Revenue Cycle Analytics

Introducing The Sift Quality Score

Post by
Bethany Grabher

For RCMs, liquidation rate (dollars collected), as well as the speed of payments, is highly dependent on the quality of accounts that their healthcare provider clients send their way. The quality of accounts that an RCM is working can vary widely between billing cycles. Account quality is dependent on their partners and their policies, insurance payers and their ever-changing reimbursement rules, facility and provider utilization rates and, as we’re all experiencing right now, the macro-environment and economy.

For RCMs (and ARMs), understanding account quality is essential for forecasting revenue, optimizing workflows and ensuring the best strategy is in place for managing Human Resources.

Most RCMs identify a dip in account quality only following a bad month. It is rare to have a forward-looking picture, making it challenging to build operational strategies that most effectively utilize available resources. Knowing the impact of this quality variance has for our RCM partners, Sift has developed a quality index to help RCMs forecast their financial performance.

The Sift Quality Score (QS) is a proxy for the quality of accounts listed in a given month. The QS is derived from Sift predictive model scores, including Sift’s proprietary Propensity to Pay score and Predicted Payment Percentage score. Sift’s QS inherently takes into consideration key factors such as SP/SPAI distributions, average list amount, age of account, number of concurrent accounts per guarantor, and many more. The QS is also normalized relative to the maximum and minimum month batch scores. This allows for intuitive flexibility in comparing the quality of a month’s batch over various periods. This intelligence goes beyond month-to-month forecasting, identifying how account quality will impact the entire early out period or billing cycle (120-days).

Sift's quality score allows for RCM's and providers to have more informed and proactive conversations regarding the trend of financial performance (liquidation of accounts) by enabling them to understand the quality of accounts of any given month or cohort. In stable times, this kind of intelligence is incredibly helpful — in turbulent times, it’s essential.

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