Beyond compliance, coding accuracy and maximizing reimbursement should be viewed more as a financial strategy than a back-office chore. Nevertheless, here are four common myths that may be impeding effective reimbursement and coding processes.
Myth 1: Reimbursement Is a Flat Rate, So E/M Level Selection Doesn't Matter
Because FQHC visits are paid through an encounter-based model, it is easy to assume that E/M level selection has little bearing on reimbursement. The reality is more consequential. E/M levels influence the cost per visit that supports the PPS rate, because each CPT code carries an associated cost that flows into the annual cost report. When a health center reports $5 million in allowable costs across 25,000 billable visits, the resulting average cost per visit is $200; Medicare then layers a base rate and Geographic Adjustment Factor on top of that cost report data.
Medicaid methodology varies by state, with some states requiring separate Medicaid cost reports and others using Medicare as a baseline with inflation updates or full recalculation. But the operational risk is consistent: If complex visits are routinely coded at lower levels, the health center artificially suppresses reported cost per visit, lowering the PPS rate across every visit rather than only the miscoded encounter. Commercial reimbursement adds a more immediate consequence, because downcoding can create a direct payment gap between codes such as 99212, 99213, and 99214.
Audit findings show what this looks like in practice. One FQHC audit found 245 down-coded encounters that represented lost reimbursement opportunity and 32 up-coded encounters that created compliance concerns. The better approach is to validate E/M levels against the documentation and monitor patterns that suggest recurring under-coding or unsupported up-coding.
Myth 2: Chart Review Alone Is Sufficient; Validating E/M Levels Isn't Necessary
The misconception is that checking provider-selected codes is equivalent to coding the encounter. In reality, reviewing codes and coding from documentation are fundamentally different processes; a coder clearing 200 encounters a day is validating, not abstracting.
A limited chart review can support clean-claim workflow, but it is not the same as coding the encounter from the full record. Review usually means checking whether the provider-selected code appears reasonable; full coding means deriving the code independently from the documentation, including the HPI, assessment and plan, problem list, orders, results, and supporting clinical context. That difference matters because a high-volume review process can miss comorbidities, secondary conditions, and documentation inconsistencies that a complete abstraction process would surface.
The risk is not merely technical. Limited review can produce revenue leakage, under-reported patient complexity, overlooked documentation gaps, and chart inconsistencies that trigger denials or audit findings. Community health centers should still use targeted validation where it fits, but they should pair it with periodic full abstraction audits to identify missed complexity and documentation trends.
Myth 3: The EHR Assigns the Correct E/M Level Automatically
The misconception is that EHR checkbox calculators can reliably assign the correct E/M level without professional review. In reality, they replace documentation-based coding logic with a simplified algorithm.
EHR calculators can create the appearance of consistency, but they are only as reliable as the selections entered into them. A checkbox-driven tool may produce an E/M level, yet it cannot fully evaluate whether those inputs are supported by the narrative documentation or whether the medical decision-making complexity is reflected accurately. In that sense, the tool can replace coding judgment with a simplified algorithm.
The result can be more variability, not less. Some providers under-select, others over-select, and both patterns create risk when no one validates the assigned level against documentation, payor rules, and medical decision-making.
The better practice is to treat the EHR-generated level as a starting point for review rather than the final answer.
Myth 4: Capturing All Chronic Conditions at Each Visit Takes Too Long
The time concern is understandable but, in many encounters, the chronic and comorbid conditions are already documented; they simply need to be reflected accurately in the coding. Capturing those conditions is not extra clinical work when they support treatment decisions, medical necessity, risk adjustment, quality reporting, or supplemental payment programs. It is representation of the record.
When chronic conditions are left out, the cost shows up in multiple places. These include lower Risk Adjustment Factor (RAF) scores, weaker risk-adjusted payment support, documentation-to-coding gaps that auditors target, degraded quality measure tracking, and under-reported patient acuity. In the FQHC model, ICD coding may not increase per-visit payment directly but helps ensure visits qualify as billable encounters, avoid denials, and count toward quality and supplemental funding programs.
Audit evidence reinforces the point. Across one engagement, coders added 1,117 ICD codes, removed 870 unsupported codes, and rearranged 346 codes to protect medical necessity linkage that might otherwise have generated denials.
Taken together, these myths reveal a larger operational issue: Coding is often treated as claim cleanup rather than financial governance. A stronger coding process connects documentation, reimbursement, compliance, and quality reporting before errors become denials, audit exposure, or lost revenue.
Why Coding Matters Beyond the Claim
Accurate coding matters because it sits at the intersection of revenue integrity, compliance, operations, and reporting. It reduces denials, rejections, rework, and delayed payment while lowering exposure to audit findings and recoupment. It also strengthens the data health centers rely on for UDS reporting, quality programs, supplemental funding, grant justification, care gap analysis, and strategic planning.
Complete coding turns the medical record into operational intelligence, supporting quality measures, risk adjustment, supplemental funding, and better population health decisions.
Paul Ferrazza is Vice President at Nivaran, formerly Nivaran, the human-in-the-loop healthcare workflow company that connects clinical documentation, coding, revenue cycle management, and virtual operations in one accountable model, helping healthcare organizations protect provider time, reduce revenue leakage, and keep care moving.