Growing recognition that social determinants are significant drivers of health and healthcare utilization patterns has increased the desire to better understand and identify these issues as well as to develop actionable steps at both the population and member levels. There has been a growing focus on developing the ability to identify the presence of social vulnerabilities among population health entities, Medicaid state agencies, risk-taking provider organizations such as accountable care organizations, and any entity with a vested interest in the reduction of healthcare spending. It is not unusual for health actuaries to get involved in this discussion. Milliman’s Ksenia Whittal provides some perspective in this article.
This article was published in the December 2018/January 2019 issue of The Actuary.
Identifying future high-risk, or rising risk, patients can help healthcare organizations reduce population health costs through early intervention. In this paper, Milliman consultants Ksenia Whittal and Abigail Caldwell examine predictive models to determine whether an enhanced model can better identify individuals with rising risk relative to traditional prospective risk adjustment models. The authors also assess the capability of these models to select members whose costs will increase in the future year.
Incorporating risk adjustment into an alternative provider payment arrangement can help payers and providers share risk, align financial incentives, and reduce health plan costs. There are many risk adjustment models on the market for payers and providers to choose from. However, both parties need to know the predictive abilities of a risk adjuster and its implications on projected reimbursement levels. Milliman consultant Ksenia Whittal provides some perspective in her article “Provider payment: What does risk adjustment have to do with it?”
Not all risk adjusters are created equal. There are multiple models available on the market and they vary in their predictive abilities, the populations they are calibrated to assess, and the time periods for which analyses can be conducted. It is no surprise that the results of any risk-adjustment analysis will be more reliable using a model with stronger predictive power. In large part, the predictive power will be driven by the algorithms underlying the model, but consideration should also be given to the model’s intended use. For provider payment specifically, it is critical to ensure that the variables used for risk score development are resistant to manipulation by providers and do not create perverse incentives. Examples of potentially problematic variables include incurred cost in a prior period, procedures, or diagnostic testing, because these items could lead to model exploitation and distort true morbidity levels.
Arrangements should also specify whether risk adjustment will be applied to actual experience at the end of a contract period (a concurrent analysis), and result in a retrospective adjustment to prior payments to account for the risk level actually encountered, or if the rate will be set prospectively, using current experience to project the appropriate rate for the next contract period. Concurrent risk adjustment is far more accurate than prospective because it seeks to explain what already happened rather than predict what will happen. However, concurrent models can introduce uncertainty during the payment year as to what the retrospective settlement may be. This uncertainty can create challenges for insurers and providers in their budgeting and financial reporting processes. Additionally, a prospective approach should exclude the use of prior cost levels to project future risk levels in order to avoid incentivizing activities that would artificially inflate costs and result in exaggerated prospective risk scores.
Beyond ensuring that the model used is a strong and accurate predictor, it is also important to choose a risk adjuster that will most closely model results for the population in question or the payment arrangement in place. Risk-adjustment models should ideally be calibrated for a population reasonably similar to the one being analyzed. For instance, a model calibrated to a commercial population will not generally be appropriate for risk-adjusting a population of Medicare enrollees, and vice versa. Broad population categories such as these have different morbidity profiles, and risk adjusters should target and be calibrated to capture these nuances, or at least a study should be done to check if a model is performing adequately if it is used on a population that differs significantly from the one used in calibration.
For more Milliman perspective on risk adjustment, click here.
The risk adjustment program, a permanent feature of the Patient Protection and Affordable Care Act of 2010 (ACA), aims to mitigate issues in the commercially insured guaranteed issue individual and small group markets. Under the ACA risk adjustment program, a member’s risk is measured using the U.S. Department of Health and Human Services hierarchical condition categories (HHS-HCC) risk adjuster model. In this particular model, carriers are incentivized to capture all existing member diagnoses that trigger any of the predefined 146 HCCs and condition groupings. By doing so, the carrier will maximize its average plan liability risk score and thus optimize its revenue transfer position relative to the market. In this report, Milliman’s Doug Norris and Ksenia Whittal explore the crucial role of diagnosis coding by focusing on certain variables—including carrier size, market share, and market size—in the ACA risk adjustment formula.