To facilitate full and accurate Hierarchical Condition Category (HCC) coding, it is important for providers to take a disciplined approach to documenting medical encounters in patient records. To this end, the AAPC suggests providers use the acronym “MEAT” to help support accurate HCC coding and proper documentation:
MEAT is a widely used documentation framework that can help support accurate HCC coding by linking provider documentation, clinical action, and claims coding. For provider organizations participating in value-based or risk-bearing arrangements, accurate documentation helps support accurate representation of the documented while contributing to care planning processes, quality initiatives, and compliant risk adjustment operations.
MEAT remains a useful framework for documenting conditions that support HCC coding, but in 2026, provider organizations need to go further. With CMS now calculating Medicare Advantage risk scores using 100% of the 2024 CMS-HCC model, and with continued RADV and OIG scrutiny, documentation should be specific, clinically supported, encounter-based, and defensible during audit.
Before MEAT methodology can be operationalized within care-team workflows, clinicians should understand why accurate HCC coding matters for documentation quality, care coordination, and payment accuracy. Clinicians are frequently overworked, and may be skeptical of new programs and processes. That’s why change management should begin early and continue throughout implementation to reinforce the clinical and operational value of value-based care.
What leadership can and should do as part of change management is test and institute systems and programs that alleviate the perceived additional operational burden. Reducing friction within the care team’s processes lowers hurdles and can help stakeholders better understand the potential operational and clinical benefits of VBC. Provider organizations often face operational challenges when transitioning to alternative payment models (APMs), and three recurring issues stand out:
In a fee-for-service revenue cycle, coding is relatively straightforward because claims typically reflect the diagnosis or procedure tied to a specific service. Value-based care models require a broader view of the patient’s medical history, often gathered from multiple sources and locations. When a patient receives care across multiple departments within a clinically integrated network (CIN) that uses separate EMRs, consolidating diagnosis information linked to HCCs becomes difficult. Without the right integrations and automation, scaling that work is challenging, especially if organizations need to improve capacity without adding headcount.
Empower clinical review specialists by giving them a more comprehensive view of each patient’s medical history, as well as tools that help them identify potential gaps in care. When done prior to an encounter, this can reduce some of the burden on clinicians during the patient visit. It can also give clinicians more time to assess the MEAT requirements of new potential conditions. In addition, AI and ML tools can help streamline HCC recapture and support continuity of documentation and data insights, provided they are paired with appropriate clinical oversight and compliance review.
Once the care team understands HCC coding as a core part of the organization’s strategy, leaders need to choose the right deployment approach and support implementation consistently. Different health systems will take different approaches; however, organizations that ask already overextended care teams and coding staff to absorb new processes without support may face resistance. Organizations should ease into the transition by starting with tools designed for value-based care that also fit existing team structures and systems.
Decisions also need to be made regarding HCC coding strategy. Leaders need to decide whether HCC coding will be addressed before, during, or after the visit. Will suspected conditions for clinician review pop up within the EMR, come through a paper list with the patient, or through custom cross-functional processes? Will coders and clinicians collaborate through electronic queries or in person? Clinicians should be part of that decision-making process. Excluding clinicians is a missed opportunity to understand how the workflow will affect day-to-day operations and may lead to implementation challenges or resistance.
Whatever tools and processes are deployed should reduce, not increase, provider workload. Continued education is important, as is minimizing alert fatigue, duplicative work, and sifting through noisy, meaningless data. If using alerts or pop-ups within the EMR, ensure they exclude conditions the provider has already addressed. This is especially important in 2026 as organizations adjust to the fully implemented 2024 CMS-HCC model, where greater specificity and accurate condition capture can play an important role in supporting appropriate representation of documented patient complexity. Most important, help clinicians understand that time spent confirming or rejecting a suspected condition now may reduce downstream administrative work later.
Clinicians play a central role in supporting MEAT-based documentation, improving completeness, and enhancing coding accuracy. But they need an engaged, educated support team with the right tools for each role. Clinical documentation improvement (CDI), compliance, and coding teams would benefit from regular retrospective audits to confirm that HCC codes are supported by appropriate documentation.
Even care teams that are fully committed to value-based care and equipped with collaborative tools may still code inconsistently. Leadership needs clinician-level visibility into coding performance to understand where education or resources should be deployed. That can be challenging without clear, data-driven insights into clinician quality risk operations.
Leadership should use performance analytics to identify variation across providers, specialties, locations, and condition categories so education and workflow support can be targeted where they are needed most. Start with the end goal in mind: show care teams the role of HCCs in supporting care coordination processes and population health management efforts. With the right visibility in place, providers can start building patient registries, identify where the patients are, and build standard care pathways to help identify potential gaps in care and support care coordination efforts, such as supporting processes that may help confirm appropriate patient medications or referrals are considered. Leadership can then identify variations in care and gather the clinicians to share knowledge.
Treating HCC coding as a discipline rather than as an administrative or financial concept—which is how many providers currently regard it—helps ensure providers and the coding team are aligned, thereby supporting improved documentation practices and operational insights that may contribute to better overall outcomes. Organizations with confidence to move into higher risk-sharing arrangements should explore automation and natural language processing (NLP) to drive scalability, collaborative tools to allow the whole care team to work in unison, and performance analytics to support ongoing process improvement across the care team.
MEAT is a useful guideline for helping care teams and coders support alignment between HCC coding and the documented disease burden of patient populations, but applying it consistently requires the right systems and technology infrastructure. In today’s environment, provider organizations need scalable workflows, compliant coding governance, retrospective quality review, and technology that supports documentation completeness without adding unnecessary burden to clinicians.
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