Grady Health System, one of the nation's largest public academic health systems and a safety net provider serving the Atlanta community, is using Jvion's Cognitive Clinical Success Machine to reduce the risk of re-hospitalization for Congestive Heart Failure patients.
Grady Health System (GHS) is one of the nation's largest public academic health systems and a critical safety net provider serving the Atlanta community. GHS provides quality, cost-effective, and patient-focused care for some of the most at-need, complex patients. Through Grady Memorial Hospital’s six community health centers and leading specialty centers, GHS is a vital and integral part of Atlanta and the region.
GHS is committed to "becoming the leading public academic healthcare system in the United States." Part of this commitment rests on driving quality outcomes for each individual patient. To help support GHS's goal, the system initiated a project leveraging Eigen-based cognitive machine technology to reduce Congestive Heart Failure (CHF) patient readmissions. This portion of the patient population is at increased risk of complications and mortality. According to a study published by the American Heart Association, close to a quarter of CHF patients discharged from an acute care facility are readmitted within 30 days.
Within GHS's patient population, there are many socioeconomic factors that pose challenges to preventing readmissions. The organization knew they needed an advanced, cognitive solution that could account for the thousands of variables impacting a patient's risk of a readmission; they needed something affordable and effective that could be used by practitioners at the point of care.
GHS contracted with Jvion to initially help lower rates of CHF patient readmissions. In the seven months since GHS deployed the solution, GHS has been better able to target and support this critical patient group. Glenn Hilburn, Vice President, Clinical Systems for GHS said, "With the information delivered by the Jvion tool, our practitioners are better able to zero in on high-risk patients. This has helped us optimize our resources and shift care to individuals with CHF who carry with them the greatest likelihood of a readmission."
The kind of patient-centered machine learning that drives Jvion's solution is designed to become more localized with every new piece of data entered into the system. And the machine has outperformed even the most advanced predictive models. The resulting outputs are driving clinicians to those patients on a trajectory toward risk and providing the guidance needed to define the most effective and efficient clinical action that will improve outcomes.
With CMS (the Centers for Medicare & Medicaid) driving higher penalties and broader adoption of value-based models, having the patient-specific information needed to enable prevention will be essential to ensuring sustainability. "We have to be able to pin point those individuals who are at-risk so that we can better align our clinical and community resources. And in the complex social and economic environment in which we deliver care, we require the kind of power delivered through a patient-centered machine’s like Jvion's to reduce suffering and help us do more for our patients," said Hilburn.