Across the healthcare system, improving outcomes while managing the total cost of care remains a critical challenge, especially in maternal health. Maternal mortality, rising comorbidities and persistent disparities have increased focus on the quality, accessibility and effectiveness of maternity care. For health plans, data analytics for payers is a key lever to identify risk earlier, coordinate care more effectively and improve outcomes while controlling avoidable cost.
In this blog, we connect with Marcos Dachary, Chief Market Strategist and General Manager of Payer Solutions at Milliman MedInsight and Sarah Quinn, Director of Marketing at Milliman MedInsight, to explore how advanced analytics and modern healthcare payer analytics solutions can help drive positive change.
Q. What is the state of maternity care in the U.S. right now?
Marcos: Maternity care is one of the most important and expensive areas to manage. U.S. maternity care costs have continued to rise, but outcomes haven’t improved at the same pace. That gap is especially concerning given that many maternal deaths are considered preventable. We also continue to see disparities in maternity and childbirth outcomes across race, socioeconomic status and geography.
Sarah: When we look at recent analyses of postpartum outcomes, a consistent signal is severe maternal morbidity and its uneven impact across populations. Studies using both commercial and Medicaid data show substantial racial and ethnic disparities in postpartum outcomes. That reinforces the need to understand what’s happening within your own member population—not just at the national level—and to focus on the postpartum window, as well as pregnancy and delivery.
Q. What role do data and analytics play in improving these outcomes?
Marcos: Analytics are foundational. By analyzing claims and outcomes, health plans and providers can identify potential high-risk pregnancies and intervene earlier. The first step is establishing a clear baseline: each organization needs to understand its own performance, variation and disparities instead of relying solely on national averages.
Analytics can also pinpoint moments in the maternity journey where improvements are most likely to matter. For example, frequent emergency department visits during pregnancy may signal gaps in access, missed opportunities for support, or unmet social needs. When you examine those patterns alongside chronic conditions like hypertension, gestational diabetes, or anemia, you can better understand what’s driving risk and cost.
This insight supports earlier, more targeted action, such as closer blood pressure or glucose monitoring, outreach to encourage prenatal visits, or care management for members with known risk factors. Many teams also apply predictive analytics for healthcare payers to prioritize outreach and allocate resources to the members most likely to benefit. Done well, these interventions help prevent complications, improve maternal and neonatal outcomes and reduce avoidable utilization.
Benchmarking matters, too. To make meaningful progress, organizations benefit from clinically relevant comparisons to best-in-class benchmarks tailored to their population. Comparing rates of C-sections, inductions, non-delivery admissions, or ED use across regions and provider groups can highlight improvement opportunities and reveal potential drivers such as practice patterns, maternal obesity, or prior cesarean deliveries.
Finally, maternal health requires a longer-term view. It’s not only about what happens during pregnancy and delivery, the quality of support across prenatal and postpartum care can also influence future pregnancies and shape how members engage with care over time.
Q. What else can health plans do to manage maternity costs and outcomes for their members?
Marcos: It starts with actionable data. Milliman MedInsight helps health plans get to insights faster by organizing information in ways that support maternity analysis without requiring teams to build everything from scratch. Instead of spending significant time parsing raw data or developing custom logic to identify emergency department visits for high-risk pregnancies, plans can leverage pre-grouped cohorts to quickly pinpoint high-risk pregnancies (with or without a chronic condition) and view key context such as age, service encounters and non-delivery admissions.
With data enrichment and advanced groupers, the goal is to make the core data points needed for comprehensive maternity analysis readily available so organizations can focus on action. MedInsight supports different needs across the organization: data scientists can work in SQL and advanced analytics, while other teams can track performance through dashboards designed for easy interpretation and decision-making.
Q. How can analytics help reduce the total cost of maternity care while improving outcomes?
Sarah: Analytics helps plans identify key drivers of maternal health costs and outcomes by bringing together claims, clinical data—and when available, social determinants of health. That combined view can surface patterns linked to high-cost cases, including preventable complications, care gaps and opportunities to strengthen prenatal and postpartum engagement.
With those insights, payers can design targeted interventions, such as enhanced care management for high-risk pregnancies, provider education, or community resource referrals, aimed at root causes instead of symptoms. Ongoing measurement is just as important, so teams can track what’s working, learn quickly and adjust programs over time. The result is a stronger ability to deliver the right support at the right time, improving outcomes for mothers and babies while reducing unnecessary costs.
