We all understand the importance of tracking denied claims. However, accessing the data and turning it into meaningful information can be challenging. Determining your top 10 denial codes may seem easy enough, but what if you want to dig deeper? For example, how many claims are you appealing? How quickly is your billing team working the appeals? How much money is at stake?  This last question in particular is essential if you want to quantify the impact of denied claims.

The first step in tracking denials is to determine how a denial is tracked in your system. While denials are typically associated with a denial/reason code, some practices use other methods to track denials. For instance, your billing team may add a tag or note to the line item that is denied. While this is an extra step, it can be helpful in identifying exactly which denials you want to track.

For the following examples, claims data has been pulled from the practice management system into PowerBi. There is no special tag, note, or other indicator to flag denials. So, we will just rely on the reason codes (keep in mind that this is sample data and will likely not reflect the real world).

This first visual is a dashboard that provides the manager with some high-level information. It displays the denied claims by payer, CPT code and by reason code. Notice the filter at the top of the visual named “reason_short”. This filter lists every reason code, and the user can pick and choose which reason codes are going to be tracked.

When conducting a denial analysis, I’m interested in a couple of key metrics. Not only do I want to know the numbery of denials, but I also want to know the total denials as a percentage of total billed line items. This metric was programmed into the analysis above, and the rate is shown on the dashboard.

Apart from the quantity of denials, I’m interested in the dollar amount that is at stake. And we’re not talking dollars in terms of charges, but rather dollars in terms of the allowed amount for the CPT code that is denied. The trick here is to tie all of your CPT codes to the contracted amount for each payer (hopefully you have your contracts loaded). This analysis pulls the allowed amount for each CPT code based on the payer. This allows us to quantity the denials.

It’s also important to understand how successful the appeal process is. We are interested in both the time it is taking to recover the money as well as how much we are recovering. The pivot table below displays a great deal of information related to denials and appeals.

The data is grouped by timeframe, and shows the total allowed dollar amount of denied claims. We can then see how much money we’ve recovered by timeframe (e.g., 0-30 days, 31-60 days, etc.) as well as the corresponding recovery rate for that time frame. You can also see the total amount and percentage not collected.

This pivot table also lets the user drill down into the denials. In the example below, you can see the breakdown of dollars by reason code. We could just as easily add CPT codes, providers, payers, etc. As in the first example of this post, we can also filter by reason code.

By moving this data into a tool like PowerBI, you can create any number of calculated measures that help you better assess your practices. Information such as the appeal recovery rate (including the rate b time frame) and the total denials in terms of allowed dollars are two examples of the kind of logic you can build into these reports.

You won’t be able to create these types of robust reports from the canned reports in your PM system. However, if you can access the raw data directly and use tools such as Excel and PowerBI, your ability to manage and make decisions will be enhanced.

If you have questions, don’t hesitate to email me at medicalpracticeintel@outlook.com