Medicare Advantage plans play an important role in providing care for patients who often do not have immediate access to treatment. To ensure that healthcare providers are reimbursed correctly, risk adjustments are used to classify patients by important risk factors. This approach helps accurately determine the average cost of treating a patient. Let’s take a closer look at these risk adjustment factors and see how they impact patient care in the healthcare industry and in the context of diabetic retinopathy.

What Are Risk Adjustment Factors?

Essentially, a risk adjustment is a number given to a person by Medicare that helps to predict the cost of their healthcare. Many risk adjustment factors determine a patient’s score. These include health conditions, demographic factors such as age and sex, socioeconomic status, disability status, eligibility for Medicaid, and the amount of time someone requires long-term-care services.

The goal of a risk adjustment is to ensure that healthcare providers and insurance companies effectively support their patients regardless of their health status or their risk adjustment factors. This is done through reimbursement. In the United States, the Centers for Medicare & Medicaid Services (CMS) use risk adjustment factors to determine the appropriate payment to insurance companies and providers that offer Medicare Advantage plans. This not only helps patients pay for the medical costs but also incentivizes health insurance providers to treat patients with more or worsening medical conditions.

How RAF Scores Are Calculated

There is no one standardized method for calculating risk scores. Most models usually require risk adjustment coding that is specific to healthcare and insurance providers. However, here is a general outlook on how  RAF scores are calculated:

Gather Patient Data

The first step in calculating risk adjustment is collecting data. Primarily, this data comes from medical claims or Electronic Health Records (EHR) and includes risk adjustment factors such as demographic and medical condition information.

Group Medical Conditions

Next, each of these risk adjustment factors is categorized into Hierarchical Condition Categories (HCC). These categories include medical conditions with similarly defined healthcare costs.

Give Each Category a Weight

From there, each group is given a weight according to the healthcare costs of the medical conditions included. These weight-based insights are primarily gleaned from historical healthcare cost records.

Determine Base and Condition-Specific Risk Scores

For the base risk score, healthcare costs are determined solely by demographic factors from past patient data. To calculate condition-specific risk scores, however, the assigned condition-specific weight is multiplied by an indicator of whether the patient has a specific condition or not, known as an HCC indicator.

Include Interactions

Some risk models include interaction factors. If interaction factors must be included, add estimates of how coexisting conditions may impact healthcare treatment and costs overall.

Calculate and Normalize Risk Scores

To calculate the final RAF score, the base score, condition-specific score, and any interactions are added together. This sum equals the expected cost of healthcare for a patient compared to the average cost of healthcare for the entire population. After adding those factors together, the risk score is normalized by dividing the patient’s risk score by the entire population’s average risk score.

The Impact of Risk Adjustment Factors on Quality of Care

The discussion about whether risk scores as a whole improve the quality of care is still being debated. One point of view is that Medicare risk adjustment worsens inequities in healthcare. This is primarily because most risk adjustment models use past medical data to decide the cost of future payments. Because of this, the data provided might underestimate the care needed for groups that use fewer services due to their poor access to medical care, not because of their lack of need. 

On the other hand, risk adjustment incentivizes healthcare systems to treat sicker patients because they can profit from the reimbursement benefits provided to them by Medicare. What these experts did agree on, however, was that the data needs to be better.

 

 

How Improved Technology Can Make Healthcare Data Better

Technology and data play a very important role in determining risk adjustment factors. Without past data, there would be no way to compare average care costs to individual patient costs, and thus this whole process would be impossible. What researchers can’t agree on though is whether there needs to be more data or less.

The healthcare industry currently is in the mindset of “more data is better.” With the help of natural language processing (NLP), scouring big data from EHRs and finding relevant insights on the severity of a patient’s condition, their day-to-day functions, and their support system is becoming easier and more effective. 

Eventually though, as the capabilities of artificial intelligence continue to grow, many of the data dilemmas that impact risk adjustment factors will be solved, including incorporating data from various providers, eliminating coding errors, and sifting through seemingly infinite amounts of unstructured data.

Diabetic Retinopathy Screening in the Context of Risk Adjustment

One of the most common risk adjustment factors that affect RAF scores is diabetes. A reason for that is because of the complications that go along with diabetes, including diabetic retinopathy. In fact, one in ten diabetic patients who receive a diabetic retinopathy exam will have sight-threatening pathology. Early detection of these threats to sight can help prevent vision loss and improve patient outcomes, helping to close gaps in patient care. However, many healthcare providers currently face challenges in implementing effective diabetic retinopathy screenings for early detection.

Here are some of the challenges healthcare providers face in screening for diabetic retinopathy:

• Many times, people in rural or underserved areas do not have access to good healthcare, let alone specialized eye care. Without proper detection or checkups on individuals with diabetes, chances of delays in diagnosis and treatment of diabetic retinopathy increase.
• Traditional screening methods for diabetic retinopathy have a high cost. Because of this cost, providers may deter offerings of retinal photography until it is too late.
• Patients who have diabetes may often be unaware of the importance of regular eye exams, recommended screening guidelines, and catching diabetic retinopathy in its earliest stages.

However, with the help of IRIS, healthcare providers can administer timely diabetic retinopathy screenings within primary care offices or through remote health providers, such as in-home or mobile care. Early detection leads to improved RAF scores, increased reimbursements for providers, and improved overall quality of patient care. 

How IRIS Can Help Optimize Risk Adjustment Factors for Diabetic Retinopathy Screening

Unlike traditional retinal screening solutions, the IRIS solution easily integrates with any EHR system and is supported by various types of teleretinal screening cameras. These fundus cameras, including handheld cameras, make it easier for diabetic retinopathy screenings within a health risk assessment to be administered by healthcare providers, especially to in-home patients or those in remote or underserved areas.

 

 

Once the fundus images are captured, the images of the retina are enhanced and both the original and enhanced images are sent to the IRIS Reading Center, where a licensed eye care physician reviews the images for signs of diabetic retinopathy. The diagnostic results are then sent back to an EHR, allowing for easy patient referral to an appropriate specialist if needed.

With IRIS’s innovative approach to detecting diabetic retinopathy early, healthcare providers can improve RAF scores, receive higher reimbursements from Medicare Advantage plans, and offer better services for their beneficiaries. 

Embracing Advanced Solutions for Diabetic Retinopathy Screening and Risk Adjustment

Risk adjustment factors are crucial determinants for providing better healthcare treatments to patients everywhere. To continue to improve RAF scores and join in the fight against preventable blindness, contact us or book a demo to see the IRIS solution in action.

 

Frequently Asked Questions

How Do You Calculate the Risk Adjustment Factor?

The risk adjustment factor (RAF) is calculated using a sophisticated algorithm that considers a patient’s demographics and medical conditions. By evaluating these factors, the RAF score generated can predict healthcare costs and resource utilization for the individual.

What Is the Medicare Risk Adjustment Factor?

The Medicare risk adjustment factor (RAF) is a number assigned to Medicare beneficiaries that reflects their health status and demographic information. This factor ensures fair compensation for caring for patients with varying health needs by adjusting payments to healthcare providers and insurers.

What Is HCC and Risk Adjustment Factor?

HCC (Hierarchical Condition Categories) is a risk adjustment model that predicts healthcare costs based on patients’ diagnoses and demographic factors. The risk adjustment factor (RAF) score derived from HCC helps allocate appropriate resources by accounting for the health status and complexity of patients in health insurance and healthcare provider settings.

What Does Risk Adjustment Mean?

Risk adjustment is a process used to level the playing field for healthcare providers, insurers, and assessment organizations by accounting for patient health differences. This method ensures accurate comparisons and fair financial allocations by adjusting payments based on the predicted healthcare costs of individuals with varying health conditions.

 

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