How lab data enables more accurate patient coding for value-based care success

As the healthcare landscape continues to evolve, value-based care is playing a pivotal role in enhancing patient outcomes while effectively managing costs. For providers engaged in value-based care programs, accurate patient coding isn’t just important—it's essential for achieving success.

Unfortunately, many ACOs and Medicare Advantage networks face significant challenges due to coding inaccuracies, which can hinder their progress toward performance goals and cost savings. Fortunately, leveraging national laboratory data offers a promising solution.

Healthcare organizations can improve coding accuracy, especially when assessing new patient panels and transitioning to the V28 HCC coding model, with lab data. By integrating robust laboratory data, you can streamline your coding practices, ultimately leading to better health outcomes and financial performance. In this article, Labcorp's Brian Buesing, Vice President of commercial efforts for large physician organizations and value-based care networks, shares his insights.

The far-reaching impact of inaccurate patient medical coding

Provider-assigned ICD-10 coding plays a key role in estimating expected healthcare costs for patients. These estimates generate cost baselines for shared savings calculations and/or per member per month payments in full-risk arrangements. When coding is misaligned with reality, providers face an uphill battle in meeting financial performance targets. 

Coding challenges are particularly acute when dealing with new patient panels, as coding data from payers often provide an incomplete and potentially outdated snapshot of a patient’s health. In Medicare programs, coding must be resubmitted each year, or prior coded conditions won’t be applied, even for chronic or permanent conditions.

For instance, a patient who has undergone a limb amputation must have the related coding submitted each year; otherwise, Medicare may incorrectly conclude the patient is no longer an amputee and exclude the additional care costs in their calculations.

This gap between payer data and actual patient health status can hinder the organization’s ability to manage patient care effectively, forecast accurately and achieve financial performance targets.

Using lab data to support coding for new patient panels

Laboratory testing plays a critical role in patient care, impacting nearly 70% to 80% of clinical decisions. It’s used to more efficiently and effectively diagnose and monitor many high-prevalence conditions.

Many of those conditions affect HCC coding, including diabetes, chronic kidney disease (CKD), autoimmune and cardiovascular disease. For new patient panels, lab data can provide a historical view of health beyond the confines of payer plans or participating provider EHRs.

By comparing lab test results to actual patient coding, you can identify and resolve discrepancies, ensuring more accurate and comprehensive coding. Working together with large provider networks, Labcorp has found coding for diabetes is generally well documented but still misses 20% to 30% of patients, leaving much room for improvement. In contrast, CKD is poorly diagnosed, with 70% or more of CKD patients going uncoded and/or aren’t accurately coded for the appropriate stage.

Using lab data, you can quickly assess these types of patients and provide an actionable patient list to work with providers in correcting coding discrepancies. 

Navigating the transition to the V28 HCC coding model

The Centers for Medicare & Medicaid Services (CMS) is transitioning from the V24 HCC model to a revised V28 model over the next two years. This change has raised concerns among many provider organizations, particularly regarding its impact on risk adjustment factor (RAF) scoring.

CMS's objective with this transition is to focus on conditions and related coding that have a true impact on healthcare costs. As a result, there’s been a reduction in the number of ICD-10 codes associated with HCC and a decrease in RAF scores for some HCC-coded conditions.

One major concern expressed by large provider organizations is the potential impact on RAF scoring for diabetic patients with complications. While it's true many provider organizations may experience a reduced rate for diabetes overall, there’s a significant overlapping population with CKD that may help offset this impact.

V28 RAF score changes for diabetes and CKD

A case study illustrating real-world coding impacts

To illustrate the power of accessing national lab data for patient coding, let's consider a recent analysis conducted with a large provider organization. Our aim was to help them assess the V28 impact and uncover potential benefits by focusing on appropriate coding adjustments for diabetes and CKD.

Labcorp conducted an analysis of 417,772 patients managed by the large provider organization. Of the patients analyzed, 95,761 had at least one lab test result originating from providers outside their network. This data was used to support patient clinical history and close quality measure care gaps.

With the ACO patients who were currently coded for diabetes, the provider organization would likely see a 15% to 25% decrease in cost baseline due to V28 RAF adjustments.

However, if this organization could address all uncoded/miscoded diabetes and CKD patients, they would experience a 300% increase in cost baseline for just those two HCC conditions. The estimated net impact was nearly $14 million annually.

Labcorp’s analysis not only provided support to prepare for the V28 transition, but also helped improve overall coding accuracy and align resources with patient needs.

Partnering for success in value-based care

As value-based care continues to evolve, accurate patient coding remains crucial for success. National lab data offers a powerful diagnostic tool for addressing coding challenges.

Labcorp stands as a trusted partner in this endeavor, offering valuable lab data that can help provider networks and hospital health systems effectively manage their value-based care programs.