The exponential growth in clinical datasets is one of the challenges in unlocking the information of patients. Fortunately, the development of modern-age technologies introduces the healthcare system to HCC coding, a technique that is used to regulate Medicare Advantage payments based on a patient’s health status. RAF scores are closely associated with HCC coding. RAF score is used in different payment models and reimbursement systems for the adjustment of payment rates.
Key Terms Related to HCC Risk Adjustment Models
For a better understanding of HCC Risk Adjustment models, there is a need to comprehend the key terms which are given:-
HCC (Hierarchical Conditions Categories)
It refers to the grouping of related diagnoses used in the models of risk adjustment. A risk score or coefficient based on the severity and management cost associated with the condition is assigned to each HCC.
RAF – Risk Adjustment Factor
It refers to the cumulative effect of all the HCCs for an individual patient. It is computed by summing the risk scores or coefficients given to every HCC.
Diagnosis Coding
As per the standardized code sets, like ICD-10-CM, the specific codes are assigned to medical diagnosis, which is known as diagnosis coding. Appropriate and comprehensive diagnosis coding is needed for proper risk adjustment.
RAF Score
It is a numerical value that is derived through the multiplication of the patient’s RAF (Risk Adjustment factor) by a base payment rate known as the RAF score. It is used for payment adjustment in risk-based reimbursement systems, showcasing the expected cost of care for a particular patient.
Prospective Payments
It refers to the predetermined reimbursement amount allocated to a patient based on their RAF scores. This amount is adjusted as per the estimated expected costs associated with the health conditions of the patient.
Retrospective Reconciliation
The process of reviewing and adjustment of payments once it has been made as per the actual health data. In this, the prospective payment is compared with the actual cost incurred to determine if it seems any adjustments need to be done.
Updates in RAF Model
There are periodic updates that take place in RAF models to reflect changes in healthcare practices, modifications in medical knowledge, and shifts in the patient population. These updates ensure that risk adjustment remains accurate and relevant over time.
Audit and Compliance
For verification and data integrity in risk adjustment models and HCC, audit and compliance reviews are used. For instance, RADV audits assist in identifying any discrepancies, ensuring compliance with coding guidelines, and protecting from fraud activities.
HCC Risk Adjustment Models
Numerous HCC risk adjustment models are used in healthcare reimbursement for the calculation of risk scores and payment adjustments, as per the health status of the patient. Depending on the payment program, specific models can vary.
CMS-HCC Model
The Centre for Medicare and Medicaid Services (CMS) Hierarchical Condition Category (HCC) model is used for risk adjustment in the Medicare Advantage and Medicare Prescription Drug Benefit Program. Based on the severe conditions of the patient and their expected needs, the HCC codes are used to predict healthcare costs.
HHS-HCC Model
The Health and Human Services (HHS) Hierarchical Condition Category (HCC) is used for risk adjustment in the Affordable care act (ACA) risk adjustment program. HCC codes are used for the prediction of healthcare costs and payment adjustments.
RxHCC Model
This model is used for risk adjustment in the Medicare Prescription Drug Benefit Program. It focuses on the cost of pharmaceuticals and the use of the HCC code takes place for medication use & chronic conditions.
ESRD-HCC Model
This model is used for risk adjustments in Medicare programs for patients suffering from end-stage renal disease. It is associated with unique healthcare needs and costs linked with this population.
Challenges Associated With HCC Risk Adjustment Models
Well, HCC risk adjustment models assist in recognizing the complexity of patient health conditions, but there are some challenges associated with it.
Availability and Quality of Data
HCC risk adjustment models are dependent on accurate and comprehensive data sources, including medical records, claims data, and other administrative data. However, the quality and availability of data may differ across healthcare organizations. Hence, it becomes challenging to ensure the accuracy of data used for risk adjustment.
Updates and Modification
To maintain the relevance of data, there are updates and modifications taking place in the models, including HCC codes and RAF scores. Hence, it becomes challenging for healthcare organizations to stay updated with compliance.
Coding Guidelines
HCC risk adjustment models require coding diagnoses for the matured specificity through specific coding guidelines. It is necessary to store comprehensive information in the documentation. However, it becomes challenging when the information obtained is vague. So, problems in documentation possess a high chance of impacting the risk scores.
Methods to Overcome the Challenges
NLP (Natural Language Processing)
It enables AI solutions to comprehend and obtain information from unstructured text, like physical documentation and clinical notes. This assists in capturing the diagnoses that might be missed.
Detection of Fraud
AI can also be employed to detect anomalies and patterns that show fraudulent activities in billing and coding practices. Hence, improving compliance and reduction of fraudulent claims.
Automated Coding
AI algorithms can evaluate medical records, clinical notes, and other significant data for the identification of potential diagnoses. Furthermore, the automation of using processes helps improve accuracy, consistency, and efficiency in coding.
Final Thoughts
HCC risk models are crucial for healthcare reimbursement, and accurate resource allocation. The use of modern-age technology, like AI in these models, helps leverage quality and better RADV audits for improved financial sustainability in healthcare organizations.