Why Healthcare AI Is So Hard to Ship
About this episode
Joanne Chen discusses the complexities of developing AI products in the healthcare sector, emphasizing the importance of privacy and quality control.
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…
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