030 - Using AI to Recommend Personalized Medical Treatment Options with Joost Zeeuw of Pacmed
Joost Zeeuw is a data scientist and product owner at Pacmed, a data-driven healthcare and AI startup in Amsterdam that combines medical expertise and machine learning to create stronger patient outcomes and improve healthcare experiences. He’s also taught a number of different subjects—like physics, chemistry, and mathematics—at Lyceo, an online education service, and Luzac College in the Netherlands.
Join Brian and Joost as they discuss the role of design and user experience within the context of providing personalized medical treatments using AI. Plus:
- The role data has in influencing doctors’ decisions—without making the decisions
- The questions Joost’s product team asks before designing any AI solution at Pacmed
- How people’s familiarity with iPhones and ease-of-use has influenced expectations around simplicity—and the challenges this poses when there is machine learning under the hood
- Why Brian thinks Pacmed’s abnormal approach to design is great—and what that approach looks like
- The simple, non-technical, but critical thing Pacmed did early on to help them define their AI product strategy and avoid going down the wrong path
- An example of an unexpected treatment prediction that Pacmed’s algorithm detected—which ended up being something that a specific field of medicine had been studying with classical research techniques 10,000 km away
- Where Joost believes Western medicine falls short with respect to new drug trials
“Pacmed in that has a three-fold mission, which is, first of all, to try to make sure that every single patient gets the treatment that has proven to work for him or her based on prior data analysis. And next to that we say, ‘well, if an algorithm can learn all these awesome insights generated by thousands and thousands of doctors, then a doctor using one of those products is also very capable of learning more and more things from the lessons that are incorporated in this algorithm and this product.’ And finally, healthcare is very expensive. We are trying to maximize the efficiency and the effectiveness of that spend by making sure everybody gets a treatment that has the highest probability of working for him or her.” — Joost
“Offering a data product like this is really another tool in that toolbox that allows the doctor to pierce through this insane amount of complexity that there is in giving care to a patient.” — Joost
“Before designing anything, we ask ourselves this: Does it fit into the workflow of people that already have maybe one of the most demanding jobs in the world?” — Joost
“There’s a very big gap between what is scientifically medically interesting and what’s practical in a healthcare system.” — Joost
“When I talk about design here, I’m talking kind of about capital D design. So product design, user experience, looking at the whole business and the outcomes we’re trying to drive, it’s kind of that larger picture here.” — Brian
“I don’t think this is ‘normal’ for a lot of people coming from the engineering side or from the data science side to be going out and talking to customers, thinking about like how does this person do their job and how does my work fit into you know a bigger picture solution of what this person needs to do all day, and what are the health outcomes we’re going for? That part of this product development process is not about data science, right? It’s about the human factors piece, about how does our solution fit into this world.” — Brian
“I think that the impact of bringing people out into the field—whatever that is, that could be a corporate cubicle somewhere, a hospital, outside in a farm field—usually there’s a re