AI & bias in UPenn's
postpartum journey
CONTEXT
Healing @ Home is an implementation with UPenn at the Hospital of the University of Pennsylvania via a chatbot nicknamed "Penny". In this program, Memora Health provided SMS-hosted support to moms after birth once they leave the hospital to expedite discharge and return moms to where they're comfortable. while maintaining the check-ins and support they would receive in a hospital. This implementation serves a diverse patient population that is majority Black (~60%), and primarily on public insurance (~55%).
This research – that I scoped, analyzed, and presented – was the first at Memora to investigate racial and other biases in chatbot accuracy, engagement, & satisfaction.
RESULTS
Thankfully, results indicated no significant differences in any measure of natural language processing (NLP) accuracy between patients by race (F=.136, p=.873, N=439) nor by insurance type as a proxy for SES (F=.059, p=.809, N=542) Additionally, BIPOC moms rated Memora's program on a 0-10 scale statistically significantly higher than white moms (F=4.905, p=.008, N=319).
CONCLUSIONS
These findings suggest this patient journey and Penny do not currently have any racial or SES biases with regards to accuracy or satisfaction. Non-white moms have historically been underserved by traditional care and the data indicates that this solution serves them equitably.
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Context
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02
Approach
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Takeaways
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Outcomes
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