ADR empowers hospitals to immediately identify attacks that in-place IT systems frequently miss. Impacted devices exhibiting malicious activity are immediately identified and quarantined to prevent attack spread while allowing continued patient care. Forensic data is then made available in the Cynerio platform to allow investigation, remediation, and recovery measures to be carried out when the device is not in use.

Cynerio enables healthcare providers to rapidly identify and reduce risk related to Iot, Medical IoT and connected medical devices within their environments. Cynerio technology is designed for rapid deployment throughout healthcare environments regardless of size, and is paired with the human expertise needed to begin addressing risk within a matter of days.

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.

Many organizations have well tested IT risk reduction systems in place, but very few of those extend to the hundreds and thousands of IoT devices within the average healthcare facility. Cynerio extends the practices of internal IT tools by providing solutions to specifically identify IoT related risk.

Many competitors focus on an inventory-first approach that results in ongoing discovery but often falls short at risk identification and reduction. Cynerio is designed to concurrently identify assets and risk, while providing actionable risk reduction guidance within a matter of hours and days.

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.