The impact of insurance claim denials for healthcare providers in 2021, and how providers' own historical payments data and machine learning provide solutions for both denials prevention and appeals prioritization.
Integrating Machine Learned adds a decision engine to the revenue cycle, enabling you to determine the next best action that should be taken with a claim, denial, patient account, payer contract or vendor. Can't envision it? Here are five examples of what ML in the revenue cycle looks like, in action...
Machine Learning makes RPA more effective in the revenue cycle. Being able to truly leverage payments data to drive decision-making makes automation efforts meaningful. ML enables RPA efforts to move from automating repetitive human processes to attacking the root causes of inefficiencies and implementing data-driven strategies around revenue cycle work efforts, vendor outsourcing, patient financial engagement and payer reimbursement.
RPA Follows Rules. Machine Learning Generates Intelligence. Your revenue cycle will benefit from both. Learn about the limitations of RPA and how machine learning provides can have a more meaningful impact on the healthcare revenue cycle.
Wonder how Sift impacts healthcare? Watch Sift Healthcare's Founder and CEO, Justin Nicols talk with StartUp Health's Logan Plaster about the impact of Sift's AI and analytics on healthcare payments.