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5 Ways To Improve HCC Coding and Risk Adjustment

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.

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.

5 ways to improve hcc coding

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.

ICD 10 CODE HCC DESCRIPTION CMS HCC CATEGORY HCC RISK SCORE
E11.621 Type II diabetes with foot ulcer. HCC18 0.307
I11.0 Hypertensive heart disease HCC85 1.351

 

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 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 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.

A few extras…

Audit preparedness in addition to the above is crucial. Organizations should make it a priority to equip their risk adjustment accuracy program with the tools needed to accurately assess the health risks of their members, hence lowering their overall risk exposure. Automated analytics solutions that give coders documented clinical evidence of accurate coding between the medical record and claim codes are an important part of a risk adjustment accuracy program designed for success. By utilizing these technologies, the reconciliation process can be sped up and the associated risk can be reduced.

Automating manual processes is essential as it allows for more and more patient records to be processed efficiently and accurately by coding teams on a bigger scale. A key component of an automated process is making it part of your coders’ daily routine, which increases productivity by a factor of 10. In order to deal with the growing number of MA enrollees, healthcare providers who use natural language processing technologies in their operations will have a leg up.

Conclusion:

Key Points to note in improving HCC coding accuracy and risk adjustment:

  • Without data, it’s impossible to assess the scope and frequency of coding accuracy possibilities.
  • A data-driven approach helps discover themes that end-users find beneficial, increasing engagement.
  • The EMR is manageable and effective during encounters.
  • Clinicians want to know how they compare.

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.