Influencing the Global Dialog on Healthcare

         September 28, 2011

httpv://www.youtube.com/watch?v=_5CUPMD0Agk

On September 16-17th, I attended the Asia-Pacific Economic Cooperative (APEC) Health Systems Innovation Policy Dialog in San Francisco. It was a stimulating opportunity to look at global healthcare concerns from the perspective of developing and developed economies. There was much opportunity to  dialog and frame the issues around transforming healthcare systems to meet pressing problems such as aging populations, the burden of non-communicable disease, regulatory harmonization, and the problem of rising healthcare costs with diminishing returns and more.

In the sphere of government and international relations, ideas and influence are the currency. APEC as a body operates mainly as a forum of ideas and provides a vehicle for harmonization of purpose across member economies. It has been described to me as being more on the leading edge of ideas than, say the World Trade Organization, particularly because member economies can openly talk over ideas without end products of treaties and binding resolutions in mind. The mindset shifts that occur at these meetings eventually percolate into public policy initiatives in individual nations and organizations such as the UN and the WTO.

APEC includes 21 member economies (Australia, Brunei Darussalam, Canada, Chile, China, Hong Kong, Indonesia, Japan, Korea, Malaysia, Mexico, New Zealand, Papua New Guinea, Peru, Philippines, Russia, Singapore, Chinese Taipei, Thailand, USA, and Vietnam) and represents more than half of world trade.

The conference included some high profile speakers such as Kathleen Sebelius (U. S. Secretary of Health and Human Services), William Weldon (Chairman & CEO of Johnson & Johnson), and numerous trade ministers from member economies, industry representatives, academics, and policy experts.

I spoke at a session: “Supporting Innovation Across the Healthcare Ecosystem – Research & Development” that was chaired by Dr. Kim Tan, APEC Life Sciences Board Member and Chairman of SpringHill Management Ltd., along with Mr. Graham Almond, VP South East Asia Sanofi; Dr. Robert Bargatze, EVP and CSO Ligocyte Pharmaceuticals; and Dr. Kenneth Stein, SVP & CMO Cardiac Rhythm Management Division, Boston Scientific.

The session was a lively and stimulating one, covering diverse topics such as new models of business for pharmaceutical companies, innovations in vaccines, regulatory and legal harmonization, and my presentation on focused innovation for disease management detection and prevention.

I’ve included a transcript of my presentation below and some of my contribution to the discussion afterwards. A recording of the presentation can be found here (or embedded video above) and just the slides downloaded here.

The presentation covers three main areas:

  1. Operational excellence -making better use of existing healthcare resources and reducing avoidable errors through innovations in operations and statistical quality control from manufacturing.
  2. Longitudinal prevention – looking ahead a few decades, presents a vision of minimally intrusive biomarker capture and monitoring that leads to dramatic improvements in disease detection and prevention.
  3. Productive longevity – raises the questions of what the world would look like with 100+ year lifespans and how that affects the economic equation of healthcare.

I close with a discussion of metrics, asking if we should continue to structure our healthcare systems around payment for treatment of illness versus measures and payment based on operational excellence, sustaining health and productive longevity. Finally, in the discussion portion on regulatory harmonization, I make some well-received commentary on the challenge of reconciling the scientific method with a regulatory and legal environment in our cultures that operates with the oxymoron: “scientifically proven”.

I found it tremendously valuable to look out decades into the future and think about the trajectory of research and development and how we can make significant contributions to leading longer, healthier and more productive lives. I hope you enjoy it.

…And that’s my 2 SNPs.

APEC Health Systems Innovation Policy Dialog
San Francisco 2011
Focused Innovation for Disease Management, Detection & Prevention

Good morning, I’m Christophe Lambert, President & CEO of Golden Helix. There’s some slides on your chair – copies that will be useful to see some of the graphs following along with me. I’d like to talk about where we can focus our innovations for disease management, detection, and prevention. I was really stepping back and saying, innovation needs to be applied to essentially all of the [conference parallel] tracks, the other tracks that we are actually not able to participate in [Preventing Disease; Detecting Disease; Managing Disease]. I come from the perspective of working in pharma early in my career and worked building a small company in the field of bioinformatics, and you really end up asking the questions, “how is what we are doing going to make a difference to the bigger picture, and if it isn’t, what are the transformations that we are going to need to think about short term, medium term, long term in order to really make a difference?”

The Problem: Healthcare Costs Rising With Diminishing Returns
In this first slide, after the title page, “Framing the Problem”, the biggie of course in America, and it is also rising in other APEC economies, is the disconnect between how much we spend of our GDP on healthcare, and on the next slide, what results we are actually getting.

Discrepancy Between %GDP Spent On Healthcare by the USA and Singapore and Life Expectancy
I was able to pull 2007 numbers for APEC member economies, looking at life expectancy versus %GDP, and you see in the upper right, the United States of America, on the lower right Singapore, and Singapore spending under 4% of their GDP on healthcare, and their longevity which is the other axis is up somewhere around 82, whereas the United States is somewhere around 78 at the time this data was collected [with 16% of GDP spent on healthcare]. And so we could probably learn something from the Singapore model, although there are arguments the society is different, we wouldn’t be able to apply some of the same controls, for instance, that have been put in this nation. But the point this makes to us is that just spending is not sufficient to get the outcomes.

How Can we Shrink Costs and Improve Outcomes?
And so there are three main facets on the next slide that I would like to touch on briefly in this talk. How do we deal with the disease management side and I’d like to talk a bit about a topic that hasn’t been covered much in this conference, which is operational excellence. There are some amazing stories of transformations of hospitals and clinics and so forth to get more out of the existing scarce resources. The assumption that we’ve got to spend more to deal with the big bulging baby boomer generation and the aging challenge we have – we might be able to challenge those assumptions by looking at some of the success stories in operational excellence.

I’d like to talk about something near and dear, which is the idea of longitudinal disease detection and prevention. Specifically speaking about biomarkers which is my area of expertise. Really looking at the idea that – we know the expenses are late in life, they are with the chronic diseases, and it has been talked about at great length how prevention is really the key. It really goes back to Deming who looked at how variability in a system gets amplified over time. If you deal with the problem early, there is less chance of it magnifying.

Finally I’ll talk a bit about productive longevity. How lifespan increases we’ve seen; it’s been something like 10 years lifespan gain in something like 50 years. What if we were to have a dramatic jump in the next 10, 20, 30 years; what would that mean for where we have to put our attention?

Operational Excellence
The next slide, talking a bit about operational excellence: really a lot of the problems in healthcare are operational. We’ve got too long wait times. The notion of waste in manufacturing – what does that mean in healthcare? It means people dying. It means adverse drug reactions killing 100,000 people in the United States. It means hundreds of thousands of avoidable mistakes – things like that. If it is avoidable, it is going to have to be dealt with through process improvement. Of course we hear about nursing shortages, the baby boomer bulge that is coming, and we know at least looking at those curves on that first slide with the United States up at somewhere like 16% of its GDP spent on health, it is not sustainable to just throw more and more resources.

It is interesting when we talk about innovation, we usually think science, right? That we are going to have these new breakthroughs in medicine that are going to be the answer. Really there are some amazing innovations in manufacturing excellence that have been developed over the last 10, 20, 30 years, of course starting to a large degree in the quality movement in Japan. There are approaches that come under different names: Theory of Constraints, Lean, Kanban, Six Sigma that have been migrating into the healthcare systems, and there are some amazing stories, and you can go read this reference and find some additional ones. The worst performing emergency room in Great Britain being transformed into the best over the course of a 3 month transformation. And what they find is you don’t have to improve everything. You have to focus on what is the rate limiting step within the organization. In the case of hospitals, it’s typically the operating theatre – you can typically get double the capacity out of it by better scheduling and making sure the resources are all there. Or [addressing the constraint in] admissions or sometimes bed capacity. It ends up being again and again the same areas of hospitals, that if you put the attention there, perhaps applying Six Sigma quality programs, on things like mistakes and so forth – you can get dramatic outcome improvements, dramatic improvement in resource utilization – essentially for almost free. It kind of seems unbelievable, but this stuff exists and is published, and how do we get it operationalized, and really ask ourselves the question in national healthcare systems, “do we have the equivalent of Chief Operating Officers?” Typically an MD is the Chief Medical Officer or Surgeon General or whatever of the country. But think about someone who is a really high level skilled person who has run global supply chains, who has transformed manufacturing operations, and so forth, having that level of expertise, brought right to our healthcare systems.
Enough on that, but it’s really: let’s exploit what we have, and there’s a tremendous amount to be done there.

Current State of Detection & Prevention
On the next slide on detection and prevention, which I really put together as a kind of continuum. If you look at the current state of it, of course in developing economies there are huge returns on spending on things like vaccinations, immunizations, doctor checkups, antibiotics, primary care, basic sanitation. Then you get to where we’re at today, where the screening of disease – look at lung cancer – 40% of people are caught at stage IV for lung cancer. And you’ve got what an 8-month median lifespan after that? The screening approaches of course give us an improvement over nothing, but the current state is of course that rather abysmal discovery [rate]. We know about half of us will get cancer at some point. This is very personal. How are we going to get to the place where we can have early detection that’s actionable?

Longitudinal Detection & Prevention
Just skipping over some of those other points for time… What I would propose, and of course we work in the field of genetic research, and to be honest and frank, a lot of what we do in the field of genetic research is discovering these biomarkers that explain only a small fraction of the variation in disease. They are what you are born with and it doesn’t represent the ongoing trajectory of disease. And so what I would propose is where we really look at making research expenditures is in the longitudinal capture of biomarkers at high frequency. And that could be genetic, proteomic; it could be metabolomic, small molecules in the urine, etc. The idea is we are getting a pretty good understanding of what genes are involved with what diseases. And when you are healthy you are going to have this constant within normal variation fluctuations of all these various indicators in your body, and then when something goes wrong or begins to go wrong, there is going to be a transition. And to transition from the place where we are talking about one-size-fits-all healthcare policy things like “salt is bad for everybody and we’ve got to lower it” – and it turns out, there is studies showing it is not necessarily so solid that science and we’ve been practicing it for 30 years. Getting it to each individual person [having] a rapid feedback loop whereby if you really want to change behavior, you can’t ask people to do something and maybe in 30 years it will make a difference to their life expectancy. You need a feedback loop that tells them, as a result of taking that drug or a result of changing your diet or a result of exercise, you’ve increased your expected lifespan, you’ve dropped certain indicators that for you — you know you are at high risk for say cancer in your family — and you’ve kept those cancer promoter genes in check.

So right now the state of that in terms of R&D – almost nothing is being spent in that area. There are a few leading edge groups working on it. It is a chicken and egg problem. You are going to need to collect the data before you can turn the informatics and bioinformatics analytics on it. And of course there will have to be all sorts of interesting questions answered in terms of infrastructure, electronic medical records, public policy and so forth. And so I’d advocate… the biggest obstacle to this from a technical point of view and sociological point of view is what does regular measuring of biomarkers mean. Well, stabbing yourself with a needle once a day – getting a blood draw and sending it in, it’s not viable. So thinking about device innovations whereby you can capture that information regularly, and start with the most at risk groups that it really matters to them, if it is going to be invasive. Right now the technologies are such that it is still fairly expensive to do some of those assays, but it is dropping exponentially. We know in a few years, it is going to be $10, $20, $50 to do a lot of these genetic tests and measurements that we are doing today.

Vision: Making Longitudinal Biomarker Capture Automatic and Minimally Intrusive
On this next slide which has a funny a picture of a toilet from the Shanghai Expo – the idea is imagine if you just flush your toilet and the mass spec was done on your urine [laughter]. In other words if you don’t have to think about it. As Rob was saying with [the benefits of] vaccination, the biggest problem [addressed] is adherence. And so how do you get systems in place whereby you don’t have to worry about it. Well it is not science fiction. There are glucose monitors that are put subcutaneously that every 5 minutes read your blood sugar levels and we really do see evidence, including people just pricking themselves with these glucose monitors, that when they have that instant feedback loop, they are making the right decisions about their diet; they are making the right decisions about their lifestyle. I think we are going to get out of this mode of trying to focus on these silver bullets, “this is bad for everybody”, “this is good for everybody”, and trying to somehow cajole and influence our populace to do that versus getting to a personalized, “this is right for me, and I can see it right in the data and make the decision myself”.

Productive Longevity
Finally of course if this all works out, productive longevity becomes the question. There is some amazing stuff happening in aging research. I’d say this prevention will lead to extensions of life, and there’s all sorts of promising molecular research on telomeres, caloric restriction mechanisms and so forth. It wouldn’t surprise me if in our lifetimes that we see some dramatic boosts in longevity. Obviously there are going to be some real questions about do we retire at 65, or does it eventually become 90, 100, 120? Seems crazy to talk about it today, but if you look at the exponential growth in human knowledge in the last century as we have followed the scientific method, these things could well be within our lifetimes, if not our children’s.

The key point I’d like to make is if we know that end of life is still going to be expensive, but if you can have a 100 year lifespan or longer, where a lot of it is productive, and I really appreciate the plenary talk that was discussing about productive life years or quality of life adjusted years, we have the potential to essentially sock away the resources to pay for when it eventually really does become important to spend on those last years of life.

Metrics
And then finally to the people thinking about public policy and metrics on this last slide: consider the prime metrics that we use in looking at healthcare systems. Especially in private systems, the way we think about measuring it is payments are basically received based on treating illness. If we believe measurement drives behavior, and essentially corporations and doctors and so forth get paid for treating illness, doesn’t that mean the more illness you have and treat and the more treatments you do, the more money you make? And eventually as we see in the crazy curves of %GDP it just sucks right into your economic output and eventually becomes unsustainable.

So I think we need think about ways to measure healthcare on throughput, looking at this operational excellence, on sustaining health and lifespan, and high quality lifespan, and essentially start looking at how we can really build the metrics that lead to the right investments in these various facets that I’ve spoken about. And the idea is we can increase our lifetime economic output, increase the funds, improve health outcomes. The real thing when you look at it from an industry perspective – what if we were to essentially eradicate disease, how are we going to get revenues? I think there will be a huge shift to where the expenditures will be on prevention and the longer [we live] and more healthy we are, the more money we can have to put towards prevention. So that is really where the markets are going to be.

Thinking about pharmaceuticals and the transformational shift, how are we going to get this personalized medicine stuff, building drugs, knowing that each person is a rather unique organism? The blockbuster model is already cracking at the seams and I think Sanofi is a lot more looking at the targeted markets and pharmacogenetics is a part of that, but gosh it is hard to envision one drug for one person. So there are these poles of personalized medicine versus you still need to apply things that can work for large segments of populations at least and looking at how we can get the R&D costs and bringing things to market costs and clinical trial cost down, or change our model so that we can treat those 6,000 orphan diseases that aren’t treated right now.

Thank you very much.

Discussion
[In discussion of regulatory and legal harmonization for the 21st century]

One of the areas that’s very interesting in genomics is this field of cytogenetics, where historically they have used karyotyping, and you’ve seen these stains of chromosomes and they basically look in a microscope and see if Johnny’s got an extra copy of chromosome 21, well he’s got Down’s syndrome. So you could see these really big structural abnormalities, and for the last 30 years, that’s the way it has been done. If you look at the clinical yield on that, any idea that it’s actually 3%? In other words, clearly the child’s got a deformity or mental retardation, or something like that and 3% [of the time] they actually find something abnormal [in the karyotype]. Well now they’ve moved up to, with microarrays, higher density, getting more information, 20% clinical yield. And then they are talking about in the future we are going to sequence the whole genome.

But the conflict is – and I wrote a whole article on this in my website if you are interested in seeing it, on cytogenetics – back when there were these large deletions or extra copies, it was pretty darn certain that is what was wrong with Johnny. You weren’t worried about being wrong. But now there are all of these variants that are: “well we’re not sure”. And then with sequencing we are going to up the information even more. We are going to have to shift things to a place where we are going to have to recognize that there is a lot we don’t know and again with the longitudinal thing we are not going be able to just give one answer today and OK now you are in the 20% who is diagnosed and you can believe that. We are going to say, “look there are these things of unclear significance and we’re going to put you on a plan where in the future we will let you know as we learn more and you will have to revise your mental model of what your disease is”.

And unfortunately we live in a society where… which is actually unscientific…. Have you ever heard the phrase, “scientifically proven”? It’s an oxymoron [laughter]. Right? And yet you see it in all of the ads. How do we take science into public policy where the truth is, science is about disproving your previous models and replacing them with better ones? Whereas in public policy we want absolute certainty and sue you if for some reason you are wrong, when science teaches us that nothing is knowable, only that we can have done our darndest to falsify it, have a good working hypothesis [laughter in agreement], and update it as we learn more. How do we get that into our harmonization of legal and regulatory?

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About Christophe Lambert

Dr. Christophe Lambert is the Chairman of Golden Helix, Inc., a bioinformatics software and services company he founded in Bozeman, MT, USA in 1998. Dr. Lambert graduated with his Bachelors in Computer Science from Montana State University in 1992 and received his Ph.D. in Computer Science from Duke University in 1997. He has performed interdisciplinary research in the life sciences for over twenty years.

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