Risk Assessment and Risk Adjustment, are they synonymous?
Risk Assessment and Risk Adjustment is a statistician tool used to moderate payments to health plans or other stakeholders based on the relative health of the” at-risk” populations. In particular, if insurers are restricted in the extent to which premiums can differ by health status or other factors that are analogous with health spending, risk adjustment can help ensure that health plans are competently compensated for the risks they enroll.
An eloquent risk-adjustment methodology is the one that properly marshals incentives, limits gaming, and protects risk-bearing entities (e.g., insurers, health plans). Currently, risk adjustment is used in the Medicare Advantage and Medicare prescription programs, Medicaid programs, some of the governmental programs, and some private plans. Risk adjusting plan paying models relies on risk assessment to establish the comparative risk of insured populations, an overview of the risk-assessment process is also provided.
Health Risk Assessment is a procedure of equitably identifying whether an individual or group represents a risk that is close to the population and, if not, of quantifying the relative divergence from the average. Individuals who are expected to experience higher spending on health are considered relatively worse (i.e., higher) risks than those who are expected to incur to spend less.
In a quintessential health risk assessment, each individual knocks up based on an algorithm that amalgamates information on the individual’s age, any diseases during the previous year, and other factors. This process works like an MCQ. The response to each question initiates a numerical value, which is combined to produce a risk score for each individual so that a collective average value can be derived and used to compare the relative risk of one population to another.
In other words, the relative risk of any particular risk category is the ratio of the average health spending for all individuals in the risk category to the average health spending for all individuals in all risk categories. Claims-based risk-assessment models use data, typically from a 1 year period, to figure out the principal conditions and allocate a risk score.
Why a proper Risk Assessment is indispensable?
Risk assessment is crucial to risk-adjust payments. It also can be used by health plans for subscribing, identifying high-cost patients for disease management programs, and measuring provider’s efficiency. Rudimentary risk-assessment models include facets such as age, gender, and self-reported health-status information. The cosmopolitan models use medical conditions or treatment information.
Risk adjustment can be used to
- Make payments more equitable, thereby protecting plan stability to avoid high-risk individuals with higher-than-average costs.
- It can also be used by a plan to internally reallocate funds to adjust for selection when actual premiums are set to reflect differences, but not the full effect of selection.
Claim’s information can be used to develop risk-adjustment systems which rely on three types of data either alone or in combination.
- Diagnosis data from inpatient claims
- Diagnosis data from outpatient claims
- Pharmacy claims data.
One and all data source has their own merits and demerits for using risk-adjustment models. However, it can be more convoluted to implement a risk-adjustment mechanism in a broader plethora unless there is a centralized administrative command. Otherwise, there could be incentives to game the risk adjustment system by enrolling certain individuals or groups inside or outside of the exchange, depending on which method is more advantageous to the plan.
Risk-adjustment procedures can help attenuate incentives for plans to flourish strategies to avoid high-cost enrollees, leading to planning competition that is based on risk selection rather than on medical and administrative efficiencies and quality. A well-crafted risk-adjustment system is pivotal to properly line up incentives and protect risk-bearing organizations.