MoneyBall Medicine: Glenn Steele and How Analytics is Changing Healthcare
Host Harry Glorikian talks with Dr. Glenn Steele, chairman of G. Steele Health Solutions, which helps healthcare organizations improve quality, and vice chairman of the Health Transformation Alliance, a cooperative of self-insured employers. Dr. Steele is the former chairman of XG Health Solutions and former president and CEO of Geisinger Health Systems, and he shares his views on how data and analytics are changing every aspect of healthcare.
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Harry Glorikian: Hello everyone and welcome to the Moneyball medicine podcast. My name is Harry glory camp, and I am the host of the show. This series is all about the data-driven transformation of the healthcare and life sciences landscape. This new landscape presents tremendous opportunities for those who are ready to embrace the data-driven reality, having the right data and knowing how to use it, will be the key to success in the healthcare market in the future.
We are already starting to see the impacts in drug development precision medicine and how patients with rare diseases are diagnosed and treated. Startups are launched every week to fill an unmet need and address the current problems in the healthcare system. Digital devices and artificial intelligence are helping doctors do their jobs faster and with more accuracy. The Money ball medicine podcast series will be one on one interviews with healthcare leaders, discussing the business challenges and opportunities arising from those working in the data-driven health care economy.
Doctors, hospital administrators, health information technology directors and entrepreneurs need to adapt to the changes affecting healthcare today in order to succeed in the new cost-conscious and value-based environment of the future. This podcast based on the follow-up to the book Money ball medicine, aims to map out many of the challenges taking place. Describe how they are impacting everyone from patients to researchers to insurers and outline some of the predictions for the healthcare industry in the years to come. Welcome to Money ball medicine.
Hello I’m here today with Dr. Glenn Steele. He currently serves as the chairman of G Steel Health Solutions, an independently operated venture launched to help health care organizations create value and improve quality. And the vice chairman of the Health Transformation Alliance a cooperative made up of 47 self-insured employers looking to get more value out of health care. He is the former chairman of XG health solutions and the former president and chief executive officer of Geisinger health systems, and integrated health services organization in central and northeastern Pennsylvania.
Nationally recognized for its innovative use of the electronic health record and the development and implementation of innovative care models. The results of these innovations have been documented and discussed extensively. Glenn thank you so much for joining me today to continue our discussion on Money ball medicine, and how data and analytics are changing every aspect of healthcare.
Glenn Steele: Good to be with you Harry.
Harry Glorikian: I want to start out broadly and get your input on in a continuation of our previous discussions of, what has surprised you most about data technology and trends in healthcare.
Glen Steele: Well, I think the usual approach to the issue of value and healthcare has been to either argue for a more reimbursement per unit of work, that is on the provider side or less reimbursement or decreased prices per unit of work on the payer side. And I think that has a floor and I think what has been transformative over the last few years is an attempt to look at inexplicable or unjustifiable utilization, and to attempt to extract unnecessary stuff that does not bring value to those who are either getting care or who are getting insured for care.
Harry Glorikian: So, based on all the work that you did, Geisinger, the tremendous examples used by I think many different presidents during your tenure at Geisinger and talked about nationally. So, in your experience what are some of the most easy to understand and profound examples of using data and innovative ways to change outcomes and lower the cost.
Glenn Steele: Well, there’s two categories of easy-to-understand examples. One pertains to a cohort of patients, let’s take for example the group of patients who have anemia secondary to renal failure. We found in analyzing that entire population cared for at Geisinger, that probably no more than 20% of the patients who are getting erythropoietin which as you know is one of the most effective breakthrough drugs to increase red blood cell mass, actually needed to be treated with Reith row potent.
They could just as easily have been treated with iron and that would be a savings of about $2,000 per year per patient. Plus, the fact is that the consequences, ill consequences of iron, particularly in patients who had significant arterial disease of vascular disease were much less than if you treated those folks for the same period of time with the reef we throw -. So, that’s you know that’s the consequence of looking at a cohort of patients, being able to analyze what was happening, being able to apply evidence space.
And then you know try to change the best practice both in terms of quality as well as cost for that cohort. For the other category and looking at individuals, when we looked at applying the nine best practices that were known to be in literature to optimize the care of our 35,000 type 2 medic patients. Obviously, what we found amongst all our primary care physicians was a bell-shaped curve of optimization. We were able to with the data feedback that we had and with real-time analytics applied, not just to the data payer but also to the provider of actual healthcare data.
We were able to provide the best practice and to focus on those who are on the wrong side of the bell-shaped curve. And over a period of time what that meant was, more and more of our patients got more and more of those nine best practices, transacted. And within three years, there was a huge effect on the probability of them having heart attacks strokes and diabetic retinopathy. So, it’s two categories where the analytics the feedback and then the ability to apply that data of those data either to cohorts or individual practitioners made a difference.
Harry Glorikan: Well, if I remember that story correctly, I think you had said to me that you saw a 23% reduction in comorbidities. I don’t remember the number exactly.
Glenn Steele: It was in the category of 25% decrease in need for hospitalization, and that’s always the first surrogate. And that applies not just to diabetics but applied to optimization of most prevalent chronic diseases.
Harry Glorikian: And that to a system like Geisinger, which is self-insured must have been huge. I don’t want to call it a profit Center, but able to really help from a revenue and profitability perspective.
Glenn Steele: Our most efficient business model was based on the decreasing total cost of care, and we decreased the total cost of care by looking at all the prevalent chronic diseases, optimizing their treatment. And the first evidence of savings was always decreasing acute hospitalization per thousand always. And obviously because in our fiduciary, we had both payer and provider, I was able to do the internal transfer pricing. So, that the profit didn’t just go to our insurance company, but it was able to be redistributed to the folks who really changed how they cared for the patients.
And that was a unique advantage that we had, because of our fiduciary with both payer and provider in the same business model.
Harry Glorikian: Yeah, I’d love to come to that that topic as we move on later. But so, when you’re implementing these data driven changes or you’re looking at the data and utilizing some of these technologies. Do you need to change the workflow of the practitioners, when we’ve looked at other industries and they try to take what the data is telling them and stick it into the existing workflow, they don’t always get out the same benefit? However, if they’re willing to change their workflows they really reap the benefit of what these systems are enlightening them to do.
Glenn Steele: Absolutely, you know if you take even if you look at the transactional EHRs, quite apart from the functional applications and the data analytics and what have you. I mean we found out in you know in 95, 1995 and I started actually experiencing it in 2000 and of 2001, I got two guys here. If you plunk in a transactional EHR and put a computer in an examining room without fundamentally redesigning what the doctor or the nurse looks at, and how she relates to the patient, you get worse outcomes you know you don’t get better outcomes. And that’s just one very concrete example of how you have to re-engineer.
Harry Glorikian: When you’re doing that, how do you manage the human element?
Glenn Steele: Well, first of all everybody’s got, you got to know at the outset and not everybody but you’ve got at least have a plurality agree that, it’s worth you know worth the challenge. That is at the outset whether you’re putting in enabling technology or whether you’re adding analytics or what-have-you they’ve got to agree that it’s worth the effort. And in general, the way to motivate the men and women who are giving care is to be able to convince them or eventually being able to prove with beta tests that there’s a better outcome for their patients.
Because at the core the motivation that led most people into people work in the health care whether they were Doctors or whether they were nurses or even you know administrative aspects at the desk, what really turns them on is to know or feel as if the change that is very disruptive at times, will lead to better outcome for their patients. A lot of people think it’s all about financial incentives, it’s you know, and aligned financial incentives are important. But I think they’re kind of a gating factor but what really turns people on is if they know that you know that the disruption that a lot of this reengineering really necessitates is worth it, because a better outcome for their patients.
Harry Glorikian: And so, you know one of the you know star examples that Geisinger put into place was the money-back guarantee on different surgeries. And when I say that to people, their eyes almost bug out of their heads. How did you get there, because most people will like to say, you just don’t understand healthcare? And I say, well if they can do it why can’t everybody else to it and I can only imagine that if every major institution was able to employ this, we might see a bending of that cost curve. So, how did you make that happen within Geisinger?
Glenn Steele: Well, first of all I got a clarify Harry, there’s the warranty that we started and that was back in 2004-2005 and then there’s been the money back guarantee based on patient satisfaction that my successor, David Feinberg has carried on with. So, let me start with the warranty since that was my baby. The warranty was putting your money where your mouth is example, what we decided to do when we re-engineered a very high prevalence high-cost, and what we learned to be high variable acute care episode. And the first one we started with was a coronary artery bypass and then we took it to about twelve others.
We basically said, if we can re-engineer the care to do exactly what our experts feel is best practice. Some of it is our own consensus but most of it was evidence-based, that was extracted from literature and from other experiences. And if we were able to guarantee for all of our patients that all of them would adhere to these must dues from the presentation of their problem until rehabilitation was finished. What would happen to the outcome, will the outcome get better and would costs go down? Because in every other complex area of life, whether it’s production or whether it’s logistics and what-have-you, in general that’s what happens.
And then we had to have outs, we had to allow people to make exceptions based on individual patient variation, but they had to justify those exceptions to their own peers. So, it couldn’t just be a voice from God or you know the claim that the training that somebody had 35 years ago was applicable now. And what we found early on with the beta test is that our and we started with a very good product, that is what we thought, not just by our own criteria but by Pennsylvania criteria had good outcome for relatively low cost compared to our competitors. The question was, would we take good and make it even better and we found out that was the case.
And that, and the really important thing of all that was decreasing the complications and at the same time decreasing the cost. So, I found out that that was an important aspect but what got the publicity was sexy packaging. And so, what we decided to do was, to try to come up with a single price that included an assumption that are complication rate would decrease significantly from two to three years before we had this re-engineering of care to post proven care, we called this stuff proven care. And we basically made an assumption between us as providers and us as payers, that our complication rate would go down by 50%, and we built that assumption into our single price.
So, we had to improve the outcome by 2x in order to break-even between our own providers and our payer and that’s what got all the publicity. So, that’s the long and the short of it Harry.
Harry Glorikian: Understand, and that required measuring everything.
Glenn Steele: Absolutely, it required knowing everything about all of the costs that were built into our baseline for the entire episode of care, including the preoperative stuff the acute care stuff. And then in the case of our warranty it included not only the rehabilitation, but it included everything that happened for 90 days after the hospitalization. So, that was the important baseline and then obviously we had to see if there’s a delta.
Harry Glorikian: Why do you think this may or may not be being adopted everywhere?
Glenn Steele: Well number one, it’s hard to do and when I say it’s hard to do you have to know your costs in an extraordinarily granular way. And when we started this even at Geisinger, those data was not available. And that’s you know, that’s an incredibly important part of what a lot of the analytics allow us to do now that was just not to be taken for granted, you know, in 2004. The second thing is the Sociology, I mean when we first published this and we’ve now taken this out and scaled it to a lot of non-Geisinger places with you know with different sociology, different market determinants and what-have-you.
Quite often the beginning approach for the clinicians when they look at this is to say, we already do this, we’re already okay, we’re perfect. And so, you’ve got to have a sociology that basically says, well let’s see, let’s look at the data and let’s see. When we started applying this to our chronic disease treatment optimization, for instance with diabetes, we knew that our men and women who were in primary care were working very hard. We knew that their panels were really optimal, and we assumed that they were already achieving a lot of these nine best practice metrics, that should be applied to almost all the type 2 diabetics that we were trying to optimize treatment in.
What we found out at the baseline was that only about 2.5 percent of our type 2 diabetics. We’re getting all nine of these metrics achieved in an optimal fashion, and that was with really good people in a great system that had a great, so just think about that. So, it’s very eye-opening when you’re able to apply the data as a baseline to your own practice. But it’s also embarrassing, and you got to get over that sociology and say, we want to improve and continuously improve in order to get done what we got done.
Harry Glorikian: I mean the other issues just to go into what David Feinberg has done. David, you know, has really been focused on patient satisfaction and he’s applied a similar approach to the money-back guarantee. If any patient doesn’t feel as if they had an adequate experience at the acute care facility, they can claim that and get the money back from their out-of-pocket portion of the reimbursement. And it you know that’s pretty frightening for a lot of CFO’s, and it’s pretty nerve-racking for a lot of clinicians. But it was an attempt by David to create, you know, a very obvious commitment that could be understood by everyone.
That would then really force reverse-engineering to make certain that if there was anything along the way that needed to be corrected it could be corrected. And the fact of the matter is the amount of money that’s been given back is really immaterial to the overall top-line revenue of the organization.
Glenn Steele: Yeah, I saw the number and it was negligible at best.
Harry Glorikian: Yeah, but it makes a big statement not just for external kind of validation, but it’s even more important to make a statement for the internal validation.
Glen Steele: Yep, going to the Sociology that you were talking about.
Harry Glorikian: Exactly.
Glenn Steele: So, when I did the research for the book and post book and so forth it’s the sensors data all these different ways of what you might call data-fi–cation of healthcare. I’m wondering if you’re seeing the opportunity now for business model shifts, because of this. If we think about what happened to the music industry, what happened to movies what happened to any industry that has been data fed. Once that data becomes free and out there it’s no longer captured within one ivory tower. And I’m wondering what you’ve seen in your experience, the opportunities that exist?
Harry Glorikian: Well, I think the most potentially transformative aspect of data is to use it to find out just how radically we can change who does what in healthcare, and where that what is performed. So, I think there’s a huge amount of, I think compelling anecdotal evidence now, that a lot of stuff that has been done in an ICU doesn’t need to be done in an ICU. There’s a huge amount of compelling anecdotal evidence that stuff that’s done in a doctor’s office, doesn’t need to be done in a doctor’s office.
But in order for a system that is very credible and has you know, what is felt to be a reasonable quality questionable value in terms of the cost part of the equation. In order for that system to feel comfortable in fundamental transforming, how much responsibility can be displaced from the doctor’s office to the home? How much responsibility can be displaced from you know the big coronary care hospital to a smaller Community Hospital, that is functioning as a stabilization site for instance? All of that, I think, is a little bit more rationally looked at by the ability to follow almost real-time data.
And I’m not talking about the payer side the claims data because that’s never real time except for Pharma. But I’m talking about healthcare, the healthcare provision side. So, the way we started to re-engineer care and fundamentally asking people who you know we’re doing what they were doing to do other stuff, or trying to ask more stuff, asked whether more stuff could be done at home for instance in chronic disease management. It was to take a look at the claims data on our payer side and try to predict which cohorts of patients would be most likely to see improvement in both their quality as well as the cost. When I’m saying cost it’s not just financial cost but you know but personal inconvenience and what-have-you in terms of people having a schlep in over 60 miles of rural roads to come to our big house as opposed to if they didn’t have to.
And then to apply that, but to see what was happening close to real-time by looking at the analytics almost in real-time on the healthcare provision side. So, the payer side allowed us to stratify, allowing us to a large extent to predict where the value Delta would be easiest to achieve. And then when we did the experiment of changing who did what and where it was done to follow that on the actual healthcare provision side in real-time. So, I think you know that’s the big picture what I think is probably going to be the biggest area of value emancipation over the next few years.
Glenn Steele: So, it’s interesting we keep coming back to the fact that you guys had the full deck of cards regarding data, just like a business would have revenue and cost of goods. You guys had your costs and what you paid out and all the data, I mean so is the transformation of healthcare utilizing data requires an institution to have both. There’s a lot of third-party payers, and so how does that work itself into it? Because I feel that you said we wouldn’t give the profit to a third party or we’re missing the data part of it.
Harry Glorikian: Well, that’s part of the experiment that we’re doing with our scaling attempts right now, because as you’re well aware most organizations don’t have the fiduciary structure that we had at Geisinger. and in a way a COS are an attempt to mimic that with big caveats in terms of, you know in terms of incentives not being you know not being completely aligned and also caveats in terms of not having prospective attribution of patients, and having moving targets in terms of quality, and what-have-you. But I think it’s certainly easier if you have the data on the payer and the providers side together.
Now the other aspect that we had is, we were you know when I left three and a half years ago, we were over thirty thousand employees. So, we had a huge amount of data on the purchaser side as well. So, I think, if you’re thinking about the payer and provider now you add in purchaser and that’s a big deal. And again, I hope we don’t have to have a reproduction of the fiduciary model of guising or because it doesn’t exist out there except for probably about a dozen organizations, but it sure made it easier for us.
Glenn Steele: So, what do you see is the next major changes coming to healthcare. I see, there’s fundamental changes, so if we look at it for specifically from the healthcare side but I see you know the Google’s and the Microsoft’s and the apples and it seems that there’s change at every, not even turn micro turn that there is affecting the system. But where do you see the biggest changes?
Harry Glorikian: Well, again if you’re looking at thirty-five-thousand-foot level, in answering your question I see continued democratization of data. I see data not from our expertise in healthcare but certainly from Amazon, from Facebook, from Google. I see them and obviously it’s a balancing act as we know, since you know there’s a downside to democratization of data. But I see them figuring out how to have much more fundamental understandable application of data to the people that we serve, you know whether their insurance members or whether they’re actual patients.
And again, that’s not without you know without risk in terms of confidentiality, it’s not without risk in terms of people tampering with it. We’re all aware of that, but I think that democratization will continue. And you know and again I’m still Pollyanna you know even though I’m so, old but I still believe that the more people know about themselves the more active, they’ll be able to participate in either maintaining their health or in you know in working with you know the partners, who are part of the health care universe to mitigate whatever diseases are gonna afflict them as they get you know, I say get into that disease state.
Glenn Steele: And how do you see those, a lot of those changes affecting all the suppliers. I mean it’s a multi trillion-dollar industry that is supplying, and everything is sort of carved in stone in their minds.
Harry Glorikian: And everything’s margin, right. So, I mean, I don’t, I can’t predict what will happen but here’s what I hope will happen, I hope a lot of those middlemen will go away.
Glenn Steele: Interesting. So, we’re talking about the sort of technology outside of the -.
Harry Glorikian: And I think it’s happening with the GPI, with the PBMs. I mean the PBMs are under tremendous pressure and they’re responding to that, they’re not dummies. I mean they’re very smart people, but you know doing what they’ve been doing with the margins they’ve had is not gonna be the future. Hence, we’ve seen the PBM you know begin to work with verticals and merge and consolidate and what-have-you. And so hopefully there’ll be more aligned incentives, but quite frankly fewer operating margins in the supply chain.
Glenn Steele: Well, and you’ve seen a lot of M&A activity. I mean if you look at the Walgreens, the CVS, the Walmart’s, the Aetna’s etc. I almost see a bifurcation of what seems like keeping people well or healthy to the sick care provider side of it and technology enabling. So, where do you see technology as sort of impacting or working outside of the traditional provider space? Again, thinking CVS Walgreens or places externally where a patient would access the healthcare system.
Harry Glorikian: Well, I thought, I mean I think it will at least potentially influence in a number of quite varied areas, for instance in kind of the healthcare universe. If we think about what is most effective in changing, the behavior of our insurance company members or our patients. My guess is we don’t have a clue compared to the sophistication of an Amazon or a Facebook or Google. And you know I’ll give you one example, when we are applying devices that could allow us immediate input for weight gain in patients, who are being followed with congestive heart failure, what we found was what I’ve experienced myself.
And that is, if a patient knew that the weight gain was going to be transmitted directly to their electronic record or their doctor or their nurse practitioner, they just stopped stepping on the scale. Now I don’t know if you do this or not, but I know when I’ve been traveling when I gain weight, I mean I just know it, and so when I come home, I stop stepping on the scale for a period of time until it goes down again. So, those are you know we just we just I don’t think we have the kind of sophistication to look at behavior change and how to get things affirming the right behavior as a lot of the folks outside of healthcare do. And that’s because we’ve been you know paternalistic for a long period of time we’ve been.
So, there’s lots of reasons for that, but I think there’ll be a huge opening up of how to get positive behavior in prevention, and certainly in secondary prevention with chronic disease that we haven’t seen before. And that’ll be because of a lot of these companies’ kind of getting involved in using non health care data to get human beings to do what they want them to do. And there’s many other areas as well, there’s no question about that.
Glenn Steele: So, you mentioned you mentioned Amazon and so from your viewpoint as former head of guising. What do you make about the Amazon JPMorgan, Berkshire Hathaway venture headed by Atal Gawande?
Harry Glorikian: Well, I think it’s a reflection of the fact that these companies are fed up with the value proposition right now for you know, for their employees. But many companies, it’s interesting and of course in my role as vice chairman of Health Transformation Alliance, I’ve gotten really comfortable with the commitment on the part of the CHROS, the chief human resource officers. And to some extent the CEOs band together to demand a look at data to see where there’s the greatest cost, the greatest variation in outcome, and to understand how together they can use their volume and their joint clout to get the best value possible.
Now as I said earlier, it’s a lot easier to use that combined volume to attack price per unit and they’re willing to do that. And you know, we can commit a significant amount of volume for hip replacement or knee replacement or the treatment of low back pain or optimizing treatment of type 2 diabetes to a certain HTA selected provider system. If the provider system does two things, number one meets our specifications for utilization. And number two you know if we get a discount on price unit depending upon how much volume is shifted. But the real question, is that scalable, is it scalable, is it easily scalable particularly the attack on over utilization or miss utilization.
But I see Amazon and Berkshire Hathaway and JP Morgan and by the way, JP Morgan and Berkshire Hathaway are both members of Health Transformation Alliance. I see them as not only symptomatic of this effort as purchasers to do something to increase the health status of their employees at a lower cost. But I see them may be coming up with products that are very specific to what their areas of expertise are. So, Amazon obviously is you know, is kind of tinkering around with the PBM game. They obviously have the capability of reaching out to human beings, whether they’re their employees or whether their you know their clients and figuring out how to become much more efficient in delivering goods in general.
So, I suspect that there will be some very specific products, that will be part of their sweet spot. And hopefully we’ll be able to take advantage of that. I think it’s a great thing.
Glenn Steele: So, what are the skills you think people need going forward in this transformation? I mean, I think what was isn’t what will be that the skill sets we need within these institutions or so forth is shifting. And what would be some key capabilities that you think any institution needs to make these shifts?
Harry Glorikian: Well, I think number one there has to be a sustainable business model. I think if you have good intent and you’re autistic and you want to do the right thing, but there’s no sustainable business model it’s not going to last. And you know I’ve argued all along the biggest problem with a CEOS is the providers believing that there’s no sustainable business model there. So, that’s number one, number two I believe that data really has to be available as an almost a public utility. Now there’s some data that has to be proprietary, there’s no question about it but a lot of data and it has to be anonymous data.
But there has to be a lot more interplay of the data outside of an unusual fiduciary like Geisinger in order to maximize what we’ve been talking about today. Number three, you know I’m a market-based guy and I believe that diversity of solutions is very important. I think if, as a big pair or as a public payer government federal government, if you try to template a single way to approach things you’re going to lose. It just doesn’t work because the markets are so varied, demography is so varied, individual preference is very important in this country, and it’s not going to go away.
So, I think that’s really important and then the last thing which, and there’s probably many other things as well. But the last thing that I’m convinced of is that the caregivers themselves are still widely respected, and if you can touch the kind of spirit and the kind of compelling pride of purpose for caregivers into a lot of this change. It happens much more readily than if they view themselves as being looked at as the enemy. So, I think that’s really important.
Harry Glorikian: On that note, I want to thank you for participating and taking the time. Is there anything else that I didn’t ask you that you’d want to add to the conversation?
Glenn Steele: No, it’s just that you know as you get older you feel freer about making predictions. So, that’s my overall caveat.
Harry Glorikian: Excellent. So, thank you so much for taking the time today. We really appreciate it and look forward to continued conversations in the future.
Glenn Steele: My pleasure Harry, thank you.
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.glorycamp.com. Hope you join us next time, until then farewell.