What do you need to know about risk adjustment in Medical Coding?
Risk Adjustment is a statistical process that considers the underlying health costs and health status of the individuals who enroll in an insurance plan while looking at their health care costs and health outcomes. If there are no risk adjustment policies, then insurers may not provide coverage to individuals that are at higher risk. At the same time, they can impose unaffordable premiums for patients with pre-existing medical conditions.
The objective of risk adjustment is to make available comprehensive insurance to all people irrespective of the risk. Another objective of risk management is to allow plans, which will cover sick individuals to charge similar premiums as plans that cover relatively healthy individuals. The model of risk adjustment sanctioned under “Obama Care” or the “Affordable Care Act” is neutral when it comes to budget. There is no increase of total payments to insurers. In fact, insurance companies that cover a relatively larger number of healthy people or individuals are required to make contributions to a risk adjustment pool, which funds extra payments to those insurance companies that cover a greater number of high-risk individuals.
Type of Data used in Risk Adjustment Typically, Risk adjustment models use the demographic data of the individual – i.e. age, sex, location, etc. A risk adjustment model also uses the process of diagnoses to identify a risk score, which a measure of the costs or expenses to cover the individual. For example, a person suffering from diabetes will have a higher risk score and that person’s predicted healthcare expenses will be greater as compared to an identical person without diabetes. In addition, the risk adjustment models understand that older people have a higher risk score than younger people. Individuals with a family history of certain health diseases might garner a high-risk score as compared to individuals with no such personal or family history.
Risk Adjustment Models Today
You will find many risk adjustments models. One of them is the “Hierarchical Condition Category” or HCC, which is used by the Centers of Medicare and Medicaid “CMS.” This model is used to calculate risk scores. The model ranks diagnose into different categories, which represent health conditions with similar patterns for cost. So, higher categories mean higher predicted health costs. For instance, cardiovascular diseases with complications are ranked “higher” than heart diseases without complications.
A person might be included in more than one hierarchical condition category. Furthermore, acute injuries are illnesses are predictive of ongoing expenses, as are long-term health conditions such as chronic heart failure, chronic obstructive pulmonary disease, Type 1 and Type 2 diabetes, chronic hepatitis, and multiple sclerosis. Nonetheless, there exist risk adjustment models, which include severe health conditions that are relevant to younger demographics – for instance, congenital abnormalities and pregnancy issues. Risk adjustment models largely depends on accurate data of patients. HCC model requires that a highly qualified health professional will identify all chronic health conditions and diagnoses for patients in order to substantiate “health profiles” for individuals.
Medical records and reports should support the presence of the health condition, indication of the assessment by the health provider, and plans for the management of the health condition. Documentation in the medical record must support the presence of the condition and indicate the provider’s assessment and plan for management of the condition. Therefore, these models seek to level the playing field by accounting for factors that can impact the cost and quality of treatment, irrespective of the care provided.
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