Managing Community Health Risk with the Cognitive Clinical Success Machine -- Testimonials Title
Actioning analytics to manage community health risk is one of the biggest challenges faced by providers. This Southeastern system is leveraging Jvion's community health predictive module to identify rising risk individuals and predict the effectiveness of specific, patient-level interventions.
This Southeastern Integrated Delivery Network (IDN), which comprises three hospitals, four ambulatory centers, a behavioral health center, and five imaging centers, employs more than 3,400 people and is the area’s largest non-governmental employer. As a benefit to working for the system, the hospital manages all health plans and is at full risk for employee health plan costs. This is part of an overall move within the system to migrate to a value-base model for the larger population. By isolating the at-risk group to employees, the system can better identify areas for optimization, potential gaps, and best practices that can be applied more broadly.
Understanding employee health and managing risk are high priorities for the system. Not only does the provider need to understand how employees will use the care offered, they need to be able to predict where expenses will rise and which individuals are at greatest risk of driving up clinical costs. Moreover, they need to manage expenditures so that they balance deductibles with co-pays and co-insurance to encourage preventative care while discouraging overuse.
The system was already using Jvion's solution to predict 30-day readmissions. They sought to extend the current use case to include community health measures that could be applied to the covered employee population. By enabling predictive technologies to identify rising risk employees and the most effective interventions, the provider is able to focus on driving care efficiency while reducing costs and improving quality.
With the initial implementation underway, the system expects to realize value across a number of key performance indicators:
- Care efficiency: by targeting care to the individuals who most need it and are most likely to adhere to a specific intervention, the system is better able to allocate resources and drive a more efficient care delivery
- Competitive advantage: while the system is the most prominent in the area, it certainly isn’t the only employer. By including predictive community health capabilities within their operations, the system is able to offer the best possible coverage to its employees and prospective talent
- Financial value: with value-based models gaining more prominence, providers are struggling to stay competitive within the split worlds of fee-for-service and at-risk reimbursement schemes. By applying Jvion’s predictive technologies, this system is not only able to optimize cost across the care continuum, they are identifying the best practices that will help them thrive under these new models
- Patient satisfaction and quality of care: using predictive technologies, this provider is not only able to reduce the risk of deterioration, they are able to improve quality outcomes while increasing the satisfaction of their employees and patients
As more ACOs, IDNs, CINs, and other coordinated care models take hold, preemptively identifying the individuals who will most likely have a significant health event becomes a key component to fighting patient deterioration and controlling costs. At this Southeastern system, they are taking the first steps along this journey with the goal of driving the best possible care for their employees.