffer from data day loose syndrome. I mean it's just aswe're data calls US anxiety. We have so much of it but we don'tknow how to put it together, to bring forth information and knowledge, togive us concept and that allow us to act, because that's the reason wehad in medic medicine. We want data so we can act on the behalfof the patient. That's what we want to do. But because Datas anddisparate sources and we don't have true in or off to this day to allowus to bring a lot of information forward, we just don't have that. Soit leadst to a lot of frustration. Really medical when medical errors happen andthe 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 manycases they injury it's caused to the patient could have been solved by athree 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 ofproblem 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 tohelp fix because it's going to be able to help us process these gigantic datasets and pull out something that's meaningful. I think it's certainly certainly helped toa 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 sourceof data, to source information. That necessarily AI machine learnings are going tohelp because they're been going to their clinician or to the provider with that dataset get whether it's actual or filtered to anything else. That just adds tothe fire hose of additional data that bread was talking about and I think wehave to be careful about the noise, you know, that's generated from allof this. I mean we just want clarity. If AI and machine learningcan give us clarity, can take a lot of information and bring it tothat single point of truth that we're seeking, I think that's wonderful. But ifit's just going to generate more, more baggage, more noise within thesystem, it's very frustrating. I mean, but I think you even you haveto back up here as you start talking about, you know, termssuch 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 tomake sure we get, you know, a diabetic known as diabetic, whereyou can take a thousand, Tenzero, a hundred thousand diabetics, where youcan put that into these algorithms and then get true knowledge coming forth. Westill have dirty data. We still have a lot of dirty data out thereand systems that don't communicate well with one another. So we're still in manyways, while we're talking about such great tools that were mentioned here, suchas AI, we still in healthcare, I think, in the trenches juststruggle. I think you know, we we joke, a few of USjoke about how sometimes how beautiful paper, paper is. You can talk aboutpapers be an archaic, but sometimes there's just a beauty of paper that youcan 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'tdo that for patients, you know, within electronic medical records. So there'sstill there's still a beauty there. You have to balance it, but reallygetting it right. That's that's where that's where I'm looking for the true innovatorsto come forth. How do we really not just data science but but bringingthat, merging that data science and data graphic work together to really help usas clinicians and patients? And it's saying that there's nothing new under the sunand 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 isgoing to be picked back to paper and we're going to in two thousand andthirty 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'retalking 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 knowthis 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 andexactly the same way. So we were doing is not measuring the data butmeasuring the change in the data from those wearables, and that actually became valuableinformation when he's we saw that change. So it's not taking the the mainthing we just look at the change in the Delta actually became a very valuableinsight 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 aninformation? Back to Jeff's comment, that's one way we actually took data thatwas not necessarily strong as it was, but made it information by just takingthe measuring the change over time. You know, we touched on a previouscall about the FDA. But if you were to understand that the information youcollect 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 inthe 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 gooddata, then the clarity becomes is amazing. Right. So that, I thinkai 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'donly had a computer helping with the data. It just doesn't happen, because that'snot the perfectly limiting factor. It's in the aggregate that is, butfor 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 havesome predictions about what may potentially happen with that patient. I think the Ithink we've yet to see the potential of that type of predictive and analytics onthese 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 getpeople more used to wear and, you know, devices that collect the biometricdata. I mean we don't we don't know what it's like to measure someone'sheart rate over ten years. We're going to know what that's like in theyears and decades ahead to see what what type of subtle variability that exists thatmay 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 hadthe ability to collect that data and that's, you know, it. Coming backto the AIML point, I think that that's what's really exciting. Ido 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 goingto 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 thatdata even more despair it because now we've got virtual care, right, we'vegot all of these remote patient monitoring all these wearables, all of these differentdevices and to collect data that's outside of the traditional healthcare ecosystem. So it'seven more siload than ever before, and so I think the the challenges thatwe've had previously with interoperability and creating some kind of centralized data set is maybeeven exacerbated by it. Mean it's good for us to finally be able tocollect this data of the person in the home instead of just in the treatmentcenter, but then it's it creates another problem, I think, for usto figure out how are we gathering and integrating that data. What do youguys 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 patientis different from heart rate for another, as to the north, as withtheir norm is. So yes, you're collecting on aggregant this data, butat the same time you need to break it down to to I think calledprecision medicine, to extent that each person is individual. So again it's understandingtheir individual as opposed to saying, well, the population does x. So thisis 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'selement square can be different from the next person. We can lower complicates itif we can lower the cost of collecting some of the day that, likeyou know, Paul Suck Cimetar ten years ago was, you know, twentyfive 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 haveback 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 bythat Avermello, the abnormality. They have been around for fifteen years and becompletely unrelated to the to the back pain. But getting baseline data and looking atchanges is really valuable. So, Brent, earlier you mentioned a termdata biome. Tell us what did you create that? Did come up withthat on your own? Where that come from? What does it mean?where? You caught me Roxy I did you know, I thought I createdit 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 Isee 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 termout there. But you know, it's not that hard data. You've gota lot of microbiomes, a lot of biomes out there, a lot ofstudy. 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 likePhi, 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 asanother line of data, and then you look at Genomic data and then youlook at consumer data. You know, I had a discussion. Someone reachedout 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 youexercise. How many times have I said Diet and exercise, Diet and exercise, and how many times of patient's not listed? Listen to that. Butwhen you really think about it, if you could look, if you hadsome idea of like, okay, this is what you're really eating, theseare the receipts. You know, I see that you have like less thanone percent of your monthly receipts in fruits and vegetables, in fresh fruits andvegetables, not can and then if you think about getting down to that consumerdata and aligning that to give you context of everything that's going on, includingsocial determinance of health. When I'm hearing a lot of people ask for socialdetermined data, you put that all together. Then you start getting this I wouldI really like the term data buy I on while others can like claimto it, I think we can use it here or approach it, andbecause that's what we're chasing. You know, we want that comprehensive view of patientsso we can treat him. Because you can. You can tell meyou have two diabetics. I can have diabetic and patient in room one andtwo, but they're going to be different based on all of that contextual dataaround them. The my doctor asked me how many beers a day do youdrink, and I said well too. They said, really, how manybeers 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'sit's an exit, a great example of back to roxy. One of yourcomments was in terms of the data and the context of the data. It'salso with all these things. How is the data requested or asked of theindividuals a patient generated? Is it? Where will related? Where's it comefrom? 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 becomeswhat? Why not? That just another factor, but that becomes a factorin 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 cannotlie versus, you know, the qualitative data, where it could bethat 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 doctorand they wanted me to fill out a paper record, which was just verydifficult for me. Cost a lot of stress in fact, for me tofill 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 timeI finished billing that perform I said can have another sheet of paper. Placehad forgotten. All right, you just can't remember. And when we createmedical 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 familyhistory, so many other related histories that you cannot you cannot remember on thefly. So it takes multiple iterations and literally months, which you can't compressin two hours. They can't. It can't be done. You gotta takecalendar time to do it. So I think a perpetual cumulative patient owned recordthat can always be audited and corrected but never recreated. And never try toremember your appendectament. All right, it's always there. You can look atit and correct it. But that's really critical for medical record accuracy. Ifyou look at an medical record for a hospitalize patient, it will be filledwith uncorrectable errors. Some of them don't cause harm, but it is ridiculousnumber of errors of omission and commission in any hospital, pit, hospitalize pensionsrecord always yeah, so when we talk about this idea, brnt that I'mgoing to just give you credit for that. You came up with the patient biome, pasion data biome. You know I mean, it sounds like adream come true. So how far are we really from that reality? AreIs there in are there any players in the market place that are either movingthe needle or best suited to help us get closer to that full holistic aggregationof data? Yeah, I think that. You know, the the term thatcomes 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, butif you think about how apple came back with the tablet, I mean thepeople were saying tablet was no more and then here you come with the tabletand people like the IPAD. I think I don't know if I can givespecific companies. I know people are working on it and I want to becorrect, but I don't know that anyone's out there doing it right or isgoing to get it. You know, there's some big companies. They allwant to do health data. I think the real question is how do weget patients involved? You know how I've always believed that patients are are sometimesthe last people thought about in healthcare. And we talked about how important itis 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 grocerystore 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 researchor 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 theearly 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, theythought 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 haveall their data perfectly, much like a credit report, like an experience TransUnit, 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 goingto get interoperability. I think interoperability is going to come through some other mechanismand that mechanism is going to look like the credit reporting bureaus, because actuallythey're their philosophy is correct. How I think? I think interruperability can comeby having the focus of the information, the ownership of it, with thepatient. So you're going to you're not trying to get a lot of differentcaregivers 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 indoing it. That patient can't manage their own medical record anymore than you canmanage your own taxes. Right. So it's too complex. Not that it'scomplex, 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 ofassistance 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 ofinteroperability. As long as there's an agreed upon structure. And today structures thecontinuity of care record and ast and thirty one and all that stuff, that'snot really that doesn't really do the job. It too complex. Yet it's toosimple. There's, I think, a better structure. But the moreimportant thing isn't the structure of the data as much as it is the ownershipof it. In the ownership will drive the apability. To your point,Jeff, with all the patients own their data, the system itself will beforeus to go, Oh, we want this data, we're going to haveto actually create this universal interoperability. So becomes a demand. Push you wantto be in business, you have to find a way of actually be ableto read that data. Hey, it's Dr Roxy here with a quick breakfrom the conversation. Are you trying to figure out what moves you need tomake to survive and thrive in the new covid economy? I want every healthinnovator to find their most viable and profitable pivot strategy, which is why Icreated the covid proof your business pivot kit. The pivot kit is a step bystep framework that helps you find your best pivot strategy. It walks youthrough six categories you need to examine for a three hundred and sixty degree viewof your business. I call them the six critical pivot lenses. As youmake 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 missingout on critical details and opportunities. Learn more at legacy DNACOM backslash kit.I want to go back to something that you said, Brent, about thecredit bureau. Talk a little bit more about that and that comparison that you'remaking. 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 likeloans to transunion. Experience Equifox, you upload that information. They do thatfor free. They do that and why do they do that? Why dothey push that information to those bureaus? Is because that they know that theyhave safer lending universally throughout the financial system...

...because that is shared through those threebureaus. Those are the main ones. So why not in healthcare? Whydon't we have that sharing? Because it's better if we share the information aboutpatients and about knowledge about research. The demand for clinical trials is I understandit. I'm not a primary researcher, but those that I know who dodo research and promote clinical trials shake their heads. I mean even at largeruniversities it's just expensive to enroll, it's expensive to conduct. It's very challengingfor for universities. Just think what we have now, the data collection toolsat our disposal, whether it's a biometric device or our phone. And whatyou know? If if patients on their data and say look, if yougot, imagine getting a text message that you qualify for this study, youknow you can roll in the study for a hundred dollars? Would you releaseyour mate? Would you release you your healthcare information from this APP? Youknow, click release your healthcare information. All of a sudden, boom you'rein roll, boom you're in Rowe. Boom your in Rowe. We've gotto wet that the information is there and we're healthcare has an Amnesia when itcomes to patient data because at every point of carry it's you Redo that workall the time. So annoying. Were Two thousand and twenty one and we'restill filling out the paper chart every time. You don't want to cause harm withthe data, and one of the things that causes harm is the requestfor 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 todo to review it, but you really should never allowed data to bere 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 helpedthat 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'slike how com I. Well, yeah, it's and that's part of the problemis it's this forced innovation. Well, we got to show we're innovatd withmaking changes. But sometimes to get to point, if there's no basisfor that except change for the sake of change, you can create more armor 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'tnecessarily good change. If you imagine if someone decided we were going to havea hundred commandments instead of ten. Well, and you know, you guys touchedon, you know, the patient being the person that owns the dataand then being the ones that are kind of determining how it gets integrated andwho it gets integrated with, and you know, kind of like the taxprep and you know what comes to mind is really almost that role being ofthe 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 thataren't going to necessarily be that tax prep person. But a lot of timesthe care giver, it just in the whole effort of being an advocate fortheir loved ones, ends up becoming kind of that text tax prep person thathas to that needs to be responsible for aggregating all of that data and indetermining who gets it. That and that...

...becomes tricky, because we went throughthat 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 andI become that advocate. We're sure of put in that position. And soyou're making a SOS. A lot of these decisions being made, yes,as a patient ownership, and it comes down to okay, well, whatdo you know about what should be transferred or what what elements of this arevalue or not valuable? So I have told people that by education of anMBA, and I said m now stands for medical just because of all thesethings I had to do and forced to be it. Now I don't thinkit's a credit anywhere, but I kept them trying, but but we're forcedto 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 thosecaregivers. We don't have the education, but all we do is, yougo online. So we did talk to a few people. Now isall we can do. So again, because it goes back to the context. But, alas, data. What's the proper context and what's the valuablecontext that we have that can actually make those decisions? Think it's certainly nota black and white scenario. Yeah, I think you've got aggregating and thenyou've got navigating, you know where the caregiver comes in, and then Ithink 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 thinkas people have more tools at their disposal, I think if we can show themhow their day they can give them greater opportunity around the knowledge of what'sgoing on with them, around the potential consequences of their disease or the outcomesor 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 againthey 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, Ithink they're going to have to use those with patients, because I think thiswhole thing around data and data blocking and not sharing data, I think healthcare systems see that is the ultimate advantage. But that all that's going to goaway. When it goes away, I'm not sure, but it isgoing to go away as patients have more control over their data and I thinkyou're going to find a patients still need their local healthcare systems as much asthey always have and trying to come go, get past that fear is going tobe, I think, a big leap for data integration. Right anothera 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 behighlighted in red, and that's very, very helpful. Right. It's extremelyhelpful because it is what you also had a color coding or some other indicationof 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 patientbecame the normal value set and you're looking at exceptions. So you canlook at clinical exceptions, obviously for all mammals or whatever you or to lookat, but in addition get in the benefit of trend for that individual patient. Yeah, I think. I think those tools are going to be greatin getting people a customed I think if you look at some basic bio metricslike walking, like step counters, things like that, just I think itreally is. I mean, I think that's where I think this is wherethe counterintuitive play comes in for success and healthcare innovation. I think you've gotto 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 bubblecharts or something that really give me more of a more of an insight andmore of an engagement with my healthcare data, I'm the patients wrinkled in, thenmaybe you know, look, you, you know, you're doing great.Oh, by the way, humanagers sent you a note. Anthem justsent you a note and said you're doing so great that they're going to youknow, you're going to get this. I mean maybe you're going to geta water bottle or or a baseball CAP, or maybe you're going to get fivedollars off your premium or something. I mean we all there's there's differenttools out there, but I think people, I think to your point earlier,Jeff, you talked about the money. How you install systems based on themoney, and I think that's where healthcare systems are. We talked abouttreating patients in the virtue of treating patients, but to the end of the dayyou're usually we are leading based on the economics and then the humanistic aspectof 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 Ithink we have to I think we have to bridge that and think about morehow we engage to get better outcomes. So who's responsible for educating consumers aboutthe role that data plays in their health care and their family and the therights 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 answerto your question, which is a very value important question, is the daywe'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 aswell, when you look at data relating to financial records, to buying habits, to where you driving, like all sorts of things that tie into themental health side, which is a whole other set of data which is considerablymore complex, but deals on the mental health side. And so to yourquestion, 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 onall 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, allthese things have even on the on the healthcare system, let alone the individualfor making those decisions. HMM. Well, and you think about the amount ofdata, just consumer data, that we provide to let's say facebook orinstagram or even just google, based upon our Google search right? Or areGoogle maps or apple maps right? There's just so much data that these otherentities 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 conversationabout keeping that information private or disclosing that information allowing these tools to have fullaccess to it, versus our healthcare data that, you know, in someways is even more critical for it to be managed well. I think allthe patient privacy stuff is, I think, according to most patients, a littlebit overblown, because they're very few medical anomalies that you have that aren'tobvious to people anyway. I mean there I guess there are some people walkingout seemingly healthy but you know, secretly. More with the obese or seemingly healthybut with the broken leg. A lot of it is not secret,right. A lot of pretty obvious. They may not know exactly what youhave, but they know what here you're sick or not. But it's whatI said, maction privacy is certainly an impediment to innovation in healthcare management.I think in your question, rocksy,... asked you know, who educatesconsumers, and I think you know calling them, using the term consumers isvery important here because it is I've gone along in my career, I I'veopened up my view of health care and I've come to the realization now thathealth care is everyone's responsibility. Whether you have a health illness, healthcare isstill going to affect you in some form of fashion, and I would anyonewho can tell me how it's not. I would love to know, becausewhether that's in your whether that's in your investment in count whether it's in yourtaxes, whether it's in your family, I mean you're going to be affectedby health care and how health care is utilized and how people treat their healthand we've got a we've got a long way to go if you think aboutwhere 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 ofscientific literacy that we're seeing around, how we're just dealing with one issue andit's not like we it's not like we've we cured cancer and obesity and cardiovasculardisease 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 withwith this most most immediate threat that we're seeing through covid. So I thinkeveryone 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 oran in story retailer, to, you know, to work innovatively, tothink about how they can interact with patients health because again, you know,go back to diet and exercise. You know, I think we need torethink those. I think just, you know, maybe a hackathon on thosewould be really good. How can we rethink how to inform people on whatthey'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'teven know what you call it. Now, after Covid, you know that needsto grab ahold of this mission and invest some dollars in this, becauseI agree with you. I think that every person in the world can benefitfrom this type of education and greater literacy. So we talked a lot of wetalked about a lot of different layers around data and patient data. Sowe think about the purpose of this show in the audience of the you knowour listenership and viewership. How does this conversation that we've had today affect thoseinnovators that are in the market place today, that are bringing innovations to market,or maybe even those people who, you know, are playing some rolein the innovation ecosystem? I think everything, every device or every invention has gotsome element of information to it. I'm not perhaps not everything, butwhen in the most part it does, and you just have to be thinkingabout your invention in the context of a much, much bigger ecosystem. Youknow, it's no longer okay to think about segregated data, and so Ithink 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 informationis coming from, where does it have to go and how do you fitinto holistic picture? Yeah, I think the use within the ecosystem is reallyimportant because said there, as we talked about earlier, there's many, manyelements 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 factit's going to be used and if it's collective's going to be potentially used andcould be misused, not nessy intentionally,...

...but just misused. Got To beaware to make sure it it's contextualized and processed in a manner that is usableunderstood along along the way. You know, I I've come to this and lookingat date and what it means and I think it all bowls down toprediction. I think it every data point, you know, insight, knowledge,however you want to frame that. I think ultimately, people who arereally playing the big data game or looking towards prediction everything that do. Howdid 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 visitor 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'sthe reason I believe it to be important is because I think the quest forprediction drives value, incredible value. Yeah, so I you know, have adifferent guests on the show all the time and there's a couple that cometo mind when I hear you describe that, Brent. So one of them inparticular is doing some predicted prediction or around bone density and the propensity tofall 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 familyand that quality of life, and so that's that would be just one reallyyou know example that comes to mind really quickly of how this is, youknow, data being used in a real positive way, in a real valuableway, not just for the sake of data, in that type of prediction. Or you think about heart attacks, right, you know all of thedifferent indicators that could potentially predict where when someone's going to have a heart attackversus heartburn, and you know, what are some interventions that could be atleast presented having that greater awareness? Yeah, the the I'm about to engage insome clinical studies for a new infusion pump, and this the world todayis very, very, very different than it was ten years ago. Sothe FDA is looking for USABILITY studies, but they're still thinking about a clipboardand somebody watching you know a dozen nurses use their product. But what we'rebuilding 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 lookat 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 amistake or you know, where did you let them make a mistake? Soit's a very different world in terms of information management. That was even eventen years ago and I thought, yeah, that goes to the point. Itjust the so much data blood. We don't even know what kind ofdata that we really have. Like we know sort of specific points of data, just and examples. We had data as a communication platform. A nailmentof data that we realized again in the mental health site. He is notjust the date, time step of when someone responded to a message, butwith, you know, measuring how quick someone does respond to a message,whether it's email, text or whatever the case may be, and back tothe change in the speed of which they change, where it takes some longerand longer longer. Again we've realized that becomes an indication of a change ofbehavior and the working the condition with that change is but that was a datapoint 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 weinvaluable for management in the decision support. So many closing words as we wrapup here. We kind of wrap up this conversation around data. Data.The big name is, you know,...

...patient centered data. That's so muchmore holistic than we had ever imagined a short time ago. It's all thestuff that Brent talked about the biome, and it it be. It's becomingeasier and easier to do it. So I'm actually hopeful that that kind ofdata will will be available because the speed with which we can do things nowand all our financial transactions is instantanins and there's really no technical reason why wecan't be doing the same thing and healthcare it Moil and come. As we'relooking at the patient and data is we talked about earlier, look at thepatient as a consumer. So in terms as a perspective, I think thathelps understanding the data, of the value the data and the need for thedata of the patient side onward. But I think until you and we're recognizeyou here. Ain't more and more, but I think until the entire ecosystemrecognizes it, you're can still going to have something stop gaps. Once it'sall recognized, then okay, at least you're moving towards solution to managing allthis. I think is as this new patient consumer moves forward and healthcare andmoves forward within the entire ecosystem, whether it's healthcare or commerce. I thinkthat what we really see need to happen is ubiquity. We really want thisdata ubiquity around around individuals so we can get the we can get the bestfrom our life. Man. I think think that sounds somewhat clasche but that'sreally I mean a lot of healthcare is oftentime seen is just being a momentof trial, a moment of sickness or illness. But I think that we'regoing to have to change the paradigm around how we view health and health datato make sure we are adapting every day to our best wellness and and thatthat's going to be an exciting time and that's where some great tools are goingto be able to be used in implemented. But ubiquity for me, that's wherewe want to drive around data and that's where we're going to see someexciting things advance. So I'm going to leave us a close us out onthis word that I think every innovator out there should go and poach a datascientist from a consumer goods company. Procter and gamble, watch out for yourdata scientists, right. These are the folks that have been dealing with consumerdata and understand how to leverage it in and out and and we could usesome 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 planninghow that data scientist and when they fit into that role, just like youwould having a CEO, a president of Business Development Person, a marketing person, thinking about the role that data science plays on really, really early onin that business model. Well, thanks, guys. Thanks again for joining me. Is another great episode on the health innovator show. Really appreciate yourwisdom today. Thank you, thank you, Roxy. Thank you so much forlistening. I know you're busy working to bring your life changing innovation tomarket and I value your time and attention. To get the latest episodes on yourmobile device, automatically subscribe to the show on your favorite podcast APP likeapple podcast, spotify and stitcher. Thank you for listening and I appreciate everyonewho shared the show with friends and colleagues. See You on the next episode ofHealth Innovators.

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