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Using Technology to Understand Your Revenue Cycle

You may recall that a few months ago I wrote a series of posts about ways to calculate key practice benchmarks, including days in accounts receivable, net collections percentage, accounts receivable greater than 90 days old and the ratio of billing staff to providers. Now, I’d like to share some ways to use that kind of data – and more – to truly optimize the revenue cycle.

In a recent discussion with the editor of an online journal, I brought up the fact that a variety of business intelligence tools now exist to help practices understand and improve their revenue cycles. Using these tools to drill down into specific details about each of your payers—instead of taking a more global perspective—can fundamentally change the way you assess your revenue streams.

I suggest, for instance, that practices focus more attention on “collection mix” than on “payer mix”. Your payer mix typically represents your distribution of charges across payers, while your collection mix shows you the money collected each month from individual payers. From a revenue perspective, that’s the real focal point. Evaluate your highest-collection payer and those payers that represent the top 80 percent of collections. Look at the CPT codes billed and important “lag times”, such as:

  • date of service to date of submission to the payer;
  • date of service to date of payment; and
  • date of submission to the payer to ERA date or response time.

This will allow you to start understanding payer-specific trends, and to measure payers against each other in key areas. In addition, it will help identify areas where you can optimize your own revenue cycle operations.

For example, review the denial messages or reason/remark codes you receive for your top CPT codes per payer. Let’s say the denial messages from a top payer on a top code all center on eligibility issues. Then you know to take steps in your front office to address that problem with those patients—perhaps through an eligibility verification tool.

Another practice I recommend is to identify, analyze and categorize your denials. By identifying those procedures with top denial rates, you can turn your attention to any snags that are hindering payment. Also, comparing your ERA data and your payer contracts can pinpoint underpayments (which may happen more often than you think: on average, 4 percent to 12 percent), and exposure to overpayments you will need to repay.

Typically, only the very best practice management systems on the market now are capable of aggregating some of these finite-level details. That’s why many companies are starting to emerge as third-party business intelligence vendors. Take advantage of available data analysis tools, and you’ll discover a wealth of revenue cycle optimization opportunities.