Integrating Snowflake and PEGA to Drive UM Case Resolution in State Medicaid

Abstract

The need for consistent data and process integration becomes increasingly more important as Medicaid programs get more complex and extensive. Even as they manage enormous volumes of administrative and clinical data, governments are under more and more pressure to deliver quick and more efficient healthcare services. Often failing, conventional compartmentalized systems create patient care bottlenecks and hinder the effectiveness of Utilisation Management (UM) case resolutions. Here is where modern technologies such as PEGA and Snowflake find use. Snowflake's cloud-native design makes scalable, safe, highly accessible data management possible, hence enabling data consolidation and analysis from many sources. Concurrent with this, PEGA's strong business process management (BPM) capabilities offer flexible, rules-based solutions responding to evolving Medicaid operations' needs. Taken collectively, these technologies offer a data-centric, effective framework allowing fast and more correct UM case replies.  Not only is speed the main goal; additionally, automated processes and real-time data help to make better decisions. States implementing this integrated knowledge approach have shown notable improvements in areas including practical ones such as speedier approval procedures, fewer administrative burdens, and more consistent, high-quality outcomes for Medicaid users. Eliminating manual handoffs and delays enables the Snowflake-Pega interface to provide access to full data sets within automated processes, hence improving the speed and efficacy of care management. This paper investigates how states are getting these results using particular case studies from actual environments where integrated data and process automation have greatly improved UM operations, encouraged provider collaboration, and drastically changed the Medicaid experience.