Learn how risk adjustment analytics can help insurers and healthcare providers receive compensation for their costs.
Until 2010, insurance companies in the US operated on a “play it safe” principle to compensate for their risk. They limited the provision of health plans to people with chronic diseases and offered different coverage options to customers who did not have such diseases. However, in March 2010, the Affordable Care Act (ACA) was passed. This law prohibited insurance companies from denying health plans to people who have already been diagnosed with chronic diseases.
A Centers for Disease Control and Prevention (CDC) study found that 129 million people in the United States have at least one major chronic disease. Therefore, one of the goals of the ACA was to make insurance plans more affordable for the public.
Risk adjustment became a crucial part of the ACA and a key measure to achieve this goal. This measure was supposed to provide adequate compensation to insurance companies that would expand coverage to people with chronic conditions. Funds from issuers with low-cost claimants would be transferred to those with high-cost claimants. As a result, issuers would “compete” with each other on criteria such as the quality and cost of the insurance policies they offer. So, the criterion of “avoid high-cost clients successfully” would be a thing of the past.
What is risk adjustment?
Payers and providers work in tandem. One payment model for health services to a provider is based on the principle of “capitation”. The “capitation” model implies that the provider receives a lump sum amount per patient, which covers all costs, regardless of the frequency and type of services from the provider.
In this way, capitation can help deliver more cost-effective, higher-quality care. However, different patients need different levels of care, and the cost of that care varies. If providers’ costs exceed the amount they were expected to pay, they risk financial losses. And if these risks aren’t managed well, providers could — and, in the past, have — avoided caring for patients who are more expensive to treat. Risk adjustment can help escape these problems.
Risk adjustment is calculating appropriate compensation for healthcare providers and insurance companies because they provide insurance coverage and medical care services to clients with significant health needs. The provider submits patient health diagnosis data to the payer (annually or monthly, depending on the payer). Payers use different combinations of variables (health diagnoses, age, gender, disability status, etc.) to calculate each patient’s risk score. Then payers adjust payments to reflect each patient’s score.
Why is risk assessment needed in healthcare?
When a new calendar year begins, the state or government entity calculates a risk score, aka the risk adjustment factor (RAF), for each client in the risk adjustment program. It is a numeric value that a government agency officially assigns to such a client based on demographic data (gender, age, etc.) and Hierarchical Condition Category (HCC).
HCC codes identify high-cost chronic conditions and some severe acute conditions. Examples of the major categories of HCC are diabetes, asthma, congestive heart failure, and other diseases. The categories display a hierarchy of diseases: for example, diabetes without complications, diabetes with chronic complications, etc. The HCC is used to determine the risk adjustment payment model. The models are regulated at the federal level so that the government can reimburse insurers and providers.
There is a basic and a final risk assessment. The basic risk assessment reflects only demographic factors (sex, age, socioeconomic status, disability status, etc.). The risk adjustment program must consider all current diagnoses to calculate a final risk assessment. To do this, the healthcare provider must submit the payer data on the patient’s health conditions at least once per calendar year. If the provider has not re-submitted a chronic condition from the previous year, the patient’s RAF score is reduced in the current year. If the provider reports about additional health conditions for the patient, their RAF score increases.
Why is risk adjustment analytics needed in healthcare?
Accuracy, completeness, and consistency of data are critical to risk adjustment. However, the provider may miscode the service or accidentally provide incomplete data. It makes it difficult to assess the true health status of the patient.
Additionally, the Centers for Medicare & Medicaid Services (CMS) has identified that some providers and plans in the Medicare Advantage (MA) program were intentionally “upcoding”. They reported that patients had more serious health problems than they were, and because of this, they were able to receive higher compensation.
The “payer-provider” tandem may use the risk adjustment analytics to act within the law, to provide evidence-based support for their compensation needs, and not to taint their reputation even by accident.
So, risk adjustment analytics helps to:
Check the compatibility of procedure and diagnosis codes
Risk adjustment analytics records updates in electronic health records (EHR) in real-time, for example, when a provider adds codes for procedures performed on patients to the EHR. Procedures are medical, surgical, or diagnostic interventions. EHR data analytics monitors procedure codes to be compatible with diagnosis codes. Belitsoft experts bring out that the compatibility of procedure codes and diagnosis codes ensures that the payer will reimburse the provider for the costs of the procedures performed.
Comply with regulatory requirements
The Centers for Medicare & Medicaid Services (CMS) and the Department of Health & Human Services (HHS) calculate appropriate compensation for insurers and providers. However, each regulatory agency has its requirements. In the HHS-HCC model, it is enough to capture diagnoses from an audio-only visit with an approved provider to calculate a patient’s RAF score. In the CMS-HCC model, the telehealth visit must be audio+video for diagnoses to be captured to calculate the RAF score.
Analytics risk adjustment combines the CMS HCC risk models and the HHS HCC risk models and considers their regulations, requirements, and HCC coding rules. It improves the accuracy and efficiency of analytics and allows the user of the risk adjustment program to avoid the risk of regulatory penalties.
Justify the right to compensation for high costs
If the provider submits unreasonably more services to the patient than they are later compensated, the risk of financial loss increases. Risk adjustment analytics monitors cost and quality indicators using medical big data (records in the EHR, access to a customer relationship management (CRM) system, etc.), studies patient profiles and takes data about their personalized experience and the entire list of medical services provided to them into account. Based on the analytics, the provider can prepare a transparent and accurate report for the payer to prove that all services were justified and that the provider did not “upcode” and is legally entitled to compensation.
In Conclusion
Risk adjustment analytics allows payers and providers to complete the risk adjustment process effectively, comply with regulatory requirements, and reduce revenue risks via government compensation.
