Pulling Back the Curtain: What Happens When Payers Review the Medical Record: Part III of a Five-Part Series

EDITOR’S NOTE: The author of this article used artificial intelligence (AI)-assisted tools in its composition, but all content, analysis, and conclusions were based on the author’s professional judgment and expertise. The article was then edited by a human being.

In Part I of this series, we explored the structured data payers receive before reviewing the medical record.

In Part II, we examined how payer analytics evaluate those data elements and identify claims that may warrant further scrutiny.

But what happens after a claim is flagged?

At that point, the case typically moves from automated analytics into the next stage of the payer review pipeline: clinical review of the medical record.

Understanding what occurs during this stage is essential for clinical documentation integrity (CDI) professionals, as it is often when payer interpretation of the patient encounter begins to take shape.

The Transition from Analytics to Clinical Review

When payer analytics systems identify a claim that appears inconsistent with expected patterns of care, the case is frequently routed for manual review.

These reviews are typically conducted by payer-employed or contracted clinical staff, including registered nurses, clinical auditors, or physician reviewers.

At this stage, the reviewer begins evaluating the documentation contained in the medical record to determine whether the services provided meet the payer’s definition of medical necessity.

This process occurs within broader utilization management programs, which insurers use to determine whether healthcare services meet coverage criteria and are delivered in the appropriate care setting. ¹

This evaluation is not performed in the clinical environment where the patient was treated. Instead, it occurs remotely, relying entirely on the documentation recorded during the encounter.

The reviewer does not see the patient.

The reviewer was not present during the diagnostic discussion.

The reviewer did not participate in the clinical decision-making process.

The reviewer’s interpretation of the encounter is based solely on what was written in the record.

For this reason, the documentation itself becomes the clinical story, from the payer’s perspective.

How Payer Reviewers Evaluate Documentation

During medical reviews, payer clinicians typically evaluate several key elements within the medical record.

These often include:

• The physician’s initial assessment of the patient;
• Documentation of symptoms, clinical findings, and diagnostic testing;
• The level of monitoring or treatment required;
• The rationale for admission or escalation of care; and
• The patient’s clinical risk profile and anticipated complications.

Reviewers frequently compare the documentation against established coverage criteria and clinical screening tools used to evaluate level-of-care decisions.

Among the most commonly used frameworks in utilization management are InterQual® Criteria and Milliman Care Guidelines (MCG), which provide evidence-based benchmarks used by insurers and hospitals to evaluate medical necessity and level-of-care determinations. ²

If the documentation clearly reflects the patient’s clinical risk, severity of illness, and the intensity of services required, the reviewer can typically understand why the level of care was selected.

However, when documentation does not fully communicate those elements, the encounter may appear less complex when evaluated through standardized coverage criteria.

The Role of Analytics in Triggering Review

Another factor influencing payer review is the increasing reliance on advanced claims analytics and predictive models to prioritize whichever cases receive manual review.

Health insurers increasingly use large-scale data analysis and machine learning tools to analyze patterns in claims data and identify encounters that deviate from expected utilization for similar diagnoses and procedures.³

These analytic models often incorporate multiple inputs, including:

• Diagnosis and procedure codes;
• Length of stay compared to expected benchmarks;
• Site-of-service comparisons;
• Risk-adjustment scoring; and
• Historical claims patterns across similar cases.

Some insurers also apply internal scoring frameworks to estimate expected severity and utilization patterns.

For example, Aetna has described internal severity-scoring methodologies used to evaluate the complexity of a patient encounter relative to expected treatment patterns, while Optum analytics platforms evaluate claim risk using predictive models and utilization benchmarks applied across large national datasets.⁴

These tools help insurers identify and scrutinize which claims warrant deeper review.

When a case is flagged through these analytic models, the subsequent clinical review often begins with an expectation that the documentation will clearly explain the clinical factors that justified the level of care provided.

If that explanation is not readily apparent in the medical record, the encounter may be interpreted differently than it was experienced by the treating clinical team.

Recent investigations into Medicare Advantage (MA) utilization management practices have highlighted the growing role of automated systems in identifying claims for further review and denial consideration.⁵

The Importance of Clinical Reasoning

One of the most common challenges identified during payer review is the absence of

clearly documented clinical reasoning.

Physicians make complex decisions every day.

They weigh diagnostic uncertainty, assess patient risk, and determine when hospital-level monitoring or treatment is required.

However, those elements are not always fully reflected in the documentation.

As noted, when payer reviewers evaluate a case, they must rely entirely on what is written in the record.

This is particularly relevant in cases where the patient’s condition evolves during the hospitalization, or where diagnostic uncertainty influenced the initial admission decision.

Without that context, the medical record may not fully convey the clinical reasoning behind the care provided.

The Limitations of Structured Documentation

Electronic health record (EHR) systems have introduced numerous tools designed to improve documentation efficiency.

Templates, checkboxes, and structured documentation fields can help clinicians capture important information quickly.

However, these tools can also introduce unintended challenges.

While structured fields may capture discrete data elements, checkbox documentation alone rarely conveys the clinical complexity behind medical decision-making.

For example, a template may document abnormal vital signs or laboratory values, but it may not explain how those findings influenced the physician’s risk assessment or the decision to admit the patient.

From the perspective of a payer reviewer, documentation that relies heavily on structured elements without an accompanying clinical narrative may not fully explain the rationale for the level of care selected.

Automation and Documentation Risk

Another emerging challenge involves increasing reliance on automated documentation workflows.

Features such as copy-forward functionality, templated note sections, and prepopulated documentation fields are designed to streamline the documentation process.

However, these tools can pose risks when documentation is generated automatically and signed without careful verification.

When portions of the medical record appear repetitive, templated, or inconsistent with the patient’s clinical course, payer reviewers may question whether the documentation accurately reflects the care delivered.

Transparency in documentation is therefore essential.

The medical record should clearly reflect how information was entered, including whether content was templated, copied forward, or directly authored during the encounter.

Regardless of how portions of a note are generated, the clinician who signs the entry assumes responsibility for the entire record!

In practical terms, the signature on a medical record entry represents confirmation that the physician has reviewed the note and that it accurately reflects the patient’s condition, clinical assessment, and decisions made during the encounter.

The Perspective of the External Reviewer

One of the most important concepts for CDI professionals to understand is that payer reviewers evaluate the medical record from a fundamentally different perspective than the treating clinical team.

The treating team experiences the patient encounter in real time.

They observe changes in the patient’s condition, discuss diagnostic possibilities, and make clinical decisions based on evolving information.

As noted, however, the payer reviewer, however, encounters the case retrospectively.

For this reason, the medical record must clearly communicate not only the patient’s clinical condition, but also the reasoning behind the physician’s decisions.

When the documentation successfully conveys that narrative, it becomes much easier for external reviewers to understand the care provided.

Looking Ahead

Understanding how payer reviewers interpret the medical record provides important insight into how claims ultimately move toward approval or denial. Clinical review is often the stage at which payer interpretation of the encounter begins to diverge from the treating clinicians’ experience.

In Part IV of this series, we will examine how findings from medical review are translated into formal denial determinations and how those decisions move through payer administrative processes. Increasingly, documentation practices themselves may also come under scrutiny through compliance audits, regulatory oversight, or legal challenges when questions arise about how diagnoses were generated or supported in the medical record.

For CDI professionals, recognizing how documentation is interpreted during payer review – and how those interpretations can extend beyond the payer level – is essential to understanding what truly happens behind the curtain.

References

1. Centers for Medicare & Medicaid Services. Utilization Management Overview.
   https://www.cms.gov
2. Optum. InterQual® Criteria Clinical Decision Support.
   https://business.optum.com/en/operations-technology/clinical-decision-support/interqual   
3. Mello MM, Trotsyuk AA, Char DS. The AI Arms Race in Health Insurance Utilization Review.
   Health Affairs. 2026.
   https://www.healthaffairs.org/doi/10.1377/hlthaff.2025.00897
4. Optum. Health Care Claims Analytics and Risk Adjustment Solutions.
   https://business.optum.com/en/insights/health-care-analytics  
5. Office of Inspector General. Some Medicare Advantage Organization Denials of Prior Authorization Requests Raise Concerns About Beneficiary Access to Care.
   https://oig.hhs.gov/oei/reports/OEI-09-18-00260.asp  
6. CMS. Advancing Interoperability and Improving Prior Authorization Final Rule (CMS-0057-F).
   https://www.cms.gov/newsroom/fact-sheets/advancing-interoperability-and-improving-prior-authorization-processes  
7. Kannarkat JT et al. Advancing Interoperability and Prior Authorization Reform.
   JAMA Health Forum. 2024.
   https://jamanetwork.com/journals/jama-health-forum