Date of Award

Spring 2-27-2025

Embargo Period

3-3-2025

Document Type

Dissertation - MUSC Only

Degree Name

Doctor of Health Administration

Department

Health Administration

College

College of Health Professions

First Advisor

Kevin Wiley

Second Advisor

Jillian B. Harvey

Third Advisor

Ruth Arthur-Asmah

Abstract

Maternal mortality remains a critical public health issue, disproportionately affecting underserved, low-income urban populations where systemic inequities exacerbate health disparities. The purpose of this study is two-fold; (1) conduct a systematic review of relevant literature on AI-powered digital healthcare tools aimed at improving maternal health outcomes, with a particular focus on how they integrate social determinants of health (SDOH) and (2) review and compare AI tools that incorporate key SDOH factors—such as transportation access, food security, and housing stability, to name a few—to assess their effectiveness in reducing maternal mortality. This review, based on the analysis of 48 studies and 51 reports, examines AI's ability to address barriers to equitable care through tools such as predictive analytics, food delivery, and transport. These innovations demonstrate improvements in maternal outcomes by enhancing accessibility, precision, and care coordination. However, findings reveal substantial gaps in integrating multiple SDOH within AI frameworks, which limits their holistic impact on maternal health. Policy implications stress the need to prioritize the development of comprehensive, multi-SDOH AI solutions to reduce maternal mortality and advance health equity. This research underscores the urgency of deploying cost-effective, scalable AI tools tailored to the unique challenges of underserved populations, thereby fostering equitable and improved maternal healthcare outcomes.

Rights

Copyright is held by the author. All rights reserved.

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