Now, that we've explored the role of risk adjustment and value based care and payment models, let's take a closer look at the Medicare Risk Adjustment or MRA Payment model, which CMS uses to pay MA organizations. MA premiums are determined and paid differently from those in original Medicare. CMS pays MA plans a capitated amount per enrollee to provide all part A and part B benefits. The CMS premium payments health plans is adjusted for each enrollee to account for the differing levels of care needed and the expected cost of that care. The diagnosis codes submitted on a medical claim for MA covered patients are more important than ever as diagnosis information is used by CMS to determine the health status of each member and therefore the reimbursement for that members care. Medicare Risk Adjustment is designed to encourage MAOS to compete based on efficiencies and quality of care rather than their ability to attract healthy individuals. This payment methodology is based not only on patient demographics, but also on the individual health status of Medicare Advantage enrollees. This model includes a crosswalk that takes more than 9,000 of the approximately 70,000 ICD-10 codes and assigns them to specific condition categories. When a claim is received that contains a diagnosis code in one or more of the categories, the value of that HCC is applied to members risk score. The total disease risk score combined with the members risk score from demographic data, indicates the members overall health status. CMS re-evaluates the individual health status of each enrollee annually, and CMS's premium payment to health plans is adjusted for each enrollee to account for that care. The MRA payment model is prospective. Data collected from one year results in payment adjustments for the following year. Conditions documented in the current year set the MA Plan's premium for the following year. To give you a sense of the dollar amount of risk adjustment in 2020, a risk score of 1.0 represents roughly $9,600 annually as the CMS average annual Medicare costs for an individual, though, the exact amount varies according to adjustments for age, gender and geographic variations each year. Thus, a physician treating a Medicare covered patient with a 1.1 risk score would receive $10,560 or 110% for care, while one treating a patient with a 10.9 score would receive $8,640 or 90%. The requirements for risk adjustment are two fold. To accurately and completely document diagnosis information for purposes of patient care and to submit accurate and complete information for purposes of appropriate reimbursement from CMS. Accurate and complete submissions of diagnosis stated to the health plan helps ensure appropriate reimbursement to the plan and to submitting providers, especially providers with value based payment models. Here you'll see a 2019 risk or calculation for a sample patient, a 72-year old female who is not institutionalized, but who has several conditions. Looking at her risk score, we see the following. First, there's a demographic risk factor. Given her age, gender and other factors, that she's not institutionalized, not also eligible for Medicaid and not disabled, her demographic factor is 0.374. Next, each medical condition congestive heart failure, or CHF chronic obstructive pulmonary disease, or COPD, and morbid obesity, each holds its own risk factor. CMS also recognizes that if certain conditions such as CHF and COPD are present in the same member, it'll cost more to care for that member due to disease interaction. A disease interaction value is then added to the equation. The model is additive. Thus, a combined HCC score for this patient is 1.114. The demographic factor and HCC factor combined to equal 1.488. CMS applies two additional factors to the calculation. A fee for service normalization factor, so payments are based on a population with an average risk score of 1.0 and a coding intensity factor, which adjust for differences in coding intensity between MA plans and original Medicare. Once normalization in coding intensity are factored in, the risk score in this example is reduced to 1.345. Continuing with the same example, let's assume the base plan payment rate is $781. Remember, the specific amount will vary. Simply, multiply the risk score and the base planned payment rate to get an average per member per month or PMPM rate for this member, which annualises to $12,605. To illustrate the importance of correct documentation and coding, without COPD, we would lose the associated value as well as the interaction value for the combination of CHF and COPD. This effectively would reduce the members risk score to 0.876, which translates to $4,395 less than the MAO would have to pay for the care of that member. Accurate coding and documentation to the highest level of specificity appropriate to reflect the members condition lead to improved accuracy of payment, allowing for stability and member premiums, and sustainable enhanced benefit plans. This slide illustrates an example of incomplete diagnosis coding, and the impact on the risk score. On the left, is how often we see coding for a member with Type 2 diabetes with the left heel ulcer. The coding reflects diabetes unspecified with no complications, and the heel ulcer is not linked to diabetes. Together, the conditions hold a value of 0.639. On the right, the conditions were accurately coded based on medical record documentation linking diabetes and its manifestation, coding the ulcer and the depression to the highest specificity. The conditions factor to 1.248. This incomplete and inaccurate coding results in thousands of dollars lost to the provider and a potential erosion of benefits for the member in subsequent years. In a second example, four diagnosis codes were submitted on the original claim associated with this medical record, as listed in the box on the left. The diagnosis codes submitted on the claim were incomplete based on record documentation. Some of the codes were inaccurate, and in some cases, the documented diagnoses were not coded to the highest level of specificity. Also, the lung cancer in the box in the left was coded as a current condition, even though it was documented as historic. This resulted in a risk factor identified as 1.35. The risk or changes based on accurate coding of the medical record documentation of historic lung cancer, the complete diagnosis codes are contained in the box in the right, which shows the actual risk factors should be 0.685. While this may not seem significant, these kinds of coding errors lead to added cost to the system and don't support the best care of the patient or the best experience for the provider.
