Whether you’re a medical coder or a health plan actuary, understanding the basics of risk adjustment is essential. It’s how a health plan gets paid for the healthcare costs of its members. Risk adjustment programs use demographic information from the member’s enrollment application. It’s then mapped to a risk score that predicts future healthcare costs.
Medicare Risk Adjustment Payment Model
The Medicare risk adjustment model differs from the commercial model in that it considers only those costs for which a Part D plan is liable (copayment and deductible costs), and the categories are different, reflecting outpatient drug cost data rather than hospital costs. It’s also a prospective model, using the diagnoses submitted in one year to calculate the risk factor used for payments in the next.
In addition, the CMS-HCCs are hierarchical: Certain conditions within a family may significantly differ in severity. The highest severity condition “trumps,” or takes precedence over, lesser-severe conditions in the same family. For example, diabetes has a higher HCC value than hypertension. This is why risk adjustment coders need to understand the specifics of each model.
Because of this, the risk adjustment process is complicated. As a result, navigating the changes that occur annually alongside the daily challenges of accurately reporting diagnosis codes requires constant vigilance. Having the right resources to ensure compliance and support your members throughout their health journeys is key.
Hierarchical Condition Categories (HCCs) List
Hierarchical condition categories (HCCs) are all the rage in healthcare as the industry moves to value-based payment models. HCCs are a set of diagnostic codes that identify chronic and serious health conditions, which are combined to calculate a risk factor score for a patient. This score then determines reimbursement for Medicare Advantage plans, ACOs, and certain Affordable Care Act (ACA) plans.
The HCC model improves on previous risk adjustment strategies that only looked at demographics by including clinical diagnoses. This process is vital because HCC coding directly impacts the amount of money a health plan receives from CMS or HHS every month for managing an enrollee’s care. Inaccurate coding will result in an underpayment to the health plan. Getting accurate HCC coding is difficult and requires more than just knowledge of medical coding rules. The coder must understand the patient’s entire health picture, including social determinants of health. A coding professional may be tempted to rely on medical necessity and ICD-10-CM codes when documenting a claim, but these may need to provide more detail for an accurate HCC calculation. In addition, the coding professional must be able to identify and capture all HCC diagnoses documented in the medical record. This diagnosis must be documented in enough detail to ensure a specificity level that CMS or HHS can use to calculate a risk factor score.
Commercial Risk Adjustment Payment Model
In the commercial risk adjustment model, healthcare providers document health assessments of their patients. These assessments lead to appropriate medical record documentation and diagnosis coding, which is then submitted for reimbursement by the plan. This information is used for various purposes, including assessing a patient’s risk level and identifying opportunities to reduce costs and improve outcomes.
Risk adjustment is a key element of Medicare Advantage and the Affordable Care Act (ACA) value-based payment models, which align payments with the risk characteristics of members in each plan. Accurate risk adjustment relies on comprehensive, face-to-face health assessments and the associated documentation that produces accurate diagnosis codes. Although no model predicts a person’s true costs perfectly, there are ways to quantify and measure model error to gauge accuracy and help determine if the models should be adjusted or replaced. One such method is a statistical process called bootstrapping, which uses multiple random samples of data from the same population to test how well the model performs.
Most health plans use a federally-certified risk adjustment model to determine their per-member-per-month capitation payment. While some states may choose to run their risk adjustment programs, those that deviate from the federal methodology must seek HHS approval and submit yearly reports to demonstrate they meet regulatory requirements.
Medicaid Risk Adjustment Payment Model
Despite differences in the specifics, risk adjustment models share several common characteristics. They are ordinal least squares regression models designed to predict an individual’s total expenditures (relative to the average person) either in the current year (concurrent model) or the next year (prospective model). They use demographic information and data from medical records to assign individuals to one of the major condition category groups.
Unlike commercial risk adjustment, Medicaid risk adjustment programs get demographic information in the enrollment application and medical record submission process, typically a comprehensive review and documentation of a beneficiary’s health status. Prospective risk adjustment uses the risk score calculated to determine Medicare reimbursements to a Medicaid plan for its beneficiaries before they receive any face-to-face encounters.
Medicaid populations are often low engagers with their providers and may be unaware of or unfamiliar with many of the chronic illnesses they have. Therefore, accurate and complete encounter and supplemental data submissions are critical for a Medicaid risk adjustment program to be successful.
