Tag Archives: Rong Yi

Equalising risk in global healthcare systems

Health insurance, like most insurance, can be priced using risk ratings, where premiums are set based on the relative risk of insured lives and the propensity to claim. This may result in unaffordable health insurance for the most high-risk members of society. As a result, many governments restrict the use of risk ratings in health insurance markets in favour of “community rating.”

In a community-rated system where all consumers are charged the same premium, many high-risk consumers are protected from paying unaffordable premiums. Other consumers, such as healthier or younger individuals, will generally pay a higher premium to subsidise sicker and often older individuals. Consequently, premium revenue collected by insurers or other risk-bearing entities may no longer truly reflect the underlying risk associated with their insured populations.

In many healthcare systems and health insurance markets around the world where risk rating is not allowed, risk equalisation is used to enhance consumer protection and market stability. Its aim is to compensate for the risk profiles of different groups of the population such that the additional medical expenses associated with high-risk members are shared amongst healthcare providers or insurance companies.

In this paper, Milliman consultants have set out a “how-to” guide to risk equalisation, or risk adjustment. They use illustrative examples from around the world to explain the challenges and practicalities that should be considered in the design and management of a risk equalisation program.

What does the health insurance landscape look like in China?

China ranks only behind the United States in total healthcare expenditures. It has at least 95% of its 1.4 billion people covered by basic, government-sponsored health insurance. But China’s annual per capita health expenditure is relatively low at around $425, or just over 5% of GDP, though it has been on the rise over the past decade.

In China, there are three types of publicly financed basic medical insurance: For the urban employed population, for the urban non-employed population, and for rural residents regardless of employment status. Funding of health coverage comes from the central government, local governments, employers, and the participants, and the level of funding varies by geographic area. On average, out-of-pocket spending accounts for 32% of China’s total health expenditures and varies greatly by geographic area.

China’s public insurance programs are facing pressure from all sides of the healthcare ecosystem. Population aging, urbanization, environmental issues, increasing disease burden due to chronic conditions and cancers, escalating unit cost in healthcare, waste, and low-value services are all adding to the growing demand for better and more efficient healthcare.

At the same time, the growth in social insurance funding, part of which is used to fund basic medical insurance, has slowed due to an aging population. And the role of commercial health insurance in China has changed significantly over the past 15 years and continues to evolve as the Chinese government implements reforms that affect the overall healthcare ecosystem.

To learn more about the health insurance market in China, read this article by Milliman’s Rong Yi and Sharon Huang, which discusses recent trends and how data and analytics could be used to address some of the challenges and support market growth.





Implication of coding on risk adjustment and valued-based contracting

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.





Pairing risk adjustment to support state 1332 waiver activities

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.





Risk adjustment for pediatric populations

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.”





Milliman perspectives on healthcare risk adjustment

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.

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