The Evolution of CMS Risk Adjustment: Past, Present, and Future

Early History of Risk Adjustment in Healthcare

This blog explores the basics and early history of CMS Risk Adjustment in healthcare.

We will examine how risk was modeled in the Medicare Advantage program and the Affordable Care Act before delving further into the successes and shortcomings of the HCC CMS Risk Adjustment model.

US Federally funded healthcare and insurance schemes have evolved through many ups and downs before becoming the CMS-governed programs that we now know them to be.

Evolution of Risk Adjustment in Healthcare

• AAPCC Model: The CMS’s AAPCC model explained only 1% of expenditure variation and under-compensated plans with sicker beneficiaries or those focusing on chronic conditions.
• Introduction of PIP-DCG Model: Replacing AAPCC in 2000, the PIP-DCG model used inpatient data but had flaws due to its hospitalization-based predictive analysis.
• CMS-HCC Model Implementation: Introduced in 2004, the CMS Risk Adjustment model improved predictive accuracy by classifying diagnosis codes into groups and focusing on outpatient expenditures with hierarchical conditions.
• MAO Risk Adjustment Programs: Effective MAOs integrate risk adjustment with quality programs, improving data collection and analytics for HCC-related measures and HEDIS quality metrics.
• Risk Adjustment and ACA: The ACA enhanced CMS Risk Adjustment by reallocating funds from lower-risk to higher-risk plans, discouraging healthy enrollee selection, and promoting efficiency and value-based competition.

Historical Milestones in CMS Risk Adjustment Development

The following programs denote the formative years that made CMS what it is today:

1. Tax Equity and Fiscal Responsibility Act (TEFRA) – 1982
2. Balanced Budget Act (BBA) – 1997
3. Benefits Improvement and Protection Act (BIPA) – 2000
4. Medicare Modernization Act (MMA) – 2003
5. Affordable Care Act (ACA) – 2010

Functional Dynamics of Hierarchical Condition Categories (HCCs) in Relation to Risk Adjustment

HCC Model and Medicare Advantage Plans

Medicare Advantage Plans (Medicare Part C) have utilized the Hierarchical Condition Category (HCC) model since 2004. This risk adjustment model identifies patients with serious acute or chronic illnesses and assigns a risk factor score based on demographics and medical history.

“For-profit” insurers manage these patients’ healthcare and receive an annual fee per enrolled member. Higher risk scores result in higher annual fees.

HCC Model Calculations

Each patient receives a Risk Adjustment Factor (RAF) score determined by:

• Age and Gender
• Medicaid or disability status
• Total chronic conditions and disease interactions

RAF scores reflect health status: lower scores indicate healthier patients, and higher scores indicate sicker patients. The average Fee-for-Service (FFS) patient has a score of 1.00.

Impact of HCC on Payment

RAF scores are cumulative, with all qualifying diagnoses contributing to the total. ICD-10 codes from the current year determine next year’s CMS payments to Medicare Advantage Plans.

Payments, including incentive bonuses for providers, are made annually and are separate from fee-for-service payments. Health status is reassessed each year, and RAF scores are reset annually.

Payments are based on HCC categories, not individual diagnosis codes. For instance, Type II diabetes mellitus with complications counts once for HCC 18.

Achieving Accurate RAF Payment

Annual Health Assessments are crucial. Implementing a program to schedule Annual Wellness Visits for Medicare Advantage members, ensuring Risk Assessments, required screenings, and chronic condition statuses are documented accurately, and coding claims precisely.

Example of Diagnosis Codes Affecting Payment:

A 64-year-old disabled female patient with Type II diabetes, Diabetic Chronic Kidney Disease, congestive heart failure, Stage IV CKD (GFR 24 ml/min Filtration), obesity (BMI 56), insulin dependence, and paraplegia would have her health status and conditions documented and coded for accurate RAF scoring and payment adjustment.

Clinical Data: The Backbone of HCC Coding

Clinical Data Collection Facilitation

Data collection is essential in modern healthcare, supporting evidence-based decisions and improving patient care. Its role in providing personalized treatments is transformative.

For instance, genomic data can identify cancer treatments targeting specific mutations, increasing success rates. Several tools and methods facilitate data collection in healthcare:

• Electronic Health Records (EHRs): EHRs centralize patient data, providing a comprehensive view for providers to enhance care coordination and analysis.
• Research Tools: Surveys and studies, like the Framingham Heart Study, uncover critical health insights, driving medical discoveries and innovations.
• Wearable Devices and Remote Monitoring: Devices such as Fitbit and Apple Watch track real-time health metrics, enabling proactive care and early issue detection.
• Mobile Health Applications and Patient Portals: Platforms like MyChart allow patients to access health data, log symptoms, and engage with providers.
• Imaging and Diagnostic Tools: Technologies like MRIs and genomic sequencing improve diagnostic accuracy and enable personalized treatment plans.

Despite the supporting factors contributing to clinical data collection, there are challenges with HCC risk adjustment coding, which we will discuss in the next section.

Risk Adjustment Coding Challenges

• Frequent HCC Model Updates: Yearly HCC model updates complicate risk management, requiring extra work. Enhancing coding accuracy and support is crucial.
• Increasing Number of Enrollees in MA Plans: Growing MA membership leads to higher disease burden and care complexity, complicating cost predictions and HCC risk scores.
• Traditional Manual Processes: Manual HCC risk coding is inefficient and error-prone, causing revenue loss. Advanced AI-driven risk adjustment methods and coder training are essential.
• An Abundance of Data: Integrating administrative, clinical, and supplemental data enhances risk adjustment accuracy, improves documentation, and reduces audit risk.

Overcoming HCC Challenges to Improve Coding Accuracy

• Leverage Advanced Data Analytics: Advanced data analytics enhance HCC coding accuracy and efficiency, preventing compliance issues and revenue losses without overburdening your team.
• Ensure Audit Preparedness: To avoid issues in CMS’s RADV audits, health plans need comprehensive member profiles and automated analytics to minimize risk and reconciliation efforts.
• Review Patient Data: Accurate HCC risk scores require identifying chronic illnesses. NLP and machine learning streamline data collection, improve documentation, and offer compliant ROI.
• Automate Manual Processes: Automation in risk adjustment reduces manual reviews, increases productivity, and helps health plans efficiently address Chase list prioritization using cNLP technology.
• Ensure Accuracy at Every Step: Effective solutions close HCC coding gaps, improve coder accuracy and productivity, and ensure precise reimbursements and better health outcomes through automated NLP data analysis.

The Impact of Technology on CMS Risk Adjustment

The Rise of AI and NLP-Powered HCC Coding

AI offers significant advantages and some challenges in HCC coding. On the positive side, AI accelerates coding processes, reduces human errors, and manages large data volumes with consistent accuracy.

Generative AI models further improve precision by refining their suggestions/insights based on new data. However, integrating AI can be costly, requiring substantial investment in technology and training, and raises medico-ethical concerns along with data security.

Additionally, there is the potential for job displacement in coding roles. Despite these challenges, the benefits of faster and more accurate coding generally outweigh the drawbacks, positioning AI as a promising tool for the future of HCC coding.

The Impact of Accurate CMS Risk Adjustment HCC Coding on Financial Projections

Accurate HCC coding is crucial for financial projections and federal reimbursements in healthcare. Inaccurate coding can cause revenue loss and resource misallocation.

For instance, a patient with diabetes complications may receive $673 monthly without proper coding, but accurate coding can increase reimbursements to $1,312.50 per month.

Accurate HCC coding is vital in value-based care and population health management, ensuring appropriate reimbursements and effective decision-making.

Misrepresenting conditions like diabetes or CHF can lead to poorly allocated investments and suboptimal health outcomes, highlighting the importance of precise documentation of patient health and risks.

RAF scores, based on diagnosis codes and demographics, assess patient risk. Scores above 1.0 indicate higher risk and treatment costs, while lower scores signify healthier patients with lower costs.

Moreover, the Updated Version 28 in the CMS HCC Model introduces changes affecting capitation rates and methodologies. Organizations must optimize coding functions to avoid financial risks and errors. Accurate coding is crucial for 2024.

Future of Risk Adjustment

Future Directions in Risk Adjustment: Spotlight on the ‘Point of Care’

Predictability is becoming increasingly valuable as healthcare providers and plans transition from fee-based to value-based care models amidst tightening budgets.

Reducing variation can present both challenges and significant opportunities in risk adjustment and quality improvement. It’s crucial for those in positions of influence to minimize this variation.

Balancing Disparities at the Point of Care Provision

Managing data variation starts at the point of care, where essential information is gathered from physicians. The principle of “garbage in, garbage out” highlights the need for high-quality data from the beginning.

Effective risk adjustment and quality improvement depend on accurate data collection, retrieval, and organization. Engaging physicians is crucial; their active participation is necessary for a successful data management cycle.

While physicians may resist extra documentation tasks, they are more motivated when they see data reflecting real clinical and financial improvements.

Minimizing disruption for clinicians is key to this engagement. In the Medicare Advantage environment, effective provider engagement aims to:

• Support accurate capture and documentation of patients’ chronic conditions in line with CMS guidelines.
• Facilitate precise diagnosis and management of member conditions.
• Enhance the accuracy of medical record documentation to improve coding guideline compliance.
• Address HEDIS measures affecting Star ratings at the point of care.

For health plans, engaging providers can lead to more accurate and comprehensive documentation, resulting in better member risk scores, improved Star ratings, and higher quality care.

Prospective Risk Adjustment Relies on Physician Engagement Strategy

An effective provider engagement program should be proactive, with coordinators supporting accurate documentation in a non-intrusive way.

These coordinators streamline processes, enhance efficiency, and reduce administrative burdens, allowing caregivers to focus on patient care.

Engaging providers is vital for advancing risk adjustment and improving clinical quality, disease management, and wellness. A holistic approach addresses redundancy and optimizes care, workforce quality, and cost control.

Conclusion

The evolution of risk adjustment in healthcare has been marked by significant advancements and challenges. From the AAPCC and PIP-DCG models to the CMS-HCC and ACA enhancements, these developments underscore the ongoing need for accurate risk assessment.

Embracing advanced data analytics and AI-driven solutions, while addressing provider engagement, will be crucial in optimizing risk adjustment and improving healthcare outcomes as we move forward.