Check out the paywall-free paper link below. I always enjoy working with Anna Zink (new prof starting at Tufts in fall 2025!)

ai.nejm.org/stoken/defau...

Great q! We found that standard ML training (incl rebalancing data, etc) still reflected the differences in EHR data quality. Later we started experimenting with different options such as adding self-report data to the model, but using regular ML training did not seem to help!

tl;dr Healthcare access disparities cascade through the entire ML pipeline.

Check out our working paper here: arxiv.org/pdf/2412.07712

Finding 3: Here's what helped: Adding patient self-reported data boosted model sensitivity by 11.2% for underserved patients. (Note adding the low/high access info did NOT help)

Finding 2: "Just train a better model" isn't enough. EHR reliability has large effects on balanced accuracy (5.8% drop) and sensitivity (9.4% drop).

Finding 1: For 78% of medical conditions we examined, data quality was worse for patients facing cost/time barriers to health. EHR reliability is defined compared against patient self-report

Very cool! Excited to check it out

jamanetwork.com/journals/jam...

Thank you thank you!!

Super interested in this use case! Do you remember who the presenter was here?

You got this Jessilyn! 🔥