Risk Adjustment Models: Breaking Down the Complexities of HCC Model V28 and CDPS

Gaining insight into the forecasting and allocation of expenses is essential for healthcare providers and HCC Model V28 recipients in the current complicated healthcare environment. Despite their complexity, risk adjustment models constitute the foundation of financial planning in the healthcare sector. These mechanisms subtly influence who receives what compensation and, more significantly, how appropriately providers are compensated for their services. However, the majority of system employees seldom stop to examine the methods used to arrive at these calculations.

What Are Risk Adjustment Models?

Risk adjustment models are statistical algorithms that examine a patient’s clinical symptoms and demographics to forecast future healthcare expenses. By projecting the amount of treatment a patient is expected to require, these models aid in the proper distribution of funding. It is a recalibration mechanism: predicted costs for older or sicker patients help level the playing field for providers and insurers.

Real-world healthcare operations use the idea daily, therefore, it is not only a theoretical one. Additionally, the need for more precise, technologically advanced solutions grows as data complexity rises.

Risk Models That Shape U.S. Healthcare

CMS-HCC Model for Medicare

The CMS-HCC Model (Centers for Medicare & Medicaid Services Hierarchical Condition Categories) is one of the most well-known and developed models now in use. It is crucial in determining Medicare Advantage plan reimbursement.

Under HCC Model V28, the CDPS  recent modifications include:

• Diagnostic category expansion from 86 to 115
• ICD-10 code reassignment for thousands
• An average decline in risk-adjusted payments of around 3.56%

These changes completely rewire the cost prediction process, making it more than just ornamental. In order to better represent the demands of contemporary treatment, several chronic illnesses have been reclassified, and payment structures have been rearranged.

CDPS for Medicaid

Specific to Medicaid populations, the CDPS (Chronic Illness and Disability Payment System) model takes into account their varied demands. Its goal is to offer financing fairness according to the health burden of disadvantaged populations.

Among the groups that CDPS prioritizes are:

• Children who suffer from long-term illnesses
• Individuals with severe needs who are pregnant
• Seniors with low incomes
• People with disabilities

The approach classifies ICD-10 diagnostic codes that have clinical significance. For an even more detailed evaluation, CDPS+Rx, an upgraded version, incorporates prescription data. This enhanced data breadth is beneficial for Medicaid clients, who frequently have inconsistent care histories.

HHS-HCC for ACA Markets

The Department of Health and Human Services created its version, known as the HHS-HCC model, specifically for ACA marketplaces. Its purpose differs from that of the CMS-HCC.

• Manage the risk for every age group.
• Add unique circumstances, such as high-risk pregnancy instances.
• Pay more attention to diagnoses from the current year than those from the past.

The main goals of the model are to guarantee that insurers receive just compensation for covering high-risk patients and to stop adverse selection, which occurs when plans selectively choose healthy participants.

How These Models Work Under The Hood

To appreciate their significance, one must comprehend the mechanisms behind risk adjustment. These models often function as follows:

Step 1: Medical Documentation

Clinical records, examinations, and observations are used to assign ICD-10-CM diagnostic codes. Here, accuracy is crucial since under-coding can significantly cut funding.

Step 2: Condition Mapping

A Hierarchical Condition Category (HCC) is associated with each diagnosis. These fall under condition categories that affect score weights for CDPS.

Step 3: Score Generation

To determine a risk score, the algorithm takes into account all mapped conditions as well as demographic information such as age, gender, and Medicaid eligibility.

Step 4: Payment Adjustment

To ensure that finances are in line with patient complexity, final risk scores modify payments to insurers and providers.

What’s New & Why It Matters

HCC Model V28, the most recent version, is a departure from previous methods:

• Diagnoses need to be adequately recorded and backed by greater clinical evidence.
• CMS eliminated HCCs that failed to fulfill cost validation standards or coding patterns.
• There is now stricter enforcement of the MEAT framework (Monitor, Evaluate, Assess, Treat).

There is increased pressure on coders and physicians to record accurately since many prevalent chronic diseases no longer map to payment-relevant HCCs.

Major Update Area	HCC Model v24	HCC Model V28
Number of HCCs	86	115
Code Remapping	Limited	Extensive
Payment Shift	Neutral	-3.56% avg
Risk Types Used	Historical	Concurrent

Challenges Facing Healthcare Organizations

Risk adjustment is no longer a back-office issue for payers and providers. The stakes are higher.

• Millions of dollars in lost income or audit fines result from incorrect coding.
• Interoperability issues cause delays in risk score updates.
• Errors arise from manual documentation, particularly when new model needs are involved.
• CMS is paying more attention to RADV (Risk Adjustment Data Validation) audits.

These days, a missed or unrecorded diagnosis can affect patient care plans, compliance, and even payment cycles.

Why Data Integration Is Non-Negotiable

Incomplete or siloed data prevents any risk model from functioning properly. Integrated platforms are important because of this.

A contemporary digital health platform aggregates information from:

• EMR and EHR documentation
• Files for claims
• Lab and pharmacy systems
• Health data generated by patients (PGHD)

Providers cannot provide real-time recording or create reliable longitudinal patient records without integration.

How NLP and AI Are Changing the Game

Structured data used to play a major role in risk adjustment. Current technologies for Natural Language Processing (NLP) are able to extract useful information from unstructured clinical notes. In order to authenticate diagnosis, they detect MEAT criteria and unearth unreported or incorrectly recorded diseases.

Then, artificial intelligence expands on this by:

• Forecasting upcoming diagnoses
• Finding holes in the code
• Making recommendations for care interventions
• Risk-based patient population stratification

Clinicians may concentrate more on patient care and less on paperwork due to this degree of automation.

Real-Time Support at the Point of Care

During patient visits, clinicians frequently do not have access to risk ratings. However, throughout the interaction, systems that incorporate EHR-agnostic capabilities might provide insights. This comprises:

• Surfacing HCCs that were overlooked
• Emphasizing previous diagnoses that require confirmation
• Requesting documentation that is MEAT-supported

The clinical team maintains alignment with financial goals by introducing risk awareness at the point of service.

Final Thoughts

Risk adjustment is fundamental; it is not discretionary. It influences the allocation of healthcare resources and the setting of priorities for treatment. Every system, from Medicaid’s CDPS to Medicare Advantage’s HCC Model V28, is essential to guaranteeing equitable and long-lasting care delivery.

Healthcare systems that use manual procedures or static data are lagging. Real-time tools, natural language processing, and integrated processes are becoming crucial as compliance standards change.

Risk Precision with Persivia

Persivia CareSpace® is unique in that it addresses all of the requirements for risk adjustment. CareSpace® has you covered whether you are working with CMS-HCC , HHS-HCC, or CDPS.  Organizations may remain fiscally aligned HCC Model V28 and audit-ready with their EHR-agnostic tools, longitudinal patient records, NLP-backed documentation assistance, and real-time clinical prompts.

The goal of Persivia’s Digital Health Platform, which is based on analytics and interoperability, is to bridge the gap between coding, care delivery, and long-term financial results by converting fragmented data into actionable insight.