Future View Productions PCORI 2019 Annual Conference What’s Right for Me? Practical Approaches to Personalized Medicine Wednesday, September 18, 2019 3:00 – 4:44 p.m. ET Remote CART Captioning

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>> Announcer: Ladies and gentlemen, please take your seats. Our program will

begin in ten minutes. Please take your seats. >> Announcer: Ladies and gentlemen, please take your seats immediately. Our

program is about to begin. >> Announcer: Ladies and gentlemen, welcome back to the 2019 PCORI annual

meeting and our opening plenary session. To begin our session, here again is PCORI's executive director Dr. Joe Selby.

>> JOE SELBY: Hi, everybody. Welcome back. I hope you had a good

break. We're going to talk about a topic now that was not on my radar on day one when I got to PCORI, even though it was tucked into the legislation. And it said that when investigators are doing comparative clinical effectiveness research they ought to look for instances where the comparative effectiveness is not the same for everybody. Look in sub-groups. Look in people defined by age. Look in people defined by socioeconomic status, right all the way down to the genetic makeup, right down to the gene and gene markers. The idea is that one size doesn't fit all, one comparative effectiveness answer may go in one direction for one group and another direction for another.

It's turned out to my amazement over time that has become one of the most defining features of what makes research -- not care, but research patient-centered. It's looking to see if the treatment works the same on everybody or if we can give more personalized advice. I'm not following my talking well enough to make the slides move. Far too often the decision is incomplete or not right for a particular person. We have trials that say on average this works better than that. Guidelines have therefore said go with this, not that. It may well be that that is actually the thing that works better for certain people.

So we have pushed this in our research. This applies to the test. Some people ought to undergo screening. For other people, they should think twice and think about actual risks are. They may be so low that the risks for getting screened may be higher than the risk from the illness in that person. Not to say that you can't have the test or must have the test, but here's the information that's going to help you make a better decision. Some treatments may work well for one group and not for a

another. You're going to hear in this panel coming up, you're going to hear from people who have done studies or are doing studies that get right at that head-on.

I don't want to say it's easy. In fact, I'll say it's difficult research. It tends to take a lot of people and careful analyses. We have a panel and a panel lead who are extraordinarily well qualified. I want to introduce Dr. Aaron Carroll, professor of pediatrics, associative Dean at Indiana university school of medicine. Aaron is also -- now I really have to get my notes because Aaron is well-known. He writes for the "New York Times," a blog called "The Upshot" and a great job on a blog he calls the incidental economists. He applies it to patients and populations in a way few others can't. Aaron was here last year. Last year he talked on a topic that was a little bit like this. It was on the right test at the right time, the right dose, the right interval. So that's a part of what we do, but this is really more about personalized medicine. Some people may think that personalized medicine is all about genes. Yes, it's about genes, but it's also about everything else about you, including what your preferences are.

Without further ado, I'm going to toss it to Aaron, again, with my great thanks for coming back for a second year. Aaron will introduce this fabulous panel. For the next hour, we're going to hear about personalized medicine PCORI style.

[ Applause ] >> AARON CARROLL: Thank you very much. It's a pleasure to be back. When we talk about research, we usually are looking at large groups, large

homogeneous groups. We analyze averages. When we publish and make recommendations, they're usually for the general public or, again, for large groups of people. I'm health services researcher, we're almost always talking about populations. In the real world, patients don't care about percentages of the population and odds ratios and P values, they care about what decision or what think is right for them.

Our plenary today is titled "What's Right for Me? Practical Approaches to Personalized Medicine." We're going to hear about research, yes. But its focus isn't on particular tests or drugs or procedures and if they work. They do. What we're going to talk about is how we can help patients determine which of those things are best for them. We're going to hear about how women might better determine which screens for breast cancer is the best for them. We're going to hear about how survives of cancer can develop care plans with their care teams that are better for them, and how we can use data and evidence to provide personalized risk estimates about diabetes to help patients determine what's best for them as well. And then we're going to talk. We're going to talk to each other on the panel about these issues more, and then we're going to take questions from you so you can all participate. We need input from the many stakeholders who take part in the health care system to make it better.

In addition, we're also going to hear from a discussant, who for each of these talks, represent a range of stakeholder health care groups, to focus lesson any individual finding and informing the kinds of decisions that people are involved in making every day in our health care system. We'll also rely on discussants to talk about what we can do to help make things better. I'm going to ask our panel to start coming up here. I will introduce them as they come.

Dr. Laura Esserman in general surgery at UCSF. Her stakeholder partner is Diane Heditsian, a founder and CEO of clarity global life sciences communications. Dr. David Kent is a director of predictive analytics at Tufts Medical center. His stakeholder partner is Dr. John Cuddeback with the American medical group association. Our third group is Karen Syrjala at Fred hutch -- that's my Seattle coming out of me. The Fred Hutchinson cancer research center. Her partner is Barry Schatz, University of Illinois at

Urbana-Champaign. We have a general discussant, Wendy Nickel, the vice president of prevention at the colorectal client. Our first talk is precision breast cancer screening, moving from debate to wisdom.

>> LAURA ESSERMAN: Thanks so very much. It's really a pleasure to be here. I wanted to say following that amazing talk from Lesley and Scott that I'm sure that although they focused on progeria, a rare disease, their findings will impact cardiovascular research and care far into the future. So from basic science, even on something very small and rare, comes great findings and insight that helps all of us.

So thanks very much for having us. Is this -- okay. So we want to talk about breast cancer screening. And the reason we say moving from debate to wisdom is because, as everybody knows, there is a lot of controversy. First, I want to say neither of us has any disclosures. So the first thing I want to ask is how many women are getting mammography for breast cancer screening? Can I see a show of hands? How many women think that there's complete agreement on which -- on when they should start and when they should stop and how often they should be screened? Can I see a show of hands?

[ Laughter ] And how many women really know their own breast cancer risk? >> DIANE HEDITSIAN: Very few. >> LAURA ESSERMAN: That is why we're doing the WISDOM Study. There's

been years of policy, controversy and conflict. Here, you can see eight different organizations with different guidelines about when to start and how often to screen. And I talk about patient reference or go to your physician and get information, but of course then they don't know.

How is that helpful to women? It puts them in the middle and polarized views confuse physicians, too, and it makes people less inclined to screen. Part of the reason is because we're in an old paradigm. Everyone got one kind of cancer and that the earlier you found a cancer, the better it was. The later you find it, the worse it was. That assumed that all cancers were the same. In that model, all we had to do was find cancers early and we would solve the problem. But after 30 years, that just hasn't been the case. So when the facts show up to tell you something, you have to pay attention. The truth of the matter is that breast cancer is not a single disease. We don't treat it that way. And if you ever went to someone who treated it that way, leave the office and go see someone else.

[ Laughter ] So there are cancers in this -- and you can see in the -- in this graph, tumor D is

a fast-growing cancer. Screening is not likely to make a difference because it's very fast growing. Systemic treatment is important whether it's small or large. For tumors like A or B, some tumors grow slowly that they probably wouldn't come to clinical attention. That's actually where screening can be harmful because it can surface a reservoir of cancers that may not really -- except some that won't ever come to clinical attention or harm people. That's where overdiagnosis that can be a problem. It's tumor C or maybe B that can make the biggest difference. Who's got what and who's at risk for what, that's a problem.

Screening is mired in controversy, it's based in science that's well over 30 years old. It's age based and age is not the biggest risk factor. Low risk women are over screened and we know high risk women are actually under screened and missing the opportunity to potentially prevent lethal tumors. The catchy public health message misses the complexity. It does not really tell the story. And by the way, it's very resource intensive. We spend 8 to $10 billion a year annually. We could be spending, if we

wanted, a quarter of the defense budget on a single test. The truth of the matter is one size does not fit all, right?

So why are we doing that? There has been an unprecedented advance in science and technology. We know about breast cancer and types and how to treat it differently. We should be leveraging some of the other things that have come up, the advances in cancer prevention from understanding what -- what can prevent, even from the idea of genetic testing and understanding how to truly prevent breast cancer. Then there's been advances in risk assessment and that can come from the now nine genes associated with breast cancer. This is rare, again, but again, those are the people at highest risk. It turns out everyone inherits a series of genes and together, each one of those may not mean much. But together polygenic risk can introduce a much higher or lower risk. So that's important. And then there have been important changes in patient rights and the idea that the Affordable Care Act, which of course gave rise to PCORI, actually also said you can't be discriminated against and you can -- that genetic testing wouldn't be a preexisting condition. And there's been changes in technology. That comes from the Supreme Court saying you can't patent the genome, which opened up the field. Changes and advances that have allowed some companies to drive down the cost of genetic testing to practically the price of a mammogram. If we can actually find that out, why shouldn't we?

We could leverage advances in biology of breast cancer, risk assessment and genetics? We can be personalized and precise for each individual woman and integrate this with risk reduction strategies. Perhaps this the most important thing. It's not just about screening, it's about prevention. But you can't prevent if you don't know where you're starting and you don't know what your risk is. Actually, it's more cost effective. That's about health care value. We don't want to waste resources, we want to use them wisely. So we put together a comprehensive risk model prediction validating high impact risk factors, the so-called polygenic risk score, with that, we can assign people a screening modality and action and it's all done in the comfort of your home, your office, it's all done in a modern way. And this pragmatic trial design is actually really important. We think every woman from 40 to 74 should participate. If you're eligible, we ask you to consent. Women spend 35 years of their life in testing -- in screenings. We want to ask would you spend five years with us and be willing to assigned a screening, annual screening one size fits all which everyone is used to and feels comfortable with or maybe this personalized approach. Or you can be assigned to annual or personalized. We're not discriminating against people who have strong feelings and over time we can learn and adapt. We want to know if it's more accepted by women, enables prevention and overall has greater health care value.

As well, we are looking -- we incorporated breast health decisions tool where we automate the integration of risk and risk reduction strategies. In this breast health decisions tool, we can actually -- women can understand what the different risk factors are and they can understand for themselves whether or not they want to embark on prevention because it's a very underutilized intervention. These are transformative trials that evolve. Women informed to screen depending on measures of risk. It's offered now across the country. And we use the PCORI principles. The results had to be ready in a timely way, we used a surrogate end point. No increase in stage two cancers. The test had to be covered by the studies, so we had to get the payers to participate. We used a coverage with progression model. We showed it's cost saving over time. Our champion was Blue Cross Blue Shield and a number of employers. We start arguing before the results before they come in so when the results come in we can move onto adopting and improving. Diane?

>> DIANE HEDITSIAN: So why would I, who's been diagnosed with breast cancer four times, want to advocate for the WISDOM Study? And why is this study so critical for all women? Well, American women need this answer, when and how often should I be screened. And we -- we want to help with that answer. So what WISDOM does, it finally brings breast cancer screening into the 21st century. It takes advantage of what Joe called personalized medicine or precision medicine. And it allows us to reduce harm to the women who are being over screened and put those resources more for the women who are more at risk for the lethal cancers because they would get screened more often. And what we can do is get the opportunity with this large dataset of genetic information and clinical information to start to see who is at risk for what kind of breast cancer.

We feel that patterns will emerge with this big data and artificial intelligence and other tools that are available to us now so we can use this knowledge against breast cancer. And we need every tool we can get. And it gives us a framework to determine three things: Risk determination, actively engaging women in their own breast health by helping them understand their particular risk, and also integrating risk reduction strategies into the whole screening process. So it's very easy to get that information to the women. So what does it take, though, to make a paradigm shift that Laura talked about? What results do we need to change the screening guidelines in the United States to be more in line with what science is telling us? What are we going to do to find out whether these early end points, which early end points will give us the most accurate information to be able to move forward with this information? And how will we educate women and their physicians about this paradigm shift?

Well, we've been asking these questions since the very beginning of the trial, and we've been modeling different scenarios to find out what we need to do. And the people we have been asking this to are our stakeholders that we see at every year, over a hundred people, and this is what we focus on. So these are the innovations that excite our advocates that are working on the WISDOM Study. We're using the genetics made possible by modern technology to their full potential, not just for the bracket genes. We know so much more now. We are asking insurers to partner with us because we both need these answers. And instead of asking women to come to the trial, we bring the trial to the women so that breaks down barriers.

Our technology platform allows our participants to access their data anytime, anywhere in an online secure portal and it enables our researchers to get insights in realtime through online realtime data analytics.

So with stakeholders at the -- coming together throughout the process, we feel we'll be able, if the results are what we hope they are, we'll be able to start the integration of these new findings into the clinic sooner rather than later. And WISDOM is a smart trial that gets smarter as it progresses because the risk models are updated as new data becomes available. And our patient-reported outcomes which we have included in this trial allow us to get more wisdom from our women in the trial. We can learn what they liked, what they didn't like, what their experiences were on the trial.

So the advocates are excited about this breast decision health tool that Laura talked about because they're learning about their own risk and they're learning what that means and what they can do about it in terms of risk reduction. And finally, the bioethicist advocates and geneticists come together in an ethics committee to handle the issues that come up in such an innovative trial. So recruitment, we're at 23,000 consented women, and we have eight open sites and we're adding more all the time. We hope to double that number within the next year. So what have we learned? We've learned that not everybody is onboard with this personalized approach

to screening. Radiologists generally still feel that 40 is the age and that annual screening or sometimes biennial is the way to go. But that's okay because we will find out if that's true in this trial or whether personalized is better.

So of course you want the perfect model, but we've learned as long as you build an infrastructure into the study where the study has the ability to evolve as the information and knowledge and new models come on board, then we're in good shape. And we've learned that it's much more difficult to get the payers involved even though we have made inroads with that. So one thing we learned is there's a steep learning curve. The advocates have worked diligently to enroll women of color in this study. We had to learn how to do that, and then we had to work hard at it, but we all have come to the conclusion that it's worth it because we want this study to have impact. And I'm happy to say that Spanish WISDOM is now online in Spanish and we're reaching out to many different populations and we've learned that everyone benefits when everyone participates. So we'd like to thank all our partners and stakeholders. We have many. And we'd like to ask you to help us by going to wisdomstudy.org, seeing if you're eligible to enroll in the study. And we'd like you now to pull out your phones and help us on social media. We want to see if WISDOM can trend today. So you see the -- the handles there on the screen. Please do that for us. And if anyone is interested and able to help us ongoing with our social media, I have easy cheat sheets for you. Please come find me. Thank you.

>> LAURA ESSERMAN: Just letting you know that WISDOM ambassadors are everywhere. This is the Grand Canyon, where I was yesterday, recruiting women even on the Grand Canyon. If you want to be a WISDOM ambassador, come see us.

[ Applause ] >> AARON CARROLL: Thank you very much for that. Our next talk is from David

Kent and John Cuddeback and their presentation is entitled improving diabetes prevention based on predictive benefits of treatment.

>> Does this work? We can go back? Okay. Well, thank you so much to PCORI for inviting us to be here, for supporting our work, for encouraging what proved to be a really great partnership between my team at tufts and John's team at AMGA. We have no conflicts to disclose. We'll tell you a story. We'll tell you a story of our project. It has three parts, a beginning, middle, and an end. It starts really with a methods grant and it ends with impact on patient care.

The first part will describe what the need is. Why do we need a predictive model for people with pre-diabetes? The second part, I'll talk a little bit about how we reanalyzed the DPP study, the diabetes prevention program trial, how we built a model and drafted it for the Electronic Health Record. And finally, John will tell us about the results from the initial use for shared decision-making of that model. John?

>> JOHN CUDDEBACK: Thank you, David. I would certainly like to thank PCORI as well and also David for doing this research to start with and for being such a great partner in applying the research for real problems that our members and their patients face. AMGA is a nonprofit trade association. We represent 400 large provider organizations. And we work to align payment incentives with improving population health, better outcomes at lower cost. What we'll talk about is how to advance the slides.

[ Laughter ] Is our together to goal campaign. We have 150 of our members who are working

to improve care for a million people with type 2 diabetes by 2021 and they're making some very fast progress on that. But from the beginning, we have had a concern about one aspect of care. There are 11 planks, evidence-based ways to improve care that are

part of this campaign, but we've had problems with one of them from the beginning. Screening is a key strategy for population health because one in four people who have type 2 diabetes don't even know they have it.

And it's really important to start early treatment because that minimizes future complications. But when we started together to goal, the campaign, we surveyed the members who are participating and we asked which planks are you going to adopt. And 31% said they wouldn't focus on screening. We were a little puzzled by this. We asked why, and they said, well, it's because we're already overwhelmed by the number of people we have with type 2 diabetes, let alone pre-diabetes.

So what's pre-diabetes? It's elevated blood sugar, not elevated enough to indicate diabetes, but it does give you an increased risk of developing diabetes, and it's about 29% over three years. The important thing is that 84 million Americans have pre-diabetes. One out of three adults. So that raises a couple of important questions. One, is there an effective way to prevent progression to diabetes, and is there a way to prioritize, a way to identify people who are at higher risk of progressing to diabetes. The first question was answered by the landmark clinical trial that David mentioned, the diabetes prevention program. And the second was answered by David's original work on a risk model from the DPP study.

So it was a study of 3,000 non-diabetic adults with pre-diabetes. The outcome was progression to diabetes over three years, and it was conducted in the late 1990s. And it was actually stopped early because the interventions were so effective. The three arms: Intention lifestyle intervention program -- what's now known as the DPP program -- taking metformin, first drug usually used to treat diabetes, and then no intervention, placebo. The placebo arm is where we know that the risk for developing diabetes if you have pre-diabetes on average is 29%, and also the on average the intensive lifestyle intervention reduces that risk in an absolute sense by 14%, metformin by 7%.

But AMGA members participating in together to goal still felt overwhelmed because for every person screened who has a result in the diabetes range, they were finding six people in the pre-diabetes range. So this brings us to the importance of having that initial research done.

>> DAVID KENT: Great. Thanks, John. Meanwhile, while John was really faced with these difficult real world issues at Tufts back in our ivory tower in Boston, we were delighted to receive one of the first pilot grants from PCORI. And this grant from PCORI was not addressing a particular medical issue or health care issue, per se. It was really addressing a methods issue and it was addressing the methods issue that Joe talked about. It was addressing the issue of how to go from the average results of a randomized clinical trial which is a group -- a study in a group to really figure out how to make decisions in individual patients.

Now, this is a surprisingly difficult problem. The conventional way of approaching this problem is through sub-group analyses and these sub-group analyses are typically done one variable at a time. We compare males to females, old to young, those with hypertension to those without, those with a particular gene to those without that gene. But for many reasons I won't go into, this is a very, very limited approach and a very problematic approach. Together with a number of other investigators, another approach we were looking at was an understanding how outcome risk can be applied to randomized clinical trials. Did this aggregate those overall results.

And we used this approach -- we -- so by outcome risk, I mean risk of the outcome of the trial using all the available information. Many different patient characteristics to predict that risk. Use this approach and applied it to 36 different

trials. I'm showing this slide just to show you two things. One is that every trial showed the same two things, it is that the outcome risk varies tremendously in these trials from the high-risk patients, have risks of the outcomes that are five, ten, 20, 30-fold higher than low risk patients. And the other thing you can see from this graph is that you don't see a bunch of normal bell-shaped curves. What you see are skewed distributions. You can see that better if we drill down on one of the trials. This trial happens to be the DPP study that John just spoke about.

So this is the risk distribution, the outcome risk in the DPP study. If you look on the X axis, the horizontal axis, you'll see it goes from patients with near zero risk, 5% risk, 10% risk, to high-risk patients with 40, 50, 60, 80% risk of developing diabetes in three years. The other thing you'll see is that most patients actually have a lower than average risk. The average risk is reflected in the summary result, but it obscures the fact that you have this variation and that most people are lower than average. Why is risk important? Risk is important because it's a determinant of how much benefit you can get from the treatment. We looked at the benefit across all these studies. What we found again is a general pattern. This is not just one case. We found that the benefits tend to track with the risk. Not all the time completely, but they -- often the high-risk patients tend to get most of the benefits. And you can see that the DPP study in this case was also an exemplar. It's in the southwest quadrant circled in -- squared off in red. We can drill down on that. We'll see that on the left-hand side, intensive lifestyle interventions which on average had a 14% benefit, the lowest was quarter of patients actually only got a 5% benefit and the highest got a 30% benefit. That's a six-fold benefit. With metformin, the average was 7%, but the benefits were even more skewed. High risk patients got three-fold the average.

So we were very pleased with those results. We published in the BMJ. We were delighted. You know, PCORI was just not satisfied.

[ Laughter ] They wanted to actually affect, you know, patient care and clinical

outcomes. Can you imagine? So they put out a call for applications for dissemination. This really was what sparked the collaboration between John's team and our team and to figure out a way to get this into the clinic from the literature into the clinic. The first thing we realized is we had to create something that works within the work flows of the clinic that John supports. So we needed something that somehow could be incorporated into the Electronic Health Record automatically. And we realized that we couldn't keep the same variables in the model because whereas hemoglobin A 1 C, fasting glucose, triglycerides, they worked well. Things like waist circumference are not. If we were developing models for tailors, this would be a good model. For doctors, no. It's not in the EHR. We had to come with up with a new model using established risk factors with EHR data. And we were fortunate enough to have access to an enormous database, the Optum Labs data warehouse. We established on 1 million patients with pre-diabetes in three regions, northeast, the south, and the west performed fairly well. There's a statistic for those who like to see that. We left out the Midwest and tested it on the Midwest. There were a million patients in the Midwest with pre-diabetes. The predictive performance was identical on the sample left out, which we were very pleased with. Then we ran the DPP study through.

The reason we ran the DPP study through this new model, that permitted us to get unbiassed risk-specific estimates of the effects of these two interventions: Intensive lifestyle program and metformin, versus usual care.

>> JOHN CUDDEBACK: Thanks David. The next step was to have two of our members use this model. The group in the Pittsburgh area has about a hundred

providers and then mercy head quartered in St. Louis spans four states, 3,200 providers. Much larger organization. I'll show you the premier results today and we're still in process at Mercy, as I will tell you.

So what we started with was asking patients and asking providers about the problem of making decisions if you know and just discovered that you have pre-diabetes. So we learned from the patients that they want a personalized estimate. They want their risk of diabetes -- of progressing to diabetes. But a lot of people were a little worried that could they deal with the probabilities. One of the things that we noticed, we learned from the focus group was every one of them could quote the ages at which three or four family members had developed diabetes. So they were already dealing with numbers, they just didn't have the number that applied to them.

So this actually has not been a problem as we have worked through the -- the application of the model. Providers also want guidance for shared decision-making. They want to be able to support and encourage the patients, especially for this fairly demanding lifestyle program. The patients who are going to get the most benefit from it. But they do feel overwhelmed and they need to prioritize. And one of the things that I think was most striking and really appeals to the clinicians, normally they just have the one test that indicated that the patient has pre-diabetes.

We have here in the multi-variable model something that's more informative than any single lab result. For example, in the highest risk quarter tile of the predictive model, a quarter of those patients had an A1C in the low end of the pre-diabetes range and vice versa for the low-risk quarter. It's a lot more valuable than any single parameter. Here's an example of how the predictive model results are displayed in the EHR premier. We have only left a low risk patient who has a 5% chance over three years. That can be lowered by either of the interventions, metformin or the lifestyle program, but the risk is already very low for this patient. Therefore, you'd have to treat 31 patients in order to prevent one from progressing to diabetes. On the other hand, you can have a high-risk patient who has better than a 50-50 chance of developing diabetes in three years. In that case, the same relative risk reduction is a much larger absolute risk reduction and you only need to treat four patients to prevent one from progressing.

So the results for 15 months' use of the model and we've tweaked the model a bit in the time frame. At any rate, of the 901 high-risk patients, only 41 were on metformin to start with. 150 were started on metformin. None had been referred to a DPP program before the model was used and almost 500 were referred during this period. So from a population risk stratification perspective, 75% of the high-risk patients had decided to take action in shared decision-making with this information. Moderate risk about 20% and low risk about 7%. And during this 15 months, 97 patients were identified as having type 2 diabetes through timely screening. So that gets back to the importance of being willing to do screening because you now won't feel so overwhelmed with the results.

We're getting personalized risk estimates into the EHRs in a couple of different ways. It was built in directly and we're working now to develop a cloud-hosted version of the predictive model, and that's what will be implemented on a larger scale at Mercy. Takes advantage of emerging EHR standards and is much easier to maintain and easier to get started for an individual provider organization. People other than us have studied this and agree on very large cost benefit for averting or delaying progression from pre-diabetes to diabetes. And we have a great story which is actually detailed on the PCORI website, but this is a patient who talked with his -- his doctor at premier medical associates. He spearheaded the initiative there. And his personal risk was very high, which was what got him to take seriously the DPP lifestyle program. He

lost 34 pounds and his health is improved substantially. So it's not just avoiding progression to diabetes. It's actually an improvement in health that was achieved in this case.

David? >> DAVID KENT: This is our last slide. It just summarizes the steps in the

process. This is -- started out as an NIH-sponsored clinical trial which proved that these interventions were effective on average and we're very grateful to the NIH and DPP investigators who did that work and made the data available. Then there was the initial methods work to create a risk stratified analysis of that trial. And then PCORI further supported the adaptation of -- of that risk model to get it into EHRs and now we're disseminating out to 50 clinics to effect patient care. Thank you.

[ Applause ] >> AARON CARROLL: Thank you. Our third presentation is Karen Syrjala and

Barry Schatz, patient survivors that receive hepatosomatic care for cancer treatment. >> KAREN SYRJALA: Thank you. Thank you to PCORI for not only inviting us

but also for supporting this research that we are very excited about. As you may have guessed, I'm Karen Syrjala. This is Barry Schatz who has been part of this project from its inception. We have nothing to disclose.

>> BARRY SCHATZ: I would like after hearing the other advocates disclose, I don't have an M.D. I don't have pre-printed cheat sheet, nor do I have statistics. So adjust your expectations.

>> KAREN SYRJALA: However, as you'll hear, he has a wealth of knowledge and he's here to share. Our focus is on cancer survivorship. The big picture that I'll talk about rather briefly and then the more personalized picture of how we adapted cancer survivorship care into the very specific process of HCT for short and how it's kind of unique from other survivorship issues but also how it's also quite similar in many respects. We'll tell you a little bit about that. We're going to focus not on the study and statistics and methodology, but more on the partnership that we've had from the inception of the project and how that partnership has enriched our process.

How many of you here in the audience today either have had cancer yourself or have a close personal family or friend with cancer? So if you look around, what you'll see is a majority of hands in the audience. And there's a good reason for that. What we know is that 41% of us will have an invasive cancer in our lifetimes. One in two men, one in three women. However, our success in treating these cancers has progressed massively in the last 20 years. So we now cure -- in other words, no recurrence in 80% of children diagnosed with cancer and over two-thirds of adults. Now, this is a huge success in our treatment, but what's the result of that. So this 41% lifetime risk has resulted in this escalating, as you see here in the graph -- an escalating number of long-term survivors.

So of the 16, almost 17 million survivors now alive in the United States after having a cancer diagnosis, over two-thirds were diagnosed more than five years ago and we're seeing this very large group of long-term survivors coming into our population and into our medical clinics. And what does that mean and why does that matter? That's a great success. As we begin to see this large group of people who are living the majority of their lives after cancer not during or before cancer, what we're finding is that they have, as you see here, a long list of organ system complications at higher rates than we see in people who've not had cancer treatment. They're also at much higher risk of having subsequent malignancy, not only recurrence, but also other types of complications. They have fertility and reproduction problems, but that's really only half the story because all the other elements that you see listed here on your right side of

the screen -- or is it your left? It's your left -- are also problems that occur in cancer survivors we know at much higher rates than other survivors.

So consequently, what we found is that as a result of understanding all these complications, the American college of surgeons commission on cancer which certifies all cancer centers are two-thirds of -- sorry, three-quarters of cancer centers in the U.S., the American College of Surgeons commission on cancer mandated in 2012 that all patients leaving their cancer care should receive a treatment summary and care plan. So the cancer centers attempted to implement this in 2012. However, they backed off more recently from that because evidence was indicating no indication that there was improved outcomes for people who received these survivorship care plans.

And so as part of our study, we're really looking at, in the face of absence of evidence, does a care plan make a difference and how do we provide that.

>> BARRY SCHATZ: The overall issue in our study here is that historically cancer researchers have focused their efforts on survival, creating more survivors. And they've been successful in that. We're now finding that there's a need to address the byproduct of that success by creating care plans that work and that are able to address the late effects of surviving cancer.

>> KAREN SYRJALA: So what does it mean when we talk about all these long-term complications? It has real downstream effects on mortality. In transplant specifically, in our own data, we found people who live at least five years after their transplant, they continue to have a nine-fold increased risk of dying after their transplant relative to the normative population which was age and gender adjusted. What does that translate to? What we saw in -- for those of you not familiar with transplant, allogeneic means you receive your stem cells from an unrelated donor or someone in your family. Allogeneic means during remission of your disease, they collect stem cells and give those healthy stem cells back to you after you've had this aggressive treatment to eradicate the disease. Both allogeneic and autologous had subsequent problems, not the original cancer that was treated. What, then, do we know about all these problems and how they're reflected in the unmet needs that cancer and transplant survivors tell us about?

Well, there are two studies where you see the percentages here of unmet needs reported by five-year survivors or longer or four to 14-year survivors, but they're very consistent in what they're demonstrating. People feel unprepared to manage their symptoms and side effects. They don't know what test and treatment they need to monitor their symptoms. What kind of things they can do for their own health. But there are also lots of emotional interpersonal problems, sexual function, et cetera. So we have a lot of unmet needs that we're aware of and that we think that these treatment summaries and care plans might be able to address.

>> BARRY SCHATZ: As you look at the slide that just went through -- >> KAREN SYRJALA: Oopsy. >> BARRY SCHATZ: It's also important to note that these kinds of unmet needs

are also things that don't just affect the patient, but also affect their families and their caregivers. So it is the added importance of a survivorship care plan is not just on the physical wellbeing of the patient at late stages. And I think overall, the primary question is: So why is the survivorship care plan important in the first place? I think reinforced even in our first presentation, cancer is a diverse topic. It's more diverse than most diseases. It has a wide range of forms. A wide range of expected outcomes, differing treatment therapies. Hence, a very, very wide range of potential complications at various stages of life. As transplant therapy is -- transplant therapy is used for a wide range of cancers, we're multiplying that diversity. So HCT therapies also are not the

same. There is a wide variety of those. Hence, for the patient it is extremely difficult to have any sense of predictability of your long-term progress, what's normal, what's not, what's expected, what's weird, et cetera. Add to this a reality from a long-term perspective that follow-up care for bone marrow transplants, cell transplants is often not -- not carried out in the long-range by clinicians who are involved with the transplant or even involved with the transplant center. Hence the patient is left to direct their own follow-up care. This winds up in them often feeling completely lost and not sure where to go for help or for direction. By the way, if you look closely in the center of that, you can see me standing in the middle of the picture going what the?

[ Laughter ] >> KAREN SYRJALA: So what we attempted to address these concerns with is

the randomized trial that you see as published here in Hematologica where we looked at personalized treatment summaries and survivor care plans. I'm going to tell you very briefly about that study. First of all, this has 16 different participating transplant centers around the country. We had as part of our protocol team members from the very onset patients and caregivers as primary stakeholders in addition to all the providers and clinicians who were a part of the project. We centralized the coordination of the project because the transplants are so distributed around the country. We centralized it through the National Marrow Donor program also called Be the Match, and we used the clinical data registry for the center for national bone marrow transplant research which retains a majority of patients around the world, but nearly all patients in the U.S.

The premise of this study is that we know -- hopefully we've convinced you -- that transplant survivors are at very high risk of late complications. Although complications vary tremendously by individuals. That we have the guidelines for what kind of ongoing monitoring is needed for whom with what kinds of difficulties during treatment or as a result of their disease. Also because of age, sex, and other factors that we've identified and specifically something you may or may not have heard of called chronic grafters host disease which is a rejection process of a donor graft. It's quite specific and unique in these transplant survivors.

So our goal was to see if this treatment summary and personalized care plan would improve cancer survivor care. Because the majority of studies that have looked at this consider distress as the primary outcome to look for improvement, knowing that 50 to 80% of long-term survivors do report elevated stress and distress after their treatment, even five to 20 years after treatment. And a large proportion of that stress is related to living with the uncertainties that Barry was eloquently describing and particularly fears of recurrence, fears of what can happen next.

So our study concept was a centralized approach using this center -- CIBMTR registry and then using the guidelines that we have for survivorship care with these individualized, personalized elements that we have defined. And then using a calling -- centralized calling registry, again, at the national marrow donor program. And we compared the provision of this personalized plan to usual care.

>> BARRY SCHATZ: So this study, just to be clear, was not about developing a survivorship care plan, but rather to specifically identify how to make plans, create plans and adapt plans that really focus on and meet the survivor's needs. If we can show, here is, for instance, a data collection sheet for a care plan that was designed by clinicians, which is the more common approach. And basically what happens is that these tend to be care plans that wind up being designed for clinicians. The plan itself in this example is how that looks. This is what clinicians think patients need to know.

So while you're looking at that, imagine how your mother would be trying to understand this and trying to explain this.

>> KAREN SYRJALA: Or trying to use it. And I think you see very clearly how overloading this information is and how difficult it might be to use.

>> BARRY SCHATZ: So this is an example of the data collection sheet revised. This time with the patient and stakeholder input. The plan that results from that, as you could see, is one that looks more like this. I think it's that the gap between the patient influence version and the clinician-derived version is apparent, both to you and your mother.

[ Laughter ] >> KAREN SYRJALA: So you can see the difference in what we were testing as

a major difference in what had been previously given to patients and to their health care providers and was even difficult for providers to use relative to what we tested in this project. Because we only had six months to look at change, we couldn't really look at health outcomes in that short window of time. So our primary outcomes, confidence in survivorship information, transplant-specific distresses, elements of managing uncertainty, of managing their health care, of family strain and stress, finances, et cetera, and the mental health score on the SF 36 which I think some of you probably know is a very standardized measure.

In this study, we enrolled 458 people from those 16 different transplant sites. As you see, we had a remarkable level of retention at 88 and 87% in the two groups, which I think is very high for those randomized intervention arms. This is the only outcome slide I'm going to show you. But just to point out to you that first in the blue and orange are the results for the survivorship care plan randomized group and the control group for their confidence in survivorship information and distress and mental health a baseline in six months. As you see, we did not significantly affect confidence in survivorship knowledge. However, what we do so different from many studies, most studies of survivorship care plan delivery, is we saw significant reduction in cancer-related stress that's reflected and replicated in the mental health score of the SF 36.

>> BARRY SCHATZ: As Karen just indicated and as we previously mentioned, it's -- quality of life issues are the most frequently mentioned unmet needs of cancer survivors, as this slide shows, reflecting some comments from our patients who were involved in our study. The revised and patient influence survivorship care plan dramatically helps the patients in relieving these greatest of these unmet needs.

>> KAREN SYRJALA: I think what you see here is that although we didn't see a general measure of confidence improve, what people told us and showing the value of the qualitative perspective is that they felt they did have more confidence in knowing what they needed to do and how to do it with their health care providers. So what we learned and where are we going next with this?

What we learned for one is that these survivorship care plans do work when people use the materials.

And that means we have to have materials that are tailored and specifically designed for and by the people who are going to be using them. As we've heard today repeatedly and bears reminding, one size does not fit all. People have to adapt these into their own lives. If you think of all those problems at the beginning that we described as more prevalent in survivors, each person's going to have different presentation, different concerns on that long list. And if we're not adapting our delivery of care to those needs, we're not going to be meeting and improving outcomes.

If we had a partner from the very beginning, if we didn't spend our first year discussing with Barry and other -- or partners who were part of our team, we wouldn't have known what we were missing. So that was central not only in that first year of

developing a revised care plan, but also as we went along and addressed other implementation issues. One thing we've learned, though, is that we can do better and that we do think that survivorship care programs are more than a plan. It's more than a piece of paper that we hand to people one time and say, go have a great life, here's your road map for your future, because lives change, things happen, and plans need to change with them and programs need to be adaptable for the rest of people's lives.

So we have learned that we need to extend that care plan into a more ongoing, living program that changes as people's lives change. What we've moved from this first PCORI study to what we now have as a inspire study which does include that copy of the plan, but it also includes a mobile app, a website, and an interactive opportunity for people to update their information, to track it themselves, and to really be in charge and to have the tools to direct and manage their own care. As Barry said, if they're not doing it, there's no other central person to manage the care for a survivor. The other element that we've added is telehealth so that we have a stepped care plan and we're able to then say, if these other tools and these remote delivery options aren't adequate for you as an individual, we have the option to increase care and to increase personalizing of the plan through this telehealth model.

So with that, we're glad to discuss more when we get to that section. Thank you.

>> BARRY SCHATZ: Thank you. [ Applause ] >> AARON CARROLL: Now we're going to hear from our general discussant

who is Wendy Nickel. >> WENDY NICKEL: Hi, good afternoon. I checked on the way up the steps to

my -- tripped on the way up the steps to my chair. So making it to the podium was an accomplishment.

[ Laughter ] I want to thank PCORI for having me here today. I'm delighted to be here. I'm

with the colorectal cancer alliance. The idea of personalized medicine is really important to our constituents and to all of us in this room. PCORI has continued to share their commitment for patient-centered care and I'm so appreciative. And thank you for allowing me to be here today.

We've heard a lot about personalized medicine in the last three sessions and I think we've heard different definitions. I'd like to poll the audience and maybe get three people to tell me in your mind what is the definition of personalized medicine. Anybody want to just raise your hand? Yes.

>> [ Off Mic ]. >> WENDY NICKEL: Great. So right treatment for the right patient at the right

time. Anybody else? Yes? >> [ Off Mic ]. >> WENDY NICKEL: Goldilocks medicine. Can you expand on that? >> [ Off Mic ]. >> WENDY NICKEL: Got it. Got it. Yes? >> [ Off Mic ]. >> WENDY NICKEL: Great. The treatment that achieves the outcome you

want. So thank you. The reason I asked that question is I think, again, the studies exemplify a lot of different definitions and different ways to look at personalized medicine. Before the session today, I Googled personalized medicine because, you know, that's the best place to get information.

[ Laughter ]

But really to figure out what -- what are the different definitions that are out there. And so one is that personalized medicine is -- addresses disease susceptibility, prevention and targeted therapies by using molecular and genetic profiling. Another definition was that personalized medicine is managing a patient's health based on their individual characteristics, including age, gender, height, weight, diet and environment.

I think it's interesting because we heard from our discussants today, we heard information about using genetic profiling. We also heard information about gathering data from the EHR. So we heard a lot of definitions about what personalized medicine is. If we were to boil it down a little bit further, I think basically what I hear is that personalized medicine seems to encompass a broad range of activities using technology, genetics, patient information, needs, and preferences to tailor care for individuals.

So, you know, as we think about the studies that were presented today, we heard about how personalized care was -- was shared across the care continuum. So we had two studies that looked at prevention and screening and really trying to figure out how to use personalized care to avoid disease. Then we had one study that looked at how to provide personalized information and a road map for care post-disease or while living with chronic illness. So why are the results of these studies important from a patient advocacy perspective, which is the perspective that I'm bringing today? And this is a little wisdom humor. Because like bras, one size does not fit all.

[ Laughter ] I mention that I work for a cancer patient advocacy organization. And for my

cancer alone, colorectal cancer, there are 1.4 million survivors in the United States today. And I literally flew in a couple hours ago from our annual conference. We had 200 people at this meeting that represent survivors, patients and family caregivers, people that are in active treatment and long-term survivors. I can tell you that their needs are as different as all of you in this room.

We have people that are living with ostomies, stage three cancer survivors and those who have metastatic cancer. What I can tell you is that listening to all of these individuals, the needs and issues for a young woman who in her 30s who is thinking about preserving fertility are very different than the needs of somebody, a 55-year-old man living with an ostomy and wants to know how to maintain an active and healthy lifestyle over a lifetime. One of the things I heard again and again throughout this conference is our attendees need personalized information. Some of the things discussed during the conference, what is a good nutrition care plan for people that are living with ostomies, for example, or if you're undergoing chemotherapy. What I heard from the survivorship study, those needs change over time, so how do we continue to personalized medicine to tailor a care throughout an individual's journey with a particular condition or throughout their lifetime.

Other comments that came throughout the conference were about clinical trials. So how do you identify what clinical trial is best for somebody based on their stage of disease, based on allergies to specific medications like heparin. And so as pointed out in the study about survivorship, tailored information really does boost confidence, it gives people the ability to avoid anxiety, it gives them the ability to manage their illness over a period of time. So one of the considerations about personalized medicine that I think is important to raise is how we get information to patients on a regular basis without burdening them, burdening their care provider or burdening the health care system. I think we heard some great examples in all three studies of how to minimize really burden on all those stakeholders.

So, for example, the diabetes study that we heard about is using

already-existing data in the EHR. It's not out looking for new information. It's using existing EHR data to develop a risk profile for patients without having to collect new information. And this alleviates burden both for patients and providers. As you heard, was a really cost effective way to identify risk. And then the survivorship study used an existing national organization who already collected data and information to disseminate the long-term survivorship plan as opposed to having to collect a lot of information through the provider's office. Again, alleviating burden for both the office staff and clinicians while still effectively creating a great care plan for the patient. And then the WISDOM Study used a fantastic integrated tool that brought the risk -- sorry, risk education and evaluation tool to individuals through a mobile app and other patient-preferred communication. So those were all tremendous ways of really reducing burden to patients.

So I want to just take a minute and talk about what I call awesome innovations that I heard from all of these studies as well as some considerations to think about from a patient perspective. So the diabetes study show that there's a way to calculate risk and provide care to individuals with pre-diabetes without screening an entire population. And this obviously has tremendous impact on the health care system as was shown, and really effectively reduced cost by helping individuals to avoid costly morbidity in the future. So when you consider the cost of lifestyle intervention to avoid diabetes versus paying for a diabetes treatment over time and the complications that come with it, the results are incredibly compelling and impressive. WISDOM made a terrific case for reducing utilization of unnecessary mammography screening. Another awesome innovation, the WISDOM Study and survivorship study showed the importance of health literacy. You heard the discussion of use of plain language, translating tools into Spanish, breaking information up into digestible chunks. This is incredibly important. I would encourage all of us to consider further easing the information burden for patients. Using pictures, using icons. Engaging people in the work and redeveloping the survivorship plan really made that tool much easier to digest and to utilize. One other consideration about exchanging information with patients, notice I didn't say "giving information to patients" because really it's about exchange, it's about communication, a shared discussion or shared exchange between clinicians and patients. To ensure that care providers are included in discussion. Individuals aren't just getting the information, but they're also getting the opportunity to ask questions. And I thought the survivorship study did a really nice job of using patient input to revise their tool so that patients could ask questions right in the tool itself related to their own personal clinical information.

So finally, under awesome innovations and considerations, I wanted to just mention social determinants of health. This is, I think, incredibly important. And one of the definitions that I heard or I discovered as I was looking around for personalized medicine information was environmental factors. So what are those environmental factors that have an impact on how patients personalize their health information. It's important to be thinking about issues such as patient access to care, their ability to follow through on health care information and treatment plans and health behaviors. And WISDOM, I thought, did a terrific job of sharing diversity among their study participants. I think they could further use that study and that information to really learn about geographic areas and communities to determine what social determinants can impact -- how they can impact risk in the future and screening behavior.

So in the context of social determinant of health, I think it's really critical to understand that while the studies made significant inroads in identifying how to personalize treatment and risk, they don't yet consider every factor that contributes to

individual health. So, for example, for an individual to follow a diabetes lifestyle change or an individual who can't access follow-up care on a regular basis due to child care or work environment, we still need to really figure out how to take these considerations into account. Additionally, in many ways, I thought it was important just to note that personalizing risk or treatment can -- can -- based on research can be a little challenging because research tends to base its conclusions on averages and not outliers. We have room to figure out this challenge and ensure that the outliers are getting the personalized medicine for their needs. One last issue I want to address is striking a balance between population health and personalized medicine. We hear a lot about population health because it has a lots of promises. We know that having access to data about populations and communities can help us to predict risk for a disease and how that population may behave from a health perspective. So superficially, it seems that personalized medicine really flies in the face of that notion.

But what I really think these studies showed is that using population health techniques, we actually can do a better job of understanding -- sorry, personalizing health care for individuals. Importantly, however, I think we need to continue thinking about the whole person and all the factors that contribute to one's health. So in summary, I think we have the opportunity to continue defining what personalized medicine is, what contributes to personalized medicine, and how it is defined not by us, but my individuals themselves. So before I close out my comments, I just wanted to say that I have a family history of breast cancer. Both my mother and my maternal aunt had breast cancer. I've had multiple biopsies. I feel I can share this information with all of you because we're close. But I also have dense breasts.

[ Laughter ] So I consider myself fairly savvy when it comes to health care, but I can also tell

you I don't have a great handle on how often should I be getting screened, is it every six months, I get different information from my PCP, from my breast doctor. Should I get an ultrasound, should I get a mammogram. I tried to join the WISDOM Study, but I live in the wrong state. Thank you very much.

[ Applause ] I'm in New Jersey. Great. We'll talk. We'll talk. >> AARON CARROLL: I know we're running a bit long and we're going to keep

going because I think this is important. Before we actually start the panel, I believe Barry wanted to make a brief

statement and then we will go from there. >> BARRY SCHATZ: Thank you. I appreciate the time given to me. I know we've

got a lot going on and run over. I did want to share a personal perspective. This is something that I wrote ten years after I was originally diagnosed with

leukemia. On the eight-year anniversary of my bone marrow transplant. I've only shared this once before, but it seemed very much in keeping with the focus of this meeting and of PCORI in general on patient outcomes and what it really means to us. It's called "in the days I've been given."

For a high school chorus, I thought they were surprisingly good. At this graduation, I thought very little about their harmony as they started to sing a song that took me back, back to a place that I really didn't want to go. But I had no choice. I heard the lyrics from fields of gold ring out from the stage. I swear in the days still left, we will walk in fields of gold. In my mind, in my heart, were gone. I see myself driving in my car ten years ago as Eva Cassidy sang these words through my CD as I played the song over and over and over. I'm driving alone with no particular destination, and I'm crying what seems like an endless flow of tears that I could not, would not share with those I

love. I'm crying tears for the days that I fear I will not be sharing with them. But

they don't tell you about -- what they don't tell you about cancer is about the hidden pain. They warn you about all the stuff that people see on the outside, how sick and vulnerable we all seem. But for me, that was not nearly the worst part. The worst part was the look of main and aching and sadness that I saw on the faces of those who loved me the most. The worst part was knowing that beyond those looks were the tears they could not, would not share with me. And that I thought would continue long beyond me. And for me, the very worst part was the day after I was told, when we told our sons just 8 and 5. And as I put my 8-year-old to bed that night, he asked, "daddy, are you going to die from this?"

And I knew I couldn't lie to him. I knew I couldn't lie. And I told him, there are too many people working too, too hard who care too, too much for me to die from this now. And he slept and I sobbed in the arms of my wife and I prayed that what I told him would be the truth. And it was.

It's been ten years, ten years with my wife and sons who have surrounded me and filled me with relatives and friends who have loved for me and cared for me more than either they or I knew until I needed them most, with doctors and nurses and caregivers who labored for me and cured me, many of whom never saw my face or knew my name. With an angel of a woman who blessed me with the gifts of the very marrow from her bones and her soul and fixed the parts of me that only God could let be fixed. And as I have seen so much beauty these ten years, not just in sharing major events and milestones, but also in gentle hugs and kisses, quiet conversations, little inside jokes, and even fleeting looks across a crowded room, I'm connected, so connected to so many wonders that I thought, I feared I would never see again. And I'm brought back to the here and now. And a tear fills my eye as he walks across the stage. I watch my 18-year-old son take his high school diploma. And I watch him smile. And I swear, I swear on the days I've been given I've walked in fields of gold, we have walked in fields of gold. And it may be easy here to get captivated by the advances in cancer treatments and the accomplishments for so many diseases. PCORI's come along and stimulated researchers to add a new set of patient outcome metrics focused on things that are not proteomes and genomes but that serve as gentle reminders that it's most important in all the work that we do to provide hope in the darkest times for patients and their families. PCORI keeps this focus clear.

As we look towards future projects to be funded perhaps one day they could even establish a database of the kind of patient-centered outcomes that really matter and are really the focus of everything. Birthdays celebrated, grandchildren held, walks to school with our children, fleeting looks and smiles across a crowded room, and perhaps even the number of times that a patient or their family looks a caregiver in their eye and says bless you, thank you. This past May, my wife and I watched our youngest son get his master's from Cornell. And we stood this past February next to our son as he -- my oldest son as he got married. It was the 8-year-old boy that I wrote about. Because of you and so many like you, what I told him 19 years ago was the truth. So if you or anyone you come across questions if it's worth all the effort and all the expense, just ask my sons. And to all of you who work too, too hard and care too, too much to let me and so many others die from these horrible diseases, bless you and thank you.

[ Applause ] >> AARON CARROLL: Well, thank you very much for that. As easy as it would

be to end on that note, we're still going to spend a short period of time having a

discussion. I'd like to start at the beginning by specifically asking Laura and Diane, but certainly others can address this as well. What is the best way you think to talk about risk with patients? Because some will respond to numbers, some won't understand the numbers, some hear 2% and panic, some hear 2% and go, that's never going to happen. So how do we come up -- how do we assess the best way to discuss even the risk once we figure it out with each individual patient?

>> So I think it's really important to put risk in perspective. And, you know, when you think about getting breast cancer, you know, one in eight women get breast cancer, so the average risk for the population is 13% chance of getting breast cancer. And it turns out actually when you're thinking about prevention, most people aren't really interested in doing something to reduce that risk until they double that risk, until they start to get into something where their lifetime risk is maybe one in four.

You know, life is full of risk. You always tell people that for some people, they know what their risk is. For many of us, even someone who has a bad cancer, look both ways before you cross the street. There are definite risks that you have. I mean, we actually have a section in our -- in our tool that shows, you know, the average risk of a heart attack, the average risk of being hit by lightning so people get a sense for what are the risks that everybody faces. So how do you -- how do you figure that? You often have to show risk in a number of different ways so that someone has a way -- some people work with numbers, some people work with figures, some people can put it in perspective of other things. You know, others -- you know, what are the risks they take in their life. If you ride your motorcycle in the rain on a -- on a steep mountain, you probably shouldn't be thinking about screening or prevention.

[ Laughter ] So I think you have to think what is your lifestyle, what do you do and how do you

think about it. There's often a lot of things. Turns out the things you can do to reduce your risk from cancer are the things that can reduce your risk from diabetes and all kinds of things. You have to put it in a broad perspective.

>> AARON CARROLL: How do you take position differences? As a physician, I can take 2% and scare a patient. I can also take 2% and improve a patient. How do we remove that bias?

>> LAURA ESSERMAN: I think the tool we developed was an effort to standardize the way in which the information is given. You know, it's about information -- there is an acronym. It's women informed and screened depending on measures of risk. It's interesting. This study stemmed from a group at the five University of California campuses getting together and saying, gee, we are working together to standardize some of our approaches for breast cancer. We thought about what was one of the most important things we could do. And that is really try and get a handle on who's at risk for what kind of cancer, you know, how do you start being able to communicate the information about risk, put it in perspective, and have a set of information that everyone's reading from the same page. So you don't -- if you saw it at a counselor, you wouldn't hear one set of questions, saw the surgeon, you hear something else, or the oncologist. We did a lot of focus groups. We spent a lot of time trying to understand how do people want to hear about it. There's different ways to look at it. You can find the papers. You can start from a broad picture and get more detailed and more detailed.

I live in an area where a lot of engineers who want all the data. So you have to be prepared, you know, to give all that information. But I think you have to have -- I think it's really helpful to have that information. You know, one last quick thing is that when we were working on this with primary care physicians, the question was, you

know, would they feel like they were -- their work was being usurped. They're like, oh, my God, we will only participate if you do the risk counseling. We set up a standard for breast health specialists. People who could be health coaches and counselors. Again, the best evidence sitting there with different ways of presenting it so people can see it so it helps -- so everyone can work from that same playbook.

>> AARON CARROLL: David and John, your study also dealt with presenting risk to patients. Did you have similar experience or do you have a different view?

>> So, you know, I'll start and maybe John can pick up. I think one of the -- you know, the questions are, you know, how is this shared decision tool actually being used in practice. If we're looking at the numbers about who is being referred, I really think that the shared decision-making tool is used by the practices themselves to decide who they would prioritize for this intervention considering that one out of every three adults have pre-diabetes. So we can't treat everybody. We can't possibly refer everybody to the why. How can we as a practice, instead of referring one out of every three patients only refer, say, one out of every 12 patients and still get most, the majority of the possible benefit from this particular intervention, labor-intensive intervention.

And I think a lot of the reasons -- we saw this steep gradient in terms of how they were doing the referral is really not the fact that the patients were saying to themselves, well, I'm only at medium risk, therefore I won't do it. I think it was really how the doctors were deciding to use the tool to target the intervention.

>> AARON CARROLL: We actually want to make sure we have time for the audience to ask questions. So I believe we have some microphones set up. I don't know if --

>> I wanted to also make one quick comment. >> AARON CARROLL: Sure. >> LAURA ESSERMAN: I've been talking about prevention for a long time. A lot

of people say I don't want to take that medicine, I don't want to do this, this is too hard. First, you say don't make a decision, try it for a month or try getting this change and see how that impacts you. Then you can weigh the risks and benefits. Sometimes people have preconceived notion of how some intervention -- whether it's lifestyle or medicine -- will be too hard. Sometimes they just have to try it and see for themselves instead of reading all the side effects and assuming everyone would have those because they don't.

>> AARON CARROLL: Did anyone have any questions? >> [ Off Mic ]. >> AARON CARROLL: Yes, please. I cannot see you, so I'm trusting if you have

a question, you'll shout it out. >> LAURA ESSERMAN: There's someone right there. >> AARON CARROLL: Please step up to the mic. >> Hi. >> AARON CARROLL: Great. >> I'm from UCF. My question is for Dr. Kent. It was very fascinating to see that

you were able to use this predictive tool for -- for prediabetes. I have two questions. One, I work on hypertension and sort of the risk profile tends to be a longer period of time. Do you see for pre-diabetes, you were looking at outcomes in three years. Have you done work in looking at outcomes that happen more, you know, on a ten-year, 20-year sort of spectrum? And how promising do you think that is to build a predictive model that would lead to a technical decision support tool based on that model? So I'd love to work on that. And the second thing I want to ask about is the tool you've been able to use -- build for pre-diabetes, how are you going back getting

physician adoption and how do you think that tool will scale across different clinical practices?

>> DAVID KENT: So why don't I take the first one and let John take the second one. So in terms of hypertension, you know, obviously specific -- specifics matter and things are going to be different, you know, a lot of the specifics of any given decisional context are going to matter in terms of how the modeling is done. But I think the same general principles are going to apply to hypertension. In fact, there are models with heart disease. I mean, it's the same thing with statins with blood pressure. So we tend to target our statins to the highest risk patients for developing coronary heart disease. And there are models for the risk of developing coronary heart disease with hypertension that also tell us it's probably not the same targets that we should be -- you probably know this better than I do, that we should be aiming for all our patients with hypertension. That there's some patients when you drive down their blood pressure below 120, the treatment-related harm of that anti-hyper tensive treatment is going to outweigh the benefits in terms of the risk reduction of coronary heart disease. There are other high risk patients who will benefit, who will get the benefit that was shown for example in the spring trial whereas patients with lower risk might do better with blood pressure target less than below 140. So I think it's pretty much the same approach. In general. John?

>> JOHN CUDDEBACK: Yeah, I -- I think the interest and uptake from clinicians and patients has not really been the issue here. I think a lot of the issue is delivering the risk predictions in a convenient way that can be used at the point of care. And EHRs have always been closed systems. And I think with the new emerging standards that are allowing us to build cloud-hosted predictive models, I think that's going to change. In fact, many decision aids are being implemented in this fashion. I think it's really the technical obstacle of just being able to get the information into the clinical work flow.

>> LAURA ESSERMAN: At the right time. >> AARON CARROLL: We'll take one more question. I think there's one on our

right. Yes. >> Hi. I'm Nicole Kelly with the American chronic pain association. For almost 40

years now, we've been advocating for a multi-disciplinary approach to managing pain with the person with pain being a key part of the decision-making team. But one of the big barriers that we've found is that payers aren't necessarily on board with some of the individualization that many people need to appropriately manage their condition.

I know that you're talking on the research level, but let's get down to where people are. How do you see payers being part of this process if we want to move towards more individualized, more person-specific kinds of care?

>> I'm happy to answer that. We've really taken the approach that -- I think everyone has skin in the game. And I think it's everyone's responsibility to generate better data and if you're testing an intervention that is either the same or less cost and you're trying to look at it instead of waiting for the data to come out and having this knowledge turn every ten or 20 years, I think we should use the resources that are in the system and repurpose it and have our payers participate. I think that's one of the things that the PCORI model has helped us to do. And it's been hard work, but we hope that this is something that PCORI will build on. And I hope that the insurers and self-employed insurers will continue to participate. I think putting pressure on the system to say it's everyone's responsibility to generate better data, help us make better decisions and offer better choices to patients, and -- and actually make better decisions about what -- what to provide to whom. Everyone needs to be part of that solution and that includes the people who pay for health care. A vested interest.

>> AARON CARROLL: We could do this for the rest of the evening. We're standing between you and the reception at this point. I'm sure the discussants here will be here after wards. If people have questions, they're welcome. I want to thank all the presenters today. It's been really great. Thank you.

[ Applause ] [ Event concluded at 4:44 p.m. ET ]