Medicare can be overwhelming, especially when your practice and patients both rely on it to receive the reimbursements they need. But many providers are uncertain how much reimbursements to expect – or how they are calculated.
This article helps you understand exactly how funding is determined. It explains the role risk adjustment factor (RAF) scores play in the Centers for Medicare and Medicaid Services (CMS) risk adjustment model – and how you can avoid missing out on the reimbursements you’re owed.
While healthcare providers want to offer care for all patients, there is a clear “risk” to taking on too many high-cost patients – especially if the provider still receives the same level of funding. This can create perverse incentives, which could make it harder for patients with higher-than-average medical costs to find a provider.
Risk adjustment is a methodology designed to solve this problem and calculate how much funding a healthcare provider should receive for each individual patient. This allows the “risk” of higher costs to be “adjusted” by also insuring patients with lower-than-average healthcare costs – all based on a county care cost benchmark calculated using historical data.
Risk adjustment simultaneously meets two needs:
This is achieved by estimating the healthcare costs associated with individual patients across a given year – which produces a risk adjustment factor (RAF) score.
A Risk Adjustment Factor (RAF) score is the numerical representation of a given patient’s predicted healthcare costs compared with the expected average cost. Technically speaking, the RAF score is a weight applied to a county benchmark rate in order to calculate the monthly capitation rate paid to a health plan:
The CMS model combines two factors to produce a RAF score:
A set of demographic factors – including age, sex, residence, and disability status – are used to determine a baseline RAF value. For example, a male aged 70-74 who lives in an institution would have a baseline RAF score of 1.224 – before any medical conditions are considered.
The CMS then uses medical diagnoses submitted by the provider to complete the RAF score. These diagnoses are submitted using Hierarchical Condition Category (HCC) coding, which groups together similar diagnoses into categories.
These codes are then given RAF values based on the patient’s demographic factors. For example, a man living in an institution who has been diagnosed with Morbid Obesity (HCC48) would have an extra 0.442 added to his RAF score.
The RAF score is the total of all HCC codes and demographic factors submitted by the provider. But this raises an important question:
RAF scores are used to adjust the payments made to Medicare Advantage (MA) plans, and therefore determine how much more (or less) funding your organization receives. The impact of this cannot be underestimated: even a small error in your HCC coding could lead to reimbursements which do not adequately cover the cost of care.
Healthcare provider submit their patient demographics and HCC coding as part of the Medicare Advantage (MA) plan’s bid submission process. The CMS uses this information to calculate the level of funding that will be provided for the following year.
There is a determined timeline to the process:
There are HCC codes used by the CMS recently increased to 115, but there are over 70,000 unique diagnosis codes – which leaves a lot of room for small distinctions. Many providers are not precise enough with their diagnostic terminology, which leads conditions to be miscategorized within the HCC coding – and ultimately produce a lower RAF score.
Many providers lack a centralized, cohesive system to document and store medical notes or diagnoses; some patient data may even be stored in a different healthcare practice and therefore be inaccessible. As a result, diagnoses that may be relevant to a patient’s RAF score could be impossible to prove or include in your HCC coding.
With gaps in medical histories or data silos that prevent HCC coders from viewing the patient’s full list of diagnoses, conditions are routinely missed or overlooked during HCC coding. This inevitability leads to lower RAF scores; Inferscience has found that fixing these gaps using the right software increases the average provider’s RAF scores by 35%.
HCC coding is a vital part of any risk adjustment flow and directly determines your RAF scores – and therefore your Medicare Advantage (MA) reimbursements. But it is a time-consuming process for most providers, either taking up providers’ time or incurring extra costs to pay a professional HCC coder.
HCC Assistant eliminates these manual processes, using natural-language processing (NLP) to automatically identify and suggest relevant HCC codes at the point of care. Not only does this save your providers time and effort – it increases coding accuracy and ensures you produce accurate documentation for the CMS to calculate the right RAF scores.
