Risk adjustment is critical to the fair and equitable application of value-based care reimbursement. However, risk adjustment methodologies can lead to abuses, intentional or not, through faulty processes and methodologies.
In fact, the Medicare Payment Advisory Commission estimated that risk scores for Medicare Advantage members in 2020, were about 9.5% higher than for traditional Medicare members – which translates to $12 billion in excess Medicare payments!
Accurately capturing the full disease burden of individual patients will ensure that health plans and providers will receive appropriate reimbursement for their membership’s health status and medical treatment.
And, as value-based care programs, e.g., Medicare Advantage, grow in popularity, identifying risk factors and measuring outcomes is key to reporting on quality and value-based performance metrics such as STAR, HEDIS and CAHPS for Medicare, Medicaid and Commercial populations.
Efficient data capture and data management are essential to the risk adjustment process. Health plans and provider groups are turning to AI/ML technology to standardize and streamline these processes, improving accuracy and reducing costs at the same time.
In particular, natural language processing (NLP) can automate chart review to analyze unstructured data (doctor’s and nurse’s notes) which contain critical pieces of information regarding a patient’s diagnosis, medical history and treatment plan.
These data can be algorithmically standardized and mapped to appropriate coding schedules (HCC, ICD-10, HCPCS, etc.) for accurate risk assignment.
Historical and current health information can be leveraged to generate individual longitudinal patient profiles which can be then expanded for population health management analysis, creating the foundation for a robust risk adjustment platform at scale.
Introducing AI/ML tools such as NLP, will provide health plans and providers a way to efficiently scale risk adjustment operations and negotiate favorable value-based contracts.
However, to ensure the integrity of risk adjustment models and NLP algorithms, health plans and provider groups should establish a self-governing body to regularly evaluate methodologies for both data operations and risk adjustment processes.
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