Undercoding in Healthcare: Causes, Risks, and Prevention

Undercoding in healthcare occurs when a medical claim reports less than the clinical documentation supports. This can include selecting a lower-level service, omitting a documented procedure or diagnosis, missing an applicable modifier, or failing to capture a billable service entirely.

Undercoding can reduce reimbursement, misrepresent patient complexity, distort utilization data, and create compliance concerns, even when a claim is paid without a denial.

A 2023 study estimating the impact of undercoding in Florida primary care found that approximately 2.6 million visits could be undercoded annually, representing nearly $114 million in potential lost Medicare reimbursement. 

This guide explains the types of undercoding, their causes and consequences, and how healthcare organizations can identify and prevent them through coding audits, clinical documentation improvement (CDI), and AI.

What Is Undercoding in Healthcare?

Undercoding is reporting a lower level or smaller scope of care than the medical record supports. It includes assigning a lower-level code, omitting supported diagnoses or procedures, missing separately billable services, or failing to report an applicable modifier.

For example, a physician may document an established patient encounter that supports CPT code 99214, but the claim is submitted using 99213. If the complete documentation supports 99214 under applicable coding requirements, the lower-level submission represents undercoding.

Medical undercoding can occur at multiple stages of the revenue cycle:

• A physician performs work but does not document the required details.
• A provider consistently selects conservative E/M levels.
• A coder overlooks a supported diagnosis, procedure, or modifier.
• An EHR-generated code fails to capture the complete encounter.
• A documented service never reaches the billing system.
• Coding decisions do not reflect current coding or payer requirements.

Undercoding can be intentional or unintentional. In either case, the resulting claim may fail to accurately represent the services delivered.

Is Undercoding the Same as Downcoding?

Undercoding and payer downcoding can both result in lower reimbursement, but they occur at different stages.

For example, submitting 99213 when documentation supports 99214 is internal undercoding. Submitting a supported 99214 and having the payer reduce it to 99213 is payer downcoding.

What Are the Different Types of Undercoding?

Why Does Undercoding Happen?

The causes of undercoding generally fall into three categories: provider documentation, coder decisions, and technology or workflow limitations.

1. Provider Documentation and Conservative Medical Coding

Physicians may document the clinical encounter without including every element needed to support the appropriate billing code.

Common problems include:

• Missing total-time documentation for time-based E/M services.
• Incomplete descriptions of medical decision-making.
• Independent interpretations or other qualifying work not adequately documented.
• Conditions addressed during the visit not clearly reflected in the record.
• Relevant clinical details buried in unstructured narrative notes.

Another cause is conservative coding driven by compliance concerns.

Some providers habitually select lower E/M levels because they believe doing so reduces audit exposure. However, consistently selecting lower codes without considering the documentation can produce inaccurate claims.

The appropriate approach is to apply coding requirements consistently, regardless of whether the resulting code is higher or lower.

2. Coder Workload and Knowledge Gaps

Medical coders frequently work with large volumes of documentation, multiple code sets, and specialty-specific requirements.

Undercoding can occur when coders:

• Review only part of the clinical encounter.
• Miss procedures or diagnoses documented in separate notes.
• Overlook applicable modifiers or add-on codes.
• Apply outdated coding guidance.
• Interpret complex documentation inconsistently.
• Lack sufficient familiarity with specialty or payer-specific requirements.

For example, emergency department coding may require reviewing physician notes, diagnostic testing, independent interpretations, and medical decision-making details across multiple parts of the encounter.

If the coder relies on an isolated note rather than the complete record, potentially reportable services or complexity may be missed.

3. EHR and Revenue Cycle Workflow Limitations

Technology can also contribute to undercoding.

Some EHR coding workflows rely heavily on structured fields or provider-selected codes. If relevant clinical work appears only in narrative documentation, the generated code may not reflect the complete encounter.

Other workflow gaps include:

• Codes generated before documentation is complete.
• Templates that do not capture relevant clinical work.
• Missing comparisons between documentation and assigned codes.
• Charge-interface failures.
• Limited pre-bill auditing.
• No feedback loop between coding, CDI, and reimbursement outcomes.

What Are Some Examples of Undercoding in Healthcare?

Example 1: An Established Patient Visit Is Coded Too Low

A physician evaluates a patient with worsening diabetes and hypertension, reviews laboratory results, adjusts medications, and orders follow-up testing.

The encounter is submitted using CPT 99213.

However, the documented medical decision-making may support 99214.

For established office or outpatient E/M visits, the correct level depends on the applicable medical decision-making or total-time requirements. The presence of multiple conditions or medication changes alone does not automatically establish a particular code.

Undercoding occurs when the complete documentation supports the higher level, but the lower code is submitted.

Example 2: Critical-Care Work Is Not Completely Documented

An emergency physician treats a critically ill patient requiring intensive clinical management.

The record describes the patient's condition and interventions but does not include the qualifying critical-care time or sufficiently establish all required elements.

Although the work may potentially qualify for critical-care billing, the coder cannot assign a critical-care code based on assumptions.

The appropriate response may be a compliant physician query requesting clarification of the missing information.

If the required documentation cannot be established, the higher-level service should not be reported.

This is an important distinction: performed but insufficiently documented work represents a potential documentation opportunity, not automatic authorization to add a code.

Example 3: A Separately Identifiable E/M Service Is Missed

A patient visits a physician for a minor procedure but also receives a significant, separately identifiable evaluation for another clinical concern.

The claim includes the procedure but omits the E/M service.

If the documentation supports both services and applicable coding rules permit separate reporting, the E/M service may be reportable with modifier 25.

Failing to identify this opportunity can result in undercoding.

However, routine evaluation associated with the procedure is not automatically separately billable.

Example 4: A Documented Diagnosis or Procedure Is Overlooked

A coder reviews the primary physician note but does not examine relevant information elsewhere in the encounter.

A separately reportable procedure or an actively managed diagnosis may be documented in another section of the record.

If that information meets reporting requirements but is omitted from the claim, the result can be undercoding.

This example highlights why complete-encounter review matters, particularly in high-volume environments where documentation is distributed across multiple sources.

Why Is Undercoding Difficult to Detect?

One of the biggest challenges with undercoding is that the claim may still be paid successfully.

Unlike a coding error that triggers a denial or rejection, undercoding may produce no obvious downstream exception.

Consider two scenarios:

This is the gap CombineHealth's independent audit is built to close. Because an undercoded claim pays and never triggers a denial, the only way to find it is to re-evaluate the encounter against the complete documentation, which CombineHealth does automatically, comparing the submitted codes to the documentation-supported code set and surfacing lower-than-supported levels, missed procedures, diagnoses, and modifiers that would otherwise go unnoticed.

What Are the Consequences of Undercoding?

Undercoding affects more than reimbursement. It can influence financial performance, compliance, patient complexity reporting, and operational decision-making.

1. Lost Reimbursement

The most immediate consequence is missed reimbursement for services that could have been reported under applicable coding and payment rules.

A 2023 study published in Exploratory Research in Clinical and Social Pharmacy estimated that approximately 2.6 million primary care visits in Florida, or 8.9% of projected annual visits, were undercoded.

The researchers estimated that this could represent nearly $114 million in lost annual Medicare reimbursement.

The study used a modeling approach rather than directly auditing every encounter. Nevertheless, it demonstrates how differences in coding levels can accumulate into substantial revenue losses across a large patient population.

Actual financial impact depends on payer contracts, service mix, coding requirements, and whether corrections are permitted.

Undercoding can be particularly difficult to quantify because the organization may never identify the reimbursement difference unless the claim undergoes an independent coding review.

2. Inaccurate Patient Complexity and Utilization Data

Diagnosis and procedure codes do more than determine reimbursement. They also contribute to reporting on patient populations, clinical workload, and resource utilization.

When supported diagnoses or services are omitted, claims data may underrepresent:

• Patient complexity and disease burden.
• Services delivered.
• Provider workload.
• Resource utilization.
• Specialty-level demand.
• Population health trends.

This can affect how organizations interpret operational performance and allocate resources.

3. Compliance and Audit Exposure

Undercoding is not automatically fraudulent, and an unintentional coding error does not by itself establish a legal violation.

However, knowingly submitting inaccurate or misleading claims can create compliance concerns.

Consistently coding below the documented level is not a substitute for a compliant coding process. Organizations should maintain documentation-supported coding practices and audit for both undercoding and overcoding.

4. Misleading Revenue Cycle Performance Metrics

Undercoding can make certain operational indicators appear healthier than they are.

For example, a lower-level code may encounter fewer payment disputes while also generating less reimbursement.

A practice may therefore see fewer denials without realizing that some encounters are not being coded to the complete documentation-supported level.

Similarly, unusually conservative E/M distributions may indicate potential coding gaps, although they can also reflect legitimate differences in patient populations and encounter complexity.

5. Administrative Rework and Missed Recovery Opportunities

When undercoding is discovered retrospectively, correcting it may require:

• Reopening and reviewing the clinical record.
• Obtaining compliant documentation clarification.
• Submitting corrected claims.
• Reviewing payer-specific correction requirements.
• Addressing timely-filing limitations.
• Reconciling revised reimbursement.

Some opportunities may no longer be recoverable.

This makes denial prevention before claim submission more efficient than relying exclusively on retrospective audits.

How Can Healthcare Organizations Identify Undercoding?

1. Compare Submitted Codes Against Complete Clinical Documentation

A medical coding audit should independently evaluate the encounter and compare the supported code set against the codes originally assigned.

Depending on the setting, reviewers should assess:

• Problems addressed and medical decision-making.
• Data reviewed and analyzed.
• Risk of patient management.
• Qualifying time.
• Procedures and services performed.
• Diagnoses evaluated or managed.
• Applicable modifiers and add-on codes.
• Diagnosis-to-procedure relationships.
• Medical necessity and payer requirements.

The goal is to identify both omitted services and incorrectly assigned levels.

2. Analyze Provider-Level Coding Distributions

Organizations can compare coding patterns among providers treating similar patient populations.

For example, a coding manager might find that one physician consistently reports a higher percentage of low-level E/M visits than peers in the same specialty.

That difference may justify targeted chart review.

However, it does not automatically establish undercoding.

Patient acuity, documentation practices, encounter types, and clinical responsibilities can legitimately influence coding distributions.

Coding distributions should identify cases for investigation, not serve as targets that physicians are expected to match.

3. Independently Audit EHR-Generated Codes

An EHR-generated code should not be assumed correct simply because it was produced automatically.

An independent coding review can compare the EHR output against the full clinical encounter and identify whether relevant information was missed.

This is particularly useful for organizations that already have coding automation but want an additional validation layer.

CombineHealth's audit mode is designed for exactly this. It independently evaluates codes generated by a provider, coder, or EHR against the full clinical encounter and flags discrepancies — the additional validation layer this section describes, so an organization can keep its existing coding automation and still catch what that automation missed, with the supporting documentation and coding rationale shown for each finding.

4. Measure Findings From Coding Audits

Organizations should distinguish between potential coding opportunities identified and changes ultimately validated.

Useful metrics include:

5. Identify Recurring Documentation Gaps

Individual corrections are useful, but recurring patterns reveal where larger improvements may be possible.

For example, an audit may show that several providers consistently omit qualifying time documentation or fail to record relevant medical decision-making details.

Grouping findings by provider, specialty, location, procedure, payer, or documentation element allows CDI teams to provide more targeted education.

How Can Healthcare Organizations Prevent Undercoding?

1. Review the complete encounter. Ensure coders and coding systems can access relevant clinical documentation rather than isolated notes or structured fields.

2. Maintain current coding and payer rules. Keep ICD-10-CM, CPT, HCPCS, NCCI, modifiers, medical-necessity requirements, and applicable payer policies updated.

3. Validate codes before billing. Compare proposed codes against the documentation to identify omissions, incorrect service levels, and unsupported additions.

4. Use compliant physician queries. Request clarification when the record suggests a possible coding opportunity but lacks required documentation. Queries should be non-leading.

5. Separate queries from CDI education. Use queries when clarification can appropriately affect the current encounter, and educational alerts when the finding is better addressed in future documentation.

6. Provide targeted provider education. Use recurring audit findings and actual examples instead of relying exclusively on generic coding training.

7. Monitor coding and reimbursement outcomes. Track validated changes, documentation improvements, and revenue actually captured.

8. Audit in both directions. Review for overcoding and undercoding so improvements do not create new compliance risks.

How CombineHealth Identifies and Prevents Undercoding

CombineHealth approaches undercoding through autonomous medical coding, independent coding audits, clinical documentation improvement, and payer-specific validation.

The platform evaluates the clinical encounter to identify the complete, documentation-supported code set rather than relying exclusively on provider-selected or EHR-generated codes.

1. Autonomous Coding and Independent Audit Modes

CombineHealth supports two approaches.

Autonomous coding mode: The platform analyzes the clinical encounter and generates the supported ICD-10-CM, CPT, HCPCS, E/M, and modifier assignments.

Audit mode: CombineHealth independently evaluates codes generated by a provider, coder, EHR, or another coding system and identifies discrepancies.

This second approach is particularly useful for organizations that already have coding technology but want to validate whether it is capturing the complete documented encounter.

2. Complete-Encounter Review With Payer Intelligence

CombineHealth evaluates coding decisions against clinical documentation and applicable coding requirements, including:

• ICD-10-CM, CPT, and HCPCS assignments.
• E/M levels and medical decision-making.
• Modifiers and diagnosis sequencing.
• CPT-to-diagnosis relationships.
• Medical necessity.
• Payer-specific coding and reimbursement requirements.

This helps distinguish between a genuinely missed coding opportunity and a code that cannot be reported under the applicable rules.

3. Explainable Coding Recommendations

When CombineHealth identifies a potential discrepancy, the audit workflow can show the existing code, the proposed alternative, the supporting documentation, and the applicable coding rationale.

It can also identify missing evidence or situations requiring coder or physician review.

4. CDI Queries and Provider-Specific Improvement Opportunities

CombineHealth connects coding audits with CDI workflows.

When missing documentation could affect the current claim, the platform can identify a potential physician-query opportunity.

When the documentation cannot support a change to the current encounter, the finding may instead become an educational CDI alert to help improve future documentation.

The platform can also surface recurring documentation and coding patterns by provider, allowing teams to address systematic issues instead of correcting individual claims repeatedly.

5. Self-Learning From Payer Outcomes

CombineHealth's self-learning approach connects coding decisions with downstream payer outcomes.

Denials, reimbursements, underpayments, and payer-specific adjudication patterns can help identify coding or documentation configurations that require further review.

Validated findings can then inform future coding and claim-validation decisions.

This allows coding improvement to extend beyond assigning accurate codes toward understanding how those decisions affect downstream revenue cycle performance.

For a more detailed explanation of this workflow, see How AI Medical Coding Audits Uncover Undercoding.

Proven Results Across Coding Accuracy and Revenue Integrity

CombineHealth has demonstrated measurable improvements in production and parallel-audit environments.

In a 90-day engagement with a 400-bed health system, CombineHealth achieved:

• 98.4% Coding accuracy
• 75% Fewer coding-related denials
• +4% Captured revenue

Customer-specific results from a 400-bed health system over 90 days

In a separate emergency department parallel audit involving approximately 1,000 charts over 15 days, CombineHealth achieved approximately 98% coding accuracy, identified 5× more CDI gaps, and reduced turnaround time by 50%.

These results demonstrate how coding accuracy, documentation improvement, and downstream revenue outcomes can be evaluated together. They are not direct measurements of undercoding reduction.

By combining autonomous medical coding, audit workflows, documentation analysis, and payer intelligence, CombineHealth helps organizations identify potential undercoding before it becomes a recurring source of missed reimbursement.

Undercoding vs. Upcoding: Which Is the Bigger Risk?

Both undercoding and upcoding represent inaccurate coding practices, but their financial and compliance implications differ.

Book a Demo to see how CombineHealth can help identify documentation-supported coding gaps across your encounters.

FAQs

What is undercoding in healthcare?

Undercoding is reporting a lower level or smaller scope of care than the medical record supports — a lower E/M level, an omitted procedure or diagnosis, a missing modifier, or an uncaptured billable service. It can reduce reimbursement and misrepresent patient complexity even when the claim is paid. CombineHealth helps by coding and auditing against the complete documentation.

How is undercoding different from payer downcoding?

Undercoding happens before submission, when a provider, coder, or system reports less than the documentation supports. Payer downcoding happens during adjudication, when a payer reduces a submitted service level. CombineHealth validates codes against the documentation before billing and uses payer outcomes, including downcoding, to inform future coding.

How does CombineHealth help prevent undercoding?

CombineHealth evaluates the complete clinical encounter — not just provider-selected or EHR-generated codes — to identify the full, documentation-supported code set, so lower-level codes, missed procedures, diagnoses, modifiers, and add-on codes are caught before billing. It validates each code against payer rules and medical necessity and flags documentation gaps as CDI queries or alerts, helping capture supported revenue without adding unsupported codes.

Can CombineHealth audit codes our coders or EHR already produced?

Yes. In audit mode, CombineHealth independently evaluates codes assigned by a provider, coder, EHR, or another system against the full documentation and identifies discrepancies, including undercoded encounters, with the supporting evidence and rationale shown. It works as a validation layer on top of your existing coding process, without replacing it.

How does CombineHealth catch undercoding when the claim still gets paid?

Undercoding rarely triggers a denial, so it hides in paid claims. CombineHealth surfaces it by re-evaluating the encounter against the complete documentation and comparing the supported code set to what was submitted, flagging lower-than-supported levels and omitted services that would otherwise go undetected because no payer exception ever appears.

How does CombineHealth avoid turning an undercoding fix into overcoding?

CombineHealth checks every proposed code against the documentation, applicable coding rules, and medical necessity, so it distinguishes a genuinely missed opportunity from a code that cannot be supported. When the record does not support a higher level, it raises a compliant query or CDI alert rather than adding an unsupported code — and it audits in both directions, for undercoding and overcoding.

How does CombineHealth handle documentation gaps that cause undercoding?

When missing documentation could affect the current claim, CombineHealth identifies a potential physician-query opportunity; when the record cannot support a change to that encounter, the finding becomes an educational CDI alert for future documentation. It also surfaces recurring gaps by provider, so teams address systematic issues instead of correcting individual claims repeatedly.