![]() It can be configured to automatically optimize HCC Risk Adjustment Coding & Chart Retrieval Solutions which do not support unreported diagnosis codes. ![]() RAAPID uses NLP Powered AI-Enabled Risk adjustment solution to optimize the retrospective risk adjustment process. Typical retrospective risk adjustment methods lacked the latest technology and tools to accurately identify medical charts that support unreported diagnosis codes. Transitioning to the traditional Medicare Advantage to retrospective risk adjustment process, Millions of Medicare Advantage medical charts are retrieved and coded manually each year to generate a more complete picture of patient health status. In retrospective studies, individuals are sampled and information is gathered about their past. In prospective Risk adjustment, data is collected as the characteristics or circumstantial changes. The result is minor chart retrieval requests, which decrease provider erosion and increase the productivity of each review. A risk adjustment program that only consists of retrospective chart reviews is short-sighted and does not support the outcomes-driven, population health management which is now innate in most of the payment models.Ī comprehensive retrospective risk adjustment solution technologies the traditional chart review process by shifting the focus from charts’ volume to precision targeting charts. ![]() Paradoxically, retrospective risk adjustment programs limit the potency of code capture. Prospective risk adjustment programs authorize timely, effective interventions including the demonstration of probable gaps in care that support conditions coded to the highest degree of specificity. Health plans, globally have begun to realize the pompous value of Prospective risk adjustment programs, and deservedly so. ![]()
0 Comments
Leave a Reply. |