Introduction
The Hierarchical Condition Category (HCC) risk adjustment model, initially introduced by CMS in 2004, serves as a pivotal method for adjusting Medicare capitation payments to private health insurance companies offering Medicare Advantage plans. Over the years, this model has undergone refinements and expanded applications, now encompassing the risk adjustment of patients in various value-based reimbursement plans, such as ACOs, Direct Contracting (CMS), Comprehensive Primary Care Plus (CPC+), and others.
- What is HCC Coding & its Significance?
- The Hierarchical Approach of HCC Models in Predicting Future Costs
- Navigating the world of ICD-10 CM and Risk Adjustment Factors in Predicting Expenditures.
- Comparison of CMS & HSS coding
- HCC Coding Operational Framework with Example
- Updates: 2024 CMS-HCC Model Key Changes
- Significant Changes to the Risk Adjustment Model: V24 Vs V28
- Challenges in HCC Risk Adjustment Models
- The Road Ahead – Meeting the Challenges
- Closing Remarks
- Furthermore, explore the below shared informative FAQs to gain insights into other relevant aspects of HCC Coding
What is HCC Coding & its Significance?
The development of Hierarchical Condition Category (HCC) coding is aimed at predicting forthcoming patient healthcare expenses. Introduced in 2004 by the Centers for Medicare & Medicaid Services (CMS), the HCC model is gaining prominence with the rise of value-based care.
This model utilizes ICD-10-CM coding to assess and allocate risk scores to patients based on their medical conditions. Each specific condition corresponds to an ICD-10-CM code. For instance, a patient with minimal severe health issues is anticipated to incur average healthcare costs over a specified time frame. Conversely, individuals with numerous chronic conditions are expected to exhibit higher healthcare utilization and associated costs.
The hierarchical condition category (HCC) coding conveys patient complexity, influences healthcare resource use predictions, and adjusts quality and cost metrics. Higher RAF scores lead to increased capitation payments, impacting Medicaid managed care and ACA plans, including Medicare Advantage members.
The Hierarchical Approach of HCC Models in Predicting Future Costs
HCC models adhere to a hierarchical approach to determine risk adjustment scores and forecast future healthcare expenses. This begins with identifying ailments and conditions recorded in a patient’s medical history, which are then translated into a precise set of ICD-10 codes. Approximately 13% of these ICD-10 codes, precisely those strongly associated with health status and cost, are systematically mapped to HCC codes across 19 categories.
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Navigating the world of ICD-10 CM and Risk Adjustment Factors in Predicting Expenditures.
Primary MCM (Medical coding Models)
The ICD-10 (International Classification of Diseases, Tenth Revision, Clinical Modification) categorizes each diagnosis documented by a physician in a medical record, encompassing symptoms and procedures. This classification system is derived from the International Classification of Diseases published by the World Health Organization.
9,757 ICD-10 codes are linked to HCC codes, each corresponding to a distinct medical condition. Hierarchies are established to connect related condition categories to specific HCC codes. The HCCs and demographic and program information are pivotal in calculating a patient’s risk adjustment score. These Risk Adjustment Factor (RAF) scores are subsequently utilized for forecasting either next year’s (prospective risk adjustment) or current year’s expenditures.
Comparison of CMS & HSS coding
The HHS-HCC model operates concurrently, utilizing codes from the current year for the corresponding year’s budget determination. In contrast, the CMS-HCC model is retrospective, relying on codes submitted from a previous year to project future healthcare costs.
Here is how they differ –
| CMS-HCC | HHS-HCC |
|---|---|
| a) Employed by CMS for compensating Medicare Advantage plans for their members. | a) Employed by CMS for compensating health insurers participating in the Affordable Care Act marketplace. |
| b) Rates for the upcoming year are determined based on diagnoses from the current year. | b) Utilizes diagnosis coding from the current year to establish risk payments for the same year. |
| c) Designed for individuals aged over 65 and those with disabilities, encompassing patients of all age groups. | c) Formulated to encompass patients of all age groups. |
| d) Diagnosis codes related to pediatrics and obstetrics are devoid of assigned risk values. | d) Encompasses categories for infants, children, adults, and incorporates obstetrical diagnoses. |
| e) Excludes expenses associated with medications. | e) Incorporates expenses related to medications. |
| f) Widely adopted by various software programs and seamlessly integrated into Electronic Medical Record (EMR) systems. | f) Less familiar to medical practices compared to other models. |
| g) The rulemaking process involves proposing changes in late December, with finalized rates released in April. | g) Reimburses health insurers for the care of more medically complex patients within the Affordable Care Act (ACA) framework. |
Secondary MCM (Medical coding Models)
- 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.
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HCC Coding Operational Framework with Example:
The HCC methodology operates as follows: A patient’s risk score is a composite of demographic factors (such as age and gender) and their primary medical conditions. With approximately 70,000 ICD-10 codes available, these codes are assigned to one of 805 “diagnostic groups” (DXGs). Each DXG represents a specific and well-defined medical condition. The 805 DXGs are then consolidated into 189 Condition Categories (CCs), wherein DXGs within a CC share clinical and cost-related relationships.
The framework further incorporates Hierarchies, exemplified by the three diabetes condition categories:
- HCC 17: Diabetes with acute complications
- HCC 18: Diabetes with chronic complications
- HCC 19: Diabetes without complications
In cases where a patient with diabetic neuropathy (HCC 18) develops diabetic ketoacidosis (HCC 17), the Hierarchy prioritizes the “riskier” HCC, resulting in the patient receiving the HCC 17 risk score over the lower HCC 18 risk score.
With 189 HCCs established, a team of actuaries analyzes extensive datasets encompassing over 1 million Medicare beneficiaries. Employing sophisticated logistic regression techniques, they pinpoint the HCCs that most effectively elucidate the costs associated with providing care to this patient population. The current iteration of the CMS HCC framework utilizes 79 out of the initial 189 HCCs
Updates: 2024 CMS-HCC Model Key Changes
In 2024, notable revisions were made to the HCC’s Risk Adjustment Model by the Centers for Medicare & Medicaid Services (CMS). Grasping these changes is essential for precise risk assessment, HCC coding, and reimbursement within the healthcare sector.
2024 CMS-HCC updates
- Vascular Diseases: Three new HCCs (263, 264, and 267) were introduced, focusing on severe atherosclerosis cases, improving accuracy in risk assessment and payment adjustments.
- Metabolic Diseases: Expansion to four payment HCCs, including a dedicated HCC (49) for high-cost lysosomal storage disorders.
- Heart Diseases: Significant expansion to ten payment HCCs. Subdivision of HCC 85 (Congestive Heart Failure) into five heart failure HCCs. Addition of HCC 221 and separation of HCC 227.
- Blood Diseases: Expanded to seven payment HCCs, categorizing conditions based on severity and specificity. Improved accuracy in risk adjustment for blood-related disorders.
- Amputation Disease: Reconfiguration to cover initial complications or ongoing costs of lower limb amputation. Accurate classification of toe and finger codes.
- Neurological Diseases: Expansion to twelve payment HCCs. Reconfiguration of HCC 75 into HCCs 193-196. Specific coding for acute Guillain-Barre Syndrome.
- Diabetes: Four payment HCCs, including HCC 35 at the top. Restructured codes for complications, providing detailed assessment and precision in risk adjustment.
- Kidney Diseases: Four payment HCCs replacing HCC 138. Addition of new HCCs (328 and 329) based on updated ICD-10 codes.
Significant Changes to the Risk Adjustment Model: V24 Vs V28
The current risk adjustment model relies on ICD-9 codes, but a transition to ICD-10 occurred in 2015. For the 2024 model, CMS reclassified HCCs using ICD-10 codes, evaluating their predictive ability for Medicare costs.
The clinical classification system was updated to enhance HCCs’ predictive accuracy, aligning with current disease patterns, treatments, costs, diagnoses, and coding practices. This led to changes in HCCs, with new ones introduced and some removed due to evolving predictive capabilities.
In Payment year (PY) 2024, risk scores will blend 67% from the current model and 33% from the finalized 2024 model. PY 2025 shifts to 33% of the current model and 67% of the finalized 2024 model. By PY 2026, 100% of risk scores will be based on the finalized 2024 model.
Challenges in HCC Risk Adjustment Models
While HCC risk adjustment models help acknowledge patient health complexity, they come with associated challenges, as mentioned below.
- Data Availability and Quality – Accurate and thorough data from medical records, claims, and administrative data are crucial for HCC risk adjustment models. Yet, variations in data quality and availability across healthcare organizations pose challenges in ensuring accuracy for risk adjustment.
- Govt Policy Updates and Modifications – Ongoing CMS updates in health care regulations & compliance aspects and modifications, such as changes to HCC codes and RAF scores, are necessary to keep data relevant. This poses a challenge for healthcare organizations to stay compliant and up-to-date.
- Coding Guidelines & Documentation – HCC risk adjustment models demand coding diagnoses with matured specificity, following specific guidelines. Storing detailed information in documentation is essential, but challenges arise when information is vague.
Also, Incomplete or insufficient documentation can present challenges, potentially leading to undercoding or inaccurate risk assessment. Healthcare providers must ensure detailed documentation to support precise coding.
The Road Ahead – Meeting the Challenges
Challenges and success stories are inevitable in the evolutionary path of the Medical coding process. However, new-age technologies like Clinical NLP-powered retrospective solutions & other transformative innovations are set to bring about considerable changes to risk adjustment coding as it has transformed other areas of health care, too.
NLP (Natural Language Processing) allows AI to understand and extract information from unstructured text found as part of physical medical records.
Using RAAPID’s retrospective risk adjustment solutions supported by explainable AI can detect anomalies and patterns indicative of fraudulent billing and inappropriate coding practices, enhancing compliance and reducing fraudulent claims.
Similarly, RAAPID’s clinical AI-supported algorithms make its prospective solution space hugely influential for assessing medical records and clinical notes to identify potential diagnoses and automating processes for improved accuracy, consistency, and coding efficiency.
Closing Remarks
HCC risk adjustment models are vital in healthcare reimbursement and enable optimized use of resources. Integrating modern technology, such as AI, enhances the quality of these models and contributes to improved RADV audits, promoting better financial sustainability for healthcare organizations.
Furthermore, explore the below shared informative FAQs to gain insights into other relevant aspects of HCC Coding
The HCC Risk Adjustment Coding Model is a methodology used in healthcare to assess and predict future healthcare costs by assigning risk scores to patients based on their medical conditions. It involves using specific codes to capture the severity and complexity of illnesses.
The HCC Risk Adjustment Model directly influences healthcare reimbursement by assigning Risk Adjustment Factor (RAF) scores based on the complexity and severity of patients’ health conditions. Higher RAF scores lead to increased payments to healthcare providers.
HCC Coding is essential in the risk adjustment process as it involves coding specific diagnoses using ICD-10-CM codes. These codes help in accurately assessing and categorizing the health status of patients, contributing to precise risk scores.
In Medicare Advantage plans, HCC Risk Adjustment is crucial for fair and accurate reimbursement. It considers the complexity of patients’ health conditions, ensuring that healthcare providers receive appropriate compensation for the care of high-risk individuals.
HCC conditions should be documented annually to ensure accurate risk assessment and reimbursement. Consistent re-documentation is particularly important for chronic stable conditions to prevent any lapses.
RAF scores, or Risk Adjustment Factor scores, are assigned based on the severity of medical conditions identified through HCC Coding. Higher RAF scores indicate increased patient complexity and directly impact reimbursement rates.
Updates in the CMS-HCC Model, such as changes to HCC codes and RAF scores, are necessary to keep the model relevant and aligned with current healthcare practices. These updates contribute to accurate risk adjustment and reimbursement.
Key components include Diagnosis Coding (ICD), the use of Modifiers for additional details, ensuring Medical Necessity, and thorough Patient Encounters and Documentation. These steps collectively contribute to accurate risk assessment.
Leveraging technology, such as Electronic Health Records (EHRs) and software applications, can streamline HCC Coding and Documentation. This helps minimize errors, provide real-time feedback, and enhance the efficiency of the risk adjustment process.
Challenges include variations in Data Availability and Quality across healthcare organizations, updates and modifications in compliance aspects, and the demand for matured specificity in Coding Guidelines and Documentation. Addressing these challenges is crucial for accurate risk adjustment.
