Innovaccer’s Quality Management providers get a consolidated view of the patient, with insights on care gaps, coding gaps (dropped codes), risk scores, acute visits, specialty visits along with Pre-visit Summary. It also makes it easy to report to CMS for the MSSP ACO program using either web interface or eCQM reporting and help providers calculate quality performance, run measure analytics, add supporting evidence, and export in payer specific format to report out to different payers/upload to their portals.

Innovaccer is a healthcare technology company that aims to revolutionize the way healthcare organizations collect, analyze, and leverage data to transform value-based, patient-centered, equitable care. Our mission is to connect and curate the world’s healthcare information to make it accessible and useful. The company provides a comprehensive suite of solutions that empower healthcare providers, payers, and life sciences companies to deliver efficient care and drive innovation in the industry. The Innovaccer platform unifies patient data across systems and care settings, empowering your organization to provide higher quality care to patients and populations, while reducing costs, improving experiences, and strengthening financial and operational performance. Innovaccer’s EHR-agnostic solutions have been deployed across more than 1,600 hospitals and clinics in the US, enabling care delivery transformation for more than 96,000 clinicians, and helping providers work collaboratively with payers and life sciences companies. Innovaccer has helped its customers unify health records for more than 54 million people and generate over $1 billion in cumulative cost savings.

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.

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.