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.

• eCART is a research-based, AI-driven software as a medical device (SaMD) that utilizes a machine learning algorithm to continuously assess hospitalized patients’ risk of impending death or intensive care unit (ICU) transfer, assisting medical staff in swift and accurate recognition of patients requiring increased medical attention. eCART draws upon readily available patient data from the EHR, rapidly quantifies disease severity, and predicts likelihood of critical illness onset, hours before it happens.
• eCART received FDA 510(k) clearance in 2024, with clinical performance data of nearly two million hospitalizations from 21 hospitals, including an unprecedented level of real-world prospective data, to ensure consistent accuracy across geography, age, race, and top medical conditions (e.g. sepsis, COPD, heart failure). 
• Built upon more than a decade of ongoing scientific research, eCART was developed at UChicago Medicine by Dr. Dana Edelson, MD, MS and Dr. Matthew Churpek, MD, PhD and is well chronicled in published literature. The analytic and workflow have been in use in clinical practice since 2016.

At AgileMD, we are building the most advanced real-time predictive analytics and clinical pathways platform for hospitals.

To date, AgileMD tools have been used on more than 4 million patient encounters by more than 135,000 clinical users in over 250 hospitals, including some of the nation's leading academic medical centers. AgileMD's eCART clinical deterioration prediction software is FDA cleared and the first AI-powered early warning device for ward patients to go through the FDA process with real-world prospective data. 

AgileMD is backed by premier start-up partners MATTER, YCombinator, and Rock Health, and our cutting-edge analytics are built on more than decade of ongoing research at the University of Chicago. In 2021, AgileMD garnered nearly $3 million in combined federal research and development funding from the U.S. Department of Health and Human Services (HHS), including the National Institutes of Health (NIH) and the Biomedical Advanced Research and Development Authority (BARDA).

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.

• Continuously monitors EHR data in real time
• Stratifies hospitalized patients by risk of impending all-cause clinical deterioration and sepsis
• Notifies inpatient providers when patient may need additional assessment and management
• Recommends clinical pathways with integrated order entry

• Authorized: FDA 510(k) cleared
• Tested: Derived and validated in nearly 3 million patient encounters across 28 hospitals
• Experienced: 8+ years in clinical use; 10+ years of published academic literature on value over other early warning scores
• Accurate: High sensitiviy and specificity, meaning fewer false alarms and more efficient focus on the highest risk patients
• Integrated: Embedded cross-team workflow ensures that risk scores are not ignored and action is taken
• Benchmarked: Robust reporting, benchmarking, and implementation support drive quality improvement