Orthopaedic Risk Stratification and Comorbidity Coding
AAHKS Improving Risk Adjustment Models
For three years, the AAHKS Risk Adjustment Task Force has been working with CMS and the Yale Outcomes Group to improve the risk adjustment models used in TJA performance measures. The goal of performance measures is to give surgeons an accurate assessment of their performance, while controlling for patient factors outside the control of providers. The current TJA performance measures being reported on hospitalcompare.gov are based on administrative claims data; therefore, unless those risk factors that are known to influence outcomes (e.g. smoking, obesity) are captured in the administrative record, your outcomes will not be properly risk adjusted.
We Need Your Help, Patients Need Your Help
We need to begin to document important clinical risk factors for lower extremity arthroplasty and have them tested to see if they improve the current risk model. We have already tested a few of these, that is smoking and obesity, and they improved the model significantly. We hope to continue to optimize the model by adding further clinical variables.
We are seeking your help in systematically capturing the risk variables known to influence outcomes. We have created an easy-to-use checklist, similar to what you currently use to document medical necessity of arthroplasty to avoid RAC audits. We understand that this adds another layer of burden to your preoperative visit, but it is important so that you will be judged fairly and maintain access for our patients.
Please forward this checklist to your hospital coders and communicate the importance of this document so that this may be coded appropriately. The checklist includes instructions on how to use it in your office and with your EMR.
- To understand how to incorporate the checklist into your EMR read the AAHKS Primer on Risk Adjustment by the AAHKS Risk Adjustment Task Force.
- For surgeons interested in improving their understanding of perioperative comorbidity coding and its necessity, please see the JBJS article, “Using Joint Registry Data from FORCE-TJR to Improve the Accuracy of Risk Adjustment Prediction Models for Thirty-Day Readmission After Total Hip Replacement and Total Knee Replacement.” Journal of Bone and Joint Surgery, Volume 97A, No. 8, April 15, 2015, 668-671.