Five Data Analytics Metrics That Help Prevent and Manage Denials

Your data will determine the overall success of your denial prevention and management efforts.

No matter how robust the clinical documentation integrity (CDI) program, there are still payer denials. Payer audits become more creative and voluminous each day. Just when we think we have a handle on the top clinical validation targets, more are quick to take their place. In addition to that, different payers often apply separate criteria. How can the revenue cycle team navigate amidst so many moving targets and mitigate the damage, to the extent possible?

In this article we will examine five actionable data analytics metrics that will give your facility the leverage to turn the tables on the payers, preserving revenue and providing a strategic approach to managing and preventing denials and recoveries.

1. Revenue at Stake per Payer Clinical Validation Policy
While we can pinpoint the most common denials (sepsis, severe malnutrition, encephalopathy, just to name a few), a tracking mechanism to capture those analytics with drill-down capability identifies the most troublesome payers and policies, as well as the revenue at stake, correspondingly. This is important for a few reasons. First, if the recoveries are legitimate, it provides a basis for engaging key members and stakeholders. Next, this information provides the ability to determine if contract language should be amended; for example, if the payer has carte blanche to any record they wish, with no limitation on the number of records they can review, this will quickly and effectively deal a sustainable blow to the revenue stream. Modifying language to limit reviews may be in order, in this case. Finally, a state law that can be invoked in your behalf may exist. For instance, New York State upholds Sepsis 2 criteria, while most payers insist on Sepsis 3 (SOFA) or a more stringent version of Sepsis 2. Hospitals in New York State have been successful at overturning sepsis denials by leveraging state law.

2. Root Cause per Upheld Denial
As mentioned, there are times when a denial is legitimate because one or more diagnosis is not supported by the documentation in the record. A root-cause analysis will demonstrate whether the diagnosis was clinically unsupported, or whether it was unsubstantiated from a coding standpoint. Reporting of this type of analytic should be increasingly granular to identify specific causes and even sub-causes; for example, analyses should identify a Coding Guidelines or Coding Clinic point that was missed or incorrectly applied, or whether the documentation was simply inconsistent. Compliance initiatives can thereby become the focus of internal second-level reviews.

3. Denials with a Query in the Record
Another important analytic is CDI metrics that determine how many denials were associated with a query on the chart. This analysis enables a more scrutinized approach of the effectiveness of the query process. For example, was a query performed solely to capture an MCC or CC, or did it honestly reflect the severity of illness? Data of this sort can optimize CDI program initiatives. 

4. Root Cause of Denial by Coder and CDIS
Closely following 2 and 3 is the analytic that identifies the coder and/or CDI specialist (CDIS) on the case. For example, if a CDIS queried for sepsis in a patient with a temperature of 100.4 and heart rate of 99 and no other meaningful criteria, this would provide valuable information. Likewise, a coder who consistently codes secondary diagnoses that do not meet the UHDDS definition of “other diagnoses” will need additional education.

5. Denials per Physician and Service Line
To identify what department (or possibly more importantly, who) needs education is data describing denials by attending physician and/or service line. Analyses of the service line (cardiology, neurology, etc.) involved helps to track down which department is generating more under- and over-documentation issues. The knowledge gained from this analytic may be the impetus to engage a physician advisor, which has proven to be an effective strategy in reducing denials.

In conclusion, it is not in the payer’s interest to continue to request and review records from a hospital with a high rate of non-recovery. Each data analytic outlined in this article represents a call to action to assist in the prevention of recoveries by the payer. What you do with your data will determine the overall success of your denial prevention and management efforts.