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September 11, 2025
Sand Technologies
The distance to care can play a decisive role in maternal outcomes. In the U.S., women who travel longer distances to reach a hospital face higher risks of complications during pregnancy and childbirth. This challenge is most evident in maternity care deserts (areas without obstetric services) that impact more than 2.3 million women nationwide. In these regions, expectant mothers travel an average of 28 miles to reach delivery care, compared with just 7 miles in fully served areas. For many rural communities, the distance can be even greater, stretching to 40 miles or more.
These added miles often translate into dangerous delays. Studies show pregnancy-related mortality ratios are more than 50% higher in rural areas compared to urban areas in the U.S.
Pregnancy-related deaths per 100,000 live births
Globally, disparities are even starker: in sub-Saharan Africa, maternal mortality ratios exceed 500 deaths per 100,000 live births, compared with 12 per 100,000 in high-income countries, with distance and lack of emergency obstetric services as key contributors.
These numbers reveal a hidden weakness: healthcare systems rarely account for geography in planning, yet distance directly influences survival. Without tools to measure and anticipate these barriers, care remains out of reach when it matters most.
Expanding maternal healthcare access is not just about building more facilities. Health systems face real constraints: limited funding restricts investments in new services, workforce shortages leave many areas without qualified providers, and infrastructure often fails to keep pace with needs. The impact of this is clear: more than half of rural U.S. counties no longer have obstetric services, forcing millions of women to travel long distances for essential care.
Geography magnifies these gaps. Poor road networks, unreliable transport, and limited emergency services can turn manageable complications into life-threatening crises. At the same time, fragmented facilities and uneven coverage make it challenging to coordinate referrals or deliver consistent standards of care. These structural weaknesses fuel higher maternal mortality rates in rural areas and highlight deep inequities.
Without deliberate planning, new investments can unintentionally widen gaps instead of closing them. The real challenge lies in designing health networks that are both efficient and equitable. That means placing the right services in the right locations, connecting them through reliable referral pathways, and making them accessible to populations that need them most.
Maternal health planning still relies heavily on static data, such as facility locations, service lists, and patient volumes. This view masks reality: a hospital may appear accessible on a map but be unreachable due to poor roads, seasonal flooding or staff shortages. Referral pathways are often unexamined, leaving clinics disconnected from higher-level care.
Travel-time analysis and population trends are rarely integrated, even though delays directly increase maternal mortality. The result is a reactive system that identifies risks only after mothers are harmed.
Healthcare generates roughly 30% of the world’s total data volume, yet much of it remains siloed. The real opportunity lies not in collecting more data, but in using existing datasets to identify blind spots and target interventions where they will have the greatest impact on maternal outcomes.
To build this form of dynamic, intelligence-driven maternal health planning model, several key datasets are essential:
Data alone is not enough; it must be translated into decisions. Intelligence planning tools, such as Sand Technologies’ Maternal Health Clinic Planner, are designed to do precisely that. The tool integrates multiple datasets to model various scenarios, including determining the optimal location for new clinics, identifying when mobile units are more effective than fixed facilities, and enhancing referral networks.
By simulating the impact of various investment choices, health systems can avoid reinforcing inequities and instead direct resources where they will have the greatest effect on saving lives. This approach has clear benefits:
At its core, intelligence-driven maternal health planning is fundamentally about equity. Every mother, regardless of her location or socioeconomic background, should have timely access to quality care. By recognizing distance as a determinant of survival and by leveraging diverse data, health systems can make smarter decisions about where to place services and how to connect them.
This approach not only helps reduce preventable maternal deaths but also strengthens community resilience and ensures resources are used efficiently. It brings health systems closer to true equity, so that no mother’s life is determined by her ZIP code. With data as a compass, geography becomes less of a barrier, and every mother has a better chance of a safe delivery.
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