Why Healthcare AI Is So Hard to Ship

Joanne Chen, VP of Data and AI at SimplePractice, joins The Tech Trek to talk about what it takes to build AI data products in a regulated, sensitive domain where privacy, consistency, monitoring, and customer trust have to be designed from the start.

This conversation gets into why AI product development feels different from traditional software, how teams should think about quality control, and why not every valuable AI solution needs to be GenAI.

Practical Takeaways

• AI products need defense in depth, especially in healthcare, where privacy, confidentiality, and security cannot depend on one layer of protection.

• The core product questions still matter. What customer pain does this solve, who benefits, and what does it take to ship responsibly?

• AI changes the development life cycle because outputs are not always deterministic and quality can degrade after launch.

• Teams need monitoring, validation, and a plan for edge cases before putting AI features in front of customers.

• AI literacy is becoming part of every role involved in building, marketing, supporting, and operating software products.

Timestamped Highlights

00:00 Joanne Chen on AI data products, deterministic outputs, and safely shipping AI features

01:20 What SimplePractice does for mental health practitioners and group practices

02:30 Why healthcare AI needs multiple layers of risk protection

05:00 What makes an AI data product different from a traditional data product

08:15 Why stakeholder expectations around AI have widened so much

10:40 How AI changes the work across engineering, CS, marketing, and support

13:10 Where AI can help reduce tedious administrative work in healthcare

16:45 Why leaders need to keep their hands dirty with new AI tools

One Line That Stuck

“Keeping hands dirty is important.”

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