Imagine you are a healthcare provider seeing a complex patient with symptoms that could point to hundreds of conditions. Now imagine you have to do it without labs, without imaging, without the patient’s social and family history. The potential outcomes are endless; sometimes you might diagnose and treat them correctly, other times you might not. The worst scenario would be if you only treated the patient’s symptom while the underlying disease continued to progress and worsen.
This is what we do in healthcare when we try to fix the revenue cycle without looking at the denial data. Here are 3 facts about denials:
The chaos we feel with denials typically stems from a lack of staff education, an ineffective/absent process, a breakdown in communication, or an unused automation.
Studies show that, on average, 19% of in-network claims across the nation are denied,1 with some states showing in-network denial rates as high as 34%.1 Industry data also reflect the average cost to work claims is high, with the average administrative burden for commercial claims at $63.76 per claim.2 What does 19% of your total claims at $63.76 look like in terms of overall staff burden?
As an industry, we have opportunities to navigate from chaos to clarity, and it all starts with your data. If you don’t have access the to the critical data needed, then work with your practice management system or clearinghouse vendors to gain reporting access. Most systems allow custom reporting, detailed drilldowns, and even custom dashboards. If you are working in an older or less capable system, consider partnering with a data analytics vendor for support.
Once you have your data, make sure they’re accurate. If you have any concerns about the accuracy of your data, fix that first. You don’t want to develop a strategic plan based on inaccurate data. Likewise, you don’t want to assume that surprising data are inherently incorrect.
Next, perform a comprehensive review by slicing or grouping data multiple ways, for example:
Looking at your data multiple ways starts to tell a story. Here is an example of how that can look:
A revenue cycle manager reviews by frequency, and the most frequent denial reason (5%) by denial code is a Current Procedural Terminology (CPT) and modifier mismatch. They slice the data again and review by department, identifying that 38% are coming from front desk activities like “the patient cannot be identified as insured” or “patient did not have coverage for this date of service.” Then they slice once more, this time by provider, and find that 18% are coming from 1 payer for 6 different credentialing-related denial reasons. In order to create a strategy, you have to identify the root causes of the problems first.
Once you know the top 3 to 5 issues, you can begin to solve for them and to prevent them from happening again. Knowing where you should focus is what keeps you from treating symptoms. From there, you can evaluate the people, processes, and technologies that are in place around each denial you’re seeing.
This may mean surveying staff, looking at practice management system rule engines, reviewing existing standard operating procedures for updates, or documenting process gaps. Once you know the problem and your current operations thoroughly, it’s time to deploy strategy.
Education and automation are 2 ways to strengthen your revenue cycle and reduce denials. For example, using the denial examples above, you could create a strategy to:
Use key performance indicators (KPIs) to help identify how your new efforts are performing. Start by identifying key metrics that align with your areas of concern. For example:
What percentage of total denials was eligibility-based in the prior 12 months?
Lastly, remember that you should review your top 3 to 5 denial reasons every month/quarter. As you become more effective at identifying, resolving, and preventing denials, you will see new and different issues taking the top spots. Don’t forget, the revenue cycle is never “done.” It is constantly moving and evolving, which requires you to do the same. On the bright side, your data have the tools you need to replace chaos with clarity. n