 Terima kasih banyak kerana menunjukkan sesi ini untuk Prosesion Medsyn. Nama saya Tan Chow Chwan. Saya Presiden National University of Singapore. Saya penjaga pelajar. Kami semua tahu bahawa dalam perjalanan medial sekarang, apa yang berlaku terutamanya, adalah bahawa apabila perniagaan berdiagnose dengan kondisi medial, kami beri mereka perjalanan standard. Perniagaan standard ini berlaku dengan baik untuk banyak perniagaan. Ia berlaku lebih baik untuk orang lain. Dan dalam beberapa kes, ia tidak boleh bekerja. Tetapi masalahnya, dalam beberapa kes, kami tidak dapat menjadikan mana perniagaan yang boleh menerima dan mana perniagaan yang boleh tidak. Jadi kami sekarang masuk dalam perniagaan Prosesion Medsyn, yang menjadikan potensi untuk kami lebih baik untuk menjadikan mana perniagaan yang boleh menjawab lebih baik ke apa jenis perniagaan. Tapi pertanyaan sekarang adalah, apa adalah janji yang benar dengan perniagaan Prosesion Medsyn? Macam mana ia boleh mengubah perniagaan Prosesion Medsyn dan perniagaan perniagaan dalam perniagaan? Dan apakah beberapa jenis perniagaan yang dapat menjadikan perniagaan Prosesion Medsyn dalam perniagaan perniagaan dalam perniagaan perniagaan? Jadi hari ini, kami mempunyai sebuah panel yang akan membantu kami lebih baik memahami beberapa jenis perniagaan dan beberapa jenis perniagaan yang kami menjadikan dalam perniagaan Prosesion Medsyn dalam perniagaan perniagaan. Saya akan menjadikan mereka segera. Kita mempunyai Cathy Hudson, yang sekarang seorang perniagaan perniagaan untuk perniagaan perniagaan dan perniagaan dalam perniagaan Perniagaan. Kita mempunyai Steve Rakowski, yang seorang perniagaan perniagaan dalam perniagaan perniagaan perniagaan. Kita mempunyai Pierre Schatz, yang seorang perniagaan perniagaan di Jemun dan kita mempunyai Victor Zao, yang sekarang perniagaan perniagaan perniagaan perniagaan perniagaan. Jadi apa yang akan saya lakukan adalah menjadikan perniagaan perniagaan untuk beri kepada kami beberapa jenis perniagaan tentang jenis perniagaan yang penting dalam perniagaan perniagaan. Selepas itu, kita akan mempunyai beberapa teman antara perniagaan, kemudian kita akan suka mendengar komentar dan pertanyaan dalam perniagaan perniagaan yang berikutnya. Jadi jika saya boleh mulakan dengan Cathy, Cathy sudah sangat terlibat dengan Presiden Obama yang keadaan terakhir dari perniagaan perniagaan perniagaan di US. Kita semua tahu bahawa kita mempunyai perniagaan perniagaan, kemudian kita mempunyai perniagaan perniagaan dan sekarang kita mempunyai perniagaan perniagaan. Jadi apa yang akan kita melihat perniagaan perniagaan? Apa yang anda fikir adalah potensi perniagaan perniagaan untuk menghubungi keadaan perniagaan? Jadi saya rasa mungkin tidak banyak distinksi antara beberapa perniagaan perniagaan perniagaan, perniagaan perniagaan, mereka semua dapat pada masalah yang sama yang sehingga untuk medan yang lama telah dilukis untuk perniagaan yang sepatutnya jika kita dapat berlaku tentang perniagaan perniagaan, perniagaan dan perniagaan, kita akan dapat mempunyai perniagaan yang lebih menarik untuk perniagaan yang sepatutnya perniagaan dan tidak perniagaan. Jadi ia sangat menarik apabila Presiden In fact, ia memperlukan perniagaan ini sebagai perniagaan sepatutnya satu tahun yang lalu hari jadi, Perniagaan Perniagaan Perniagaan. Dan kita sekarang baik dalam perniagaan dan memperkenalkan perniagaan untuk memperkenalkan perniagaan yang kita perlukan untuk memperkenalkan perniagaan. Dan apa yang telah menerima saya tentang ini, bukan hanya tentang perniagaan yang akan diperkenalkan tetapi juga tentang memperkenalkan perniagaan yang kita memperkenalkan. Jadi sangat memperkenalkan perniagaan untuk bagaimana kita akan meminta pelanggan atau lebih banyak amerikans untuk memperkenalkan perniagaan mereka, perniagaan hidup mereka, perniagaan genetik mereka, perniagaan elektronik mereka, perniagaan lain tentang diri untuk membuat itu sebuah pelajar yang terbuka dan mengenai itu, kita membuat para pelajar bersama kita dalam penjara penjara penjara memperkenalkan mereka bahawa kita akan menerima penjara penjara penjara penjara untuk mereka. Dan juga bahawa semua data dari ini akan diperlukan untuk semua pelajar penjara penjara penjara sehingga itu adalah sebuah keguguran dan keguguran yang kita membuat dari awal kita juga dalam penjara penjara penjara apabila kita pertama membuat penjara penjara penjara penjara penjara penjara beberapa bulan lalu, orang berkata kita tidak mempunyai banyak masa untuk membuat aplikasi kita dan memperkenalkan aplikasi kita ke NAH. Kenapa kamu bergerak dengan cepat? Dan kita kata, seorang Presiden adalah penjara penjara penjara penjara penjara penjara penjara dan ada sebuah fokus untuk memperkenalkan dengan sebuah tahun dari sekarang untuk sebabnya, sebabnya Presiden akan memperkenalkan sebuah masa yang terakhir dan kita mahu mempunyai sebuah fokus untuk memperkenalkan sebuah masa yang terakhir sebabnya, sebuah masa yang terakhir dan kita mahu memperkenalkan sebuah masa yang terakhir sebelum dia pergi ke pejabat. Jadi, sebuah keguguran ini akan memberikan banyak informasi? Apa yang kamu harap untuk menggabungkan? Saya akan memperkenalkan dengan baik. Jadi, awal-awal kita akan memperkenalkan informasi dari orang yang berlaku. Kita akan meminta untuk meminta untuk memperkenalkan dalam dua cara. Satu adalah, memperkenalkan organisasi kejayaan yang sederhana dan rutin untuk kita di NIH. Kita akan meminta atau memperkenalkan sesiapa-siapa yang berlaku dan memperkenalkan, saya mahu memperkenalkan inisiasa ini. Itu sangat menarik, dan juga sangat menarik dan sangat menarik, tetapi dalam masa mobil dan teknologi modern saya rasa kita akan dapat melakukan ini. Jadi, di atas batu kita akan dapat informasi yang berlaku dari orang-orang. Kita akan dapat memperkenalkan dengan kejayaan baru dalam rekaman elektronik. Kita akan dapat informasi yang berlaku tentang orang-orang. Dan saya rasa, dengan jauh-jauh sebegitu, kita akan dapat memperkenalkan pertanyaan dan pertanyaan untuk diperkenalkan. Satu-satunya perkara yang kita lakukan dalam memperkenalkan inisiasa ini adalah untuk memikirkan bagaimana kita dapat membuat ini dalam cara yang menarik pertanyaan yang dapat diperkenalkan dan pertanyaan yang akan menjadi pertanyaan kepada pelajar dan pertanyaan dan pertanyaan kita dalam sebuah sektor privat. Jadi, kita memikirkan tentang sebuah ujian yang berlaku dengan sebuah ujian yang berlaku. Di U.S., ada 140 ujian yang berlaku dengan informasi genetik di dalam labu. Tapi bagaimana banyak kita tahu apakah tidak kita mempunyai ujian yang lebih berlaku atau lebih berlaku untuk menjawab ujian yang berlaku? Kita tidak tahu. Tapi apakah jika kita menerima informasi kepada pelajar yang berlaku di sebuah ujian yang berlaku apakah tidak ujian yang berlaku untuk semua orang? Dan untuk dapat menjadi ujian yang berlaku untuk ujian yang berlaku. Jadi, saya rasa ia akan menjadi ujian yang berlaku. Jadi, saya mungkin akan bergerak kepada Steve sekarang. Steve, ujian yang berlaku adalah ujian yang lebih berlaku dalam ujian yang berlaku dan juga berlaku dengan pelajar yang berlaku. Dalam ujian industri, apa yang anda fikir adalah ujian yang berlaku? Boleh anda juga berkata bahawa ujian yang berlaku sebagai ujian yang berlaku dalam ujian yang berlaku. Mereka boleh faham ujian yang berlaku atau ujian yang berlaku untuk memperkenalkan siapa yang lebih berlaku daripada ujian yang berlaku. Tetapi, bagaimana ujian yang berlaku untuk membuat ujian yang lebih berlaku? Terima kasih, Con. Ketika saya memperkenalkan kata-kata, kami ada banyak kata-kata. Ketika orang tahu kami mempunyai ujian yang berlaku di United States. Kami sebenarnya memperkenalkan kata-kata satu-tiga kata-kata setiap tahun. Kami percaya kami mempunyai kata-kata ujian yang berlaku pada kata-kata ujian yang berlaku. Yang menarik adalah kata-kata kata-kata yang berlaku adalah kata-kata kata-kata, tetapi ia Halilah eruesen yang besar berm Organizasi Kehidupan Dan bahagian, kami sudah city menerima takranya daripada keadaan konnte didalam mechaniresia dari Kegembangan att смогat Kita cuba mencari cara untuk menggunakan data itu dengan cara yang betul sebagai perusahaan. Tapi kita sangat berminat. Jadi apabila kita minta siapa yang ingin berpartisipi, kita sangat berminat dalam berpartisipi. Dan juga saya mempunyai perusahaan Klinik Klinik Klinik. Jadi saya dan kawan-kawan kita dalam perusahaan kita sangat berminat tentang cara kita boleh berpartisipi sebagai perusahaan dan memberikan data yang betul bersama. Kerana jika kita boleh menghubungi data bersama dengan data lain, itu selalu lebih berguna. Dan juga cara yang kita telah menghubungi, kerana selalu susah untuk mendapatkan data seharian. Kita sebenarnya membuatkan perusahaan dengan perusahaan di United States yang tersebut adalah The Novel On. Dan kita menghubungi data yang tersebut untuk melayani sekarang. Data keberkatan, data keberkatan yang berguna untuk mengahwapakan kisah sejarah sejauh. Jadi jika kita memikirkan apa yang kita nak melalui, itu boleh menjadi berguna. Kerana kamu meminta soalan itu untuk kita gunakan. Kita sebenarnya mempergat dengan Center for Disease Control di Hapatatasi. Pada masalah besar, di United States alone, ada sekitar 80 juta bayi-bayi yang mempunyai risiko untuk mempunyai kapitaliti. Kita mempunyai sekitar 4 juta bayi-bayi yang mempunyai. Dan dengan data itu, kita dapat membantu menemukan keadaan kita untuk membuat perjalanan itu. Tetapi, pentingnya, dengan kemampuan ayah, kita tahu bahawa kita boleh sekarang efekatiusnya mempunyai kapitaliti. Kita boleh, dengan cara yang betul, mempunyai kapitaliti pada masa. Dan apa yang kita juga menemukan di perjalanan ayah adalah di masa depan ayah yang bekerja di farmah. Ia semua telah dikawal oleh pesakit. Ia lebih penting untuk menemukan pesakit. Dan dengan mempunyai data itu, kita dapat lebih efekat dan lebih precise untuk menemukan pesakit yang kita cuba mempunyai untuk mengenai efekatiusnya. Dan dengan pilihan yang lebih baik untuk pesakit itu, kita dapat membantu menemukan perjalanan ayah untuk mengenai perjalanan ayah untuk membuat perjalanan ayah pada masa yang lebih cepat. Jadi, dua cara yang kita memperkenalkan pada perjalanan ayah ini. Tapi data adalah sebahagian besar. So, dari perjalanan perjalanan ayah, apa yang paling penting untuk perjalanan ayah untuk memperkenalkan perjalanan ayah ini? Apa yang kamu akan katakan? Bagaimana perjalanan ayah? Kamu mengatakan perjalanan ayah dalam perjalanan ayah. Dan keadaan ayah yang penting adalah sebahagian besar yang terbentuk perjalanan ayah. Ia mempunyai kemampuan ayah yang diperlukan oleh perjalanan ayah. Jadi, kita memperkenalkan perjalanan ayah untuk memastikan kita berusaha awal. Pastikan kita mempunyai asas yang betul dan untuk perjalanan ayah. Tapi ketika perjalanan ayah memastikan kita melakukan perjalanan ayah untuk perjalanan ayah, maka pentingnya, kita kongsi perjalanan ayah. Dan kemampuan perjalanan ayah ini. Jadi, jika perjalanan ayah yang diperlukan oleh perjalanan ayah dan itu sangat menerimah maka kita perlu membuat ...untuk memperbaiki framework untuk menghubungi diagnostik di dunia dan seluruh dunia... ...dengan platform yang berlainan dan membuatnya bersihkan bersihkan. Jadi, kita berfikir, bagaimana kita melakukannya? Bagaimana kita melakukannya di negara dan bagaimana kita melakukannya di global? Terima kasih. Jadi, saya mungkin boleh bergerak kepada Peer. Kehidupan Kehidupan, tentu saja, adalah... ...pada sebuah pelajar diagnostik yang lebih besar di dunia... ...dan juga sangat terlibat dalam prosesion medicine. Peer juga telah menjadi sebuah memberi... ...penghubungi di global agenda di prosesion medicine. Sesetengah perkara yang Steve beritahu... ...berkata-kata beberapa jenis perasaan... ...peribadi, perasaan kepercayaan, berkata-kata... ...berlainan lebih berlainan dan sebagainya. Ada banyak perasaan sebelum prosesion medicine... ...boleh menyebabkan prosesion medicine... ...dan menjadi sebahagian sistem kebaikan. Jadi, apa yang kita katakan adalah... ...berapa penting perasaan yang paling penting? Sebelum itu, kita dapat benar-benar menerima... ...potensi prosesion medicine... ...dan melihat mengapa kita menggunakan sistem kebaikan. Terima kasih, Professor Chan. Sebuah situasi yang teruk untuk menjadi sebuah... ...pengguna-pengguna-pengguna... ...dan anda memberikan sebuah pertanyaan. Tapi saya akan cuba menjawab... ...berkata-kata daripada perspektif. Kita telah berada di tempat ini sejak 20 tahun... ...dan saya akan melihatnya... ...dalam perasaan kepercayaan. Pada masa awal, ada banyak perubahan... ...sebab orang tak percaya... ...masukkan perasaan kepercayaan. Saya berfikir ia akan sangat susah... ...untuk membuat perasaan kepercayaan... ...untuk membuat perasaan kepercayaan yang lebih kecil... ...dan kemudian, ada banyak perubahan. Ada banyak perubahan... ...dalam komuniti kepercayaan general... ...semasa komuniti kepercayaan terkenal... ...sampai kita perlu pergi. Pada masa awal, ia sangat susah. Apa yang terjadi adalah... ...beri perubahan berlaku segera. Saya akan membuat ini juga dari perspektif optimistik... ...dan menunjukkan keadaan tetap berlaku. Perubahan pertama itu... ...beri perubahan pada konsep. Perubahan kepercayaan yang dipercaya... ...menerima bahawa... ...beri peluang yang sangat teruk... ...untuk membuat perubahan lebih kecil... ...dan membuat perubahan yang lebih kecil... ...untuk membuat perubahan lebih kecil. Jika mereka tak berikan perubahan yang sama... ...vieh bahawa adalah terena dengan mengembangoutsi... ...mencari tarikh rasih berlaku menjadi... ...petua-petua. Apa terjadi di peluang yang kedua-petua datang adalah... ...beri peluang yang sangat gy 라고 diperlukan... ...arena membuat pengir principles... ...untuk mengg periodliche kita... ...membuat perubahan ter kompensi dengan distinta. Keluarga musuh udik ini... ... pu gadget, theybotell model... ...ן, masih mengalami... ...perubahan biksu tentang pertikaan kita que 100 teh belakang... In the meantime are going quite well. So we today have about 20 personalized medicines in the market and oncology in the United States. That's the largest market. But the more important number is that 60% of drugs in pharmaceutical pipelines are actually being developed with a diagnostic as a companion diagnostic. This is spectacular. So we don't have to push that button anymore where that train is going. The next one was the regulatory barrier and for many years it was thought that adding a diagnostic to a pharmaceutical drug would delay or put at least risk onto the development timelines and also opportunity for a drug. The first co-approvals were done about five, six years ago. We were actually fortunate to have been part of that where the first single gene test together with EGFR inhibitors for colorectal cancer came through and several other cancers around the same time. And so this in the meantime seems to be a solved issue. That is not the problem anymore, at least in most jurisdictions around the world. So now I'm going to get to areas that are still a challenge. The first is the whole reimbursement community. And this is currently still a challenge because there are so many different pillars across the healthcare industry. And what personalized medicine does, it combines several of these pillars, not only diagnostics and therapeutics but also the providers and other constituents and creates an ecosystem. And as soon as you do that, you're creating something that traditional payers are not really accustomed to because normally what you want to do is to take a certain element within a pillar, let's say diagnostics, and reduce the cost and increase the efficiency or improve outcomes. This is not the case with personalized medicine. You definitely introduce a new diagnostic, new cost, but you dramatically lower the cost in other areas, be it hospital stays for instance, reducing the time during which therapy has to happen. So this is something that we are still looking for the right models to evaluate to say the benefit to an ecosystem is this and this is why we will also have the correct payment components behind it. As an example, there is hardly a lab in the United States and Steve will be able to talk to that as well that is actually making money off genetic testing for the administration of therapies. It's currently very often a loss making proposition and so this has to be improved. The next barrier that still is up there is education. There was a recent study by Harvard Medical School said that 36% of physicians self reported that they don't really understand what personalized medicine is. And this was self reported, so there's a lot to do and that's why I applaud initiatives like Dr. Hudson, the NIH and others are doing because this is tremendously important to really understand how this can be administered and not create a quagmire in terms of complexity for the various constituents. One way is to think about integrated education versus disciplines. Right now, people study genetics, people study medicine, should we have more combined education and think about educating on other disciplines as well. Then the last one, data sharing. Why is this important? It's often talked about. Personalized medicine is working with DNA very often which is an incredibly complex molecule. There are over 3 billion letters in our DNA and variants can have so many different combinations of effects on the outcomes or the efficiency or effectiveness of drugs. So the only way we can really solve this is if we pool all the data we have to start seeing patterns, start seeing where a drug worked, where a drug didn't work because using rational science we can get to certain information but not to all of it. So we have to pool that information to start creating this working living ecosystem that we can mine for science and that we can apply for healthcare. And that is 30% of all medical data stored on paper today, even in the United States. And it's an incredibly fragmented industry and even if digitalized, the interoperability of that data is a problem. It is not only in terms of terms so the famous one is a fractured leg and broken leg. The two won't fit in a database. We're working on international classifications of disease so-called ICD-10 but also the privacy issues in the cloud. People are very often concerned about what it means for their genomes to go up to the cloud and to be mined and processed and stored there. The only way for this to happen is in the cloud and this is something that we're still working through to solve and Victor with the IOM has done tremendous work in helping us now separate clinical research and new findings from actual routine use of that genomic data. So we're moving in the right direction but it's not moving fast enough because science is moving so fast. And we should never forget that science is at a blazing speed years ahead of what we're even thinking about today in terms of implementing clinical practice. So there's still a lot to do but it's a fun time. Okay, thanks Pierre. Sorry to ask you a pessimistic question but I thought you did extremely well. But I mean since you brought up reimbursement I want to turn to Victor. I mean personalized medicine much targeted much better targeted therapy. It sounds really good but one of the greatest concerns about most policy makers in most countries is rapidly rising healthcare costs. And also the need to have greater productivity in health system that is using less health professionals to do the same job. So the question is will personalized medicine really be able to reduce costs and improve productivity in health system? And how could it actually go about doing that because if we do not address this question then there'll be very little likelihood that personalized medicine become widely adopted. But that's certainly a question for Steve and for Pierre. I will speak from a physician perspective. At the National Academy of Medicine previously known as Institute of Medicine is also a health policy advisor. And previously Pierre and I were on the Global Agenda Council on Precision Medicine here. So there's a whole bunch of things I can look at but I guess particularly I want to think about how do you implement precision medicine in the caring of the patient? And this is where I think your point is well taken because the issue is there's a lot of concern about are we adding more workload to doctors? Are we increasing the cost of care? Do you increase more complexity by adding more tests? The education issue? And are we really at the end of the day? How do you be sure that this is implemented in a way that can demonstrate clearly that we providing better outcomes, low cost and more productivity among the healthcare providers? So I'm going to talk about three things. One is the need for evidence. Second is adoption. And third of course is payers and policy. Do you throw a need for evidence? I think there's no question in my mind from this point on, of course it's really happening that all the precision medicine, diagnosed therapeutics should be put against the test of outcomes and the health economic analysis of health economics. Because without that, we are truly worried about is it going to help the patients and we're introducing a lot of refinement but that drives up the cost. In that context you think about this, traditionally we think about randomized control trials and indeed there are such trials that show certain tests and certain therapeutics are going to be better than the standard of care. But that's not always the case and if you look at how diagnostics are being approved in this country and how they paid, it doesn't always show necessarily go through that kind of litmus test of whether you've improved better outcomes and reduced cost. So now with what Steve and Pierre and Kathy said is this huge amount of information now we're able to collect. This information from biosensors, biomarkers to electronic health record and the research as well as the various omics allows us to have a big data to do some serious analysis whether retrospectively or prospectively about some of these promises of personalized medicine. So it should, a good test, should reduce the downstream need for testing. Therefore you're able to get to the answer faster without subject patients to a lot of tests and talk about diagnosis. We just had a report talking about diagnostic errors. How many errors to occur and how much cost not only to patients but patient safety as well. But also I believe that it's essential that you think about patient satisfaction as well as reduction in downstream cost. So let me give you an example. 3 million US in the United States, 3 million people have symptoms of chest pain, coronary artery disease. And testing to know whether coronary artery disease cost about $6.7 billion a year in US. Now many patients undergo first noninvasive imaging and then cardiac catheterization. By the way, I'm a cardiologist. That's why I use the example. So at Duke, we actually work with the database of American College of Cardiology, cardiovascular database. About 663 hospitals and 30,000 caths. And look at the yield of cardiac cath. You'd be amazed. Only 30% of patients with cardiac catheterization have obstructive coronary disease. And some other reports go down as low as 10%. So imagine that you had a blood test that you can look at gene expression scoring and such test exist. It's actually called Chorus CAD. They demonstrated that if you have patient with symptoms with no known coronary artery disease and you have this test, a low score has a very high negative yield, which means 90% of people with low score have no coronary artery disease. So if you do that test, reduce the need for imaging, need for coronary artery catheterization, and of course risk to a patient. Now the data show that if you use a commercial health plan, which they did, they publish, that 23% fewer patients receive imaging non-invasive and 20% fewer patients underwent cath, overall resulting in 10% reduction in health plan cost. That's substantial. Let me turn to therapeutics because we talked a lot about diagnostics. And here, there are many examples of targeted therapy, whether you're GLEVAC, whatever, antibodies to HER2. I thought hepatitis C is an interesting issue. After all, this is so controversial. Sovaldi, which cost about $100,000 per treatment, 12-week treatment, but it really... It's an antiviral agent. Antiviral agent, it cures hepatitis C. So there's a lot of argument about that is going to break the bank. I think there's an analysis done to say if every Californian receive hep C treatment, they will bankrupt the MediCal. But that being said, there's analysis look at long-term effect, with downstream in terms of cirrhosis, liver transplantation, et cetera. And if you look at a 10-year period, you save actually over $300,000 from treating this patient. So it raises a lot of interesting questions, long-term, short-term issues. I want to come back to that. So let's say that we have tests and therapies that clearly show a benefit to the patient and reduce cost and benefit outcome. But once I mention, how do you make sure it's adopted in treatment? Physicians are overwhelmed with so many different new things. I think the important thing is being able to incorporate into evidence-based guidelines and protocols of treatment so that people adopt those things. So whether you have to do extensive education alone, I think the first thing is to make sure that's written in the guidelines. Once you use that, you would reduce the cost of care and the need for other diagnostics. So I think that's an important issue and obviously reimbursement is important. But there are now with the ability to have clinical decision support tools, electronic health record that can easily be accomplished. And it's also important to measure these things so that physicians who are practicing, you can see that in fact they're practicing according to the best standard of care and have incentives around those. So I say so evidence to adoption. The final issue about policy I want to bring out is a paper we wrote in Lancet. Denner Goldman myself out of the gag that we had talking about modeling in terms of health economics. And what we did is we look at six different disease states, cancer, cardiovascular disease, et cetera, and over a span of many years. When you look at that, the value proposition is very different. Those who are in prevention whether prophylactic therapy to delay the onset of cardiovascular disease to prevent it saves a lot more money than the short term outcomes such as cancer. So it is important in my opinion closing the loop in the first part of the conversation that payers and policy makers need to think about what to encourage, what to incentivize, what to reimburse and how to actually maximize those which will give you much greater long term effect saving money and better outcome than the short term ones. So those are the things I want to talk about. So this is very interesting because many policy makers, many payers are very focused on short term benefits. And there's a question of yes, in the longer term you save the entire system money but the distribution of those savings will vary across different constituencies and the timeframe. Any thoughts on how that might be addressed? Well, certainly I was saying that outside the US when you have a single payer system that's much easier because you're looking at long term cost of care of healthcare to the country. I think in our country we're going to have to think about aligning as the photo care act is doing looking at the outcomes of the population looking at disease in the continuum. When you start doing that you recognize that you have to start think about savings. So instead of fee for service you're looking for outcomes and value. That encourages providers to look at what test to use or therapies to use in order to increase savings. And that savings can be converted into incentives for the providers. Terima kasih. Saya fikir Dr. Zahal telah menghubungkan hal yang menarik tentang jenis dan langkah yang berbeza tentang hepatitis C dan saya berikan sebuah jenis sebuah jenis sebuah jenis sebuah jenis sebuah jenis di United States. Seperti yang anda tahu untuk sebuah audience kita biasanya tempatan-tempatan mempunyai populasi besar. Jadi kita mempunyai pekerjaan yang mempunyai kelas diagnostik. Jadi tahun lalu kita mempunyai perusahaan untuk hepatitis C jadi kita mempunyai hepatitis C dan kita membuat tesinya untuk pekerjaan. Dan kemaren itu adalah kita sekarang melepaskan lovesan diberi sebuah pekerjaan. Dan terima contoh pada tahun lalu ia berdapat sebuah hepatitis C pertanutan kelas diagnostik pada sekurang-kurang conquer yang kita sendiri mencari sekitar 10 jr. Jadi saya kira ini kurang awal kelas diagnostik yang berpunyai oleh pekerjaan. Apakah pekerjaan mempunyai klas diagnostik dengan kelas yang dipercewakan di bawah-dawangan? Mungkin di tahun medikaya atau oleh pekerjaan lain. Ini adalah sebuah contoh dari kebanyakan versus kebanyakan dan mekanikkan mekanikannya. Saya mahu juga balik ke dua soalan yang penting. Satu soalan adalah soalan mengapa sekarang. Kenapa kita fikir kita berdiri di sebuah kebanyakan semasa medik peribadi boleh mengambilkan? Kenapa sekarang? Soalan ini sebuah idea medik peribadi yang telah berdiri semasa lama. Apa sebab masa sekarang yang membuat kita fikir itu tentang kebanyakan? Cathy, Emily? Saya rasa ada beberapa sebab. Dalam kebanyakan kita, kita fokus sedikit di sekitar genetik dan genomics. Sudah tentu sebuah kebanyakan telah memiliki sebuah kebanyakan dan dengan itu membuat kebanyakan untuk orang untuk mempunyai genome dan mempunyai kebanyakan untuk bekerja. Tapi itu sebuah teknologi yang saya rasa akan membuat sekarang pada masa yang betul. Apabila kita pertama memperkenalkan kebanyakan medik peribadi atau kita memperkenalkan kebanyakan kebanyakan yang menyebabkan saya pada malam adalah sebuah kesilapan bagaimana kita dapat kebanyakan kebanyakan medik peribadi. Dan ibu saya baru-baru memperkenalkan dia dan apabila saya memperkenalkan dia kita memperkenalkan kebanyakan dan rekaman. Sudah tentu kebanyakan medik peribadi tidak akan bekerja tanpa kebanyakan tersebut. Dan kita memperkenalkan itu dengan cepat. Jadi saya rasa informasi kebanyakan teknologi, teknologi genomik dan anda mengatakan teknologi mobil yang diperkenalkan. Apa yang orang boleh beritahu tentang mereka dan apa yang boleh diperkenalkan dengan kebanyakan daripada orang-orang adalah sebuah banyak data. Jadi saya rasa sekarang kebanyakan kebanyakan dan kebanyakan teknologi dan kebanyakan data dan saya rasa sekarang itu kebanyakan tersebut untuk mengelukin inisiasa ini. Saya akan mengalami itu banyak-banyak. Saya rasa sekarang kita memperkenalkan salah satu perkara yang paling menyerahkan dalam bahagian biologi yang saya pernah memperkenalkan sehari dan selama 20-25 tahun. Tapi ia adalah perkhidmatan biologi dan informasi teknologi. The two telah memulainya untuk mengulangkan bersama-sama. Kita adalah kebanyakan tersebut yg yang selalu terkenalkan untuk mesin dan reajans. Tapi beberapa orang tahu setiap pekerja yang berbicara menggabungkan perniagaan kita adalah pengalaman bionfamili. Kita sudah ada 6-700 orang yang berusaha melakukan pengalaman bionfamili. Jadi kita akan bergerak dari peti untuk melakukannya, yang dipanggil. Jadi ini adalah sebuah latihan latihan latihan. Dan sehingga ini berlaku, tekanan yang berlaku untuk menggabungkan medis yang berharga. Satu, pengalaman genomics dan perjalanan dan cara kita dapat mengingatkan itu. Dan dua, senjata. sebab tanpa kemungkinan untuk menggantikan data itu dan menghubungkannya dan memasukkannya dengan cepat dan memasukkan informasi yang paling tepat dalam kedua. Kami tidak akan dapat memperkenalkan keputusan peribadian. So, salah satu teman yang penting yang kami sedang memasukkan sekarang, dan ada beberapa barisan yang penting, saya sangat menggantikan, adalah bahawa ini tidak mungkin untuk labu-labu untuk menghubungkannya dalam makhluk ke luar tawar untuk membuat perisekoan memperkenalkan data yang paling tepat dan ke shaking untuk merupakan literatik jealousy dengan sistem yang mendengar untuk membuat keputusan untuk lebih banyak kemerk ini adalah sesuatu yang di-lain negara sebanyak orang yang masih tak tahu. Saya menghargai untuk membantu mengalukkannya bagaimana pentingnya ini. Yang menarik juga di sini bahawa ia menjelaskan lebih baik daripada sebelumnya yang kita pernah lihat adalah pulang dari pesakit di atas janji. Jadi pesakit sekarang menjelaskan diri kepada pesakit dengan lebih banyak informasi daripada yang mereka pernah ada sebelumnya. Dan mereka dengar tentang janji. Dan mereka faham pilihan perjalanan. Dan kita sebenarnya... Untuk melihat ini, kita telah bekerja dengan Memoria Sloan Kettering dan kita telah mengambil 34 jenis yang diperlukan di New York City di Memoria Sloan Kettering. Dan kita telah mengambil set ini dan kita telah menggunakan sekarang di seluruh Stateng, seorang komuniti-anak. Ini adalah sebuah pelajaran yang kita cuba jelaskan kepada komuniti-anak. Yang penting adalah cara kita menginterpret data apabila mereka sebenarnya memperkenalkan seorang pelajar bukan di New York City tapi di Kansas. It's a different... it's a different challenge. So I think the patient is pulling, is pulling for this and so I think it's creating a lot of catalyst in the system for more information and more progress. I just want to make sure we look at the implementation side as I said. And the important part is that all systems are now beginning to not only have great integration and information through information technology but they're clinical support decision tools. So I come back to the earlier point. If a test is proven to be useful, better outcome reduce cost, then it should be integrated into pathway of care. And this way we really don't even have to worry about the education piece. In many ways it's ready to move forward, right? I still challenge my industry colleagues to say they've got to meet that limits test which is have you put it through a health economics analysis? At least if not show some real tangible data to show their reduced cost? And actually where's the outcome? Once you've got that, then I think you should be really ready to go. So in looking at the Precision Medicine Initiative where you can bring a lot of information, research to integrating with clinical care to the implementation of this once you've got the evidence that they're useful. So I think time is right because all the tools are there. The question is, how do we bring them all together? Fantastic. I think maybe it's time for us to open up for comments and questions from the floor including people. Please, go ahead. Can you just briefly introduce yourself before you ask the question? Thank you. Thank you. My name is Barney Dougal. I represent the Bahaya International Community to the UN. I live in New York. My question is about mental health. You know, with all the information we have with the genome project, the understanding of the brain, one would imagine that we'd have a lot more personalized medicine available for mental health care. And I think that the panelists will probably agree that there's a big divide between physical health and mental health. And could you share a little bit more about why that is and what you might be foreseeing in the near future about personalizing medical care for mental health issues? Excellent question. I think a lot of what now is focus on cancer but how about mental health? Let me just make a couple of comments about this issue because I think it's particularly important in the way that we've been thinking about it for the Precision Medicine Initiative cohort and how do we make sure that we have a diversity, the full diversity of the United States as a part of our cohort. And that means that we need to be open and accessible to people of all ages and all life stages and there are people who are going to be more difficult and probably more expensive to enroll into the cohort. And certainly high on that list is folks who suffer from mental illness and that touches all of us or somebody that we love, right? So this is really an important issue and what I am now coming to learn is how accessing medical record information, particularly psychiatric medical information is going to be particularly problematic. So what we're queuing up as we start to think through the challenges and opportunities here is where are their policy barriers or policy opportunities that we need to tackle in order to make sure that we're open and receptive to making the cohort meaningful for people with mental illness. So we're just starting to get into that and be happy to talk with you and work with you on these issues. Peace. The whole field of neurology and advanced diagnostics around neurology continues to evolve. We actually have a big portion of our genetics business based on advanced diagnostics for neurology related to Parkinson's Alzheimer's and we're working with the University of California in San Francisco in that regard around dementia. So now you understand what's the risk? What's the clinical evidence to do something about it? And this is where we're working with the payment system to say this is why we think that information has value because going back to Dr. Zell as an example if it can end the medical odyssey of why my mom or dad is not feeling well this could have savings to the system but we need the evidence to support that claim. So there is a lot of progress being made. Most of the large genomic programs starts off with certain disease pilots and usually it's cancer or genetic disease, rare genetic diseases. There should be a big push towards looking mental health but when you reach the million cohort you're obviously collecting huge amount of data and there should be sufficient data to subset looking at mental health. The main barrier as Kathy pointed out is the information access and the definition of mental health and how you measure some of these things. So it's a big problem. I think ultimately what people love to see is that everybody gets the genome or the omics or biomarkers measured and then it's integrated into electronic health and care and that probably you get a lot of information on mental health but I think ultimately is how you define the phenotype is a big issue. So I'm Alan Wilcox, Social Entrepreneur and President I'm just wondering if the benefits of personalized medicine are available in the near term only to those living in high income countries or are they available to those living in low income countries or will they simply have to wait? That's an extremely important question and I'd like to highlight when we talk about personalized medicine even in the United States the treatment of cancer is not predominantly done in very advanced academic healthcare institutions but over three quarters is in community hospital levels and if you go international you see an even more diverse ecosystem people who really could benefit from this so the key thing to us at least has always been not only to look at the United States but to immediately go global and even developing world there are a number of programs that many companies including us have been working on to make these new drugs available at a very manageable cost also in the developing world however cancer is the primary target and obviously this is not always the highest priority for a lot of these countries and as the indications are expanding infectious diseases coming up we talked about HCV as being a very important one mental health we're working on two programs already but out of 40 programs that's still far too few so there is going to be much more diversity in this area and there are great systems that are only good for a very few but to think about the social aspect and to make sure that this can go global as quickly as possible there is another dimension which is there are some inter-ethnic types of differences in treatment responses so you also need to determine say Asian responses different types of treatment differ substantially from Caucasian ones completely new trials so please you had a question could you have a mic over here please you're on now many things indeed the platform is open to many different users and stakeholders and the great thing too Liz is that if somebody generates an idea or a question by mining through this extensive dataset these volunteers are going to be expressing their willingness to re-engage or engage with scientists so that you could build additional sub-studies within the cohort to answer a specific question so I think it will be an exciting resource for idea generation give me a quick response from Steven I think there is a lot of focus on the cost of next gen processing and that cost will come down but the real value added is finding the insight in the collection of the data and more aggregation of that data with multiple data sites and actually do the interrogation of the data and this is not computer science it's more mathematics and it's interrogating the data to find the insight associated with that data and that's where the brilliant is finding that insight and when you find that insight that will develop the capability and more data and then also explaining that data in a useful way for the practice of that data throughout the world is another important part of this and so what we have found is even with good, valid data and with the analysis presented to a physician is explaining that with genetic counselors and experts in the field because it's evolving so quickly they can't keep up so it is an important part I have a quick comment I would like to all of these initiatives I think it's absolutely key that the emphasis on supporting academic basic research should be underscored we can apply let us take care of this mundane translation we need some help on the bureaucracy and all the alignment of the constituents but the only way the reason why we're here today is because of the pandemic community contributing to science and building this base and we're minding that we're basically translating this now into improvements in healthcare but we've seen over the past few years that there has been an increasing trend into applied sciences from basic and we would greatly support and we've been doing that very vocally increased emphasis on basic sciences many thanks both among the various EHR proprietary companies and between those products and the data extraction and statistical analysis programs my question is what can payers such as HHS Medicare and other third party payers due to enhance and encourage better interoperability of data Victor, it sounds like a question directed to you but if you have a question or answer I think that I have a feeling that the way things are progressing the pressure will be on more and more because not only are we talking about patients within the system but patients across systems where you need in fact the ability to have sharing of information and so in many regards you can probably look at the way the payment is right now which is looking at outcomes as a driver towards interoperability for example frequently one would imagine that you take care of a patient in one health system that in fact the patient seeks help elsewhere and that is a great need for interoperability to begin with and so as you begin to look at payment along outcomes I think more and more pressure will be on in fact the vendors to find interoperability I think the most reasons that to date they are not aligned is because the vendors in fact of all their preparatory approaches we as providers can put a lot of pressure on this and at this point I think ultimately that may be the direction to go I will be interested in what you think Kathy So I appreciate the question We have these waves of requirements from the Office of the National Coordinator for Health IT in order to have a set of incentives through CMS for interoperability and a lot of that has been pretty simple frankly we are now into meaningful use 3 and looking forward to new standards for the first time certainly since I've been back at the NIH on meaningful use of electronic medical records for research and this is coinciding with the real movement among inpatient patients to have access to their own data and so the notion that people would be able to download rapidly and easily their own medical information that's really putting a lot of pressure on interoperability and new rules coming out about data blocking there's sort of a series of forces that are coming into play that are going to move us forward with interoperability we are certainly not there I was at a meeting of the Health Information Management Association which is a big interoperable group and I had a problem and went to the University of California San Francisco to a clinic and was told that I could download my lab results from an online system and I sat there for 45 minutes during a session on interoperability unable to download my own laboratory results I ended up getting a group of about a dozen health information technologists experts around and they couldn't do it either and I finally picked up the phone and not called the lab Steve It is evolving so for instance what Kathy referred to it is a now requirement in the United States that patients get access so we're all required as providers to serve up if you want that data and so at our company we actually we have developed a smart app it's called MyQuest and now if you have your testing done by Quest Diagnostic you can load up the app and you can get access to your laboratory results and also we'll do a historic look back on your laboratory results so actually we have 2 million users so far in a short period of time about 12 months to get access and some of this interoperability is a broad term some of this is just related to how to communicate from an electronic standpoint there's been a lot of work on that front what Pierre has talked about earlier is common definitions and common information model so we could have searchable databases to really do the clinical research necessary and that's where we need to make some progress is that information definition we have one final question can we have a Mike? Hi, I'm Ashwin Naik I'm a YGL and a Healthcare Social Entrepreneurs and coming from a world where there are not enough doctors, hospitals and go to Alan's point about affordability the discussion seems to be around precision medicine focused on individual diseases and to me that seems like a bigger challenge unless we focus on precision health we seem to be breaking it down into individual components your thoughts please Chair? Victor? I mentioned earlier using the modeling of health economic analysis there's no question prevention and health is going to be by far better than late stage therapy and the model is really quite impressive we found that over a period of multi years $600 billion if you're able to reduce just the incidence of heart disease by 50% whereas if you look at other models and other diseases so your point of prevention is critically important but I think when you talk about emerging economies and low income countries infectious diseases is a very important issue and the ability to point of care diagnosis is critically important that too should be emphasized as another way and I totally agree with you about health equity issue I think that is still going to be a substantial problem in long run just looking at hepatitis C drug as an example now having two pricing of two different levels in different countries highlights the issue of health equity and so I do think that there are a lot of issues to be addressed in moving this thing forward Steve? Precision health it's an interesting area that we're putting some thought into the value of a patient and the understanding of a patient of the value of health information from their family and making sure that they fully have a grasp of what their family history is before presenting themselves to a physician and yesterday we had a panel and Toby Garzgrove brought up opportunities around prevention to get it really in the preventive side if you think about smoking sensation and obesity at a real personalized level you can have an effect on some of these very expensive diseases which will affect differently all of us if you have a better understanding what's happened with your family and also what you're potentially subjecting yourself to but it's a growing field and I think more content at the point of interaction between the physician is something that we're looking into Also, Chef, please Quick question I think there's always this tension at least perceived tension between precision medicine and public health people have written about this to say that in fact that's the end of one or the other actually I think they're fully aligned because what I said earlier by using the tools of precision medicine you're looking at population risk when you look at individual risk so you know what population or what populations are more vulnerable at greater risk and therefore you can come in with much early intervention so I think the two are aligned if applied properly and that's the point you're making So a very rich discussion I'm afraid we're running out of time maybe I just summarized the first is the why now question and it's not just the advances in biomedical sciences it's also the advances in data science, the ability to put data aggregate them in the cloud and to understand more use it to understand disease better and how patients can be targeted susceptible populations defined and we also are seeing now a pull from patients who demand or would like to have this sort of care The second is really there are many constituencies it's a complicated and challenging area and we need evidence and the evidence is not just of clinical outcomes and usefulness it's also about whether or not they can reduce cost increase productivity and also make treatments more convenient for patients and finally there's this issue about reimbursements which I think would be a major barrier like the widespread implementation most of these things there's a short term long term issue the alignment of incentives across different payoffs and in the long term we need to align all these incentives in order to realize the potential and yet in addition reap the benefits of cost effectiveness so with that I think you agree with me we had a fabulous set of contributions from distinguished panelists and I'd like to invite you to thank them in a traditional way