Population health improves only when the health outcomes of many individuals get better, which is the focus of value-based healthcare. Costs also accumulate from the care provided for individuals. By organizing teams to care for individuals with similar needs, a value-based approach enables expertise and efficiency, rather than rationing to drive costs down.
Empowering the clinical team to make decisions rather than an insurance utilization management administrator supports practitioner-client relationships. These relationships enable the delivery of effective and appropriate care. Broadening and integrating the services provided to members with similar clinical profiles achieves better outcomes by identifying and addressing gaps or obstacles that undermine clients’ health results.
Our Sevida Analytics enables healthcare organizations to continue to evolve their capacity to understand and meet the needs of their members. It runs algorithms on population segments to identify trends in zip codes, age groups, chronic conditions, and other targeted attributes, thereby categorizing members into risk groups and enabling population health initiatives to mitigate future health conditions and disparities.
Plans require payers and providers to report on and ultimately be held accountable for performance against measures aligned to a range of specific goals and objectives, which are used to drive quality improvement and operational excellence. Our Sevida Analytics engine supports the computation and reporting of these NCQA and State quality measures to support incentives for improved performance and reduce the reporting burden on providers and payers.
Population health management requires effective risk stratification and scoring. Risk scores are standardized metrics that predict the likelihood of a member experiencing certain outcomes of interest. Healthcare organizations can leverage data aggregated in the Sevida Data Pod to better understand patterns of what is likely to happen to members.
Payers and providers can also leverage machine learning algorithms to track member progress along common clinical pathways and develop insights to plan personalized interventions. Our Sevida Analytics support multiple risk scoring algorithms and enables trend analysis for each member.
Sevida predictive modeling helps to proactively identify members with the highest risk of poor health outcomes and offers improved risk management insights.
With increasing caseloads, it is difficult for healthcare staff to analyze and predict outcomes for each member in a timely manner, as and when any health event occurs. Our Sevida Analytics encapsulates the staff know-how into rules and algorithms and triggers them in real-time as new member health information becomes available. This intelligence identifies the next best action to create the desired outcome.
Our Sevida Analytics Engine supports rich drag and drop tools to build reports and dashboards that can be configured to meet any customer reporting need. Sevida reporting tools support exports to PDF, CSV, and other formats.
With healthcare moving from a fee-for-service model to a value-based payment model, stratifying and managing member populations and proactively managing care has gained utmost importance. Our Sevida platform identifies and understands the clients whose health and social circumstances create a consistent set of needs.
