January 7, 2009

RAC Readiness: Identifying Risk – Now or Later

By

nhirschl120ds

 

Hospitals and healthcare providers need to identify their unique RAC risks to be financially prepared for RAC implementation realities. This article presents best practices for data mining, risk assessment, and mitigation initiatives.

 

I strongly recommend that healthcare providers employ a data-driven approach to RAC readiness. What better way to assess your organization’s RAC risk than to mine and analyze your claims data the way the RACs do? Of the many lessons learned from the RAC demonstration project, hospitals know that the RACs utilize a focused, targeted approach to complex review account selection. CMS mandated a non-random approach in order to emphasize detection of improperly paid claims.

 

So where does your organization begin?

 

Step 1 – Get to Know Your Data

 

Using algorithms that drill down into your DRG, ICD-9-CM codes, length of stay, discharge disposition, and charge data, the RAC has a fairly clear picture of its potential findings before the review process begins. So can your organization. While PEPPER reports serve as guides, H&A suggests that providers use the same kinds of data mining tools that the RACs use to drill deep. Whether using an internally developed or a state of the art, industry-expert tool, start data mining now.

 

Step 2 – Use Your Data to Determine Risk

 

Once you get to know your data, you will be able to determine potential financial risk, extrapolate forecasted reserves, and begin developing performance improvement initiatives.  Let’s use the following scenario as a guide.

 

In 2008, Hospital A had 1,000 one-day stay Chest Pain accounts. After mining and analyzing the data, 100 accounts were selected and reviewed to determine actual RAC risk. Review findings indicated that 30 of the 100 accounts reviewed were originally inappropriately billed as inpatients, resulting in a potential 30 percent RAC change rate.

 

To determine RAC risk:

 

  • Multiply Chest Pain DRG payment rate by 1,000
    $DRG rate = 3,700 x 1,000 = $3,700,000

 

  • Multiply this result by 30%
    $3,700,000 x 30% = $1,111,000 = Potential RAC Risk

 

As a result of the review, Hospital A identified that most of the inappropriate admissions were due to insufficient physician documentation and lack of comprehensive Emergency Department (ED) admission screening protocols.

 

Final data mining outcomes: Hospital A allocated $1.1M for potential RAC recoupment and hired an ED admission review team to mitigate future risks and improve performance.

 

Step 3 – Use Your Data to Identify Reward

 

Data mining also allows you to identify opportunities for potential financial reward. Let’s use the following scenario as a guide.

 

In 2008, Hospital B In 2008, had 200 one-day inpatient stays grouped to DRG 640, Electrolyte imbalance with Manor CC. After mining and analyzing the data, 30 accounts were selected and reviewed to determine actual RAC risk. Review findings indicated that 10 of the 30 accounts reviewed had originally been incorrectly coded (30% change rate) Review of medical record documentation indicated that the correct principal diagnosis assignment would generate an additional $1,600 in DRG payment.

 

To determine RAC reward:

 

  • Determine 30% of 200 DRG 640 accounts
    = 60 accounts

 

  • Multiply $1,600 by 60
    60 x $1,600 = $96,000 = RAC Reward

 

Final data mining outcomes: Hospital B compliantly improved DRG revenue and implemented an on-going coding education and audit program.

 

In summary, hospitals are best prepared for RAC when data has been mined and analyzed and when empiric information can support performance improvement. Remember, it’s all in your data.

 

Nancy Hirschl is President and CEO of Hirschl and Associates
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