One of the nation's largest public academic health systems and a safety net provider serving the Southeast, is using Jvion's Cognitive Clinical Success Machine to reduce the risk of re-hospitalization for Congestive Heart Failure patients.
The provider is one of the nation's largest public academic health systems and a critical safety net provider serving the surrounding community. The system provides quality, cost-effective, and patient-focused care for some of the most at-need, complex patients. Through the provider's six community health centers and leading specialty centers, this provider is a vital and integral part of the region.
The hospital 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 this 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 the target 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.
The provider contracted with Jvion to initially help lower rates of CHF patient readmissions. In the seven months since the hospital deployed the solution, clinicians have been better able to target and support this critical patient group.
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.