HCC Coding and Risk Adjustment Tool model is specially designed to estimate future health care costs for patients. its main objective is to consider the well-being of the executives alongside exact repayments from medicare Advantage Plans.

1. Maximized Your Reimbursement with
Better HCC Coding and Risk
Adjustment - Inferscience
Healthcare organizations must be able to forecast future healthcare financial
resources and predict appropriate remuneration for physicians as the number of
Medicare Advantage plan enrollees rises. The HCC risk adjustment model is used
to predict anticipated costs for Medicare Advantage beneficiaries by CMS, and the
results have a direct impact on the compensation healthcare organizations get. As
value-based payment models gain traction, the HCC risk adjustment approach is
becoming more widely used.
Hierarchical condition category (HCC) risk adjustment is a critical and popular
topic in healthcare management today, notably in the medical coding industry. In
the United States, Medicare Advantage plans now cover nearly one-third of all
Medicare members, making it more important than ever for healthcare firms to
pay attention to this model. This would assist in the appropriate classification of
illnesses as well as ensure that HSPs receive adequate compensation.

2. Patient-specific Risk Adjustment Factors (RAFs) are calculated using data from the
HCC model and applied to capitation payments for Medicare Advantage plan
members. As stated by the American Academy of Family Physicians, “hierarchical
condition category coding helps explain patient complexity and construct a
picture of the full patient,” assisting in the proper measurement of quality and
cost. HCC coding and risk adjustment accuracy can therefore have a substantial
impact on the financial viability and service delivery of healthcare organizations.
Hierarchical condition categories are based on the fact that different illnesses
within the same family (or group) might have varying degrees of severity. Thus,
the most severe takes precedence over the others and be used in calculation of
patient’s risk score. As in the case of Diabetes as seen above, and in the chart
below you can see how certain HCC codes have different risk scores attached to
them. The following is an example of HCC codes in the chart of a 70 yr old woman
who lives in an institution with type 2 diabetes, diabetic foot ulcer and
hypertensive heart disease.

3. In addition to diagnoses, certain demographic factors are also considered in
calculating the RAF. These include:
• Age.
• Sex.
• Socioeconomic Status.
• Disability Status.
• Medicaid eligibility.
• Institutional Status.
Using this article as a guide, we’ll go through the fundamentals of risk adjustment
models and our five recommendations to Improve HCC Coding and Risk
Adjustment.
5 Steps to Improve HCC Coding and Risk Adjustment:
→ Having an accurate and updated problem list.
→ See patients at least once a year.
→ Public education and information dissemination.
→ EMR optimization and better decision assistance.
→ Monitoring performance and spotting potential.
A multidisciplinary approach to our 5 HCC coding improvement
recommendations:
Accurate data management is a key step in making improvements in the HCC
coding sector, this will involve a collaborative effort of key sector players. It is
therefore important that a structure is set up in such a way as to include a
multidisciplinary team that would be responsible for enhancing documentation
and HCC coding accuracy. ACO team members, doctors, clinic managers,
operations, and medical coders could all be included in this working group. This
multidisciplinary effort could then be geared towards spearheading a working
approach to drive improvement. The basis for formulating this approach will rest
on actionable items which include:
→ Having an accurate and updated problem list: As a result of years of data
entry into an EMR, many healthcare organizations now have a large

4. problem list that is likely inaccurate. Duplicate and inactive diagnoses must
be eliminated, key areas with discrete data identified, and a diagnosis
preference list used to include HCC suffix codes and RAF values as well as
prioritize results are all part of the process of ensuring an accurate problem
list.
→ See patients at least once a year: Identifying patients with chronic illnesses
who haven’t been seen in a calendar year is the first part of the process. If
that’s the case, the next step is to try to bring them in to be seen (which
may be more logistically tasking). A clinical dashboard that provides a
snapshot of both EMR and claims data, as well as a complete picture of
patients who have not yet been seen in a calendar year, is one way to
accomplish this quickly. These patients can be found and matched to visit
and HCC coding gaps once they have been identified by the work team. At
the system, region, clinic, or provider level, the team should collect and
review data on a regular basis with clinic staff (such as on a quarterly basis).
Frontloading visits for these patients early in the year, when clinics have the
capacity, is an excellent best practice.
→ Public education and information dissemination: Clinicians will need to be
trained along the way, as this is unfamiliar and counterintuitive practice. In
terms of teaching, the most crucial aspect to convey to physicians is that
accuracy is more important than a precise score. Clinicians can benefit from
group education on the importance of specificity, which can be beneficial
both clinically and financially. The relevance of risk adjustment and impact
quantification should be stressed in training on a systemic level in order to
justify resource allocation and regulatory compliance. Additionally, clinic
employees can gain an understanding of the specialized tools and
workflows required for patient monitoring and reporting.
→ EMR optimization and better decision assistance: Accurate HCC coding
cannot be achieved without both education of clinicians and the integration
of suitable coding into everyday clinical encounters. An ACO/Medicare
Advantage identification highlighted in the EMR, decision assistance tools
that may be engaged for chosen populations, and HCC diagnosis warnings
for prior codes are all possible options. Understanding the main data
elements in the healthcare ecosystem will help accurately depict risk scores
at both the individual and population levels. For better patient care

5. documentation and the detection of potentially incorrect disease
documentation, administrative, clinical, and auxiliary data informs your risk
adjustment accuracy program. In case of poor data management, one could
end up with wrong HCC risk adjustment coding, incorrect remuneration for
quality of care, and an elevated audit risk if several data streams are not
utilized.
→ Monitoring performance and spotting potential: Another and perhaps the
most important responsibility assigned to the group is to keep a watch on
the results and seek places where they might be made better in the future.
The workgroup can present to stakeholders evidence of an increase in the
average RAF score, an increase in major problem list diagnoses, a decrease
in the number of members who do not receive an annual visit, and an
increase in the proportion of persistent condition diagnoses that have been
resolved. Identifying possible areas for improvement follows data collection
and dissemination among stakeholders, which is the next stage after that.
Unresolved chronic conditions affecting specific populations can be
investigated to aid in the detection of these conditions.
Conclusion:
Changing how healthcare organizations document and categorize chronic
diseases can help them collect more thorough diagnoses, leading to greater and
more appropriate compensation as well as better healthcare delivery for
complicated patient populations. As the number of Medicare Advantage
beneficiaries grows, healthcare facilities are finding it harder to stay financially
sustainable. Patients with chronic illnesses should be examined annually, and
decision-support and EMR optimization should be improved. A great way to
achieve EMR optimizations is with integrations to your EMR, the HCC Assistant is
a great software tool that integrates with a variety of EHRs and suggests HCC risk
adjustment codes at the point of care.