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.

Patients placed in the wrong bed status with improper documentation results in massive revenue loss and patient dissatisfaction.

Physicians can’t keep up with constantly changing criteria needed to admit patients to the hospital, and hospitals spend tons of money and resources fixing bed status issues retrospectively.

AdmissionCare provides the admitting physician with automated admission criteria - such as MCG - integrated directly into the EHR workflow to help document medical necessity that increases payer reimbursements and reduce denials.

How does it work?

• Integrate into the clinician's EHR workflow
• Determine the most appropriate bed status for each patient at admission
• Synchronize payer criteria with the clinician's documentation
• Collect revenue for the care provided, while avoiding costly denials

EvidenceCare is a unique type of clinical decision support system (CDSS) with its EHR-integrated and content-agnostic platform that empowers better care decisions by improving clinical workflows.

Founded in response to the professional experience of emergency physician Dr. Brian Fengler, the platform provides clinicians with evidence-based insights and measurable outcomes that improve hospital margins.

Based in Nashville, Tennessee, EvidenceCare is a 2x honoree of the INC 5000 list of fastest growing companies, a 2x Fierce Healthcare Best Product winner, and one of Modern Healthcare's Best Places to Work in Healthcare.

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.

EHR Integrated on the front end of the clinician workflow

EHR Integrated on the front end of the clinician workflow