HCC coding is a crucial component of healthcare value-based payment models. When it is not correctly done, providers can miss significant reimbursement opportunities and negatively impact their revenue cycle performance.
Healthcare organizations should regularly audit and monitor HCC coding and risk adjustment models to ensure compliance. This helps identify coding gaps that can lead to an inflated RAF score, resulting in lower reimbursement.
Streamlined Claims Processing
If you’ve ever tried to submit a medical claim for coverage, you know that it can be incredibly complicated. After all, it’s not just a bill from a doctor or hospital; it’s an invoice for all the services and fees involved.
When a claim is submitted, it goes through many stages and involves a lot of people. And these people can make mistakes, so it’s important to keep things organized and error-free.
One of the most efficient ways to keep claims processes organized is through digital recordkeeping. This allows you to share status information with patients and insurance providers easily. It can also help improve communication between workers. In addition, it can help your customers check their status and see if they’ve been paid yet.
Another way to streamline your claims processing is to automate recurring tasks and eliminate unnecessary work. This can save your team valuable time and ensure consistency across all departments.
A streamlined process can help you avoid delays and errors, improving customer satisfaction. This will help you retain your existing customers and attract new ones. Moreover, it can also help you control operational costs.
Hierarchical condition category (HCC) risk adjustment models are now being used in Medicare Advantage, Medicaid, and private health plans. CMS developed them to help predict and manage patient healthcare costs in the coming year.
Each HCC carries a weight or score within the model, and these are added together to create a risk score, which is used to determine a patient’s expected annual expenditure. This is then normalized to a value of 1.0.
This risk factor helps to ensure that beneficiaries with chronic illnesses have access to care at a fair cost while still providing providers with the resources needed to provide high-quality care. It also helps CMS, responsible for the model, direct funding where it will impact the quality of life for patients.
The most effective way to achieve this is through a strong and comprehensive HCC coding and risk adjustment program. Using specialized patient-centered software systems, organizations can search and capture the appropriate conditions of their entire population to ensure that all relevant HCCs are properly documented.
This will help them find gaps in the documentation process and identify risk score-relevant conditions that can be closed and reclaimed. This will ultimately result in more accurate HCC coding and RAF scores and higher reimbursements from Medicare.
To achieve the most success, healthcare organizations should use a centralized data hub to capture and consolidate patient data in a single place, ensuring that all relevant risk adjustments are captured and reviewed in real time. This will allow them to find and close any coding gaps that may be present and reduce the risk of audits or missed revenue opportunities.
Better Patient Care Documentation
Hierarchical condition category (HCC) coding is a key part of CMS’s risk adjustment model, assigning patients a risk adjustment factor (RAF) score. This score is then used to determine healthcare costs for patients enrolled in Medicare Advantage plans.
The use of HCC coding also improves the accuracy of risk-adjusted reimbursement. The RAF score is calculated using the information submitted on medical claims from face-to-face encounters with qualified practitioners. It is based on the patient’s underlying disease state and other factors, including age and gender.
To achieve better documentation, educating all staff members about the importance of capturing patient-level data is necessary. This includes educating care professionals on the significance of examining social determinants of health and how to capture and evaluate information on those factors.
A specialized review process that uses medical record review and supplemental data files are needed to verify the accurate documentation of HCCs. This includes aggregating and collating data from multiple sources, such as hospital claims, provider claims, in-home assessments, and other supplemental data files.
Reduced Audit Risk
Risk adjustment models are becoming increasingly prevalent as healthcare shifts to value-based payment models. While these models are designed to help health plans insulate themselves from the higher healthcare costs that come with enrollees with high medical use, they also create a strong incentive for physicians and healthcare organizations to focus on coding accuracy.
To ensure accurate coding under these risk-adjustment models, health systems and medical groups should prioritize having a team in place to manage the documentation and HCC coding process. This group could include members of the analytics team, the Accountable Care Organization (ACO) team, and clinicians, among others.
In addition to improving the quality of care for patients, correct coding under risk-adjustment models also improves healthcare quality through reduced audit risk. This is because incorrect coding can lead to a health plan paying for sicker patients when it should be reimbursed for healthier ones.
One way to mitigate audit risk is to implement technology solutions to help speed up the reconciliation process and reduce the likelihood of errors. For example, automated coding technologies that provide documented clinical evidence of accuracy between the medical record and claim codes are useful tools for risk-adjustment coding and reconciliation.
Another approach to reducing audit risk is to ensure that all diagnoses and conditions are captured in the medical record each year. This can be done through proactive screenings and annual wellness visits or by utilizing preventive testing options to detect more serious conditions.