Elevate medical practice by earlier disease detection and improved diagnosis via more accurate data collection, better risk and symptom assessment, faster charting, and increased revenue by higher level E&M billing. Now, achieve better outcomes in less time for more money.

Discover SOAP, a patented and clinically validated, Epic, AthenaHealth, etc. EHR-integrated, conversational AI-powered patient and physician-facing interface for more accurate patient data collection and analysis; an unrelenting focus on improving patient outcomes and eliminating administrative burdens. 

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.

Patented and clinically validated AI-powered new "Gold Standard" of more accurate, truthful and comprehensive patient interviews in less time for more money, risk and symptom assessment, differential diagnosis and note creation.

Patented and clinically validated AI-powered new "Gold Standard" of more accurate, truthful and comprehensive patient interviews in less time for more money, risk and symptom assessment, differential diagnosis and note creation. Less time to provide better care for more money.

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.