heath tech

Professor of Computer Science and Professor of Medicine at Dalhousie University, Dr. Abidi serves as director of the Health Informatics graduate program and leads the university’s NICHE research group, bringing the benefits of knowledge management technologies to healthcare enterprises. Read Blog Post Here!

 Episode Highlights 

• The primary driver of digital health progress
• The two sides of AI, and where machine learning falls in this paradigm
• Two key problems Dr. Abidi and his team address using AI and ML for decision support
• How Explainable AI can help build physicians’ confidence in decision support tools
• How an AI decision support system can double as an educational device
• The adoption challenges inherent in black-box models
• Why more data is not necessarily better
• Why Dr. Abidi considers data collection the weak link in data quality
• Distinguishing between biases: which impact a model’s performance and which do not?
• Current NICHE team projects
• How the rise of e-therapeutics grew virtual care, and a crucial clarification of terms
• Barriers to digital health innovation, and countries/entities that are leading the way

Links  

https://www.linkedin.com/in/raza-abidi-73b769111/?originalSubdomain=ca

https://niche.cs.dal.ca

https://niche.cs.dal.ca/raza_abidi/

Quotes  

• “The onset of digital health was the need to collect all the patient information and then make it available at the point of care.”
• “From a mindset standpoint, we have moved from that data-intensive digital health to more knowledge-and-services-oriented digital health.”
• “I think [the target] is: how can we actually use decision support to impact the operationalization of healthcare services?”
• “The entire reason for Explainable AI is to build trust, and once we build the trust, then the uptake of that decision model will actually be high. Otherwise, it will be used on and off, and there will always be ‘can I have a second opinion?’”
• “Biases come at two different levels, which we need to understand: Is it an ethical bias, or is it a bias that would affect the performance of the model?”
• “There are two sides of research: one is fundamental research, and one is applied research. And it is the fundamental research that is supported by these collaborations, because that is where we are asking the hard questions.”
• “One cannot discount the utility and applicability of artificial intelligence in health. It comes from all sides.”

Today’s episode is all about the history and future of infusing tech into healthcare with the goals of improving outcomes and lowering costs, and features one of the leading voices in this field, Jonathan Bush. Jonathan, aka JB, started his career in healthcare as an ambulance driver and army medic, and then met Todd Park, another Bio Eats World guest, while at Booz Allen. Together they founded Athena Women’s Health Clinic, which evolved from a clinic specializing in maternity care to one of the original digital health companies providing cloud-based services and point-of-care clinical and back office tools for providers, later called Athenahealth. In this conversation with a16z general partner Julie Yoo — who is also a digital health builder — JB discusses this evolution, how it mirrors the bigger trend shifts in healthcare, and how it has informed the mission of his new company, Zus, which he compares to a Github for healthtech. JB and Julie cover what’s changed since the launch of Athena, 25 years ago, how to disrupt an entrenched system like healthcare, the role regulation plays in the space, and the under appreciated importance of bottom-up sales. 

Please note there is some colorful language used in this episode, in case you have young children listening.