Predicting and preventing serious pregnancy complications using medical and social data
Official title: Using Multimodal Clinical and SDOH Data to Develop Risk Models for Predicting Severe Maternal Morbidity
Funded at Arizona State University-Tempe Campus · NIH-11471296 · Data updated 2026-11-12
This project combines medical records with social and neighborhood information to identify pregnant people at higher risk of serious complications so clinicians can offer help earlier.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Arizona State University-Tempe Campus NIH-funded |
| Lab location | 1 site (Scottsdale, United States) |
| Project start | March 10, 2025 |
| Project end | December 31, 2030 |
| Funding year | 2025 |
| Award (this year) | $427,356 |
| NIH project number | 7R01HD116103-02 |
| Project ID | NIH-11471296 on NIH RePORTER |
Who could benefit from this research
✓Could help
Pregnant people or those recently postpartum who receive care in the participating health systems—especially individuals from groups with higher rates of severe maternal morbidity—are the most relevant population.
✕Less likely to help
People who do not receive care in the participating health systems, whose social data cannot be linked, or whose health issues are unrelated to pregnancy may not directly benefit from this work.
What this research studies
This project will combine my medical records with information about housing, income, and neighborhood conditions to build computer models that spot who is at higher risk of severe problems during pregnancy or after birth. The team will link long-term clinical data from health records with social determinants of health and use advanced modeling to recognize patterns of physical, mental, and social risk. The goal is to flag people who need closer monitoring, tailored care, or social supports and to reduce racial and geographic disparities in severe maternal morbidity. Participation or inclusion likely depends on whether my care or data are part of the participating health systems and the linked datasets.
Why it matters
Potential benefit: Could help clinicians detect high-risk pregnancies sooner and target care or supports to prevent severe maternal complications.
How similar studies have gone: Prior risk models exist but often lack detailed social-determinant information and have shown only modest accuracy, so combining rich clinical and SDOH data is relatively new and promising.
Where this research is happening
-
Arizona State University-Tempe Campus
Scottsdale, United States Active
Researchers
- Principal investigator: Pathak, Jyotishman — Arizona State University-Tempe Campus
- Study coordinator: Pathak, Jyotishman
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.