103 - Helping Pediatric Cardiac Surgeons Make Better Decisions with ML featuring Eugenio Zuccarelli of MIT Media Lab

Today I’m chatting with Eugenio Zuccarelli, Research Scientist at MIT Media Lab and Manager of Data Science at CVS. Eugenio explains how he has created multiple algorithms designed to help shape decisions made in life or death situations, such as pediatric cardiac surgery and during the COVID-19 pandemic. Eugenio shared the lessons he’s learned on how to build trust in data when the stakes are life and death. Listen and learn how culture can affect adoption of decision support and ML tools, the impact delivery of information has on the user's ability to understand and use data, and why Eugenio feels that design is more important than the inner workings of ML algorithms.

 

Highlights/ Skip to:

• Eugenio explains why he decided to work on machine learning models for cardiologists and healthcare workers involved in the COVID-19 pandemic (01:53) 
• The workflow surgeons would use when incorporating the predictive algorithm and application Eugenio helped develop (04:12)
• The question Eugenio’s predictive algorithm helps surgeons answer when evaluating whether to use various pediatric cardiac surgical procedures (06:37)
• The path Eugenio took to build trust with experienced surgeons and drive product adoption and the role of UX (09:42)
• Eugenio’s approach to identifying key problems and finding solutions using data (14:50)
• How Eugenio has tracked value delivery and adoption success for a tool that relies on more than just accurate data & predictions, but also surgical skill and patient case complexity (22:26)
• The design process Eugenio started early on to optimize user experience and adoption (28:40)
• Eugenio’s key takeaways from a different project that helped government agencies predict what resources would be needed in which areas during the COVID-19 pandemic (34:45)

Quotes from Today’s Episode

• “So many people today are developing machine-learning models, but I truly find the most difficult parts to be basically everything around machine learning … culture, people, stakeholders, products, and so on.” — Eugenio Zuccarelli (01:56)
  
  
• “Developing machine-learning components, clean data, developing the machine-learning pipeline, those were the easy steps. The difficult ones who are gaining trust, as you said, developing something that was useful. And talking about trust, it’s especially tricky in the healthcare industry.” — Eugenio Zuccarelli (10:42)

 

• “Because this tennis match, this ping-pong match between what can be done and what’s [the] problem [...] thankfully, we know, of course, it is not really the route to go. We don’t want to develop technology for the sake of it.” — Eugenio Zuccarelli (14:49)

 

• “We put so much effort on the machine-learning side and then the user experience is so key, it’s probably even more important than the inner workings.” — Eugenio Zuccarelli (29:22)

 

• “It was interesting to see exactly how the doctor is really focused on their job and doing it as well as they can, not really too interested in fancy [...] solutions, and so we were really able to not focus too much on appearance or fancy components, but more on usability and readability.” — Eugenio Zuccarelli (33:45)

 

• “People’s ability to trust data, and how this varies from a lot of different entities, organizations, countries, [etc.] This really makes everything tricky. And of course, when you have a pandemic, this acts as a catalyst and enhances all of these cultural components.” — Eugenio Zuccarelli (35:59)

 

• “I think [design success] boils down to delivery. You can package the same information in different ways [so that] it actually answers their questions in the ways that they’re familiar with.” — Eugenio Zuccarelli (37:42)

Links

• LinkedIn: https://www.linkedin.com/in/jayzuccarelli
• Twitter: twitter.com/jayzuccarelli
• Personal website: https://eugeniozuccarelli.com
• Medium: jayzuccarelli.medium.com