[This Transcript is Unedited]
Hubert Humphrey Building
200 Independence Avenue SW
Washington, DC
Agenda Item: Welcome and Introductions
DR. CARR: I am Justine Carr, co-chair of the subcommittee.
MS. KANAAN: Susan Kanaan, writer
DR. STEINWACHS: Don Steinwachs, member of the subcommittee.
DR. MIDDLETON: Blackford Middleton, member of the committee.
DR. GREEN: Larry Green, member of the subcommittee.
DR. HORNBROOK: Mark Hornbrook, member of the full committee.
MS. MC CALL: I am Carol McCall, on the full committee and member of the subcommittee.
Agenda Item: Defining Meaning of "Quality" in Quality Subcommittee
DR. CARR: Paul is en route. I think that we can start. As you can see, we put together an agenda based on our last call. As you know, this afternoon we are going to be hearing from a number of folks on novel sources of data.
What we had outlined today as talking about, when we say quality, are we talking about quality of care, quality of data, quality of what. Then we are going to talk about innovative data and sources. Carol is going to share some of her experience, and Blackford as well, and then planning for our freestanding quality hearings.
But I think I want to raise one issue. Yesterday we heard a lot about the Recovery Act, the dollars available and then a number of new issues raised in that, PHRs on privacy, on HIPAA, on stewardship, a number of themes that we have seen before. So I think it is worth pausing for the moment to say, did we hear anything yesterday that we feel should call for a mid-course correction of something we need to do immediately, or are we good to go with the themes that we have established already.
So your thoughts on that?
MS. MC CALL: I think it is a great question. The thing we have to ask ourselves is, given where we are, is there anything we would change about what we do.
There is nothing that I would change in the immediate term. I will reserve the right to change my mind by the end of the meeting. The hearing that we are getting set to have I think is still the right one. I was glad to hear themes around data stewardship. I think it has to do with new sources of information and broadening the lens with which we view data, its sources, its uses, what it means to quality, or what quality means with respect to health care, health data, all those different types of things. We will hone in on those.
I don't think we should change our path. I still think the next step is the right one. We may add some different textures to it. It may help inform exactly what we ask, what we do, who we bring.
DR. MIDDLETON: I'm so sorry I had to miss yesterday, but I was gainfully employed reviewing some stuff with AHRQ down South. Could someone characterize the summary if you will of the presentations yesterday? I have heard a lot of things from a lot of different sources about how the stimulus bill is going to impact IT obviously, and potentially quality and patient safety and care, et cetera.
One of the things that I raised on the list via e-mail earlier was, how does our role in NCVHS change or not? Given the call for new FACA committees from the legislation, what do we do as NCVHS? Does that change our mission or goals or activities?
DR. CARR: We talked some about that yesterday. I think that -- others chime in, but I think our sense is the same sort of presence we have had for 60 years will continue. A lot of this is about health IT, and we are about some of that; we are also about other, broader questions. So I don't think there is any sense of that.
I think there was a call yesterday to put together here are the things that NCVHS is well equipped to do, and get that out there. I think there is some skepticism about whether these two FACAs will be able to be pulled together in 90 days and implemented, especially because we are still waiting for a Secretary.
I think also, well, I don't know, we will see, I am just reflecting on conversations, but it may be that they are going to be very implementation focused, the standards in particular. I think the question that I heard more of is what does it mean for the new AHIP 2 committee. Did others heard anything different? Are we going to be impacted, is NCVHS impacted?
MS. MC CALL: I actually had to leave right when that question was being asked, and I heard a hard stop and I didn't get to hear the answer. But I've got the same question, which is, are we elevated, demoted, or are we in a different position, so that what we should be doing --
DR. MIDDLETON: Two out of three of those are bad, demoted or pushed to the side.
MS. MC CALL: Right.
DR. MIDDLETON: The specific questions I might ask the committee to consider, either the subcommittee or the full committee, is just looking at the legislation, now that it is law, things like the focus on HIE. Not only has it standards impact, and will draw upon a bunch of the standards thinking, I think, but there are clearly implications to data integrity and quality as we think about their transit source, authentication, non-repudiation, et cetera, and how they are used for measurement or care delivery.
Then secondarily, this whole issue of meaningful use, that is, the Medicare/Medicaid funds will be allocated based upon demonstrations of meaningful use of HIT. So far, all that means is, DRX, decision support and quality reporting. So we own a third of that right there, quality reporting; how can we help outline strategy in the standards or quality data issues for quality reporting, but further, might we think also about measurement of these things.
MS. MC CALL: Thank you. The things that I saw woefully missing, and I brought it out both in the full committee and then offline, and I think is the task that the Populations Subcommittee just walked away with, which is, there is no money there for the health statistics enterprise for the 21st century, I mean none. That is one of the biggest misses that if we don't make sure is shored up, we are going to regret it, maybe not today, maybe not tomorrow, but soon and for the rest of our lives.
DR. STEINWACHS: So Carol, you shouldn't have left the meeting, because your name came up several times.
MS. MC CALL: There you go. But I think our role with respect to that is to say what else needs to be done. So it is not just quality of data. There is more that needs to be done around metrics, what is a good metric. Nobody really talks about that, but they matter and they matter a lot. What they are, how they are couched, are they consistent.
Then, even a metric is not just reporting, it is analytics on same. There are going to be more ways to analyze them than there are different elements and bits. There is very little talk about dichotomies, there is very little talk about meta-data.
DR. CARR: Some of the work that Paul did with high tech was to say, here are all the things that are out there, and here are the things that we can make meaningful.
I think that taking that one step further is to think about these electronic health records, what are the structured fields, looking toward what will we want to be measuring, because right now, because you can get it on your computer doesn't mean that -- it is a little bit electronic, but in terms of queryable fields or creating registries for measurement, it is not quite there. So I think that might be one thing that interfaces with that, what are those things.
Over my now five years of being on the committee, there is a balance that we need to strike between granularity and high level. I think again, it is teeing up the issues, asking the questions that are answered by others.
One of the things that in the NEHIC presentation that caught my attention yesterday is the high level ideas articulated, followed by a -- I forget what they call it, but a specialist, kick the tires on the concept. We need to remember that even though a number of us have a lot of expertise in this area, we meet four times a year plus some hearings, so we can't be doing the complete drill down. So I think striking that balance of how you tee up the important questions and hand it off.
We heard yesterday from the CDT that did a project on de-identified data. We had raised it as part of our secondary uses hearing, but we realized we did not have enough information. They brought all the thought leaders together. They haven't answered everything, but they brought it farther than we were. I think that is the model that we want to think about.
So as we think about these things today, maybe what we do is hear a little bit from you and Blackford about things that you are doing, and then come back to this issue at the interface or the areas of focus in the upcoming Recovery Act that relate to quality and how we would interface, where we think we should be. This afternoon, we are going to hear about data from non-traditional sources. That is interesting, but it may be not intersecting as much as we want.
So if you could talk a little bit about information that you were talking about with the pharmacy and predicting adverse events.
Agenda Item: Innovative Data Sources and Data Streams
MS. MC CALL: Actually there were a couple of things I wanted to do. One of them was talking about data creation and data use, and then some non-traditional players that I see in general, certainly not a complete landscape, but just some examples.
On data creation, there is a lot that we do at Humana. I am part of our Innovation Center there, which is something that we started back in 2000. It was a time of big change at Humana, but also in the industry toward consumerism.
The Innovation Center is this investment in being intentionally different. It has been this Chinese calendar year of different themes and all of that. Where we are today, we have done a variety of different things.
Around health behavior change, there is a strong belief that that is one of the keys, and it is really all about how do you engage people, how do you identify them, all of that. There is a lot that we do with personal technologies, there is a lot that we do with incentives and rewards.
We have launched health games which are absolutely fascinating. There is one in particular I will tell you about. We have a lot more emphasis on social networks and social programs.
Some examples. This is a spinoff company, it called Sensei. It is a personal coach on your cell phone around diet, exercise and fitness. It allows you to customize, personalize not just diets, but prompts based on the particular issues that you have, whether you tend to be the midnight raider or whatever it is.
This is something that is not just for Humana members. You could go out and buy it today. So the whole idea is to have something that is personal, intimate and always on you, so it is really quite delightful.
Another is called health models. We created this with Virgin Life Care, Richard Branson, Virgin and all of that. It is an incentive and reward program. I can earn, and I am part of it, $450 a year by essentially keeping track of not what I eat, but in this case, what I do with my feet. There are contests and there are programs, and there are winners and losers and all this kind of stuff. You can compare yourself against others, and it is really interesting. There is data in there that is being generated and tracked, and you can compare and contrast. We have tracked the impact, to your point, Blackford, and we have seen changes in weight, we have seen changes in blood pressure. You step in a health zone and it will keep track of some of this stuff. It is very easy.
Another one is called Horsepower Challenge. This is a health game literally, for kids in schools. There is a pedometer. They wear it on their shoe when they walk in the building. It uploads -- those steps earn points. The points allows you to pimp your horse that is now part of the game. The horses are now racing each other. The best school wins, school on school, and the winner gets to go to the Derby, in a whole big splash.
The feedback on that one is that it harnesses the kids' natural desire to be creative, what they do with their horses on this game; they compete. But what the teachers say is, it not only increases awareness of activity, which it does, it changes their relationship to it. It also changes it not just in school and with each other, but in their families. The parents are saying they are having different conversations. So it becomes harnessing what is naturally fun to do, where the emergence is healthier behavior.
Last but not least in this data creation and programs is essentially a social bike program called Freewheeling. It is spun out to a company called B-Cycle. It is just like the bike sharing program in France, but unlike that program, it also allows you to accumulate points, a la Health Miles. So you can keep track of your own activity. There are kiosks that are around. You come in, you sign up, you use it, you drop it off at the other kiosk. We put one here in Washington, D.C. It was launched at the Democratic National Convention. We also had it at the Republican National Convention. We have sold the city of Denver, and we sold it in Miami Beach.
But the whole point is that these types of things from a groundswell up become about health and health behavior. So if you think more broadly about the data that is being generated and how it is going to be used, there are implications everywhere. People are going to want to track and compare. So it steps even outside the PHR, at least in the way most people think about it.
So those are some novel data creations. I can give you some more examples, but those are delightful highlights.
In terms of data uses, what I do in my world is, I do scary stuff with data. I have got a Health Services Research Center that does the things that you might traditionally think it does. It takes advantage of the data that we have, administrative though it is.
We do some interesting things in terms of fusing quantitative and qualitative, but those are recognizable sets of activities. What we do that is kind of unusual, we have a portfolio of predictive models. I was telling Justine of one that we have created which is really quite delightful, folks.
What we did was we took all of our claims data. We then processed it to create a lot of meta-data, a lot of tags about conditions and situations, everything from, yes, you do have cardiovascular disease or not, things you would recognize, but also indicators of depression, et cetera. We fused it with a knowledge base of every known possible drug, serious adverse drug reaction, drug or disease, et cetera.
When we fused those, we came to the point of view of every person who believed had a serious adverse drug reaction over a two-year period, across our entire Medicare population. It added up to be quite a sum. It added up to be over $500 million of impact just for the inpatient admission or the ER visit period, no follow-on costs, no other costs.
Then we build a predictive model off the whole mess. This is a tough, tough predictive problem. These are rare event issues. They don't happen often. They are categorical data, longitudinal, all kinds of hard computational science, and they did it.
So they have an area under the ROC curve for certain types that is over 80 percent. That is very good. That is with the time boxed, can you be better than random at all. There are a lot of situations where they will come back around. They will tune, they will refine, et cetera.
So that is the type of thing that could become -- it is not prefect, it is far from, but what do you do with something like that? You could make it the beginning part of a clinical decision support that says, your doctor, if he could put it into some of these information exchanges.
Humana has a company, they are called Availity. It is a nonprofit, all payor, all provider. It uses claims data, snap-together payor based health records, and you could have this sitting on top.
Another thing we do is, we use our data, but also now our members, to power up clinical studies, observational life science/biomarker studies. So we are powering up what we call the bioimage study, which is part of a huge set of research around high risk plaque. They are closing in in about 18 months. They will have a blood test that they are ready to launch that identifies people at high risk near term of first heart attack or stroke.
What we have done is, we have said, we will have 7300 people into a clinical trial by June of this year, that have been identified as qualifying, outreached with a call center, enrolled, and they come to a mobile clinic.
So it is a really exquisite example of a novel and different use of, in my world, administrative data to massively speed up, scale up and diversify the moment from science to solution. So it is a very novel use. No other health plan has done this in this way at this scale, and it makes possible things that would traditionally be much slower or not at all.
Last but not least around all things geeky, we are pursuing a simulation. Think about the Sim City of health policy. One of the predictive models that we built is an obesity inference model. We don't have height and weight and therefore BMI on our membership, we just don't. But for a small group of people, we did get it. We got it because we built an HRA, and we got that we built that off of some other predictive models that we have. But to the HRA we then asked other questions.
So for about 100,000 people, we had a whole bunch of stuff about health behavior and height and weight and all that. We said, okay, we can build an inference model that says, for people that we don't have height and weight, I can use your claims data and I can figure it out. That has an area under the ROC of 287, so it is pretty good.
What you can then do is take that, run it on your entire adult population, and you can infer obesity in every single adult. You can now create, and we will be creating, an obesity index, a health index that ties it to things I know about metabolic syndrome. Do you have diabetes, what is my opinion on that, do you have hyperlipidemia, do you have high blood pressure, are you in fact obese. You can then combine these in different ways to understand, broad side of a barn, who is likely pre-diabetic. You can go out and you can get them. BPP says, one thing you want to do is have them reduce their body weight. We have the interventions. So you can use data in novel ways.
I want to take it one step further. What I want to do is do some simulation. I want to take a particular city and I want to simulate the health of a city. The way you do that is, you grow a city. So we will go back, and I will be doing some data fusion that is really cool. I want to take a city, it will probably be Louisville, and I want to be able to take all this different information about it over the last 20 or 30 years, everything from what is the walkability index, how many sidewalks do they have, what have they done with public transportation, what is happening in public schools. These are all of the forces that come to play on the health of the city.
You use those as guideposts. You can use agent based modeling and other simulation techniques to grow that city and its forces to where it is today. Where it is today health wise is going to be how I represent it in this health index. But that health index is an emergent property. It is not an input, it is an output. So there are ways that you can use these other forces to get you to that answer.
Then what you have is, you have the ability for policy makers to understand the major influencers on health as they go around making decisions about urban planning and how they handle their businesses. So it really does become the Sim City of health policy.
It is fusing data at levels that were never meant to go together, so you do it very carefully. Some of it will be at a very high level, others will be at this level, and you basically do good enough, so that you can have a model that is good enough to make better decisions.
So those are some non-traditional uses of our data being fused with other data. We expect the health index to be ready second quarter. We expect the Sim City of health policy to be ready third quarter. So that is data uses, non-traditional players.
There is some good stuff here in Tab 6, 6 or 7, that for the first time talks about some of the health data players. So in the packet, social networks come to health. There is discussions in here about companies like Patients Like Me, James Heywood, Ben Heywood, they are in Boston; they are delightful. These are places where people come and put in some of their own data, and can volunteer for clinical trials, all kinds of stuff.
There are some other things out there. Adam Bosworth who used to head Google Health, he now stepped out to start his own company, Keiss. Full disclosure; I am on the advisory board of that. That one is going to do some interesting things around what I call do it yourself labs. There will be a way for people to order their own labs and step around the gatekeeper, of having to go to your doc and pay that cover charge. It will all be within regulation, no 360 network behind it. So it is not an issue of regulation; it is the fact that there is going to be a fundamental sea change in terms of people's own ability and accountability for their own health.
Another technology that I have seen is truly a do it yourself lab kit. This is something that once shrinkwrapped, it becomes a do it yourself kit that you can buy literally at a Walgreen's. It takes a drop of blood. It is to perform a lot of the know your numbers tests, whether it is cholesterols or a variety of things that you might normally get in one of the comprehensive panels at a doctor's office, and literally bypass even Keiss entirely.
There is a lot out there on body sensors, everything from heart rate that is done remotely, sitting on your bed, to how you are sleeping, to motion, to gate. These are things by Intel and Natrills Research Center, Foster Miller, Physic Ventures, Unilever.
DR. CARR: Carol, you are always a wealth of rich ideas. It is fantastic. Having heard that, Blackford, I want to give you a chance to talk about other perspectives. We are just talking about what we have said out to do and a little bit about what was talked about yesterday, about the structure fields.
DR. TANG: Can I ask two questions of Carol?
DR. CARR: Yes.
DR. TANG: One, in discovering -- I don't know whether ADEs or all kinds of complications, --
MS. MC CALL: No, ADEs.
DR. TANG: You said you sort of heard things. Did you infer them, or did you use claims data that also pointed out that this in fact was a complication due to an ADE?
MS. MC CALL: Only six percent of our claims are coded that way. It is in peer review with people at the University of Arizona. We have had I know are ghosts in the data, so it all depends on how well those are accurately defined. But the way it was done was, you made sure to strip out the underlying incidence of admission. It was only something that the literature said was coded up. It said, if you see these combinations, then you could see an admission for X. So only if it was an admission for X, and then even then, some of those got stripped out.
So there are still some ghosts in there. We believe that 80 percent of them are more --
DR. TANG: Six percent is about the right number, actually. The other question is, you described very innovative ways of using public data fusion. It is the use of that information that would determine whether this is a positive thing for the individual or not. How do you determine that?
MS. MC CALL: At this point, once you actually get the end of the result, there are a number of different ways you could do it. Can you see an individual in -- this is single play, just like a video game; do you want to be able to see individuals in it or not. If the answer is no, then all of that detail will get stripped out.
DR. TANG: So there are a couple of ways to do it. I could go find the individual to go help them, renting the bike or whatever, or I could say, you ain't getting insurance from us. How do you decide?
MS. MC CALL: There is the Sim City of health policy, which can strip out all the detail. You can strip all of the individual detail out of that, or you can present it in a way that can say, I can show you globs or clumps of people.
So that is one thing that you can do. But then you quickly transitioned over to, what do I do with the information to decide if I am going to insure them?
DR. TANG: You have used these things in novel ways. You have gotten new information. You have to separate what is good and what is not, but anyway, new sources of data. Depending on whether you are a provider or an insurer, you could do one or the other thing to with it.
MS. MC CALL: Having been cast in the role of evil empire, I understand, but you are going to have to get more specific because of the implications. Are you saying it is impossible?
DR. CARR: Is this how we want to spend our remaining 25 minutes? I think it is very interesting, but we are not going to get through the whole conversation.
DR. TANG: I want to hear from Blackford in response to this offline.
DR. MIDDLETON: Well, a hard act to follow, so I am going to go from 100,000 feet and dive in.
It strikes me thinking about this is that we are fundamental in the midst of a sea change. A provider oriented view of data, a provider oriented way in which data is created, is going to be supplanted by a patient oriented view of data and a patient oriented means by which many data are created or acquired and used, et cetera.
So I go back, Paul and Justine, to the original NCVHS depiction of patient and provider and public health. I think we need to redraw it as a patient centered view of health care, self and proxy, workplace sources of data, home, and then social populations, social networking 2.0, whatever you want to call it. It is a completely different depiction of the way we think about these data. I think that allows us to think about the principles by which these data have to be considered in their multiple uses and infusions, et cetera.
I think the same principles apply about accuracy and precision and reliability, validity issues of data, as well as when repudiation issues will be perhaps more important. I will always trust your data. I will always assume that it has not been changed in transit. However, if a patient is accessing their data, or even creating some of their data, I may be very interested in changing it. I wish I could change the number of zeroes on my bank account. But it is that level of non-repudiation, if you will.
So I think that raises a set of issues about how we think about patient centered data and how we might articulate the principles that we have to consider around these issues of validity, reliability, accuracy, precision, integrity, maintenance of validity across time and space.
Then finally, what are the value metrics that we will use to decide these data of this type are worth gathering and sorting and using. There may be other purposes and other businesses and all the rest of it and we might be interested in those data, but we might provide some guidance on this.
This came up the last time we met. I am particularly interested in value and value measurement and assessment.
DR. CARR: Yes, thank you.
DR. MIDDLETON: So I think we have to bring that on. So after that high level guide, then let me go a little bit more down to the ground level.
What we have been doing, Partners has had a long interest in gathering data from a wide variety of sources, not only across the enterprise and all the typical health care side of the equation way.
We have done as much as we possibly can to create an integrated data model and gather these data in two primary forms. One is a clinical data repository to the transactional needs of care management, but also importantly a research patient data registry, a separate denormalized, more population oriented view of these very same data that have been cleaned up dramatically from their clinical source and messiness to be available and used for research.
Now, the third primary focus after clinical and research focus on these data is to think about how to organize the genetic genomic data, how to marry the genotypic representations we all have with the phenotypic representations, and do so in a meaningful way that has value from the get-go.
In addition to that, very tactically, we have had a bunch of experiments ongoing with personal health records and patient source data capture, specifically things like showing the patient an abstract of their record in the form of a personal health record, tethered to the EMR, tightly coupled to our EMR. We find interesting things. Just showing the patient the med list can detect discrepancies in the doctor's understanding of the med list from the patient's actual use of meds. In a significant number of times from around randomized control trials, physicians are informed by just having the patients review their own med lists, so extremely interesting stuff.
In addition, we find -- and this replicates some other findings -- that in diabetes care, not only can you activate the patient to be more engaged in their own diabetes care in all the usual and right ways, by engagement and participation in data gathering and submitting a journal to the physician for their review, perhaps most importantly, that patient involvement activates the physician. The physician then is more likely to actually do things to manage the diabetes beds.
MS. MC CALL: We have seen the same thing.
DR. MIDDLETON: So this is a very funny thing; how do we overcome inertia? Maybe we have to stop throwing water at the provider, and use the patient to activate the physician.
Other kinds of experiments. The Center for Health has done a bunch of home monitoring for QBNI and CHF and all this different kind of stuff, and the same kinds of findings are coming to be.
So I don't have anything more detailed to offer. I would be happy to send in more detail if people are interested. But I think we have to reorient -- we have talked about this before -- 180 degrees from the provider view of data to a patient centric view. Maybe it is a concentric set of circles, or maybe it is five bubbles, but patient, community, social network, and maybe somewhere out here is provider. I would love to do that.
DR. CARR: Or maybe from your observation it is providers, since we are using the patient to activate the provider.
MS. MC CALL: Although I would still go back and say patient in the center, and then maybe there are some different rings around it.
The other thing that strikes me is that given NCVHS and given the realities of changes in the landscape, but also the realities of our own frequency, et cetera, we cannot engage in trench warfare on what the right fields are. It is not our job; we will lose the battle anyway. There are people that have a lot more knowledge about what the right things are.
I think part of our role is to stay a couple of steps ahead and say, so glad you guys have made the investment in HIT. I am going to assume you are going to get that one figured out. Congratulations.
Now, where do we need to go next and start essentially creating a landing, a beachhead, for what the next issues are going to be. I do believe it is a telescoping out from a health care and a provider centric to a health and a person centric, and start basically doing the work to create a point of view around that. So that is one.
The other is your whole bit on measurement and hey, did it work. This is CER for everybody. It is not just head to head competitions on giant class effect in studies. This is any program.
DR. GREEN: I really like what I have been hearing. It can save us a lot of time, if you want to see a third set of examples that are practical, from the ground. The November issue of the American Journal of Preventive Medicine.
DR. CARR: Which journal?
DR. GREEN: The supplement to the November issue, 2008, to the American Journal of Preventive Medicine reports out six years of RWJF work that sounds like what these guys were just talking about. You can see untraditional sources of data that, you will laugh when you start trying to do this. It comes from the YMCA, it comes from unusual places.
One practice group replicated health behavior counseling with LISS. LISS of course was a computer. One of the data points that came out of that one was that there was a patient that used LISS every night at 1:30 in the morning. Talk about a quality measure and access and availability and the patient can do it.
Where I really want to go is, I think I want to pretend I am on the NCVHS for a minute as a member. You have got key questions on the agenda here. I did not set out to organize the set of comments here, Justine.
I think quality is an attribute of health care, is the fundamental issue. Quality of health is very, very important. I think the Committee for National Bio Statistics, we have a monitoring function for health, but that is not what I think the word quality actually means. I think the word quality is in play in our country and our world in the stimulus bill, and where we are right now. We have got to do something about an unsustainable, unaffordable, run amok health care delivery system, and we have got to get some money out of it to do the sorts of things that Carol is trying to get to.
So quality as far as I am concerned, we should be thinking about as an attribute, not a real result outcome sort of thing. Quality is a means as far as I am concerned to the end of improved population health. That is the way I have started thinking about it.
I have been in a number of discussions at the IOM and elsewhere. We can spend the rest of our life arguing about this, too, and I don't want to argue about it, but I just want to come clean. I am just confessing.
I think what I heard this morning is the visionary leadership that is uniting bio medicine and the socioecological framework in our time, in our place, in our country right now. We have really solid data that you don't get to health if you don't address both.
You are the only person that said genetics so far this morning. We believe that is 30 percent of the variance in whether you live your full life span or whether you suffer when you didn't have to. We think that about 40 percent of the behavioral stuff that you highlighted in 14 different ways, that is 70 percent of the variance. We think that health care is about ten percent of the variance.
But we also have pretty solid evidence that health care serves as a nexus. It is a point of integration when a lot of things can happen where you can connect it with the school, with the church, with the work, and personalize it and prioritize it. That is the answer to the question about the interface with person centered health care, not person centered health. We have learned about that and we know about that.
Our structure in the NCVHS is displayed this morning, I think. There is no really clean way to isolate for me the work of the Quality Subcommittee from the work of the Standards Committee and the privacy group and the Populations Committee meeting that we just had. The good news that I see in this meeting is that they are coherent, and they are tending to be convergent right now.
So I am struggling to understand where the Quality Subcommittee --
MS. MC CALL: Yes, but I am less worried about organizational design right now, committees and all that, as I am what I heard you start with, which is, the focus should be on capturing, measuring and then trying to assure quality of health care. That is what I heard you say, which is very different than taking a view to say we are going to go beyond health care.
DR. GREEN: I don't want you to hear that. That is not what I am trying to say. But we do have new opportunities to assess health care that we need to seize.
But the way this meeting was structured as I read our book, and what we are going to be listening for this afternoon from my perspective is, we sense, we smell, we have examples at the table that you can unite administrative data, the survey data that is population based and that the National Center for Health Statistics operates, and clinical data through the new world. It needs to flow in the Nationwide Health Information Network, and out of that circle that Bob Kolander showed us over and over again, deriving that is our assignment, as far as I am concerned. Using data to develop the information is what we should be talking about.
We are enriched with the tension and the conflict between all of these measures being intensely personal and there being intense interest in keeping it completely private.
DR. CARR: I think what might be helpful is to say what we are not about, and then what we are about. I think what I have heard is, there is going to be a huge influx of dollars into HIT, and there will be lots of granular HITSP type looking at all these different things and merging information. It is going to be fast paced and specific. We with our time frame, our expertise and so on, don't see ourselves as participating in that level of granularity, so that is it.
MS. GREENBERG: When you say we, you are talking about this subcommittee?
DR. CARR: Right. But what we do think is that our role is to go where the puck will be, as Carol has said three years ago from that book. We are hearing today about some of that remarkable leveraging of new technology, blending of databases and imposing thoughtful integrative questions on top of data.
I like what Larry said. As we think about where we are, health care is ten percent of the variance. Genetics is 30 percent and behavior is 40 percent. So if we are looking to make a difference, what Kayla is talking about is behavior, and what Blackford was talking about was genetics. That is not part of the conversation today.
So health care, all the ways of delivering care is getting lots of attention, but it is only going to be ten percent of the solution, and behavior and genetics are 70 percent of the solution.
MS. GREENBERG: But I also really liked Larry's point about -- because that is my song -- all of our money, all of our focus, all of our research is on health care, and it really isn't the biggest thing that influences health care. But on the other hand, we certainly know it has got to have an influence.
I really liked your concept of data access. I think it could be. If you had a person centered health care system, it could be the nexus. I don't think it necessarily is for most people, but it certainly has that potential.
DR. CARR: And maybe the medical home and all of that. But I think what is intriguing is, it is the nexus informed by genetics and the behaviors. I think that is so key. If it is just about ACE inhibitors and ARBs, we are doing that with the core measures and all that, but there is this whole other thing about activity, and even designing cities. We have the note on the stairway telling you it wouldn't be a bad idea to walk. Just those simple things makes me walk.
MS. GREENBERG: I want to make a case that this Quality Committee's work really could be galvanized around examining the union that Carol illustrated this morning. She talked about, she just had administrative data, she didn't know what something meant, or she didn't have the rest of the clinical data. What we believe is that we see a future where she could have that, too.
MS. MC CALL: Oh, yes, I am all about fusion.
MS. GREENBERG: When you do the fusion, the opportunity to assess the quality of health care and its outcomes, and to get to the value equation becomes possible.
We have got people sitting around this committee that envision how that could happen. It undergirds measuring the quality. It is a measurement issue. It is about quality. It is about getting the right data in the right place, getting these intersections. It was on our agenda.
Part of our chore to listen for today, as we look at these models, what can come out of the administrative data, where are the holes and where are the gaps. I think our committee should be making contributions to envisioning how we get the data we need to assess the quality of the health care delivery system and the health of the nation in the world that is coming.
MS. MC CALL: We have got it written down here, so let's not rewrite our charge.
MS. GREENBERG: Let's stick with the charge.
MS. MC CALL: Right, so let's stick with the charge. I think what I am going to do is push us back toward health, monitoring the nation's health data needs to identify emerging health data issues, methods, technologies, put a star next to technologies, to identify strategies for evolution from single purpose, narrowly focused to more multi purpose integrated shared data.
DR. MIDDLETON: There is a tension though I just want to draw out, that I am feeling, that I wonder if others feel, or if I am missing something.
On the one hand, I think there is a clear drive toward m fusion and aggregation and pulling together data from multiple data sources, and then using it, exploring those large data sets. At the same time though, there is a pressure towards hyper segmentation or mass customization that has come up clearly in our conversations with the ACME group in the recent meeting.
It may be in the end that there is a treatment for Blackford Middleton that is different than yours, Larry, and different than yours. When we think about this from a traditional epi small cell numbers problem, then there are different issues about how we trust and use the data integrity and reliability.
So I don't want to lose that micro data focus, while we think only about the data fusion kinds of issues.
MS. MC CALL: I understand. I would encourage us to go back and look at this page, guys. The question is, rather than reinvent this wheel.
DR. CARR: What tab is it?
MS. MC CALL: It is under Tab 4, and it is the last page in the tab. It is the charge of the Subcommittee on Quality. It says we are focused on two areas, and that we have near term priorities that are specifically two things, and ask ourselves whether or not, given the current state, we believe this has changed or needs to change.
DR. TANG: I would guess that what we heard both in the main committee and this morning is a reaffirmation of where we are headed. One is person centered, and two is health and health care.
I think that the novel uses just gives you more hope that you can do something, one, by combining the data and two, by sharing it back with the individual. We shouldn't all of a sudden stop and focus back on the ten percent.
`I think the ten percent, rather than being a nexus, right now it is end of life tail of the dog, and why chase that? But it could be if we refocus on the patient centered health, we could have an infrastructure that allows us to permeate rather than be the end of life tail. That just changes the whole -- and I think it is still where we need to go.
MS. GREENBERG: It is so hard in this committee to talk about the redesign of health care, which is the first transformation since 1830, without getting that reaction. I wish we could put that to bed. There is not really a conflict here. There is not really a conflict about wanting to know if the redesign of the largest health care delivery in the country works or not. There is not conflict in wanting to know the answers to that, having the data sets available to do that. We are taking a person approach that acknowledges the socioecological framework of what you do at work and what you do at school really matters.
What we are looking at here is to quit segregating these things as if they are different, and to put them into the center of the table and say yes, all of those matter. This genetics thing is so neglected, and it is going to be blow things up really soon.
DR. CARR: I am going to be the Chair here. What is the output and what --
MS. MC CALL: What do we need to come away with today, guys?
DR. TANG: One is reaffirmation. I think that is pretty good.
MS. MC CALL: Yes, but we have a hearing to plan.
DR. CARR: And the audience is the Secretary, the customer.
MS. GREENBERG: Can I just make one very quick statement? That is, I wouldn't worry either about this organizational thing. You can talk better in small groups, in subcommittees. But this group can be more the visionaries to some degree, not that they are concrete. But I don't think you have to worry as much as some of the others do about hitting the stimulus package, so it is okay.
DR. TANG: I think we can reproduce the substance of this discussion for the broader committee.
DR. MIDDLETON: So Paul or Justine, just a question following up on Marjorie's comment. This was said earlier. I kind of don't get it, that we are removing ourselves from the stimulus part of the package, which is about quality data reporting. I don't get that.
DR. CARR: I think that maybe we are speaking too narrowly. There are things we have done before, stewardship, privacy, HIPAA, those kinds of things, the connectivity of HIEs, stuff like that. But this piece of it I think is very noble to put forward as an area of focus and development, taking what we have with the administrative data and blending it with other data sources.
DR. MIDDLETON: But I am just worried now at the very tactical level. NQF, we are doing our thing with high tech two, with the quality data set and the work flow. There are others thinking about the AMA collaborative measures and what is going to come out of an EMR. I don't know if NCVHS has ever really put a stake in the ground about HIT based quality data reporting, and it couldn't be more relevant or germane than right now.
DR. CARR: What would that look like?
DR. MIDDLETON: I think a well thought considered piece, white paperish thing on the principles surrounding HIT based quality data reporting to give guidance to all the effort that is going to flow right now about how to make that part of meaningful use really be meaningful.
MS. MC CALL: Can I ask a question, a very naive question? Is that job not already taken by somebody? What I don't want to do is wrestle with somebody over whose job it is to do.
DR. MIDDLETON: It is a fair question. I thought you were going to ask, is it done.
MS. MC CALL: No, I know it is not done.
DR. MIDDLETON: So is it being done by someone else.
MS. MC CALL: Or has somebody else been charged to do it. What I don't want to do is scale a hill, only to find out that somebody gets to the top and I go, what the heck are you doing here. It gets to our role.
DR. MIDDLETON: I guess my observation -- Paul, I would be interested in your response -- my observation is that there is a lot of related and disjointed efforts that may or may not be pointing all to the same goal. If NCVHS' mission in life is to define the goal, in this case what would be quality data reporting from HIT, that is our bread and butter, I think.
DR. CARR: Let me ask this. Susan has helped us put together a primer on data stewardship. It is nothing earth shattering; it comes off of our earlier hearings, but it is taking what this group said, here are all the observations, and putting it together as a compendium, not making a recommendation, but creating a compendium. Would that serve this purpose?
DR. MIDDLETON: That is one part of it, maybe even a small part frankly, data stewardship.
DR. CARR: I don't mean data stewardship, but what represents quality. It is a primer.
DR. MIDDLETON: I might try elevating its stature from primer to white paper or something.
DR. CARR: We have testimony today or presentations today, the beginning of a 2.0 hearing about that, and we were talking about going in that direction. The two things that we have heard about, what Carol is doing, what you have talked about, to me are things that we don't hear about elsewhere, and that need truly to be elevated and that we need to hear more about. So I feel like that would be a reason to focus right here.
DR. TANG: So Blackford, I think high tech two can cover what you just said. That was our discussion a couple of days ago, for clinical data for quality measures, and quality measures that have to be up to snuff to accept clinical data.
I think that plays a little bit to what Carol was just saying. We certainly can accept that kind of work as part of our quality charge, but I think what you were talking about this morning is new and not being done by anybody else.
MS. MC CALL: These are less about the specific tactics. The big theme in here is whether or not we see an unmet need that we can do in a big way around quality measures.
DR. CARR: So we will listen to the presentations this afternoon. Cynthia will set up a call in ten days, and we will then come up with a plan.
(Whereupon, the meeting was adjourned at 10:03 a.m.)