Assertion Supported AI assessment confidence: 95% certainty 4/5 debate potential 3/5

Batniji: Waymark's ML model boosts acute care prediction accuracy to 90%

Rajaie Batniji · Scaling Medicaid Innovation with Rajaie Batniji, Sanjay Basu, and Afia Asamoah · Mar 20, 2025 · at 24:00

Rajaie Batniji, co-founder of Waymark, discusses how their machine learning tool Signal improves care team targeting efficiency for Medicaid populations.

0:00 / 0:21exact quote · 21.7s

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“If you didn't have signal, you'd have about 30% accuracy in identifying Future acute care use. I mean, seven out of the 10 patients that you reach out to, you're not going to actually benefit in a way that changes the outcome. With Signal, you have 90% accuracy. So that means one out of 10 of the patients that you reach out to, you're not going to benefit.”

quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →

The claim, plainly

Waymark's ML model, Signal, achieves 90% accuracy in identifying Medicaid patients facing future acute care needs, compared to 30% accuracy without it.

a quote alone can be ambiguous, so this restates it with the references and context of the conversation around it resolved. AI-written; the tape below the player is the source.

Assessment

The claim states that Waymark's Signal ML model achieves approximately 90% accuracy in identifying Medicaid patients facing future acute care needs compared to 30% accuracy without it. According to the research notes, both a published peer-reviewed study in Nature Scientific Reports (January 2024) and a technical white paper published by Waymark ('Beyond Risk Scores: Waymark's Signal Suite', October 2025) confirm these figures. The model demonstrated >90% accuracy in predicting avoidable emergency department and hospital utilization, while baseline traditional models identified at-risk patients at one-third the rate (~30.9% baseline sensitivity). This directly satisfies the resolution criteria.

A January 2024 peer-reviewed study in Nature Scientific Reports evaluated Waymark Signal across 10 million Medicaid recipients, finding >90% accuracy in predicting future avoidable acute care utilization, outperforming conventional risk models by 3x.
An October 2025 Waymark technical white paper confirmed that Signal achieves >90% accuracy in identifying future avoidable acute care needs compared to standard cost-based baseline models which had a ~30.9% detection sensitivity.

Checked against

[1] waymarkcare.com [2] nih.gov [3] waymarkcare.com [4] hubspotusercontent-na1.net [5] waymarkcare.com [6] hubspotusercontent-na1.net [7] hubspotusercontent-na1.net [8] nih.gov

"Waymark Signal" "30%" "accuracy" "Prediction of non emergent acute care utilization and cost" Patel Waymark Signal ML model accuracy Medicaid acute care Rajaie Batniji Waymark Signal 90 accuracy 30 acute care

assessed as of 2026-07-28

This assessment is an AI reading of a public statement, made against the cited references with the confidence shown. It can be wrong. The quote, the tape and the references are all on this page; if something is off, use report an error below. full disclaimer →

Mentioned

Medicaid Signal Waymark

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Batniji: Waymark's ML model boosts acute care prediction accuracy to 90%

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