Healthcare is a Team Effort—And So is Accurate Coding
Exceptional healthcare is not the result of isolated efforts—it’s the outcome of true collaboration. When physicians, nurses, coders, and administrative support staff work together as a cohesive team, they combine their unique skills to deliver comprehensive, patient-centered care. This collaborative model ensures that all aspects of care are addressed, from clinical excellence to operational efficiency, ultimately enhancing outcomes and improving the patient experience.
The same principle holds true when it comes to accurate documentation and medical coding, particularly in today’s value-based care environment. Capturing the complexity of a patient’s health status requires more than just clinical acumen; it demands a coordinated effort between providers and coding professionals to ensure that every diagnosis is supported by clear, complete, and compliant documentation. By involving the right stakeholders at the right points in the workflow—whether at the point of care or during retrospective reviews—organizations can improve code specificity and reduce documentation errors.
A team-based approach to coding also leads to more accurate claims submissions that truly reflect the disease burden of the patient population. This not only supports appropriate reimbursement but also minimizes the risk of financial penalties tied to inaccurate or incomplete coding. Additionally, such collaboration can significantly reduce the workload on retrospective audit teams, streamlining internal processes and increasing coding efficiency across the organization.
With CMS increasing the frequency and scope of Risk Adjustment Data Validation (RADV) audits—and now applying error extrapolation dating back to 2018—the financial exposure for Medicare Advantage plans is more significant than ever. In this climate, organizations cannot afford to overlook the importance of precise, compliant documentation and coding practices. A proactive, team-based strategy is essential to protect against costly audit findings, maintain reimbursement integrity, and support the long-term success of value-based care programs.
Our AI-driven platform uses advanced disease detection algorithms and natural language processing to analyze the EHR data and clinical notes you already have, surfacing real-time risk adjustment insights directly within your workflow. When coding is done correctly during the encounter, it’s more than best practice—it’s empowering. It instills confidence, drives efficiency, and enhances clinical decision-making. ForeSee ESP® is built to amplify human intelligence, not replace it—supporting care teams with the insights they need to document accurately, improve outcomes, and move forward smarter.
Here’s how we can help
Reduce Audit Risk
Inaccurate coding by providers increases the risk of RADV audits, where CMS can reclaim millions in paid claims. ForeSee Medical ensures coding accuracy, reducing compliance risks and financial exposure.
Boost Clinical Outcomes
Earlier diagnosis through accurate coding leads to earlier treatment, improving patient outcomes and care quality.
Improve Medicare Contract Performance
With accurate coding, your Medicare Advantage claims will reflect the true disease burden of your value-based care population, leading to better contract performance.
Empower Providers
Physicians, often untrained in CMS-HCC coding, may undercode or overcode their encounters. ForeSee Medical helps eliminate these errors, mitigating risks and supporting compliance.
Proactively Address Documentation Gaps
Our software identifies deficient documentation from past encounters, allowing you to proactively address current RADV exposure during future patient visits.
Enhance Productivity
ForeSee Medical streamlines workflows with pre-encounter chart reviews, ensuring every patient and every HCC code is reviewed before each visit.
Let us help you protect your organization, improve patient outcomes, and enhance operational efficiency through accurate, AI-powered coding.
Blog by: The ForeSee Medical Team