35AMGA.ORG MARCH 2019
The U.S. healthcare system is steadily transitioning from fee-for-service (FFS) reimbursement to fee-for-value (FFV) payment. This change has already started
to affect medical practice revenue, and it will
have an even bigger impact in the years ahead.
Unfortunately, most physicians and practice managers understand only part of the FFV
equation. While they know the quality data they
report to payers under FFV will affect their reimbursement, many do not understand exactly how
payers use this data to adjust payment.
What is the missing piece of the equation?
Patient risk scoring.
Under many value-based payment models,
payers adjust reimbursement to reflect the
relative health or sickness of patients. These
adjustments are meant to reflect expected costs,
so they can have a big impact on payment. In
fact, depending on what risk factors are present, appropriate risk scoring can double or triple
The challenge is that patient risk scoring is
complex. It is easy for medical practices to
under-report risk and, therefore, to miss out
on full reimbursement. There are some crucial
challenges they must confront in order to understand and properly utilize patient risk scoring.
The Nuts and Bolts
Physicians and practice managers must first
understand the nuts and bolts of patient
The overall goal of value-based payment is
to reward physicians who provide high-quality
care. However, because of differences between
patients and patient populations, physicians
may see wide variations in outcomes and costs
regardless of the quality of the care they provide.
If value-based payment is to be fair, there must
be a way to account for variation in patient risk.
The solution is risk adjustment—using
statistical modeling to convert a patient’s indi-
vidual health risk factors into an overall patient
Payer risk models are technically complex.
The most commonly used system is the Hierarchical Condition Categories (HCC) risk
adjustment model. The Centers for Medicare &
Medicaid Services (CMS) introduced
the HCC in 2004 to adjust capitated
payments for beneficiaries enrolled
in Medicare Advantage plans.
The HCC model also includes
patient demographic information
(age and gender) and patient Medicaid status. Within the model, each
HCC and demographic category is
assigned an individual risk factor.
The sum of each patient’s individual
risk factors is their total risk score.
This patient risk score is also known
as the Risk Adjustment Factor
In general, the RAF is low for
young, healthy patients and high for
senior patients and those who have
Medicare Advantage determines
plan payments by multiplying the
base capitated rate by the patient’s
By Gene Rondenet and Lucy Zielinski
The Hierarchical Condition Categories (HCC) model incorporates
79 diagnostic categories covering
high-cost chronic diseases and
some acute conditions. Specific
HCC categories include:
X Diabetes without complication
X Diabetes with chronic complica-
X Morbid obesity (HCC22)
X Rheumatoid arthritis and inflam-
matory connective tissue disease
X Drug/alcohol dependence (HCC55)
X Dialysis status (HCC134)
X Major depressive, bipolar, and
paranoid disorders (HCC58)
X Congestive heart failure (HCC85)
X Acute myocardial infarction