Jill Inderstrodt, PhD, MPH

Contact : ji3@iu.edu

All pregnant people deserve the pregnancy, birth, and infancy they want for themselves and their babies.

Initiatives

Research Scientist, Clem McDonald Center for Biomedical Informatics, Regenstrief Institute
Assistant Professor, Health Policy and Management, Richard M. Fairbanks School of Public Health
Affiliated Scientist, Military Family Research Institute at Purdue University

Dr. Jill Inderstrodt designs intelligent clinical and public health surveillance systems that seek to eliminate the effects of health disparities on mothers and their babies.

Dr. Inderstrodt’s research follows two complementary tracks. First, she leads the design of multi-modal public health surveillance systems that integrate clinical, administrative, interview, and policy data to improve understanding of maternal and infant health—an approach that reaches beyond traditional reliance on quantitative data to see individuals as more than the data points their health records provide. Second, she leads teams that apply artificial intelligence (AI) and machine learning (ML) to develop products that improve care for pregnant women, especially those living in under-resourced areas like obstetric deserts.

She serves as Principal Investigator on a five-year CDC cooperative agreement to enhance Congenital Heart Defects surveillance in Indiana, directing a statewide collaboration for improved surveillance infrastructure, clinical practice, and maternal-infant health policy. She is also Co-PI of the IU Better AI for a Strong Rural Maternal and Child Health Environment (IU-BARE) Lab, a multi-campus effort that uses AI/ML and community-based participatory research to embed predictive models into culturally responsive remote monitoring tools designed to advance equitable pregnancy care.

Dr. Inderstrodt is also an active public scholar whose commentary on maternal health policy has appeared in The Boston Globe, IndyStar, Slate, Education Week, and Ms. Magazine, among others. Her work embodies a commitment to designing data-driven, human-centered systems that move beyond measurement and effect meaningful change for mothers and infants.

Featured Projects

Indiana University Better AI for Rural Maternal and Child Health Equity (BARE) Lab

National Institutes of Health Artificial Intelligence/Machine Learning Consortium (NIH AIM-AHEAD)

(2025-2026)

Role: Principal Investigator

Major Goals: Leverage Artificial Intelligence (AI) to address maternity care deserts and racial, ethnic, and geographic disparity in Indiana, and to develop and pilot-test predictive AI tools for pregnancy-associated cardiometabolic complications in rural communities

Project Number: PAIRC2-SUB00453

Advancing Surveillance of Congenital Heart Defects: Using Unparalleled Informatics and Data Science to Improve the Health of Children, Adolescents, and Adults

Centers for Disease Control and Prevention (CDC)

(2024-2029)

Role: Principal Investigator

This project will link clinical, administrative and public health data for surveillance of congenital heart defects (CHD) to do the following:

identify CHD cases among Indiana children, adolescents and adults
integrate clinical and administrative data on individuals with CHD
utilize integrated data to measure outcomes for CHD
work with policy partners to inform policymakers and make recommendations so individuals with CHD can experience long-term health and continuity of care.

Linking Data Across Clinical and Public Health Systems to Enhance Maternal and Child Health Surveillance in Indiana

Centers for Disease Control and Prevention (CDC)

(2023-2026)

Role: Co-Investigator

Build a registry of pregnant people-infant dyads and link discrete public health data sources for surveillance of stillbirth, neonatal abstinence syndrome and congenital syphilis.

Late-term Preeclampsia in Birthing People Without Early Pregnancy Biomarkers

Protocol for a Prediction Model for
National Institutes of Health Artificial Intelligence/Machine Learning Consortium (NIH AIM-AHEAD)

(2023-2024)

Role: Principal Investigator

Develop a machine learning model to predict preeclampsia in pregnant people who present late for prenatal care