# Analytics & Machine-learning for Maternal-health Interventions (AMMI): A Cross-CTSA Collaboration

> **NIH NIH U01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $1,146,879

## Abstract

PROJECT SUMMARY
African-American women across the US experience alarmingly higher rates of maternal mortality than
their white counterparts. Factors associated with social determinants of health (SDoH), including
education, housing, transportation, and nutrition are recognized as potentially contributiing to this
disparity in maternal health outcomes, along with clinical risk factors including hypertension and heart
disease. However, the complex associations among these factors, along with the causal role they play in
increased risk for maternal mortality, are not well understood, nor are there comprehensive health care
interventions that take these combined factors into account to provide decision and communication
support for patients, providers, and community support workers. The Analytics and Machine-learning
for Maternal-health Interventions (AMMI) initiative, a collaborative effort from researchers at UNC-
Chapel Hill, Duke, and Wake Forest, aims to address these gaps by developing a machine learning-
enhanced health technology framework to reduce downstream risk of maternal mortality in African-
American women. By integrating data across the three institutions that includes both clinical and SDoH
factors, and by building machine learning applications grounded in this data, AMMI’s goals are to: 1)
clarify and track contributions of biological, clinical, and SDoH factors toward specific maternal
morbidities associated with eventual mortality, 2) conduct efficient and accurate risk predictions to
determine whether patients fall into defined target risk groups, and 3) translate these risk predictions
into interventions appropriate for providers, patients, and community support organizations. A key
focus of the initiative is to create an advanced technology infrastructure supporting connectivity and
communication among these three types of stakeholders, with the goal of building trust and awareness
based on automatically curated decision support aids and ultimately mitigating patient risk. To this end,
Aim 1, focused on establishing system requirements, begins with the formation of a stakeholder group
that brings together patient, provider, and community support organization representatives to engage
in design and evaluation with AMMI researchers throughout the project. Aim 2 focuses on systems
development, including the creation of 1) a custom-built clinical and SDoH data mart, 2) clinical decision
support software using machine learning algorithms, and 3) three user-facing apps aimed at providers,
patients and community support personnel, and AMMI researchers. Aim 3 focuses on pilot-level
deployment of the system, integrating the AMMI apps through Epic to provide informational
interventions to providers, patients, and community support personnel. Aim 4 engages stakeholders in
formative and summative evaluation during and after the deployment phase (Aim 3), including both
testing of the software function and measurement of the impact of AMMI i...

## Key facts

- **NIH application ID:** 10843848
- **Project number:** 5U01TR003629-03
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Metin Nafi Gurcan
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,146,879
- **Award type:** 5
- **Project period:** 2022-07-22 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10843848

## Citation

> US National Institutes of Health, RePORTER application 10843848, Analytics & Machine-learning for Maternal-health Interventions (AMMI): A Cross-CTSA Collaboration (5U01TR003629-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10843848. Licensed CC0.

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