By: Gina Stinson, Senior Director, Revenue Cycle Management
Did you know there are over 293 reasons why a claim payor doesn’t pay a health insurance claim as billed? These reasons are called Claim Adjustment Reason Codes (CARC) and insurance carriers or claim administrators apply these codes to claim remits to identify why a claim, or line-item thereof, was not paid as billed. Identifying and managing these codes is critical to the financial health and success of medical providers that want to successfully navigate recover zero or partially paid claims – also known as denials.
Medical providers use denial mapping in their quest to combat denials. The extent to which a provider can map their denials is contingent upon system and reporting capabilities. Denial mapping should support system workflow, reporting and analytics, as well as denial prevention efforts. Denial mapping begins at the CARC level as not all codes represent denials – in other words, providers need to eliminate the weeds to get to the wheat.
Let’s take a closer look at the CARC code and its associated codes found in the 835 EDI file. As previously touched on, the Claim Adjustment Reason Codes (CARC) is used to identify why a claim, in whole or part, was paid differently than billed. CARC codes may be further supplemented by a Remittance Advice Remark Code (RARC) which is when a CARCs description states “at least one Remark Code must be provided…” or when the description is classified as an Alert.
Did you know there are 1,076 RARC codes in addition to the 293 CARC codes? It’s true! CARC codes are also categorized into one of five Claim Adjustment Group Codes which are used to identify non-payment responsibility (e.g., patient, contractual, etc.). Understanding how these codes work together is important to robust denial mapping.
Before denial mapping begins, determine:
- Who will own the denial mapping and who modification authority (e.g., business office, denial committee, standards committee, etc.)
- Who will ensure the denial mapping is reviewed and modified timely in accordance with X12s published updates which occur three times per year; will this review be pro-active or re-active?
- How will initial vs. subsequent denials be identified?
- How will a claim identified as denied, yet also paid in full on the same remit be identified? (It does happen.)
- How will the mapped and identified data be used?
- Will it route denied claims within the system for workflow purposes?
- Will it push claims/patient accounts to an external system or vendor for a follow-up?
- Will it be used to optimize existing workflows to drive performance and efficiency?
- Will it be used to create and categorize analytics and build reporting?
- How will those reports be used?
- To follow up on claim or appeal status?
- To support denial prevention/mitigation efforts?
- To support payor behavior or performance findings?
- For general aggregated reporting?
- How will those reports be used?
Answering these and other questions will help determine how granular the denial mapping needs to be. Denial mapping used to support or optimize workflows should be very detailed, specific, and pointed; whereas mapping for general reporting doesn’t require the same level of granularity. Once it’s been determined how the mapped data will be used proceed with the general denial mapping process outlined. For the purposes of this blog, let’s assume we’re using denial mapping to support denial prevention/mitigation analytics and reporting.
General Denial Mapping
Step 1 – For each CARC listed identify if the code is a denial or not
Step 2 – For each denial, CARC assigns an upstream data owner for associated denial prevention and reporting data (e.g., patient access, utilization review, case management, coding, etc.)
Step 3 – For each CARC listed identify if the denial is controllable or uncontrollable
Robust Denial Mapping
Providers with more advanced analytic capabilities are positioned to create a more robust denial mapping table – one that takes denial analytics from good to great. Providers can accomplish this by mapping sub-grouped data. To tackle this…
- Use the most recent 6 months of 835 data,
- Generate existing data output of all CARC and RARC code combinations, and
- Review this data output in both the aggregate and payor levels.
- Payor level data enables to the identification of one-off payor variations.
- Want to kick it up a notch further? Review CARC and CARC/RARC combinations at the insured member group number level and peak into your ERISA payor behaviors.
- It’s important to note that for items a and b, the provider is mapping at an even more granular level that includes the payor, the CARC, the RARC (if required per the CARC), and potentially the group number level based on analytic functionality.
- Initial denial
- Lastly, for each combination, follow steps 1-3 in the General Denial Mapping section above for each CARC/RARC combination.
While denial mapping is both tedious and laborious it is an eye-opening endeavor well worth the journey.