What You Should Know:
– Anomaly, a precision payments company streamlining healthcare billing and payments for providers and payers, today announced its new offering Anomaly Smart Response.
– Smart Response uses AI to enable providers and payers to reduce avoidable claim denials and rework and increase payment transparency, top priorities for healthcare stakeholders. Smart Response complements Anomaly’s planned suite of products powered by its AI claim prediction engine, including overpayment prediction, which enables payers to predict and prevent overpayments by learning from previously overpaid claims, and instant payments, which will enable providers to immediately get paid upon claim submission.
Predict Claims Denials with 97% Accuracy
The product gives providers a real-time response predicting actionable denials and payment amounts—so providers can correct and avoid denials before they occur. Smart Response operates directly within providers’ native workflows by integrating with payers, practice management software and claims clearinghouses.
Smart Response is powered by Anomaly’s breakthrough AI claim prediction engine. The engine analyzes thousands of parameters and billions of claims to learn payer-specific rules and nuances for each provider. By continuously analyzing data in real-time, Smart Response stays up-to-date and automatically adapts to changing trends and new payer rules. Smart Response accurately predicts claim line denials and reasons with over 97% precision, based on an analysis of over $100 billion of billed charges through June 2022. The engine is focused on actionable denial scenarios, and is able to achieve this best-in-class level of precision for up to half of total denials (recall) across thousands of payers, all 50 states, and all lines of business (Commercial, Medicare, and Medicaid). Anomaly plans to extend its engine to predict final payment amounts (real-time adjudication) and patient responsibilities, which will further increase transparency in the payment process for all stakeholders.
