 Good afternoon. Thanks, everybody. So I will share the insurer's sort of perspective on personalized and genomic medicine and some of the financial impact. And like Naomi, I just wanted to, just a few words about it, so it's a national health plan. We have 18 million medical members, about seven, I'm sorry, 9 million pharmacy members. We contract with more than a million health professionals, about 600,000 PCPs. Why that's important is our ability to sort of get physicians, clinicians to do what we think is best. What the experts tell us is best is a little limited by the fact that it's so large, it's so diffused, they're organized under various systems, so it's very difficult. So I also wanted to kick it up to the highest level just to set the stage for how insurers think about this area and insurers are proxies for the people who are paying the bills, which in most cases are employers in this country through self-insured plans. So Naomi commented on this, that we are somewhere in the neighborhood of spending between 18 and 20 percent of our gross domestic product on healthcare. We are soon going to infuse another 30 or 40 million people into the system. Most of the health policy issues are related to health care reform or related to increasing access to care, but not really driving towards better affordability of care. So the pressure on the cost side of the equation is going to be even more than what it is now. There's well-documented poor quality and misallocation of resources. The IOM has looked at this, up to 100 million Americans die each year from medical errors. We spend $70 billion a year on incorrectly prescribed drugs and adherence to evidence-based guidelines is at best about 50 percent. We can argue that these guidelines really are customizable to everybody, et cetera, but even where they are and they're straightforward adherence to them are pretty poor. So into this picture is emerging genetics, and we've all seen these slides before this rapid availability in new genetic tests. There actually are not databases to Eric's earlier point this morning to sort of talk about how much extra volume there really is. Some of that is limited by these stack codes. But for some of the labs that have reported out on this, they report that there is a 20 percent increase utilization in genetic tests every year versus about a 1 to 3 percent rise for non-genetic tests. And the market is somewhere between $3 and $5 billion today and estimated to be heading to the $10 billion category. From an Aetna perspective, the absolute dollars that are spent on genetic tests are relatively modest. We look at costs with a metric that's called PMPM per member per month costs. And for us, it's about 75 cents per member per month. That's less than a half 1 percent of total medical spending. So these are modest costs. And I'll say that that number changes as a function of what you include in the cost of a test. Are you including the pathologist costs, for example, the E&M charges, et cetera? So that can change. But still, it's a relatively modest amount. About a third of that cost goes to cancer diagnostics, a third to reproductive genetics, and about a third to others, which we include infectious disease within. But importantly, our trends in the cost of genetic tests rise by about 11 percent per year. So it is important. And Dr. Frucke will point out that even though the total spend is small, these tests are said to influence somewhere between 50 and 60 percent of medical decisions. So it's important to get it right. What we see beyond the rise of the number of tests that are available and in the pipeline are the emergence of blockbuster diagnostic tests. And these are concerning. Simply if the number of those new technologies come out with these price tags, again, it becomes sort of unsustainable. So BRCA led, I think, the pack at a $2,400 price tag. There are $5,400 tags now. We have seen some panel tests come in for things like vaginitis panels at $4,000 and $5,000. So some of it is absurd, but it is, in fact, the reality of what's happening. And then, tagged to the economics of the diagnostics or the economics of the therapeutics. And we're seeing, of course, the rise of biologic therapies, 25 percent of new drugs that are approved by the FDA are these specialty drugs or biologics. The price tag, the rate of inflation for those is about 12 to 16 percent a year compared to about 3 to 5 percent for the non-specialty drugs. And oncology plays a huge part of it. So it is good news that many of these biomarkers are in the area of oncology. And whether they're effective and they will actually reduce some of the costs of the drugs is not known. And then the individual, you know, the treatment costs per year for some of these things are just eye-popping. So when you look at these economic trends, it's really not sustainable as to what Naomi said, that we are the stewards of the system. And we've got to be careful that the development of any new technology is, you know, starts at the point of what benefits the patient and not how do we just get new platforms out into clinical medicine. This is a slide I was referring to earlier, that when I put this together a few years ago, there were only about three or four different sort of markers. Now there are actually, I just did two slides worth of them. But there is lots of it, a lot of companion diagnostics. I put the treatment costs next to the drugs that they are companions to just to get a sense of maybe what the opportunity might be, you know, to sort of use them perhaps more rationally. And then on the right side, which ones are covered. But there are a lot of them. So I think to Eric's question, is it here? Yes, it is here. And this is my favorite cartoon. It's sort of the value proposition of where we're going. And I use it a bit. But it's the important question to keep our eyes on. And it's that, will this area improve quality, safety and the cost effectiveness of the care that's delivered? Or will it just be additive cost, marginal improvements? And given the, again, the costs of these, the unit price of these technologies, I hope that it's the first bullet. The challenges that we see as payers are the same as the challenges that everybody has talked about. So concerns about effectiveness, cost effectiveness, the science limitations, these kind of underlie clinical policy development and coverage decisions. Clinician and patient and consumer preparedness to use them effectively. And what do we have in the pipeline to really drive engagement of patients, physicians, health systems to use them effectively? What is the impact of consumer, direct to consumer marketing and consumer diagnostics? I'll show you some slides on that and the CPT coding challenges. And then for payers, particularly in the background, are always the issues about privacy, protection of the data. And then what we see in some of our data is the emergence of disparities and the use of the access to these tests. So I think that PCORI has a third of its efforts focused on this, is we're certainly seeing some early indications of that. So I'll go over this fast. It's been touched on by many people, but it's just the principles of how you decide what gets covered or not. And plans cover services that are related to prevention, the diagnosis, and the treatment of illnesses. So it's not information only. That's no one doubts that information is good, but they are related to the prevention, diagnosis, and treatment of illness. The information has to affect the course of treatment of the member. The care or treatment is likely to improve outcome. Improvement should be attainable outside of investigational settings. There are some exceptions to that, especially in the cancer world. And that services, importantly, are consistent with plan design. You can have a terrific technology where if the plan sponsor doesn't want it, it's simply not covered. And these principles of how you make coverage decisions are the same for genetic technologies as for non-genetic technologies. This was an issue a few years ago about whether there's something exceptional at the area of genetics. I think that's mostly been resolved. Or at least there's some consensus around it. The standards of, you know, the evidence standards for coverage are that we look to the published peer review literature for scientific evidence that permits conclusions to be drawn about the effect of the technology on health outcomes. So analytic validity, again, is assumed. Clinical validity, clinical utility. We do look for the final improvement of appropriate governmental regulatory bodies when it's required. So in the drug category, yes. In the diagnostics, no. If it comes with some FDA approval, that gives it a little extra heft or lift, but it's not required. And evidence that demonstrate that there's an improvement in the net health outcome so that it's as beneficial as what the established therapies are. So again, the evidence standards for genetic technology assessment is the same as for non-genetic technology assessment. In terms of what about the role of cost and cost effectiveness, in spite of what many people feel, that actually the role of cost is not a determinant in whether something gets covered or not. You see some very expensive technologies on the market. It is looked at, though, in terms of whether the cost effectiveness of the technology is. And for technologies that are very costly, health plans often put in processes in place to help manage that. And those processes are things like pre-certification, meaning if a physician wants to use something, they have to call the health plan. And there's an internal check that maybe is bumped up against NCCN guidelines or ACMG guidelines or something else. Predetermination disease management programs that really try to maybe specifically talk to a patient about the value of intervention A versus intervention B. Pharmacy management programs that might require as a condition, for example, of ordering urban talks that a K-RAS test is done or for peglated interferon that some genotyping information is provided. And then the duration of the treatment might be influenced by what the genotype of the virus is. So I've just listed a few examples of some of the pre-certification processes. These are, again, I think sort of values that health plans try to basically get the care to conform more to what the evidence-based standards are. These are five of many that are on pre-certification lists for us. But I show it because it's an example of sort of how these technologies are actually used in the community. If you believe the premise that the guidelines that we base these policies on are the guidelines of the professional colleges and those guidelines themselves are rational and appropriate, then this is what happens when these technologies are rolled out into service. So for Oncotype DX, for example, 9% of the requests don't meet what the FDA approval of that test was based on. For urban talks, 5% of the requests don't come in with the K-RAS. For hepatitis C, basically applying the genotype testing and then duration of treatment based on genotyping, there's a 10% reduction in avoidable drug therapy. For pre-implantation genetics, 40% of the requests that we get are not consistent with what ASRM. We get a lot of them for infertility and for infertility management. So 40% don't match what ASRM says or appropriate reasons for it. So it's quite high. And then for BRCA, we've been managing this for about 10 years, maybe a little bit longer. And what we see over time, actually it's the next slide, what we see over time is an increase in the rate of non-evidence-based use of the test. I don't want to say it's inappropriate, but the tests just don't match what the guidelines are, which are based on NCCN. And I could tell you that in the early years, most of the requests that we got around this were from, I would say, genetics-informed providers, genetic counselors, medical geneticists. As it gets disseminated into the primary care community, the OB-GYN community, that's what you start seeing. And then we have conversations many times a day over this test with physicians. And there is confusion about what are the guidelines? What is the checklist, if you will, that the physicians are using? Many of them are using myriad guidelines. So it just speaks to, and I don't say dismissively, but it just speaks to the need to be clear about what the standards are as clear as one can be and then try to think about what kind of tools do we have to support for decision support? Some of them are passive, some of them are active. And I think somebody asked a question earlier about the influence of marketing on the use of these tests. I do agree that having a guideline around it is probably the most influential. We see the same thing with KRAS and others. But this slide looks at the impact of direct-to-consumer marketing in BRCA, and I somehow deleted the arrows of when those campaigns took place. But one of them was around late 2004, and CDC evaluated this, that after these campaigns, they saw a 244% increase in the demand for the testing. Now, some of that is appropriate, but a lot of it is inappropriate. So it's this balance between trying to pull the density bullet into the population so appropriate stuff is done, hopefully supported by genetic counseling and people who know what they're talking about. But when you pull that bullet forward, you're gonna get a lot of inappropriate stuff, and you have to be prepared to deal with the harms and the costs that come along with that. Our clinical policies are also developed by a dedicated team of clinicians, and only clinicians, no business people. Policies are reviewed annually or more frequently as needed by either a new FDA decision, a new policy from a medical professional organization, practice pattern changes, we get requests from manufacturers, sometimes professional colleges to look at it. Some of the policies are updated, like seemingly monthly, the genetic testing one is. They're all in the public domain on etna.com, and most of the payers have theirs out there as well. And in the clinical policy bulletin, you'll see background, detailed rationale for the policy, what the associated coding is, the references, and then in the upper right hand, you can see a comprehensive history of when they were reviewed to see what the evidence change was or the rationale for changing the policies. So that's sort of what we do, how we do it. I think the big economic picture of the context in which this is taking place, I just wanted to touch on maybe two or three barriers that I think are particularly relevant to this room and the effect of use of these technologies. So it's the clinician and consumer preparedness, I won't go through the bullets, I think you've all seen them before, but most people really just don't know what they're doing. And to Dr. Faroukis point, no, actually somebody else said this, that within health plans, we don't have molecular geneticists, pathologists. I'm an OBGYN by training. So we could stand to use and understand your expertise when making these clinical policies decisions as well. Some of the tools that we have developed for all areas of medicine, but we're also applying them in the genomics environment are things like this, which is basically we aggregate data from many, many sources and the capability to aggregate to pull in data is expanding dramatically, dramatically. The pipes are being laid across health systems for great sharing of this data. We input member self-reported data, so at the equivalent of their PHR, the data they wanna share with us, claims data, lab data, radiology data, plan design, some demographic data that patients wanna give us, eligibility data and some external databases. And it all goes, gets aggregated into something that's called a CARE engine and it just has algorithms, best practices sort of programmed into it. Some of those are again the guidelines of the professional colleges and the pharmacy world, it's drug-drug interactions, et cetera. And then the data is sort of interpreted and then alerts are sent to physicians and or members and they are then plugged into disease management programs where they exist, might be used in a wellness counseling environment and they're also put back into the patients' PHRs that's their Aetna PHR. I just wanna highlight the genetic lab data piece because that's really been difficult. But again, with the STAC codes, we can't look at our claims data and really get any sense of, meaningful sense of what's in there. When there are specific codes, I think that it was gonna give greater capability to sort of push alerts, push best practices out to patients and physicians. And while I recognize that they don't, the new codes won't cover panels, the new codes that are coming do represent 95% of the tests that are currently being used today. So it is gonna be a way to sort of, again, pull the density dots forward. These are some of the messages we call care considerations. These are just some of them in a genetics context. You have breast cancer less than 40, consider genetic counseling, male breast cancer, et cetera. And we have variable uptake, I think of, and we do collect uptake of how patients close those gaps. So we can mine our data to say how well do they comply with these things and it varies. And it gets to this issue of patient engagement. Who talks to patients? How do you really get them involved in their care? And they're bombarded with messages all day long. The other challenge, we talked about this coding issue. I think we've probably beat a dead horse here, but I just put out two examples for people who haven't been that close to this. On the left is the existing coding for BCR ABLE testing for Gleevec monitoring. And on the right is UGT1A1 for Iranatecan testing. And you see they're the same stack codes. You can have different numbers of units of it. But what does it really mean? There's not really a systematic way to put it together to rebuild the stack and say, ah-ha, I understand this was a BCR ABLE test. The new codes that are coming out in 13 are what are in those green boxes. So I think that it will help us move towards sort of better decision support capabilities. Some of the other things that we have done to sort of promote evidence-based use of genomic services or some online resources, some physician CME that we've just finished up here. Andy Fawcett helped us with the development of some of those. We have member guides and then telehealth. And I would say they're also very invariably used because people are overwhelmed with these messages. There is an opportunity for deeper engagement. We talked about this last night. There's now a lot of good mobile application technologies. One that we have is called eye triage where patients can put in symptoms or clusters of symptoms that then sort of lead them through possible algorithms. So it is possible to program those with genetic content, but the question is what genetic content are you programming? What's the leading edge of the most important stuff from a public health point of view? And then things that health plans can do. Clinicians, not so, I think this is one of sort of our strength is to look at how you develop networks of clinicians that actually can promote the platform that you're interested in. If one of those things is the more effective use of genetic technologies, one of the things that you can do is there's a lot of work now going on in oncology network strategies to basically create medical homes for cancer patients and to have reimbursement models move from essentially a fee for service environment to adherence to care pathways. Let me show you sort of on the next slide. These are some of the metrics that were developed by ARC. I circled and read the ones that could be relevant in a genetic space. So one measure that an oncology practice could be reimbursed based on would be the percent of chemotherapy treatments that have adhered to NCCN guidelines or pathways. So to the extent that the NCCN guidelines have incorporated the best evidence we know around the biomarkers, then you have a mechanism to sort of encourage and send physicians to try to adhere to that. And then another network thing that we've developed over the last four or five years is telephonic genetic counseling capability to try to get beyond this issue of limited resources. We had done some early work with Fox Chase to understand what are the factors that encourage physicians to use genetic counselors. And the key thing is the distance that they are physically to a counselor, not the patient or necessarily the medical issue at issue. So we've developed this, it's actually has taken off dramatically. It started with cancer counseling and now it's used across a range of conditions. We have it available passively and the places where we actively steer to that are things like in a pre-certification environment where a patient, for example, a request for a BRCA doesn't meet the criteria and then you have a conversation with a physician. The next step is to say, go to a counselor. If you don't have one, you can go here. And what we found in a pilot that we ran with them that the rate of non-approval of BRCA tests when counseling preceded it was very low. So 25% in primary care offices versus 5% or so with genetic counseling. And then finally, some of the efforts that we've undertaken are in the research area. We actually do have a foundation that has funded, I forget who spoke about this, but we do have a foundation that has funded a few small genetics-based projects. A lot of those are looking at systems of care. So what is the pathways? What's the care that surrounded the decision to have a test or to not have a test? We've looked at clinical utility of two tests, HER2 and oncotype that we were particularly interested in. The HER2 issue actually came from some data that said 60% of her septin users never had a HER2 test. We thought that many oncologists thought that couldn't be. And so that was the genesis that, and actually for us in our networks, 95% of people use it. So we said, fine, it seems to be taking place. We have nothing to add here. What else? We are actively in the middle of a BRCA study looking again at patterns of care to try to understand what we can put around this very high rate of inappropriate use that could help support this. Should we require genetic counseling? What happens in these settings that gets this inappropriate treatment rate to be so high? And then finally, we are just about to start on a disparities project with UCLA and Harvard trying to get at an issue. What we found with the oncotype study was that utilization was relatively low, although the technology was new, but minority patients, African-American women were twice as likely to not have them given the fact that they have coverage for it. So we're trying to understand what that's about. And then the last thing that I wanna say that we are acutely aware as payers that we live in an environment of distrust, of heightened emotional concern about the fact that healthcare services sometimes are rationed. We won't use the word rationing, but by saying no in limiting policies, that's essentially what you're doing. And so the issues of GINA and HIPAA, et cetera, even though they've kind of a pain in the neck sometimes, especially HIPAA, we're very supportive of them because we understand that the only way that we can use our data is if we have a very high sort of standard of how we use that data. So that's it, thank you. Okay, David, Valley. Yeah, so I had a couple of questions. Thank you very much for that talk, which I found really interesting and useful. And not surprisingly, it reminds me that I think in our clinics, we're always pleased to see that when a patient is insured by Aetna, I had two general questions. One was much of individualized medicine is focused on the idea ultimately of doing more prevention. And I wonder if Aetna has modeled what that would mean for the economics of healthcare if we could, by some measure, improve prevention and do less reactive treatment. What would that do to the spiraling upward costs of healthcare? And the second question is just a small one, but I wonder if Aetna pays for genetic counselors. In other words, in a genetic counseling bill in the clinic, does Aetna pay for that? Yeah, so the second one, yes, counselors are paid for face-to-face action. Could you, yeah. Yeah, so counselors are reimbursed either face-to-face or telephonically. And then the first question, which is what's the economics of preventive health care? You probably don't mean just in the genetics context. Well, you're talking to somebody who sees genetics in every illness, so yes. So no, we've not looked at preventive health care in the context of genetics. I think it's, I don't know that the data on the effectiveness of each of these tests is sort of strong enough to complete the modeling all the way through. But the answer is we haven't done that. Michael? Okay, so once again, we're eating into our break, but, John, did you? So don't run away, John, or? So I was going to ask the speaker. The, you're insuring about 10% of the entire population and have tens of billions of dollars coming in. It seems that the whole industry is, works in a way that's reactive. You say you want to see certain kinds of standards of how convincing the testing or the new procedure is, and you just sit back and wait for it to come. It seems we're working in an environment in which the health care is increasingly rationed. I mean, we are obviously not going to be able to afford the health care that can be provided even if all the guidelines and everything else is perfect. And so it seems that the industry ought to be invested in trying to bring to the marketplace the kinds of tests and criteria for what works in a way that would be more proactive. I mean, in being involved in helping design the implementation of new technologies like what we're trying to do here at this meeting. And yet the whole industry looks like it's just reactive and waiting, just sitting back waiting for all the different ways that it could come to it. Eric promises that there's going to be an RFA on the NHGRI support for re-engineering the health care system. And K through 12 education follows right after that. Yeah, I mean, I could try to answer it. I would say, again, in a genetics context, the spend for genetics technologies are less than a half of 1%. So any innovation that's gonna be done about sort of figuring out what technology can change an outcome, today it's not gonna be genetics. So that's it, you know, no disrespect to genetics as I'm a champion of it inside my company, but it's still relatively new. I would say, and Reid can answer this probably better than I can, that health plans do spend a tremendous amount of money in innovation. But what they innovate in is systems innovation, plan design innovation, a lot, we've pilots all over the country in a whole range of medical home, you know, accountable care organizations, et cetera. So a lot of it is looking at the system of care because there's such a disconnect. You know, this inappropriate stuff is not used, appropriate stuff is not used, inappropriate stuff is used. You know, this is this mismatch. So there's a lot of work done in sort of system, I won't call it redesign, but system work, but not in a specific genetic test itself. So I'll.