Eric Topol wears many hats. He is a practicing cardiologist, genomic researcher, chief academic officer of Scripps Health, and the head of the Scripps Translational Science Institute, one of more than 60 biomedical centers supported through an ambitious National Institutes of Health program created nearly 10 years ago to move basic medical research into the clinic.
Before he moved to San Diego-based Scripps in 2006, he helped create a medical school at the Cleveland Clinic, where he was cardiology chairman. He was a vocal critic of the painkiller Vioxx, which was eventually taken off the market. He left the Cleveland Clinic amid controversy after it revoked his academic position.
Topol is a leading proponent of the use of emerging technology to make medicine more transparent and empower patients to make more informed decisions, a topic laid out in his 2012 book The Creative Destruction of Medicine. Xconomy met Topol in San Francisco before a healthcare conference in late May (at which he riled up some of the physicians in attendance). We discussed technological progress—and lack thereof—in medicine and how doctors, patients, drug makers, technologists, and other actors in the world of healthcare are adapting to the rapid changes. What follows is an edited and condensed version of our conversation.
Xconomy: In recent years one of your top initiatives has been to create a medical school and a training situation that’s much more tech-savvy. You were lamenting how little med students were using technology and integrating it. Is that still an initiative you want to pursue?
Eric Topol: It’s been difficult to pursue. We were trying to get a medical school going at Scripps. It didn’t come to pass. We had funding but the president at the time at the last minute didn’t want to go through with it. I had set up a medical school at the Cleveland Clinic, but it wasn’t until the latter part of this first decade that it became apparent that there wasn’t any curriculum anticipating the dramatic changes. A couple med schools are trying to get their students ahead of the curve by giving them high resolution ultrasounds instead of stethoscopes, by having genomic medicine in the program, but overall there’s not much progress. It’s actually misplaced to put the priority on med students. If we wait for them, we wait for a generation of physicians. Turns out almost 55 percent of physicians are age 55 or greater in this country. We want to get to them. Problem is they’re busy and not inclined to do Internet courses. The governing organizations—the AMA and such—are not exactly what I would call tech-supportive or savvy. I do think we have the power of education through Web-based remote learning, but we’d have to make it a certificate, or obligatory. It has to have some teeth in it.
X: So that’s one interface between doctors and technology. But we’re only just now putting together the infrastructure to combine layers of big data, all the “’omics,” clinical data, and so forth, into some sort of actionable data that doctors and clinicians can use. What will it take to get that “soup” of data we’re creating into a form that doctors and clinicians can use?
ET: The New York Times had a story that some basketball players are hiring their own personal data scientists. Doctors aren’t necessarily going to have that. But we’re going to have more data per individual, plus of course what we’ll get when we collate lots of individuals. It spans not only all the different ‘omics, but sensors, imaging, electronic records, social graph stuff. That can all be had now, this panoramic view of a person, but making it useful is not going to be the physician’s charge. What we need are the algorithms, the deep learning from that data, not just at the individual level but at the cohort level. That has to be processed in a simple way to connect the dots.
The problem here in San Francisco, all these great people can do this work, but they’re not medical or health related. They’re working for Twitter or Pandora or whatever, they’re not working in this space because they’re young and this is not their thing right now.
X: How much sensitivity do people coming from the tech side have around regulation and privacy? They’re entering a new world that people in healthcare have inhabited all their working lives.
ET: We’re talking Facebook stuff versus medical information. You download an app and press “I agree” but you haven’t read anything, which no one has. There’s a big disparity between selling your data that you liked something, versus do you have some kind of mental disorder and the whole world’s going to know you’re taking lithium for bipolar disorder or whatever. There’s [a] big issue here of lack of protection in this kind of medical big-data story. HIPAA [the federal health information privacy law signed in 1996] doesn’t touch this. This is about selling your data unwittingly, with the potential—if you have somebody’s genome it’s not unlikely you could identify them. Same holds true with other pieces of each person’s data. De-identification is a concern. The two biggest obstacles of bringing big data into medicine and reaping the benefits of digitizing human beings: number one, we don’t have the analytics and informatics tweaked right, because we don’t have the expertise dedicated to it so far. The other is privacy and security.
X: What are some early examples of healthcare applications and benefits that have come about with this confluence of data, connectivity, and so forth?
ET: There are many examples. You can do your cardiogram on your phone and get a real-time interpretation with an algorithm built into your app. It’ll tell you what your cardiogram shows, and that can pre-empt a visit to the emergency room or an urgent physician appointment. I can get an e-mail now from a patient who I wasn’t sure was having a rhythm problem from the symptom, and I got the subject line, “I’m in atrial fibrillation, now what do I do?” The algorithm tells them their diagnosis. You have to have a circuit of the heart with two fingers. You can put two thumbs on your iPhone, or just carry it in your pocket.
X: I didn’t know there was something that simple to get actual information to the physician.
ET: Or it can just tell patients it’s normal, and they don’t have to bother the doctor. That’s what provides lots of reassurance. I give that example, because it’s the beginning of where this is all going. Automated interpretation.
X: Nike pulled out of FuelBand. People are down on that category of “quantified self” or wearable devices. Is that ominous for the type of applications you’re talking about?
ET: These Fitbits, FuelBands, Jawbones, they do help patients walk more. People have them and look at their phone 100 times a day, and they want to walk more. But the accuracy is a problem, and usually in a matter of weeks, people give up and say, “I can’t get my 10,000 steps.” It’s not the same as having a medical condition and the app is a way to get your arms around it. Almost every patient I have has high blood pressure. I used to ask them to write it down. I’d barely get a couple a week, if any. Now, they just have to press start with a wireless cuff on their wrist, keep that in their pocket. They get as many blood pressure [readings] as they want per day, and the app will tell them what percent is out of preset range that the patients and I would set.
The [genome] sequence is another part of the story. The biggest value of that today is pharmacogenomics. Let’s say you go to your doctor and he prescribes a medicine for you. Because you’ve had your sequence or your pharmacogenomic profile, it already knows that the medicine and you are incompatible because you’ll have a major side effect, or you should only take such and such dose, or it isn’t going to work. Everyone should have that, and it can be done inexpensively.
X: The arrythmia example is a simple layer of information that’s actionable. But with pharmacogenomics, isn’t it deeper? Is there a framework for getting from someone’s whole genome sequence to actionable data that you can apply with confidence?
ET: Not many people have had their whole genome sequenced. The tally a couple months ago in the world was 100,000 people. What I’m talking about is like out of a [particular] 23andMe genotype, they have 30 drugs they screen for. There are other companies, too, doing pharmacogenomic profiles inexpensively. If you get one of these direct-to-consumer panels, the whole kit and caboodle and it comes with upgrading, it’s cheap. Relatively cheap. $99 is not free, but for the information you’re getting it’s a pretty good value.
We need point-of-care genotyping, whether at a drug store or at your doctor’s office. Genotypes could be done for pennies. Quickly. In minutes. No one’s doing that yet.
X: Will it be common five years from now?
ET: I hope it doesn’t take that long, because a lot of people will get hurt with the wrong dose. It could be done today if there was a real will to do it. We’re talking about screening particular genotypes that are well established for drug interactions.
X: With all these data layers, will some become actionable sooner because they’re easy to mesh together?
ET: Every one of these layers—of this Google map of the person—you can make a case that if a person’s perfectly healthy, they’re not suited for all this high-definition drilling down. The problem has been that we want to jump to, “Oh, everyone will have any one or all of these ‘omics’ studied.” I think it’ll be different than that. I think it’ll be very specific and particularized. We’ll sort it out. For many people, obesity isn’t due to their gut microbiome. But for some people, it may well be. It’s going to be like a lab test when you work up a patient: You say I’ve ruled this out, I’ve seen this, I have these sensors for caloric intake, I have all these different layers of information. And when do I get this next layer? When do I go for a methylome of the whole genome? Or get sensors of the environment?
X: What do you mean, “sensors for caloric intake”?
ET: There are devices, hand-held spectrometers, you point at the food, and they tell you the exact caloric intake. These mass-spec devices, if you could tell exactly not just how many calories, but how much fat, protein, carbohydrate, it’s going to be pretty big. Supposedly the technology is getting pretty darn accurate and with multiple companies competing.
X: How do we start creating the protocols you’re talking about, where you don’t look at all the panels at once, you winnow things down?
ET: We have to find our way. We have powerful tools that are all coming alive in a short period of time. How to use them and how to compartmentalize those of academic interest and those of actionable potential, that’s really a big difference. If it’s not actionable, who’s going to pay for it? And we’re in an economic crisis. Doing this in a promiscuous way is going to hurt the field and it’s going to be like these proton beams and robotic surgery, we’ve never had the data to support their use. We don’t want to go that way. We have to get these things validated and show that they change the outcomes of people [for] the better, and that the expense was well worth it.
X: It’s been nearly a decade of the NIH translational program and the 60 centers. Is there a way to measure results?
ET: The first funding group was in ’06, so it’s been eight years. [Scripps is] into our second five-year cycle, we just got renewal. The metrics are many: what papers did you publish, how were they cited, what impact did they have in changing practice in the field in a substantive way. There are a lot of other things coming into play, like what new collaborations were you able to develop with the life sciences industry. And new monies you brought into the programs because of these new paths that you built. It’s become clearer than ever before that we can’t survive without better collaboration with industry, especially with sequestration budgetary hits that are profound. Even without that, we’re not reaping the natural benefit of innovation.
The biggest line item in the entire NIH budget is these 60 centers. Hundreds of millions of dollars a year. What are the stories from each site and collectively to tell how they change medicine. It has to be bigger than grants, and publications. If you publish one paper and it markedly improves the lives of millions of people, that’s the whole story right there: a single discovery.
X: But we won’t see those products, or those improvements, for many years.
ET: We think at Scripps we’ve made several. For example, we were the first center to [replace] a real stethoscope [with] a high resolution ultrasound because you get so much more immediate information than with “lub-dub” or bowel sounds. We also were first to show a tiny band-aid could capture every heartbeat for two weeks instead of a monitor that had all these wires on it. We were also the first to start pharmacogenomics systematically for Plavix for all patients undergoing stent. I’m sure that has saved some major heart attacks.
But if it only gets implemented in Southern California, it’s not a big deal. One of my biggest frustrations [with translational] medicine: “translating” means [applying this data] to the real daily practice of medicine. But medicine moves so slowly. And you’ve seen how many years it takes to get from ideation to innovation to practice.
As you point out, it’s not easy. Sometimes you need to go downstream for a decade to see the impact of something. We’re very fortunate because Francis Collins, the NIH director, is masterful at communicating that [need for patience] to Congress and President Obama. It’s not an on-off switch for the most part. Takes some time to have independent replication and adoption. Some things we can speed up for sure, and hopefully it will change. I hope the consumer will drive the future and not wait for doctors.
X: Is academia the best place to create these translation centers?
ET: I think it’s ideal. It’s complementary to the pharma-biotech industry. And also device and diagnostics. You’ve got brilliant people on both sides, but their talents are very different. Why is pharma now turning to academic centers more than ever before? Why are they signing up these large multiyear agreements?
X: A lot of people in pharma are wondering that, too, because they haven’t produced much. Maybe because they’re desperate?
ET: I don’t think so. It’s because if you go through the history of where drugs come from, most of them start in academic centers. The idea is, let’s not wait and futz around, let’s accelerate this path. So as soon as you find this variant that prevents type 2 diabetes, for example, the idea is let’s run with it.