Proper Coding for Risk Adjustment Helps Reduce Healthcare Spending
Two questions put risk adjustment into perspective on how it affects coding and physician payment.
If you wonder what exactly risk adjustment is, how it impacts coding, and why it has become an important part of diagnosis coding, we have answers.
What Is Risk Adjustment?
Risk adjustment is basically a tool that determines how much money is needed to care for a patient based on how healthy or unhealthy they are.
The American Academy of Actuaries defines risk adjustment as, “An actuarial tool used to calibrate payments to health plans or other stakeholders based on the relative health of the at-risk population.”
For example, providing care for a 24-year-old male with no chronic conditions is much less expensive than caring for an 86-year-old woman with diabetes, chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). There are basically three risk adjustment models: The Centers for Medicare & Medicaid Services (CMS) funded Medicare Advantage plans, U.S. Department of Health and Human Services (HHS) funded risk adjustment that provides capitation to the Affordable Care Act (ACA) health exchange plans, and state-funded pools that are used to reimburse commercial insurers for specific high-risk patients.
The HHS risk adjustment model is for the non-elderly population and is used when HHS is operating risk adjustment on behalf of the state. This model uses a hierarchical condition category (HCC) system like that of the CMS-HCC system, where diagnosis codes are placed into categories and those categories are then weighted. Unlike the CMS-HCC system, HHS uses a concurrent model where this year’s diagnosis data is used to predict this year’s expenditures. The overall process includes calculation of individual risk scores from claims data, calculation of the average score of the plan, adjustments, and finally transfer of payment based on the adjusted plan average risk score.
How Do Conditions Impact Healthcare Dollars?
According to HealthIT Analytics, in 2015 Medicare found that patients with common chronic diseases such as cardiovascular disease, diabetes, and asthma, were 2 percent more likely to experience a preventable hospital admission if they fell into the highest quartile of care coordination as opposed to the lowest. High fragmentation was associated with $4,542 more annual spending than lower fragmentation. The 2016 white paper published by the Partnership to Fight Chronic Disease, referencing the New England Journal of Medicine, said that 86 cents of every dollar spent on healthcare goes to treating people with a chronic condition. For each additional chronic condition a person has, that patient’s medical costs increase by more than $2,000 a year, on average.
It is no secret that the U.S. healthcare system suffers from high costs that do not yield high levels of quality.
“Almost half of the nation’s health care spending is driven by the top 5 percent of the population with the highest spending, while the top 1 percent account for more than 20 percent of total health care costs,” according to Harvard Business Review’s, “Redesigning Care for High-Cost, High-Risk Patients.”
The state and federal governments use programs such as the CMS Medicare Advantage plan, the HHS ACA risk adjustment, and other state-funded high-risk pools to reimburse commercial payers for high-risk patients. These pools are exactly what they sound like: CMS, HHS, or the state have a budgeted amount of money in a pool and, using the risk adjustment methodology, distribute that money among the various plans and payers with higher amounts allocated to those that cover higher risk and higher cost patients.
So what’s the problem? Payers with a higher population of high-cost patients get more money, right? Well, maybe, but in a lot of cases no. This information comes to CMS, HHS, and the state through diagnosis coding and the onus lies on the provider (many times the primary care provider) to accurately document, code, and submit the claim information for these patients. The payer submitting this data to the government entity doesn’t see or treat the patient. Unless they conduct rigorous audits of the claim data submitted to them for the populous of these programs, they have no idea whether they are accurately capturing the health status of their populations. This establishes the opportunity for a program that will effectively lower costs and increase revenue for payers.
Unlike overpaying for services that are billed, risk adjustment payers aren’t losing money in the traditional sense. What happens is that the payer doesn’t realize the true cost of the patient attributing to higher risk. In Table A (on the next page), you can see the difference in attributed cost depending on the specificity of the diagnosis coding.
Table A: The difference in attributed cost depending on the specificity of coding.
Based on 2019 Final HSS Risk Adjustment Model Coefficients
|Variable||Demographic Info Only||Inaccurately Coded||Accurately Coded|
|45-year-old Male: Silver||0.164||0.164||0.164|
|Diabetes, with Comorbidities
Inaccurately Coded: E11.9
Accurately Coded: E11.22
|Chronic Kidney Disease, Stage 5
Inaccurately Coded: N18.9
Accurately Coded: N18.5
|Amputation Status, Left
Inaccurately Coded: Not Coded
Accurately Coded: Z89.512
|Risk Adjustment Factor||0.164||0.627||5.141|
|Annual Payment (assumes $800/mo)||$1,574.40||$6,0190.20||$49,353.60|
Looking at Table A, consider the following examples:
Example 1: The provider sees the patient and simply codes a well visit. There are no conditions and the risk adjustment factor (RAF) is simply the demographic information.
Example 2: The provider codes for the patient’s diabetes, but again is not specific. The RAF now increases, and assuming a baseline annual payment of $800 per month, the payment increases by approximately $4,500 — meaning, that this patient with diabetes will cost the plan approximately $6,000 a year instead of $1,500 a year.
Example 3: The provider accurately codes all the patient’s chronic conditions, including diabetic CKD, stage 5, and the amputation status code for his prior left leg amputation. The RAF is significantly higher, and cost of this patient rises by more than 800 percent. Payers with a higher than average high-cost/high-risk population and inaccurate reporting will end up spending a disproportionate amount of funds on those patients rather than what was budgeted.
In this example, the payer may have budgeted for $1,000 to $6,000 for the year for this patient, but at the end of the year the patient’s actual cost is closer to $50,000. Those costs are not then offset by the federal or state governments, since their population is considered of low or average risk. To recoup that overspending, payers and plans begin to charge higher premiums or exclude populations based on pre-existing or chronic conditions. Risk adjustment mitigates those issues, allowing for lower premiums and non-exclusion, but only if population health status is accurately reported.
It’s All about Documenting and Coding Conditions Properly
If the problem is that providers aren’t accurately documenting, coding, and submitting diagnoses that accurately represent a patient’s health status, then the solution is to implement a risk adjustment audit program, specifically one focused on commercial risk adjustment. There are a few vendors, such as Optum and Leprechaun, which search by using claims analytics algorithms for unidentified excess risk in health plan populations and then confirm the findings with audits to help plans increase their revenue from government payments or high-risk pools; however, providers can implement their own HHS risk adjustment audits in the office. By taking steps to verify the accuracy of the diagnosis codes according to the supporting documentation prior to claim submission, the provider will accurately report the level of risk for their patient population. Accurate risk increases positive patient outcomes and reduces the need for high premiums.
Amanda Turner, MBA, CPC, CDEO, CPB, CPMA, CRC, is the manager, service operations at Zelis Healthcare. Her experience includes managing audits and provider education. Turner holds a Master of Business Administration in Innovation Management from Temple University. She is a member of the Blue Bell, Pa., local chapter.
American Academy of Actuaries, “Risk Assessment and Risk Adjustment,” 2010: www.actuary.org/pdf/health/Risk_Adjustment_Issue_Brief_Final_5-26-10.pdf.
Health IT Analytics, Poor Care Coordination Raises Chronic Disease Costs by $4,500: https://healthitanalytics.com/news/poor-care-coordination-raises-chronic-disease-costs-by-4500
Partnership to Fight Chronic Disease, 2016 white paper: www.fightchronicdisease.org
Harvard Business Review, Eapen, Zubin J., and Sachin H. Jain. “Redesigning Care for High-Cost, High-Risk Patients:” Harvard Business Review, February 2017: https://hbr.org/2017/02/redesigning-care-for-high-cost-high-risk-patients
CMS, “Risk Adjustment Methodology Overview,” 2012: www.cms.gov/CCIIO/Resources/Presentations/Downloads/hie-risk-adjustment-methodology.pdf.