Healthcare providers are measured on certain performance metrics that dictate their payment amounts under value-based contracts. Risk adjustment plays an integral role in determining financial performance. In order for these contracts to be equitable for insurers and providers, risk adjustment must accurately capture changes in population morbidity to effectively measure the provider’s true cost impact.
In this article, Milliman’s Rong Yi, Howard Kahn, and Jared Hirsch highlight common data issues that affect risk scores. They also discuss practices that can improve coding efforts related to risk adjustment.
Section 1332 of the Patient Protection and Affordable Care Act (ACA) allows states, starting in 2017, to waive certain ACA market rules to allow for more tailored commercial individual and small group market solutions. When states consider market reforms such as reinsurance under the 1332 Waiver with the aim of stabilizing the market and providing affordable coverage, it is important to consider the challenges and options in the context of their effects on other market stabilization mechanisms like risk adjustment. Milliman consultant Rong Yi offers some perspective in this paper.
The use of risk adjustment in provider reimbursement arrangements has increased as alternative payment arrangements are becoming more widespread in health insurance. Risk adjustment has been used by Medicare Advantage and managed Medicaid programs to reimburse health plans for the unique risks and populations in their care. More recently, as carriers have transferred utilization risk to providers through alternative payment arrangements such as global budgets and bundled payments, risk adjustment has been used to reflect a provider’s patient’s severity. Also, under the Patient Protection and Affordable Care Act (ACA), beginning in 2014 risk adjustment will be used to transfer payments among all fully insured individual and small group plans.
Many existing risk-adjustment methodologies have been developed and used on populations that include a mix of adults and children. Because adults form a larger proportion of the average population, the disease states recognized in these methodologies were optimized with greater emphasis on adults. A chosen risk-adjustment methodology should reflect the characteristics of the underlying patient population, so organizations such as children’s hospitals, pediatric provider groups, and health plans that enroll a large proportion of children have begun to question these standard risk-adjustment models.
Milliman consultants Howard Kahn, Rob Parke, and Rong Yi explore this topic in their paper, “Risk adjustment for pediatric populations.”
Risk adjustment is commonly used by health insurers to increase the accuracy of cost models by taking into account the risk characteristics of different populations, usually by providing a risk score for each individual. Milliman consultants are commonly asked to perform risk adjustment and are always looking to advance its analytical utility. In a recent paper, Milliman consultant Ksenia Draaghtel demonstrates that
…segregating risk by service category better represents the differences in utilization and cost within each component, and is an important aspect in actuarial pricing. Inpatient, outpatient, physician, and pharmaceutical services possess different characteristics with respect to the utilization frequency, cost severity, speed of claim payment, and underlying trends.
The simulations used in the paper show how these techniques can increase profitability.
Risk adjustment has become more newsworthy recently with the resurgence of interest in provider cost-sharing as a mechanism of healthcare cost control. Two recent Milliman papers discuss the issue. The first, “Risk adjustment and its applications in global payments to providers” by Rong Yi, Jon Shreve, and Bill Bluhm, is a great place to start if you are new to the topic. It starts with a rationale for using risk adjustment in this field. Take two patients, one healthy, one with chronic diseases, each patient being treated by different providers. Assume that healthcare payments are determined by patient age and gender. Without risk adjustment, the provider with the sicker patient would appear overpaid and inefficient and would miss out on efficiency-based performance bonuses. The paper goes on to discuss risk adjustment methodologies, concurrent and prospective models, data issues, and other important considerations.
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.
The Massachusetts Medical Society has published two papers on risk adjustment:
The papers provide an overview of risk adjustment and where it fits in health reform while offering practical suggestions for using risk adjustment in Massachusetts.