Peer-reviewed study demonstrates Waymark Signal is 90 percent accurate in predicting avoidable ER and hospital visitsSAN FRANCISCO, CA / ACCESSWIRE / January 23, 2024 / Waymark, the Medicaid provider enablement company, today published a peer-reviewed study in Nature's Scientific Reports comparing the performance of Waymark SignalTM, the company's proprietary machine learning technology, to conventional Medicaid risk models. The study found that Waymark Signal was 90 percent accurate in predicting avoidable emergency room (ER) and hospital utilization for patients receiving Medicaid - stronger performance than leading Medicaid risk models in the field.Waymark Signal combines data on social risk factors and patient risk trajectories with healthcare utilization to identify patients at risk for preventable ER and hospital visits. As one of the largest and most representative comparisons of Medicaid risk models to date, the study assessed Waymark Signal's machine learning approach against traditional regression models for Medicaid, which rely primarily on patient demographics, healthcare diagnostic codes, and medications to predict at-risk patients. Researchers found that Waymark Signal was 3x better at identifying at-risk patients and 10x better at predicting costs compared to conventional models."This study demonstrates the potential for machine learning models like Waymark Signal to more accurately identify at-risk patients and drive more effective interventions," said Sadiq Y. Patel, MSW, Ph.D., an author of the study and Data Science Lead for Waymark. "By enabling care teams to better recognize and act on social and clinical risk factors, Waymark Signal can help them intervene to prevent avoidable disease complications, ER visits, and hospitalizations that negatively impact both patient health and costs."Additionally, the study found that Waymark Signal also reversed the Black-White prediction bias observed in most risk models

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