Artificial Intelligence And How It’s Transforming EHR Systems
Harry’s guest John Glaser, senior vice president of Population Health at Cerner, speculates on how business models in healthcare are changing and how artificial intelligence and EHR systems will work together in the future.
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Harry Glorikian: Welcome to the Moneyball medicine podcast. I’m your host Harry Glorikian, this series is all about the data-driven transformation of the healthcare and life sciences landscape. Each episode we dive deep through one-on-one interviews with leaders in the new cost-conscious, value-based healthcare economy. We look at the challenges and opportunities they’re facing and their predictions for the years to come.
My guest today is John Glaser, who is the senior vice president of Population Health at Cerner. Cerner is a health IT company that is one of the largest suppliers of electronic health record systems in the United States. John joined Cerner in 2015 as part of the Siemens health services acquisition, where he was the chief executive officer. Prior to Siemens, John was vice president and chief information officer at Partners HealthCare. He also previously served as vice president of information systems at Brigham and Women’s Hospital.
John received his PhD from the University of Minnesota, he has written over 200 articles and three books on the strategic application of IT and health care. Including the most widely used textbook on the topic, “Healthcare information systems a practical approach for health care”. John is on the faculty of the Wharton School at the University of Pennsylvania, the medical university of Southern Carolina, the School of biomedical informatics at the Texas Health Science Center and the Harvard School of Public Health.
John focuses on strategic relationships with Cerner clients and advancing Cerner’s population health solutions and services. John, welcome to Moneyball medicine, it’s great to have you here.
John Glaser: Harry, it’s a pleasure.
Harry Glorikian: John, tell me what does it mean to be vice president of Population Health. What is Population Health?
John Glaser: Well, it’s a fuzzy term in some ways but basically the idea is that there are organizations. They’d say I’m accountable for the health care and the health of a group of people, it might be an employer who says I’m responsible for my employees or the state Department or a health care provider, who has a series of lives attributed to the – health plan, but the point is they’re accountable. And so they need a series of tools and technologies that help them manage health and manage health care this is analytics to see you know who’s receiving what care, how costly is it.
This is a series of care management to the degree they need someone to help them navigate the care process or social determinants. So anyway, at the end of the day accountable organizations need technology to help them fulfill their obligations to those who they are to serve, and that’s what population health IT staff intends to do.
Harry Glorikian: You know medicine has been historically based on a fee-for-service model, where you’re paid on what you do. And now that we’ve seen sort of a shift not as much as I’d like to see, but a shift towards value-based medicine, in other words paying providers based on outcomes. Do you see what you’re doing or and/or the business model sort of shifting towards how we do, what we do?
John Glaser: Yeah, I think Harry we’re in the early stages of an extraordinary change in the business model of delivering health care, and it runs along a couple of different dimensions so to speak. One is we’re moving from reactive sick care to proactive management of health, so you know you show up. We’ll fix you so we got to make sure that you remain healthy. So, that’s one dimension, the second dimension is fragmented, where you go here for this type of care there for that type of character this integrated continuum of care that occurs across.
So, we manage you throughout all the steps that need to be taken to you know replace your hip for example. The third is moving from volume or rewarded for volume to where you’re rewarded for quality and efficiency. And then last but not least is a shift from where we’re cantered on the clinician to where we’re cantered on the patient’s. So, these four dimensions represent an extraordinary shift in business model. I think frankly you could argue that the business model shift that healthcare is undergoing, now is the most profound business model shift of any industry in the last 100 years.
Where you look across transportation, telecommunications and all kind of financial services etc. Now business model shifts are hard and they take time to play through. So, I suspect that we will be spending decades frankly to make this particular shift occur and to occur well. So, population health and other technology are being brought to the table as organizations prepare for this future that awaits all of them.
Harry Glorikian: Well, I think it’s interesting right, I mean every once in a while it sorts of strikes me. I think it’s because I’ve been in the business for such a long period of time is, every other industry has been digitizing or measuring for forever, it seems like. And it seems like for us, it really hasn’t been that long. I mean if you think before the Reinvestment and Recovery Act and you walked into a doctor’s office, the entire wall would be paper. And so in, I think digitization has only been eight years maybe on a grand scale.
John Glaser: Yeah, I think that’s fair Harry, we’re less we’re not as far along as other industries, now you have to be a little careful because the degree to which digitization will occur or to which it will be impactful varies. So, organized religion has not been digitized and unlikely to be to any material degree, similarly the legal profession has to degree. But at the end of the day when you have a sort of cohort of people who are experts and whose knowledge is really the asset here. It’s hard to digitize it, you can digitize a lot of stuff surrounding them but to digitize a smart financial planner or the smart lawyer or smart doctors, just challenging.
So, but that being said we are late, you know I think in a way sometimes health care gets accused of being behind. You know as if it’s full of a bunch of button heads who didn’t know any better while their colleagues, and retail or leapfrogging them. So, well you know there are button heads in health care, but by and large they’re pretty darn smart. They are thinking, they have made perfectly rational economic decisions based on the business model at the time. So, if I’m rewarded for volume I don’t need all this other IT stuff, I just need to keep the beds filled in the clinic schedule fill.
So, I’m investing like a perfectly rational person would do. Now we’ll see that shift as that occurs but nonetheless there’s been decades of non-investment, because the economics didn’t warn it.
Harry Glorikian: That begs the question of, what have been the challenges that the healthcare industry is faced with respect to this digitization of data?
John Glaser: Well, I think there’s a number of challenges Harry, one is the range and complexity of the data is just off the charts. So, if you say well how would I describe a person in their phenotype and all the different types of data that were brought here. It’s a much more complex record than your financial record, and now we’re going to add to it by saying, golly we really understand the social determinants that influence you and we really need to understand how to motivate you, and we really need to understand your genome, you know. So, we have this incredibly complex set of data that comes to be.
The second thing that is a knowledge base, it probably has few knowledge bases which are challenging to master. One of the ways you can see that is if you go back 40 years ago and see how many specialties were recognized by the American Board of Medical Specialties roughly a dozen. You say well how many are recognized, today roughly a hundred. Well, why does that happen because the knowledge base expands and becomes so challenging, you get to increasingly narrow what we expect a human being to master.
The third is you have these very complex processes that occur. This is not manufacturing the hospital people show up they have complications they go south all this kind of stuff; you have to be able to sort of manage the workflow on an ad-hoc basis. So, I think it is, this complicated data world complicated, knowledge world complicated work process role and then last but not least, so now when I was in graduate school Harry I was an organization theorist. And one of things you notice is that for sociologists hospitals of the most studied organizational form of all time, because sociologists can’t figure out how they work.
They’re too complicated, parallel power structures doctors and administration, lots of committees etc. Anyway the whole thing is one of the reasons, I think healthcare is behind in IT partly because the economics didn’t warrant it, but partly you say it’s arguably the most complex arena to apply IT that we’ve ever had. And so that’s hard, she was very challenging to get apps at work effectively in this complexity.
Harry Glorikian: Interesting, it’s funny because I think to myself sometimes it’s a product of the way the system paid itself that caused some of these shifts to happen, where in other sciences we come up with a way of organizing the information about, what we need to work with. Because we’re looking for a certain outcome, that we’re trying to measure, and where you’re being sort of remunerated based on what you do, not what the outcome is. That rubric of organizing sort of becomes looser -.
John Glaser: Yeah.
Harry Glorikian: In a sense. So, helping this along I mean I you know we hear so much about artificial intelligence, the analytics machine learning. I mean the definitions that worlds are keep expanding it seems like it. Where do you see whichever term the AI, the machine learning, the analytics and the electronic health record system sort of intersecting and what does that look like? Do you have some examples?
John Glaser: Yeah, here I think I mean it’s something back a little, but one of things I find interesting about IT is about every decade a class of technology arrives it changes the world. So, you go to the 60s is the mainframe, 70s is the minicomputer, the 80s is a networked personal computer, the 90s was the web. Year 2000 was the mobile device, you know the iPhone debuted in 2007, see well what is it this decade. I say well frankly, I think it’s this broad umbrella called AI, that will change the world just as the others have changed the world. And just like the others to have time to change the world, so all this.
Now it’s, this broad term in a way you get all hung up about what AI really means and all that stuff. But I think frankly listen it’s the whole field of advanced analytics applied. Now in a way they’re sort of the, we see sort of four broad arenas in which you would apply this one, is determination of structure. So, you say the machine is reading an image and saying, we got a you know this is what’s going on here. You know whether it’s an eye disease with your eyes or a tumor or whatever or the machine is looking at a pattern arising, listen this drugs these drugs are hurting people. You can see this in the pattern you know or treatment A is better than treatment B.
So, there’s a creation of structure artists but this mess, frankly that we have so that’s one category. The second is increasing contextual awareness. So it’s okay, John Glass was taking care of Mrs. Smith, the Machine says I know what’s going on with Mrs. Smith, I got a record, I know they’re presenting conditions, I know what preferences. I know John’s preferences etc. I know what the evidence says I know what the payment is. And so I’m going to present data to John in that context. Here’s what you should pay attention to, here’s likely your next series of actions etc., I’m going to shape myself to this particular interaction. Sometimes we see this on retail sites with a sort of attempt to shape.
The third is I’m going to do operational flow; an example of this remember my days at Siemens with the smart city. So, the city Siemens we got a traffic jam on the Main Street, so I’m going to alter the light sequence to sort of move it along a little bit here. And so in a process sense you say the Machine says I got to take Mr. Smith down to get his radiology exam, I can tell that there’s a 30-minute wait in radiology. I can see he needs his blood drawn, the phlebotomist is one floor below, I’m gonna let the phlebotomist play through, draws blood and then I’m gonna send it on a writ, it’s sort of choreographing a process.
And then last but not least, it is the sort of clinical decision aids. You know it’s the readmission algorithm, it is the thing that says you know this person is likely to be better off in this type of skill facility versus that’s, a lot of the predictive stuff you know comes in there. So, you look at all that and say wow there’s some real power here, and a lot of that role leverage EHR data and particularly golly, if we can bring it all together and if we can deal with some of the, you know the mess that is in there and if we get better at social context etc. So, I think we’re learning Harry and it’s an exciting time and there’s you know the gazillion start-ups, there’s some big gorillas.
You know the Google’s of the world and Amazon’s of the world, Microsoft’s etc., we’re all playing in this thing. So, anyway we’re in the early stages of this decade of this very profound change, which will in a way preserve the EHR as the core. I mean you still got to collect the data Docs and nurses stuff to work with something. I mean they’re interacting with something that goes on, but the nature of that interaction will be quite different in the years ahead.
Harry Glorikian: Do you see the EHR, do you see this integrating with the HER? Do you see the EHR becoming the data like that something reaches into and then does an analysis for a specialization? How do you see this melding?
John Glaser: All the above, I think in some cases the intelligence will be part and parcel of the HER, because of nothing else speed you know. So, when you enter an order and the thing comes back what are you serious, there’s got to be a better way to do this. You know that will be part and parcel in that by this. On the other hand, which you see for example in population health is to extract data from lots of different EHRs. Because – regions have the plus you bring the claims and the devices and all those other stuff. And what I think that increasingly the population health will be is and I got to keep Harry healthy.
So, and I need to pull together enough that can characterize him, you know clinically characterized them, socially characterized and how do I motivate in etc. Now I haven’t characterized Harry what’s the plan to keep Harry healthy so I got to come up with a plan and then I got to monitor the plan -. You know all sudden he stopped taking his drugs or all of a sudden his blood Sugar’s wobbly etc. So, the plan has to alter itself so I think we need to do some that’s all intelligence. You know the intelligence of rationalizing the date of the intelligence I’m going to infer a plan and the intelligence is enough to monitor.
So, there’ll be this layer that sits on top of EHRs and I think frankly, you know is people begin to say I want to bring my data into my mobile device and integrate it there. There will be intelligence applied there, you know there’s quite local or a cloud-based, but specific to the device guiding you or me and decisions we might make.
Harry Glorikian: What have you seen from either the start-up companies or are new things that are going on, but what do you see that’s really exciting like what sort of application area do you see where it’s improved an outcome for a patient and lowered the cost, so in that sense?
John Glaser: You know we see, I mean there’s lots of spot examples and what I think that’s kind of interesting about this whole arena is, at times the we talk about the sort of general purpose intelligence. That’s kind of a Watson thing or how you know in the 2001 Space Odyssey, but in fact the real powerful stuff is really quite targeted. You know it’s the intelligence and a Siemens MRI, this is part 62 is feeling you know get over here and fix it before it really fails. Which is different from a part that says in your glucometer your blood sugars are bounced around through something, which is different from, If we don’t do something, now you’re gonna be readmitted with it for the, anyway very targeted intelligence here. So, you see lots of neat examples, you see neat examples of sepsis algorithms that say, we’ve got to do something now before this because you can go south in a hurry. We see neat examples on readmissions where they really do drop readmission rates. We can see examples where we say rather than send this person to this skilled level facility upon discharge send them to a lower or higher. And we see actually a third of the time decisions get changed you know be to the right place to go up and do this though.
So, and we see examples of more effective ordering, we see examples where you can do post Marcus, you can really pick up signals in the data. This is a drugs hurting people or treatment is better than for treating to be. So, you go through this range of things that are, wow it’s pretty darn nice alright add off all those stuff. I think Harry one of these sort of you know, there’s some broad big challenges that are out there. I’ll give you an example of a broad big one here. So, when you look at an EHR see, how many instances of medical knowledge are there and of typical EHR say, well what’s an instance of medical knowledge or an order set you know a health maintenance reminder.
You know all are sort of instances of medical knowledge, in general there’s in excess of a hundred thousand. So, wow you know how, who’s maintaining this. I don’t know and what day was a recent update, I don’t know about either. So, one of the challenges we have is we introduced all this intelligence to these systems, and it just grows this sort of body of knowledge. It becomes brittle you know, because nobody’s watching the store so to speak there. So, you say wait a minute how about if we have the machine watch and the Machine point out that this thing is updated and then actually look at the data machine learning and make the updates itself etc.
So, you know, no way you have machine healing and management of its content. I said wow that’s pretty neat frankly, we’ll have to do something if this stuff is going to get brittle break and hurt somebody. But anyway how we figure that out beats me, and I think that’s becomes one of those great challenges that confronts industry in addition to continue to find and leverage. Lots of very specific point examples of where the intelligence has really made a, to much more gain although quite focused game.
Harry Glorikian: So, that begs the question of, I mean there’s got to be either new capabilities people need to learn or new people we need to hire, that are going to get involved. But this, it seems like healthcare is going to be a booming area for jobs and new types of jobs that, or new skills that they’re gonna have to teach doctors in medical school just to be keep up with all of this. So, what do you see as the opportunities?
John Glaser: Well, I think there’s lots of opportunities and those young people aren’t even mid-career people looking to shift here healthcare and healthcare IT and informatics is data science all this stuff is gonna be a rich and fertile area for quite some time. So, I think Harry, they range from what I call the methods of guys, you know men and women who really understand how to apply different analytical techniques to make this stuff, and really quite you know a lot of you know machine learning techniques and other types. So, there’s the methods people that got it one here.
The second is a series of people who actually understand the clinical context, because sometimes the massive people come up in the person of clinical context, says no I mean I’ll give you an example this goes way back when. We were looking at data on how you do have the Machine determined smoking status you know. So, can the machine go through and say, Harry’s a smoker or non smoker and it gets complicated maybe stop five years ago or whatever what happened to be. And we were looking at one particular note and it said smoking status unclear, and so what does that mean.
And the physician who is working with us miss said, that means the resident is tired and just didn’t want to go down this rabbit hole, and just wrote smoking status and clear. Well, you got to know the context to know that are these kinds of things. So, we’ll always have to have people who understand the move of where that’s going on. The third is understand, people who understand workflow so where and how do I introduce this you know. Do I do this we’re in the middle of the exam, do I do it after the fact, I do it to the doctor, do I do it to some staff in there. I mean where do I fit this and if I fit this what do I want them to do.
So, you might have logic that says the social determinants indicate this person is in a nutritional wasteland. We, got to deal with it okay, who does what when that is informed. So, there’s a series of what is the process and their choreography that goes with it. And then last but not least it is people who design stuff. You know my wife recently bought a Volvo xc90, you know. I know Harry if you said which has more lines of code, a high-end SUV a 787 or the space station. The answer is a high-end SUV by factor two, sheer lines of code you know to park correctly avoid crashing somebody to dim one other light. You know it’s amazing here on these kinds you couldn’t crash that thing if you wanted to.
So, the point is how do I help my wife or anybody with that bring the knowledge in and sort of you know what to do, a guide the interaction that is going on and these kinds of things. So, I think there’s between the methods people, the context people, the workflow and the computer human design people. There’s lots of opportunities to do this, and then the last one at least obviously people who evaluate, say is it doing any good.
Harry Glorikian: So, that begs a question of I’m looking at all the other industries is, when they’ve tried to apply these advanced machine learning applications AI etc. And their first go, they tried to obviously do what we always do, take it and melt it into my existing workflow. And they never seem to get the return that they were expecting. And then when you see them shift their workflow based on the power that the system provides, they seem to get much more. So, how’s that gonna work in healthcare? Because we’re pretty rigid in our workflows. So, how do, do you see that influencing the workflow and what we learn?
John Glaser: And I think you know we will iterate, because we know a lot of smart people in this industry, and they’ll say, I don’t really know, but let’s try it here and they say well you know we’re off 15 degrees and so you’ll have to iterate. You know I think one of the ways that organizations of all this what I always root for short cycle learning try this too, try that too, they just sort of short cycle their way into you know better to do this. In some ways we do have rigid workflows but on the other hand, I think I find over my forever time in this industry is that, if you help a caregiver save time or do a better job of delivering care, they’ll adapt quite readily. Where they’re not happy is if you cost some time you know or they set notice.
So, you know subject to regulation and reimbursement because there’s certain things you’ve got to do here. So, I think what will iterate and you know define novel ways of doing this kind of stuff, and frankly one of the great things that you know the is learning from your colleagues, you know about what did you try in your organizations. The chief medical officer is talking to chief medical officers and vendors learning from their clientele etc. We’ll get better at all this.
Harry Glorikian: There’s a lot of new entrants into the field, right. You’ve got the Google’s and you’ve got the Apple’s and there’s and the list is incredibly long of wellness company sort of budding, right up against the line there of regulated versus non regulated care. Where do you see or Amazon’s for ran into this? Where do you see their impact of the system and I don’t want to say good or bad, but how do you see that changing things?
John Glaser: I think that’s unclear to me is how that will evolve, and you see multiple threats. You know you see a thread of you know health plans and providers fusing and merging, you know you see the Walmart’s making moves. You see the CVS is making moves, all you know a pharmacy and retail and health organizations, they’re all making moves in ways that are quite striking. And you see them trying to take out the middleman of the PBM by mu, you know worry.
So, this is restructuring that is occurring and it’s not just in the sort of non-provider’s side of this thing. You know a couple years ago at CERN or you know maybe decade ago, we would have said well what’s a large Health System we said well, about five billion in revenue annual revenue. And so today what would you say, about twenty billion in revenue. So, there did the bigger getting bigger in lots of ways here. So, even the provider side of restructuring these very large health systems with a whole lot of regional systems that are going on. So, you have all that and that’s on the non IT side. Let alone the tech giants you know coming in, let alone the consumer guys coming in some of which are tech giants.
Let alone the traditional EHR vendors Cerner being one epic being and other all scripts etc. Let alone to your point a gazillion starts of a remarkable talent, some in the consumer side, some on the analytic side you know all over the place. You see well how will it all send a lot, good question how will that all settle out. So, I don’t know that we know in a way, what that will look, I think some things are clear. You know one of which is you could who poo poo the tech giant say, well they tried that before and it turned poorly for them, it’ll turn poorly for them again. Don’t count on it, the world is a different place, the technology is better, there’s got smarter etc.
The other is if you said the traditional boundaries, you know providers on one side, health plans on another, reach pharmacies that are those boundaries are blurring fast. And so you see that and providers getting into medic right or other stuff. So, I think a lot of it if I were a health system sir what do I do about all this stuff, well I’d start forming relationships with a lot of people who may have been traditional antagonists in the past, you know the health one, the retail guys. And it’s they’re learning just as you’re learning and starting to go through all of that kind of stuff here.
And I think you know you it becomes harder you turn to a core vendor, you say well jeez you know certain or at they’re going what do you think of all these guys you know help me navigate this technology stuff or consulting firms, you can go off and do all this. All right very complicated, very confusing time, and I think the other sometimes you know you know Harry healthcare straddling two business models is, a fee for service and the value-based care, you know what a pain in the butt, it is a pain in the butt, how long will it last decades. It’s not one of these two years and it’s over and done, so settle in for a multi-year period of forming. That will go on across the board.
Harry Glorikian: That, I’m sure that doesn’t make physicians or anybody listening to that -, but so I you know as I look forward in the next, I hate even saying like you know three, five, ten years there’s a lot of digital disruption coming. Yeah, I try to stay up on all the bleeding edge which moves in to tech very quickly. Whether it’s AI, dare I say block chain, virtual and augmented reality things like that. Do you see, what do you see having impact on the healthcare arena?
John Glaser: You know, I think it’s hard to do that I mean and at the end of the day for me, you know what I think is you step back and say for any particular class of technologies at a very fundamental level, tell me what it does. And then I will tell you whether, I think that’s important. So, I’ll give you an example, what is flight do. So, as well enables you to get from point A to point B a lot shorter period of time without the infrastructure, you don’t need railroads you need a landing strip at either end, well that’s pretty remarkable. I could see where that’s valuable to me. What does refrigeration do? It allows perishable things to last longer, they say well jeez would I do that. Well, I’m you know I’m moving in pharmaceuticals across the globe that matters a lot. So, you step back at a very fundamental level and say, why is this, what is it and why is it profound. So, I for example look at block chain and you say what is it it’s a new way of doing accounting that has the ability at perhaps to remove the middleman, like the bank or the Law Offices Center. Do I think that will fundamentally alter health care? No, I don’t do, I think we’ll see it sure you know we’ll see it as people do credentials for doctors etc.
So, now on the other hand you say what about this intelligence, say well really could help a series of decisions contextual where structure of data is that a big deal. Absolutely, it’s a big deal. Do I see the fact that you and I might have technology on us? You know our mobile device that can communicate with us knows, where we are etc. Is that going to be a big deal? Absolutely a big deal. So, I think it’s hard but the trick is to step back and say at a very fundamental level what does it do. And the answer, you’ll get an answer and the salience of that answer will vary by industry. Block chain may be more important for financial services than it is for healthcare, than it is for you know other religions there.
But I, but even when you do that, they say ok. So, I think consumer stuff is really important but there’s a zillion companies, how do I sort through we through chat. That’s still an issue you know even if you believe the area’s really critical.
Harry Glorikian: So, what other topics or subject areas, I don’t want to say keep you up at neither, but are very salient to what’s happening in this whole digitization or movement of healthcare in this direction.
John Glaser: Well, I think it’s be careful here, because I do believe it’s a profound business model change, and I do believe it takes time for those occur. You know if you look for example here last year, what percent of retail in the US was done over the web versus in a store. And the answer is 12%, you say wow you know how long we’ve been at this. Well you know Amazon incorporated in 1994, Google in 1998. So, 20 years later it’s 12%, and so that’s not fair because gasoline isn’t sold over the Internet.
So, okay well it factored out some of that stuff and you say, but still and some industries has been devastating you know consumer electronics, but other industries it hasn’t groceries are still largely untouched by the internet, you know jewellery large the untouched etc. So, it takes long periods of time and differential impact. It’s not a universal impact that comes across the border. So, I think we’re gonna big business model change is going to take time. We have some extraordinary technology coming, so what is critical. When there’s no inherent reason to believe it’s all going to work out well. You know you sometimes what golly is gonna try, that’s not a given here at all.
You know there’s and you can see it in the EHR burnout issue of the doc, we could very well drive a whole bunch of people out of the business at a time one certainly I, and beginning to need them as I decay slowly but surely in the years ahead here. So, we could break it in some ways are ever, so what matters a lot is that the industry with all the competitive juices that flow around here is that, it learns from each other and guides us. Yes, there’s businesses here but there’s also you know a moral and a civic responsibility, we collectively have that it turns out well, as we go through this.
So, the thing that I get really pretty well at night but the thing I worry about is that we don’t learn and learn together to make this thing as effective and efficient and as highly tuned as possible, and for God’s sakes we don’t break it along the way.
Harry Glorikian: So, where do you see, I mean I always think to myself some of the big shifts have happened because of the way that government has influenced those shifts. In other words, if we kept paying everybody based on everything they did, they’d be perfectly happy. But you know we came up with this thing called the Affordable Care Act. We said, well you know maybe we should pay based on outcomes. How much do you believe the government is playing a role in this shift versus competitive dynamics which I don’t believe necessarily exists in healthcare?
John Glaser: Well, I think the government’s, the single most potent actor in all of this. You know it accounts for half the payment you know between Medicare and Medicaid, it accounts. So, how it decides to pay is enormous consequent. It is also because it is government has the ability to absorb being the first mover disadvantage. You know the free rider effect and so government can make those moves bear the free rider effect. Because it’s government, whereas any individual player first of all isn’t big enough to do that, but you know it suffers that consequence.
So, it is the big gorilla and it can deal with the first-mover dissident. Now it has challenges in that, it is a political animal. It is surrounded by Congress, it is surrounded by elected officials who come and go. So, you know it’s gonna get buffeted by those particular wins and all the stuff that makes politics complicated, you know that we go through. And it’s got a big task who’s trying to figure out how in the world you take a country or 330 million, people are very diverse and sort of satisfied them all. And I remember it spending time at ONC and I thought golly meaningful use. You know what does meaningful use me you get 3,000 ideas and you can only take 12.
So, this is a they have a tough job to go off and to do this whatever they do the industry will follow, the payers will follow, the plans will follow etc. So, that’s on the top of the regulatory though for example the FDA see we’re gonna loosen up you know the process by which you get new stuff approved. It has an enormous influence on whether that goes on or not or you know advances occur within biomedical discovery. Last but not least for example, on HIPAA where it’s kind of striking to me as hip as 20 years old. It covers provider’s health plans and intermediaries but it doesn’t cover Amazon and it doesn’t cover Apple.
So, if government has decisions to make about the privacy context, you know what it does or doesn’t do. Anyway I think it is the most significant actor that exists in the landscape today and as it moves, so well the industry.
Harry Glorikian: Any closing thoughts or anything that you think, you know the listeners would want to hear about in these in these shifts, before we sign off here.
John Glaser: No, I think first of all it’s been a pleasure. I appreciate the opportunity spend a little time with you Harry and also with those who are listening in to this stuff. I think for all of you the, we are being, you’ve probably gathered from my comments and perhaps comments rather. It’s a remarkable time to get through it we’ll take our collective intelligence hard work and thoughtfulness. And so I look forward to working with everybody, who’s listening to this stuff to let’s go make this thing happen.
Because at the end of the day the consequences are real, I mean I think about this every now and then area, you know you if you go to pick a hospital that’s near us. There are people, the people who are in there are some, there’s somebody’s spouse, there’s somebody’s parents, there’s somebody’s siblings. So, this is real people who are loved by others going through a bad time unless they’re giving birth to a kid, which is usually a pretty good time.
And so it’s very real it’s very personal little level and we ought to recognize the magnitude of that and the importance of that as we collectively work our Fannie’s off to make this thing as good as we can be, and learn as we go through this. Anyway I feel like a sermon, but nonetheless go forth and make this world a better place. We all need it and look forward to it.
Harry Glorikian: Thank you very much for the time John.
John Glaser: Thank You Harry.
Harry Glorikian: And that’s it for this episode. Hope you enjoyed the insights and discussion. For more information, please feel free to go to www.glorikian.com. Hope you join us next time, until then farewell.