The Harry Glorikian Show Harry Glorikian

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.

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.

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.

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."

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.

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.