Seeking Alpha offers a high-level view of risk adjustment and what it means for health reform. Here is an excerpt:
The process of measuring risk factors for the purpose of risk-adjusted reimbursement is called simply risk adjustment: something that Wall Street does quite efficiently in a marketplace setting.
OK, so how does risk adjustment work in the reform context?
As with any type of modeling, risk adjustment is an imperfect science, especially as it pertains to a price discovery process that involves thousands of beneficiaries but only one payer and one provider.
In a recent white paper written for the Massachusetts Medical Society, Milliman, the actuarial consultancy, tackles the intricacies of risk adjustment and submits key principles that both the payer and provider need to heed when looking to form ACOs. [Read “Risk Adjustment: Important Considerations for Global Payments to Providers”.]
Many people, the Milliman paper explains, view the ACO concept as a viable alternative to the existing fee-for-service payment system. Not waiting for the U.S. government, commercial entities have already conceived and deployed ACO-type models, which included both a risk-adjusted global payment and a performance-based payment.
The government model continues the fee-for-service system but introduces additional payments based on a set of benchmarks for health care costs, outcomes, and quality. HHS is expected to recognize risk adjustment tools that will determine how much it reimburses groups for exceeding these benchmarks.
Milliman lists the following five key risk adjustment design principles:
- The groupings of medical conditions in a risk adjustment model should be clinically meaningful and reasonably specific, in order to minimize opportunities for gaming or discretionary coding.
- Diagnoses within the same condition category should be reasonably homogeneous with respect to health care cost and utilization, in order to optimize predictive accuracy and robustness of the model.
- Condition categories should have adequate sample sizes, to permit accuracy and stability of model predictions.
- The risk adjustment model design should encourage specific coding and discourage vague coding. Vague codes and nonspecific diagnoses should be excluded from the risk adjustment model
- The risk adjustment model should not reward coding proliferation. Providers should not be penalized for recording additional diagnoses. In other words, coding more diagnoses should not reduce the risk scores.