The Fort Worth Press - Waymark Signal Identifies High-Risk Pregnancies 55 Days Earlier Than Traditional Methods, Study Shows

SAN FRANCISCO, CALIFORNIA / ACCESS Newswire / January 26, 2026 / More than 80% of pregnancy-related deaths are preventable, yet maternal mortality continues to rise in the United States. This is particularly true for people receiving Medicaid benefits, who account for nearly half of all U.S. births and face some of the steepest barriers to early intervention. Current tools used by most healthcare organizations identify risk only after clinical problems appear in claims or electronic health record data, missing the critical intervention window to prevent pregnancy complications among patients with limited prenatal care access. The result is preventable tragedy for patients and preventable costs for health plans and providers struggling with labor complications, neonatal ICU admissions, and poor performance on maternal health quality measures.

A new peer-reviewed study from Waymark published in Nature's npj Digital Public Health offers a solution. Signal for Maternity Risks, the newest addition to the Waymark Signal^TM platform, predicts adverse pregnancy outcomes for Medicaid-enrolled pregnant women with 89% accuracy. The technology spots at-risk pregnancies a median of 55 days before traditional clinical indicators emerge by detecting subtle signs of risk (e.g. domestic violence or undiagnosed heart disease), giving care teams time to deploy community-based support before complications develop.

The study analyzed data from the complete population of 190,698 Medicaid-enrolled pregnant women across 26 states and Washington, D.C. By combining clinical data with social determinants of health - including healthcare workforce availability, distance to obstetric care, and community infrastructure - Signal for Maternity Risks achieved 89.4% accuracy and 81.3% sensitivity at predicting maternal or infant morbidity or mortality. The model predicts adverse pregnancy outcomes including preterm birth, severe maternal morbidity, and neonatal intensive care unit admission. Importantly, Signal for Maternity Risks works with existing Medicaid claims and administrative data, requiring no new data collection infrastructure.

Traditional risk tools rely on diagnosis codes like hypertension or diabetes showing up in claims. By the time these flags appear, opportunities for early prevention have passed. Signal for Maternity Risks identifies risk 55 days earlier, giving population health teams time to connect pregnant members with care coordinators, pharmacy teams, licensed therapists, and community health workers before problems develop.

"Individual clinical factors alone poorly predicted outcomes in our analysis," said Dr. Sadiq Y. Patel, MSW PhD, lead author of the study and Vice President of Data Science and AI for Waymark. "Adverse pregnancy outcomes often arise from complex interactions between biological, social, and structural factors. By systematically integrating clinical data with social determinants, we achieved substantially better prediction than traditional approaches."

The model integrates comprehensive data sources including pharmacy claims for fertility medications, emergency visits for early pregnancy symptoms, historical pregnancy outcomes, and social determinants like healthcare workforce availability and distance to obstetric care.

Significantly, the technology also eliminated algorithmic bias between Black and White patients. While a clinical-only model showed lower sensitivity for Black patients (71.5% vs 73.0%), incorporating social determinants achieved equivalent performance for both Black and White patients (81.3 vs 81.5%). Addressing these social determinants could reduce adverse outcomes by 31.8%, with the greatest benefit for Black women.

"Black women face three times higher maternal mortality rates than White women," said Dr. Sanjay Basu, MD PhD, Co-founder and Head of Clinical at Waymark and senior author of the study. "This 55-day detection advantage gives health plans and providers time to intervene early and prevent complications that we know are avoidable."

Signal for Maternity Risks joins the Waymark Signal Suite of predictive models, a unified approach to early intervention for Medicaid populations. Unlike traditional risk models that identify patients after crisis patterns emerge, Waymark's technology predicts who will benefit from intervention and recommends what interventions to provide. The platform includes Signal for Rising Risk (>90% accuracy predicting avoidable hospital/ER visits), Signal for Quality (85% accuracy identifying patients who benefit from HEDIS outreach), and Signal for Dual-Eligible Populations (80% accuracy predicting avoidable hospital/ER visits).

The full article titled "Early Detection of High Risk Pregnancies Using Clinical and Social Data to Improve Health Outcomes" was published in npj Digital Public Health, a peer-reviewed journal published by Nature. The authors were Sadiq Y. Patel MSW PhD of Waymark and University of Pennsylvania, Chitra Akileswaran MD of Alameda Health System, and Sanjay Basu MD PhD of Waymark.

About Waymark
Waymark is a public benefit company dedicated to improving access and quality of care for people receiving Medicaid. We partner with health plans and primary care providers-including health systems, federally qualified health centers (FQHCs), and independent practices-to provide community-based care to people enrolled in Medicaid. Our local teams of community health workers, pharmacists, therapists and care coordinators use proprietary data science technologies to deliver early interventions to hard-to-reach patient populations. Waymark's peer-reviewed research has been published in leading journals including the New England Journal of Medicine (NEJM) Catalyst, Nature Scientific Reports, and Journal of the American Medical Association (JAMA)-demonstrating measurable improvements in health outcomes and cost savings for Medicaid populations. For more information, visit www.waymarkcare.com.

Contact Information:
Iman Rahim
Communications
[email protected]

SOURCE: Waymark

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M.T.Smith--TFWP