Risk Adjustment 101: Ignoring these Could Cost You Millions
Guest post by Abhinav Shashank, CEO and co-founder, Innovaccer.
For a long time, healthcare insurance companies used to overlook people who were likely to be high-cost. As the landscape changed with new regulations, insurance providers have started offering new policies in the individual market without identifying any pre-existing conditions while enquiring about their health status. Even so, there have been many loopholes, and every administration has and continues to aim at minimizing these gaps. The one good answer thus far: risk adjustment.
What is risk adjustment?
Risk adjustment over the years has become a key mechanism used in healthcare to predict the costs incurred and ensure appropriate payments for Medicare Advantage plans, Part D plans, and health plans. Historically, it was only used in Medicaid and Medicare but lately has been an actuarial tool to ensure that health insurance plans have adequate funding and no financial hindrance in providing care to high-risk, high-need patients. Insurance companies and their plans are compared on the basis of quality and services they offer, providing a strong foundation to value-based purchasing.
Why is risk adjustment so important?
Risk adjustment advocates fair payments to health insurance plans by judging them on their efficiency and encouraging the provision of high-quality care. Beyond that, here’s why risk adjustment is important:
- A critical tool in quality reporting and monitoring measures.
- Provides a neutral field where providers and payers can be compared to their peers on the basis of their quality and efficiency.
- Combining risk scores and evidence-based models with risk adjustment helps providers and care teams design post-discharge plans with intense follow-ups.
- With predictive analytics, risk adjustment models may be used to capture all the dimensions of relevant patient risk.
How is risk adjustment used in healthcare?
Healthcare risk adjustment methodologies can be used to account for changes in severity and case mixes for patients over time. Risk adjustment has been critical in reducing “cherry picking” among health plans. Dimensions of risk in care can broadly be categorized into three categories:
- Health status
- Patient health-related behavior
- Social determinants
It’s important to ensure that by providing incentives to enroll high-cost individuals, there are necessary resources available to provide efficient and effective treatment to the relatively healthy population without overcompensation. The methodology used to risk-adjust premiums varies on the following:
- Patient population and their breakdown
- Source of payment
- Healthcare market regulation
On the macro level, unless the state combines its individual and small group markets, separate risk adjustment systems operate in each market. The Department of Health and Human Services (HHS), developed a risk adjustment methodology, where individual risk scores are assigned to each enrollee. The diagnoses are grouped into a Hierarchical Condition Category (HCC) and are assigned a numerical value which is averaged to calculate the plan’s average risk score. Payments and charges are calculated by comparing each plan’s average risk score to a baseline premium.
How ignoring risk adjustment can cost you?
In the current payment models, payments are risk adjusted, which makes it extremely important for every risk to be tracked and accounted for during reporting processes. Generally one risk point equates to $10,000 in Medicare contracts, therefore downcoding any patient’s data erroneously could mean thousands of dollar loss for the healthcare organization. It has been observed that around 15 percent to 25 percent of the risks are downcoded.
So for instance, if an organization has downcoded 15 percent of the risks erroneously equating to 1,500 points, that would be roughly equal to savings opportunity worth $15 million.
Data fueling risk adjustment
Access to key data on enrollees’ health conditions is paramount for risk adjustment to work. Clinical data contains the most important details about patient health and risk that simply can’t be obtained from claims data alone. However, there are several obstacles:
- Thorough data documentation: Healthcare data comes in from various sources, and to collect, incorporate, and document patient data incoming from EHRs to wearable trackers is a costly and a time-consuming process. It’s important that physicians and health coaches are able to document patient data on the job, to maintain consistency in documentation.
- Data handling: The storage of data, integration of data sets, retrieval, and use of data are only a few of the subsequent challenges that are not covered by the infrastructure health plans manage. Also in later stages, data sharing becomes a challenge as interoperability between disparate systems is yet to be realized.
- Comprehensive analysis: Majority of clinical data is unstructured, and no meaning can be derived out of it without advanced analytics. Although healthcare IT has developed some sophisticated analytics, the frontier of clinical data analytics is yet to be explored conclusively.
With healthcare IT going leaps and bounds, there’s little doubt that healthcare organizations will leverage these insights and apply them to clinical data management in the future. Advanced analytics capabilities have the potential to not only impact the bottom line of a health plan, but to also improve population health management and help makes organization informed decisions about premium pricing, membership expansion, bid rate calculation, etc.
The road ahead
Risk adjustment models and their correct implementation is central to achieving high-quality care. With the growing importance of value in care, the challenges in implementing a broad risk-adjustment framework should be countered to protect physicians from inadequate compensation and provide high-need patients with adequate care. Regardless of changes in administration and patient population, there is one common belief that stays true: complete, accurate and value-based care. Only time will tell how successful any one risk adjustment model will be, but the fact that risk adjustment is fundamental to value-based care delivery is indispensable.