EMR Integration & Relevant Hardware:
Epic, Cerner, Meditech, Allscripts, NextGen, athena, GE, eClinicalWorks, McKesson
Desktop, Mobile / Tablet (web optimized)
HOPE-CAT is a machine-learning-based risk-assessment algorithm that identifies factors that may indicate the development of cardiovascular conditions that lead to maternal morbidity and mortality. By monitoring a pregnant patient's aggregated health data (e.g., medical records, wearables and device data, and self-reported data) in real time, HOPE-CAT stratifies a patient's risk and alerts providers to changes in a patient's risk status.
Detect risks related to cardiovascular events and disease in pregnancy and postpartum.
Aid in earlier diagnosis of conditions leading to maternal morbidity and mortality.
Patients, Practitioners, OBGYNs, Midwives, Nurse Practitioners, Doulas, Cardiologists, Emergency Medicine
Current efforts to reduce cardiovascular-based maternal morbidity and mortality are reactive. Often occurring in a disjointed system, patient encounters and, consequently, data collection occur every few weeks or months over the course of a pregnancy, and even less frequently postpartum.
HOPE-CAT constantly monitors a patient's aggregated health data (e.g., medical records, wearable and device data, and self-reported data) in real time, stratifies a patient's risk, and alerts providers to changes in a patient's risk status.
The machine-learning-based technology identifies signs of risk sooner than would be discovered in a clinical setting, prompting proactive intervention and reducing outcomes of maternal morbidity and mortality.
HOPE-CAT is a proactive tool that identifies signs of risk before a patient experiences complications or a medical emergency, prompting intervention and fostering proactive care, unlike other diagnostic and treatment models.