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.

The solution helps health systems to take a step-by-step approach to manage risk from population- to patient-level with easy to use dashboards powered by clinical and claims data. First, it helps understand the current state of risk management in the network with historical comparison to previous years risk scores. Second, it can answer “where to focus” by identifying the cohorts with the least risk recapture rates or maximum dropped/suspect codes, chronic disease distribution, month on month risk capture trend and more. Third, it helps prioritize patients with the largest number of coding gaps and get patient-level risk information to redirect resources strategically that reduces overall risk for the population.

Innovaccer is the data platform that accelerates care innovation through innovative AI technologies. Innovaccer’s AI-powered healthcare platform unifies patient data across systems and care settings, and empowers healthcare organizations with scalable, modern applications that improve clinical, financial, operational, and experiential outcomes. 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 $1.5 billion in cumulative cost savings. The Innovaccer platform is an industry-leading data and analytics platform, the Best in KLAS CRM solution, and the #1 rated population health technology platform by Black Book

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.

Innovaccer's coder assistant identifies missing codes based on claims data and suggested clinical markers, helping avoid improper data submission and related consequences such as lengthy audits and repayments to CMS. The solution enables physicians and coders to save time by identifying information that could not be directly found in the patient's clinical or claims data. Failing audits, repayments to CMS, and legal ramifications are some of the unfortunate outcomes of improper data submission. Automation helps overcome these challenges by detecting suspect and dropped codes while reducing administrative burden.

Innovaccer’s risk management solutions support these efforts to help healthcare providers achieve their risk goals. The AI-powered coder assistant helps providers address coding challenges by analyzing dropped codes and reviewing claims data over multiple years to identify prospective coding opportunities and surface suspect codes based on evidence. This enables them to receive appropriate reimbursements by providing high-quality care to patients