In a value-based world, accurate risk adjustment is essential to healthcare providers. Health plans rely on physician medical record documentation for accurate RAF (risk score) projections.
But executing an accurate prospective-first program requires more than just good predictive algorithms. It also involves supporting physicians with education and resources that encourage them to document their risk-adjusted diagnoses accurately.
How Risk Adjustment Works
Risk adjustment estimates each individual’s care cost based on their specific health needs so that healthcare providers are paid fairly for the patients they treat. It’s important because otherwise, insurance companies could be incentivized to deny coverage or charge higher premiums for those with pre-existing conditions. Risk adjustment allows providers to be fairly compensated for the care they provide, and it’s a necessary step as healthcare continues to transition from fee-for-service models to value-based ones.
Risk-adjusted reimbursement is based on the Hierarchical Condition Category (HCC) codes that physicians document during patient visits, so HCCS must be accurate and complete. This is especially true regarding Medicare Advantage plans, where HCCs drive reimbursement.
One of the most effective ways to improve HCC accuracy and optimize coding productivity standards is by arming the Medicare Advantage coding team with automated analytics tools to validate member risk assessments before sending claims to CMS. By giving coders access to a more comprehensive member profile that includes social determinants of health (SDH), physician office staff can reduce clinical documentation workload and mitigate the risk of over-coding. The result is more precise medical records and claims data supporting accurate coding of current diagnoses. Physicians can also be backed by collaborating with care coordinators and case managers who can help them document SDH for coding purposes and better inform member outcomes.
Why It Matters
Many health plans use RAF scores to determine eligibility for programs like physical therapy, case or disease management, transportation services and other patient-centered supports. Getting this right is critical for both health plan costs and enrollee benefits.
Ultimately, reducing coding gaps is a low-hanging fruit for improving health plan financial performance that’s relatively easy to achieve compared to changes in care patterns. That makes focusing on prospective risk adjustment a smart strategy for physician practices and health systems participating in global payment models such as accountable care organizations (ACOs) or Medicare Advantage.
Prospective risk adjustment starts with a comprehensive medical record review, typically performed by clinical coders working with physicians. This ensures that all diagnoses documented to the highest level of specificity are included in each patient’s RAF score. Ideally, these diagnosis codes also support selecting the best ICD-10 code to capture the complexity essential for accurate risk adjustment coding guidelines. As new healthcare delivery models shift more of the financial burden from payers to providers, the need for this type of quality control is only expected to grow.
Accurate Coding Is Key
With accurate coding, healthcare organizations can save money, avoid HIPAA violations and risk being fined by federal regulators. It’s no wonder that healthcare organizations are constantly looking for efficient ways to improve coding accuracy.
One of the most important ways to achieve accuracy is by using software programs that automatically match every patient encounter to a specific medical code. These programs can quickly scan unstructured clinical charts and notes for relevant information, cross-reference multiple coding directories instantly, and compile results into standardized files that can be easily exchanged between coding teams, billing staff, clearinghouses and payer systems.
Using software to help determine HCC codes also allows for a faster, more consistent review of claims and identifies potential errors that could have been missed. This can save significant time and effort that would otherwise be spent scouring medical records for the right diagnoses and procedures.
Other important steps include:
- Setting up a working group to focus on key action items, such as an accurate problem list.
- Ensuring chronically ill patients are seen once per calendar year.
- Improving decision-support and EMR optimization.
- Educating clinicians and staff.
- Tracking progress and identifying opportunities for improvement.
Health systems are also encouraged to develop a leadership message around the importance of accurate coding. This can have a trickle-down effect, and it is not uncommon to see organizations with high coding compliance rates perform better than those who have not prioritized coding accuracy.
Getting Started
Achieving accurate risk adjustment coding in a health plan is a big challenge, but it must be tackled to ensure patients stay healthy and programs have the revenue needed to manage their care. With the right strategies in place, it’s possible to reduce coding gaps and boost HCC scores.
Unlike regular physician office coding, which focuses on the patient’s chief complaint, risk adjustment coding emphasizes the importance of all diagnoses related to the individual’s medical history and current risk level. This means that physicians must focus more on the entire patient and document each diagnosis, even if it doesn’t directly relate to their current treatment plan. Failure to report a diagnosis or misreporting a condition can skew the patient profile and affect funding for that member’s management.
To minimize the impact of coding errors, educating clinicians on accurately reporting all diagnosed conditions is critical. Workgroups and targeted communication can help providers use the most up-to-date clinical guidelines, understand hierarchical condition categories (HCCs), and capture and report all relevant diagnostic codes.
Another way to improve coding accuracy is to adopt prospective solutions that analyze data before the patient encounter. This can include looking at prior claims, lab reports, and EMR problem lists to identify conditions the provider may not have coded or documented. Prospective coding can reduce retrospective “chase” lists and increase overall productivity.
