This Central Florida Integrated Delivery Network (IDN) is the largest provider in the area and delivers health insurance for thousands within the community. As a stand-out innovator and leader in the adoption of value-based/at-risk models of care, the system is continuously looking for new and more effective ways of improving patient health outcomes.
When leadership turned their focus to reducing readmissions across all of the provider’s inpatient facilities, they knew they needed a powerful cognitive machine. They wanted a solution that could pin point patients at risk of a readmission and provide the intervention effectiveness information that would help ensure a healthy discharge.
Jvion's Cognitive Clinical Success Machine delivers readmission risk propensities and clinical recommendations at the patient-level to help providers reduce readmission rates and drive improved health outcomes. The solution, which combines Eigenspace and Eigen Sphere technologies, renders the most effective clinical risk and intervention information available in the market.
Jvion's Cognitive Clinical Success Machine uses the data that is already on hand and can account for incomplete and inaccurate data elements because of the advanced capabilities driving the engine. The resulting power extends patient-level risk propensities to interventions and likelihood of patient engagement. Moreover, Jvion's solution is equipped for much more than readmissions. From nosocomial events to community health efforts, Jvion's solution is pre-seeded with hundreds of clinical vectors designed for all types of provider environments.
In the first month of results, 175 individuals were identified at high risk of readmissions across all facilities. With each readmission costing the system an average of $11,200, the one-month savings adds up to a potential total savings of more than $1.9M.
It is important to note that these results were rendered without historical data information from the system. The Eigen-based platform underlying the machine’s engine enabled insights three-time more precise and effective than that of a LACE score even without initial tuning to the system’s patient population. And as the machine processes more of the system’s data, effectiveness will continue to increase.