Insurance Revenue Collections

 

EFFECTIVE ANALYTICS AND CONTROLS ON INSURANCE REVENUE COLLECTIONS CAN PAY BIG DIVIDENDS

91% of healthcare industry executive respondents from the HealthLeaders Media Industry Survey cite reduced reimbursements as the No. 1 threat to their organizations. Medical practices should have access to useful and data interrogative analytics to evaluate payer reimbursement measures such as cash collections, accounts receivable, denials, claim rejections and net collection rates among others. In the short-term, practices can immediately address reimbursement outlier transactions. And monitoring practice analytics allows practices to develop longer-term solutions by improving internal controls and financial condition without adversely impacting the patient experience.

Research from the Medical Group Management Association estimates that payers underpay practices in the U.S. between 7 to 11%, on average. While 7 to 11% doesn’t sound like much, let’s do some simple math. Let’s assume your practice collects $300,000 from insurance per month, and let’s further assume your underpayment rate is 10%. At this revenue collection and underpayment rate, payers are shorting the practice $33,333 per month or $400,000 per year. Engaging a reputable payment integrity auditor can not only allow the practice to refile and collect on retrospective claims but provide concurrent reviews to ensure you are getting paid right the first time.

There could be one to a dozen known or unknown factors causing your practice to receive lowered or underpaid reimbursements. Using data analysis techniques, a payment integrity auditor could perform various tests to identify and measure a set of known or potential causes for underpayment. For example, an auditor may perform a Relative Size Factor (RSF) test on contractual provisions affecting reimbursement such as billed procedures or ambulatory surgical center groupers on zero-balance accounts and easily observe payment variances requiring further investigation. This type of data analysis is referred to as supervised scripting for potentially known root causes such as a pricing configuration defect programed in the payer’s system. Alternatively, the auditor can aggregate data sets from its billing system creating dependent and independent variables and perform data-mining techniques such as regression analysis. After univariate and multivariate studies are performed, the data can be further analyzed using algorithmic calculations. A regression technique will help auditors identify the most common variable(s) that are present when an underpayment is made. Combining audit, data-analysis and data-mining techniques typically can provide sufficient evidence to verify where underpayments are coming from and insights as to why they happen.

Let Us Help You

Oasis Practice Solutions, Inc. can provide medical providers with retrospective and concurrent payment integrity solutions. Our audit and revenue assurance team offers a complimentary assessment of underpayment risks. Contact us today to better analyze and maximize your revenue cycle management opportunities.