Anyone who has taken the CRC™ (Certified Risk Adjustment Coder) curriculum can tell you that risk adjustment was first utilized in the mid-90’s for Medicaid purposes. The goal of risk adjustment has always been to collect data on patients so that money being allocated could plan for not only the current diagnoses, but to allow for a model where that value is increased in correlation with the increasing costs of caring for patients with manifestations or complications of those known chronic conditions while also considering all comorbidities.

Risk Adjustment does not collect diagnosis codes in order to audit back to a specific encounter, but instead seeks to document all current diagnoses that are annotated or documented by an approved provider type in connection with a face-to-face encounter.

The diagnoses collected are aggregated for a whole number (Risk Adjustment Factor or RAF) for the patient for each year. The diagnoses are not required to be addressed or treated, but they must be actual current diagnoses. The goal isn’t to account for that specific visit, but to collect all current diagnoses for each patient for each year.

There aren’t additional payments for finding the diagnosis more times. Once a diagnosis is picked up, the value for the diagnosis is applied to every month of that same year of service of that encounter year because it is the overall yearly risk score that is being adjusted. It is not a risk score per encounter in the way there is an RVU (relative value unit) of work for each encounter.

This is the hardest concept for some to understand. Even some elected officials called foul without understanding this new modeling concept. They accused health plans of suddenly gaming the system because ICD codes were being reported at an all-time high while not realizing the truth is that we have been underreporting diagnoses for decades and risk adjustment was correcting this problem.

I decided to share this information below because I believe in risk adjustment and I believe that if we do it properly that we can improve the health of millions. The train is clearly running off track and it will take all of us to correct its course.

I have had the privilege of being hired by one of the very first risk adjustment vendors nationally in 2008 and subsequently was invited to the very first CMS RADV training because one of our insurance clients didn’t have a coding director yet. I have worked closely with people at both CMS and HHS. I have met people at RTI who developed the HCC models for CMS and I have worked on many cases before the DOJ and OIG.

Providers do the best they can to accurately document diagnoses, but many of them are unaware of certain coding rules and are shuffled through a chaotically packed schedule day. Health plans are held accountable for getting diagnoses submitted right, but they are often only forwarding on what they themselves have received on claims.

As the original CRC™ curriculum author, and creator of the TAMPER™ acronym, I feel the need to speak up so that we can all code on common ground. This is vital toward the success of risk adjustment’s purpose.

Risk Adjustment was created because FFS cannot help us become proactive clinically

Fee-For-Service (FFS) is when a provider submits a claim for an encounter or visit that was provided in the past and they seek reimbursement for the services or procedures that were provided. Procedure codes are supported by diagnosis codes to show “why” the encounter or visit was necessary, and they need to correlate with one another in a way that the diagnosis explains the medical necessity of the service that had been provided.

We have long known that this model is a disservice to the providers on the front lines as they do not gain credit for complex medical decision making with the chronically ill patient population and can only bill for the Relative Value Units (RVUs) that were provided directly on that visit date. We also have no correlation with FFS billing and coding with quality-of-care initiatives. It is difficult to determine if a provider who sees a certain type of patient more often does so with great success or if those visits were needed due to poor clinical outcomes, patient noncompliance, or some other factor.

It is important to note that 5 to 10% of all claims are still being submitted using paper superbills which are not updated timely and do not allow for robust reporting. Another variable is the selection of EMR systems as some limit the number of diagnosis codes that can be added and others populate commonly used codes, both of which impact risk adjustment.

While providers are incrementally paid for each visit or encounter, the issuers or health plans themselves are not paid this way by CMS or HHS in risk modeling. This disparity is part of the problem. Health plans are the guardians overseeing the care of patients and they have often been caught off guard with diagnoses that they were not aware the patients even had, and this is directly due to the underreporting of health data (diagnosis codes in this case).

The biggest losers in this scenario are the patients themselves. Diagnoses that are not reported are left out of analytics and planning. It is nearly impossible for health plans to prepare for the financial needs of manifestations and complications of diagnoses when they are unknown. 

It is commonplace for health plans to run all kinds of analytics. They compare what is known for each member from year to year and watch for diagnoses that “fall off” (of claims reporting for the year) unexpectedly. They run suspecting analytics to try to estimate clinical outcomes and what may be needed for differing patient trajectories and individualized to each patient. They analyze a host of factors from age, race, gender, socioeconomic status, geographical territories, known life-long and permanent conditions, medications that are being used, common clinical progressions of certain diagnoses, and more.

When diagnoses are left out of this mix, two major negative impacts occur. One is that the financial reserve to help pay for those manifestations and complications is terribly underbudgeted and the other is that patients may not get enrolled for wellness programs that aim to keep patients out of the ER and avoid unnecessary hospitalizations. There are many patients who, with appropriate proactive clinical efforts, could avoid complications and manifestations.

Read the Brian’s complete article below (.pdf) where he covers topics such as:

  • Does MEAT (Monitor, Evaluate, Assess, or Treat) have a place in Risk Adjustment?
  • Is MEAT needed during RADV or HRADV audits?
  • What does CMS & HHS have to say about all this?
  • High level Risk Adjustment audits & actuarial firms, are they getting it right?
  • Should we trust Risk Adjustment Model design?
  • Concept vs. Reality, what does it really take to be a Risk Adjustment Coder?

Brian Boyce, MHA, BSHS, CRC, CPC, CTPRP, Cert. Coding Instructor, Cert. Clinical Bioethics

Brian is original author of the CRC® (Certified Risk Adjustment Coder) curriculum. He is recognized nationally in risk adjustment as an expert serving on cases before the DOJ and OIG. He has special interests in bioethics, patient safety, disease management, and the leadership of people. Brian is a veteran of Desert Storm, the former CEO of ionHealthcare, and the current Director of Risk Adjustment Integrity Group at Advize Health.