November 22, 2022
About half a million babies are born every year through IVF. That number would probably be a lot higher if the procedure were cheaper and more accessible—but making that happen would mean transforming IVF from an artisanal craft into something more like a modern automated factory, with AI helping doctors and technicians make faster and better decisions at every step. And that’s exactly what Harry's guest Mylene Yao, the co-founder of Univfy, is doing. Univfy helps patients with two aspects of the IVF process. The first is using machine learning to provide patients with a more accurate assessment of the odds of success, before they decide whether to invest in one or more IVF cycles, which can cost up to $30,000 per cycle. The second is financing. Univfy works with a bank called Lightstream to provide up to $100,000 in financing for up to three rounds of IVF, with a large refund as part of the deal if the treatments don’t result in a baby. Harry talks with Dr. Yao about the prospects for far broader access to IVF, now that the field is finally adopting more ideas from the worlds of technology and finance.
57 min 19 sec
2.1.22
Healthcare is one of those areas where more data is almost always better. And I talk a lot on the show about how data is helping doctors and patients make smarter decisions. But a lot of the data we’d still like to have is stuck in those arcane Electronic Health Record systems or EHRs that medical practices or hospital systems use to track their patients. These systems tend to be closed, proprietary, user-unfriendly, and incompatible with one another. And we've repeatedly made the case here on the show that EHR technology is holding back innovation across the healthcare market. That’s why we like to meet companies that are working to make EHR data more useful. And in this episode we welcome a pair of guests from a company called Verana Health that’s trying to do just that. The company recently brought in $150 million in new venture capital funding to help scale up its data services, which currently focus on the subspecialties of ophthalmology, neurology, and urology. Verana takes data on patients in these fields, cleans it up, analyzes it, and pulls out insights that could be useful—both for clinicians who want to increase the quality of the care they’re providing, and for pharmaceutical companies who need new ways to measure the effectiveness of their drugs and better ways to find patients for clinical trials. Here to explain more about all of that are Verana’s CEO, Sujay Jadhav, as well as its senior vice president of clinical and scientific solutions, Shrujal Baxi. (If you’re a longtime listener you might remember that we had Shrujal on the show once before, back in 2018, when she talked about her previous company Flatiron Health.)
46 min 38 sec
1.18.22
As we say here on The Harry Glorikian Show, technology is changing everything about healthcare works—and the reason we keep talking about it month after month is that the changes are coming much faster than they ever did in the past. Each leap in innovation enables an even bigger leap just one step down the road. Another way of saying this is that technological change today feels exponential. And there’s nobody who can explain exponential change better than today’s guest, Azeem Azhar.
57 min 47 sec
1.4.22
Genialis, led by CEO Rafael Rosengarten, is one of the companies working toward a future where there are no more one-size-fits-all drugs—where, instead, every patient gets matched with the best drug for them based on their disease subtype, as measured by gene-sequence and gene-expression data. Analyzing that data—what Rosengarten calls "computational precision medicine"—is already helping drug developers identify the patients who are most likely to respond to experimental medicines. Not long from now, the same technology could help doctors diagnose patients in the clinic, and/or feed back into drug discovery by providing more biological targets for biopharma companies to hit.
44 min 00 sec
12.21.21
When the coronavirus pandemic swept across the world in early 2020, Spain was one of the countries hardest hit. At the time, Nuria Oliver was a telecommunications engineer working and living in Valencia, one of Spain's 17 autonomous regions. She’d spent years working for companies like Microsoft, Telefonica, and Vodafone, using AI to analyze data from mobile networks to explore big questions about healthcare, economics, crime, and other issues—so she realized right away that mobile data could be an important tool for government leaders and public health officials trying to get a handle on the spread of COVID-19. With the backing of Valencia's president, Oliver put together a team of scientists to analyze network data to understand among other things, how much people in Spain were moving around. That helped them predict infection rates, and to see whether lockdowns were really helping to contain the virus's spread. The team's predictions were so accurate, in fact, that when they entered an X Prize Foundation contest seeking the best AI-based pandemic response systems, they won first place. Nuria Oliver joins Harry to explain how they did it—and why mobile data makes a difference in the fight against the pandemic and other health threats.
59 min 11 sec
12.7.21
We've learned from previous guests that machine learning and other forms of AI are helping to identify better disease treatments, get drugs to market faster, and spot health problems before they get out of hand. But what if they could also help patients find the best doctors for them, and help doctors frame their advice in a way that patients can relate to? This week, Harry's guest, Briana Brownell, talks about the computational tools her company Pure Strategy is building to find patterns in people’s personal preferences or cultural identities that can enable better matchmaking between patients and doctors, predict which patients are most likely or least likely to go along with a treatment plan, or help doctors communicate their recommendations better. "Not everybody makes decisions in the same way," Brownell says. "Not everybody values the same things. But by understanding some of those psychological and value-based drivers, we can get better health care outcomes."
49 min 46 sec
11.23.21
Your medical records don't make pleasant bedtime reading. And not only are they inscrutable—they're often mutually (and deliberately) incompatible, meaning different hospitals and doctor's offices can't share them across institutional boundaries. Harry's guest this week, Ardy Arianpour, is trying to fix all that. He’s the co-founder and CEO of Seqster, a San Diego company that’s spent the last five years working on ways to pull patient data from all the places where it lives, smooth out all the formatting differences, and create a unified picture that patients themselves can understand and use.
59 min 09 sec
11.9.21
Chances are you or someone you love has had a biopsy to check for cancer. Doctors got a tissue sample and they sent it into a pathology lab, and at some point you got a result back. If you were lucky, it was negative and there was no cancer. But have you ever wondered exactly what happens in between those steps? Until recently, it’s been a meticulous but imperfect manual process where a pathologist would put a thin slice of tissue under a high-powered microscope and examine the cells by eye, looking for patterns that indicate malignancy. But now the process is going digital—and growing more accurate.
49 min 57 sec
10.26.21
Many of us wear wireless, battery-powered medical sensors on our wrists in the form of our smartwatches or fitness trackers. But someday soon, similar sensors may be woven into our very clothing. Harry's guest this week, Nanowear CEO Venk Varadan, explains that his company's microscopic nanosensors, when embedded in fabric and worn against the skin, can pick up electrical changes that reveal heart rate, heart rhythms, respiration rate, and physical activity and relay the information to doctors in real time. Nanowear’s leading product is a sash called SimpleSense that fits over the shoulder and around the torso, and last month the company won FDA approval for the software package that goes with the SimpleSense sash and turns it into a diagnostic and predictive device.
53 min 25 sec
10.12.21
Today we bring you the second half of Harry's conversation with Dave deBronkart, better known as E-Patient Dave for all the work he’s done to help empower patients to be more involved in their own healthcare. In Part 1, we talked about how Dave’s own brush with cancer in 2007 turned him from a regular patient into a kind of super-patient, doing the kind of research to find the medication that ultimately saved his life. And we heard from Dave how the healthcare system in the late 2000s was completely unprepared to help consumers like him who want to access and understand their own data. Today in Part 2, we’ll talk about how all of that is gradually changing, and why new technologies and standards have the potential to open up a new era of participatory medicine – if, that is, patients are willing to do a little more work to understand their health data, if innovators can get better access to that data, and if doctors are willing to create a partnership with the patients over the process of diagnosis and treatment.
44 min 54 sec
9.28.21
The podcast is back with a new name and a new, expanded focus! Harry will soon be publishing his new book "The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer." Like his previous book "MoneyBall Medicine," it's all about AI and the other big technologies that are transforming healthcare. But this time Harry takes the consumer's point of view, sharing tips, techniques, and insights we can all use to become smarter, more proactive participants in our own health. The show's first guest under this expanded mission is Dave deBronkart, better known as "E-Patient Dave" for his relentless efforts since 2007 to persuade medical providers to cede control over health data and make patients into more equal partners in their own care.
51 min 13 sec
9.14.21
Harry's guest this week is Matthew Might, director of the Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham. Might trained as a computer scientist, but a personal odyssey inspired him to make the switch into precision medicine. Now he uses computational tools such as knowledge graphs and natural language processing to find existing drug compounds that might help cure people with rare genetic disorders.
48 min 55 sec
8.31.21
This week Harry is joined by Kevin Davies, author of the 2020 book Editing Humanity: The CRISPR Revolution and the New Era of Genome Editing. CRISPR—an acronym for Clustered Regularly Interspaced Short Palindromic Repeats—consists of DNA sequences that evolved to help bacteria recognize and defend against viral invaders, as a kind of primitive immune system. Thanks to its ability to precisely detect and cut other DNA sequences, CRISPR has spread to labs across the world in the nine years since Jennifer Doudna and Emmanuel Charpentier published a groundbreaking 2012 Science paper describing how the process works.
67 min 12 sec
8.17.21
Harry traveled to the San Francisco Bay Area this summer, and while there he interviewed the co-founders of three local data-driven diagnostics and drug discovery startups, all of whom participated in the same graduate program: the Biomedical Informatics Program at Stanford's School of Medicine. Joining Harry were Aria Pharmaceuticals co-founder and CEO Andrew Radin, BigHat Biosciences co-founder and chief scientific officer Peyton Greenside, and Inflammatix co-founder and CEO Tim Sweeney. The conversation covered how each company's work to advance healthcare and therapeutics rests on data and computation, and how the ideas, skills, connections each entrepreneur picked up at Stanford have played into their startups and their careers.
50 min 38 sec
8.3.21
Harry's guest this week is Jeff Elton, CEO of a Boston-based startup called Concert AI that's working to bring more "real-world data" and "real-world evidence" into the process of drug development. What's real-world data? It's everything about patients' health that's not included in the narrow outcomes measured by randomized, controlled clinical trials. By collecting, organizing, and analyzing it, Elton argues, pharmaceutical makers can it design better clinical trials, get drugs approved faster, and—after approval—learn who's really benefiting from a new medicine, and how.
47 min 37 sec
7.20.21
In a companion interview to his June 7 talk with Stanford's Michael Snyder, Harry speaks this week with Noosheen Hashemi, who—with Snyder—co-founded the personalized health startup January.ai in 2017. The company focuses on helping users understand how their bodies respond to different foods and activities, so they can make diet and exercise choices that help them avoid unhealthy spikes in blood glucose levels.
49 min 21 sec
7.6.21
This week Harry sits down with Vangelis Vergetis, the co-founder and co-executive director of Intelligencia, a startup that uses big data and machine learning to help pharmaceutical companies make better decisions throughout the drug development process. Vergetis argues that if you put a group of pharma executives in a conference room, then add an extra chair for a machine-learning system, the whole group ends up smarter—and able to make more accurate predictions about which drug candidates will succeed and which will fail.
53 min 02 sec
6.21.21
From her TED talks and her appearances on PBS, geneticist Wendy Chung is known to millions of people as an expert on autism. But thanks to funding from the Simons Foundation, she’s also known to tens of thousands of people with autism and their families as the leader of history’s largest study of the genetics of autism spectrum disorder (ASD). It’s called SPARK, for Simons Foundation Powering Autism Research for Knowledge, and it's a big-data exercise of unprecedented proportions.
50 min 59 sec
6.7.21
Having helped to bring big data to genomics through the lab techniques he invented, such as RNA-Seq, the Stanford molecular biologist Michael Snyder is focused today on how to use data from devices to increase the human healthspan. Some cars have as many as 400 sensors, Snyder notes. "And you can't imagine driving your car around without a dashboard...Yet here we are as people, which are more important than cars, and we're all running around without any sensors on us, except for internal ones." To Snyder, smart watches and other wearable devices should become those sensors, feeding information to our smartphones, which can then be "the health dashboard for humans and just let us know how our health is doing." (You can sign up to participate in the Snyder lab's study of wearables and COVID-19 at https://innovations.stanford.edu/wearables.)
55 min 49 sec
5.24.21
Angeli Moeller is a molecular biologist, a neuroscientist, a systems biologist, and a data scientist all rolled into one—which makes her a perfect example of the kind of multidisciplinary executive needed for this new digital health ecosystem defined by big data, AI, and machine learning. She's a founding member of the Alliance for Artificial Intelligence in Healthcare, does extensive work for the nonprofit rare disease advocacy group Rare-X, and has spent almost five years managing global data assets and IT partnerships at Bayer. At the beginning of 2021 she became the head of international pharma informatics for Roche, the world’s largest drug company. Harry caught up with her on Zoom in February, and the conversation started with the role of informatics at Roche, but quickly expanded to cover all the areas where deep learning and other forms of AI and data science are transforming drug discovery and healthcare, and what life sciences entrepreneurs need to do to get on board.
57 min 41 sec
5.10.21
The discoveries medical researchers and drug developers can make are constrained by the kinds of questions they can ask of their data. Unfortunately, when it comes to clinical trial data, or gene expression data, or population health data, it feels like you need a PhD in computer science just to know which questions are "askable" and how to frame them. This week, Harry talks with the founders of a startup working to solve that problem.
47 min 03 sec
