AtlantiCare, the largest health system in southeastern New Jersey, will allow its healthcare providers to better manage patient populations and track clinical and financial objectives with quality-focused tools from Elsevier/MEDai, a leading provider of predictive analytic solutions.
AtlantiCare's vision is to build healthy communities. It will use MEDai's Risk Navigator solutions to provide analytics for its Health Engagement program, which is positioned to prevent injury and illness, and manage health risks, chronic illnesses and healthcare utilization. Additionally, AtlantiCare is part of Premier, a collaborative to design and implement an Accountable Care Organization (ACO) in New Jersey. ACOs are designed to keep patients healthy and out of intensive care settings, while simultaneously shifting reimbursements to be based on quality outcomes and efficiencies.
AtlantiCare will use MEDai's Risk Navigator solutions in conjunction with its patient-centered medical home model to enhance cooperation and communication between plan, provider and nurse case managers. Risk Navigator provides the ability to integrate data from multiple sources and systems and produce information that is needed to provide proactive patient care.
"The collaborative nature of ACOs is an extremely important step in the evolution of our nation's healthcare systems," said Clayton Ramsey, COO, MEDai. "MEDai's predictive analytics and healthcare expertise aid clients in ensuring they have the necessary resources to make quality, efficient patient-care decisions based on accurate information."
For more than a decade, MEDai's predictive modeling suite has accurately forecasted prospective high-risk patients to improve patients' health and quality of care while controlling cost. Risk Navigator Clinical is built to fit the clinical workflow by wrapping business intelligence around a member-centric database providing evidence-based medicine guideline compliance information and cost and utilization forecasts. Clients can also leverage MEDai's motivation index to determine the level of member engagement most appropriate based on the members' motivation to self manage their conditions.