PROJECT ABSTRACT In the U.S., from 2011 to 2014, 7,208 maternal fatalities, with the trend worsening year-on-year (CDC, 2016). In addition to the 700+ fatalities, at least 50,000 woman experienced life-threatening complications, annually. According to CDC (2019) for every fatality, 70 more women suffer avoidable, traumatic complications as a result of pregnancy. Medstar Health Research Institute and Invaryant, Inc, propose the evaluation of a cardiac risk assessment tool for pregnant and postpartum women; this tool updates automatically directly from patient medical records; wearable devices; and patient surveys. Success implies disruptive improvement in women's health. Proposed research involves three key elements: technology, data, and social determinants of health (SDOH) using geospatial mapping of patient locations. Technology: Study technology shall be based on the Invaryant Health Platform (IHP), a technology that automatically ingests data, from medical records, wearable devices, and other sources using proprietary AI based interoperability technology called Mesh-Complex Method Exchange (Mesh-CMX). We propose using the IHP in conjunction with novel prototype-level technology, namely Healthy Outcomes for all Pregnancy Experiences-Cardiovascular-risk Assessment Technology (HOPE-CAT) and the Invaryant machine learning technologies to monitor the patient, based on signals, out-of-range “trip-wires”, and trends in the mother's health data that merit medical intervention. By extending the proof of concept into an early commercial version of the software, and integrating it to the IHP which will automatically update changes in the patient's medical record, we will provide an “early warning” system for mothers and their providers. Data: The study will be tested on patients' medical records using the MedStar's Analytics Platform (MAP), a registry of over 5 Million unique patients. The tool will subsequently have the potential to be leveraged to over 90 million medical records for the MedStar and Cerner hospital systems, distilled down to meet specific eligibility criteria including, gender, age, race, pregnancy and medical outcomes. A second phase of this project would take the findings from the retrospective study (this grant request) and use the technology within the Medstar hospital system, to validate the efficacy of the findings in a “real-world setting”. Using our proprietary AI technology, we will compare each mother's progress against a cohort of retrospective data to enhance diagnostics and provide real-time feedback to caregivers and patients. Geospatial mapping: Mapping the patient medical record and the health information to their social setting is vital for understanding the underlying social constructs that affect the health of mothers in different regions.