Pulling Back the Curtain: How Payers Decide Which Claims Receive Review: Part II 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 examined the first stage of the payer review pipeline: the structured data that payers receive before they ever request the medical record. Diagnosis codes, procedure codes, Diagnosis-Related Groups (DRGs), length-of-stay information, and other structured elements transmitted through claims and authorization transactions provide payers with substantial information about a patient encounter before any clinical documentation is evaluated.

But receiving data is only the beginning.

Once that data enters payer systems, it typically passes through a series of analytic and operational screening processes designed to identify claims that may warrant closer scrutiny. This stage of the pipeline remains largely invisible to many hospital teams, yet it plays a critical role in determining which encounters move smoothly through adjudication and which are selected for further review.

Understanding how this stage works helps explain why some claims receive immediate attention from payers, while others pass through the system without interruption.

In many ways, this is the point at which payer systems begin deciding which claims deserve a closer look.

The Analytic Screening Stage

When claims data enters a payer’s adjudication environment, it is commonly evaluated by analytic tools used in payment-integrity programs to identify potential payment risk. These systems process large volumes of claims data and apply statistical comparisons, utilization patterns, and policy-based rules to determine whether an encounter appears consistent with expected clinical and financial patterns. ¹

The purpose of this screening stage is not necessarily to identify incorrect claims. Instead, it functions as a triage process that helps payers determine where to focus review resources.

Claims that align with expected utilization patterns may pass through automated adjudication with minimal intervention.

Claims that appear statistically unusual, however, may be routed for additional evaluation.

At this stage, analytic systems may evaluate elements such as:

• Diagnosis and procedure code combinations;
• DRG assignment and expected resource utilization;
• Length of stay relative to historical norms;
• Cost or reimbursement levels relative to benchmarks; and
• Facility-level utilization patterns.

Importantly, these evaluations are typically based on structured claims data, rather than the clinical narrative contained in the medical record.

For clinical documentation integrity (CDI) professionals, this stage highlights an important reality: documentation captured in the medical record ultimately shapes the coded data that payer analytic systems evaluate.

Pattern Recognition in Claims Data

One of the most common approaches in payer analytic systems is pattern recognition. Using large datasets of historical claims information, payers establish baseline expectations for common clinical scenarios.

These baselines include expected relationships between diagnoses, procedures, length of stay, and resource utilization.

When a claim deviates from those patterns, it may attract additional attention.

For example, a hospitalization with a length of stay significantly longer than historical averages for a given diagnosis may trigger additional evaluation. Similarly, certain combinations of diagnoses may appear statistically uncommon, based on historical claims data.

Hospitals reporting unusually high rates of specific diagnoses, complications, or DRGs may also attract analytic attention.

These statistical signals do not necessarily indicate that a claim is incorrect. Rather, they indicate that the encounter differs from expected patterns.

From the payer’s perspective, these differences represent potential payment risk, and therefore justify additional review. Payment-integrity programs increasingly rely on advanced analytics and artificial intelligence (AI) to detect these types of anomalies in claims data.²

From the hospital’s perspective, these cases often reflect greater clinical complexity or documentation patterns that differ from statistical norms.

Outlier Detection and Payment Integrity

Outlier detection is another analytic technique commonly used in claims evaluation.

Because healthcare claims data is highly standardized, payers can compare encounters across large populations of patients and providers. Through these comparisons, they can identify claims that differ significantly from expected benchmarks.

Claims generating higher reimbursement relative to expected norms may be flagged for further evaluation.

Similarly, hospitals whose utilization patterns differ significantly from those of peer organizations may attract analytic attention.

For example, if a hospital reports higher-than-expected rates of certain DRGs or complications relative to regional or national benchmarks, payer systems may flag those encounters for additional review.

Again, the presence of an outlier does not automatically mean the claim is inappropriate or incorrect.

It simply means the claim falls outside expected statistical norms.

Once identified, however, these encounters are more likely to undergo additional scrutiny.

Policy-Based Screening

In addition to statistical analysis, payer systems apply policy-based rules that reflect coverage criteria and clinical guidelines.

These rules help payers determine whether the services associated with a claim appear consistent with their coverage policies.

For inpatient hospitalizations, screening processes may evaluate factors such as:

• Severity of illness indicators;
• Expected intensity of services;
• Anticipated length of stay; and
• Diagnosis and procedure relationships.

If the coded data suggests that these factors may not align with the payer’s coverage criteria, the claim may be routed for additional evaluation.

Importantly, this determination may occur before the payer has reviewed the medical record itself. Instead, the decision is often based on structured data and analytic signals generated earlier in the review process.

Timing and Early Payer Evaluation

Timing also plays an important role in payer review.

In many cases, payer evaluation begins shortly after the hospital submits an initial admission notification, authorization request, or other early communication through its access or insurance verification workflow.

This communication often occurs within the first 24 hours of admission, or by the next business day.

At that point, the payer may begin evaluating the encounter using the limited structured data available early in the hospitalization.

Because the encounter is still unfolding, the full clinical narrative may not yet be fully documented.

Recent regulatory changes have also accelerated payer decision timelines.

Under the Centers for Medicare & Medicaid Services (CMS) Interoperability and Prior Authorization Final Rule (CMS-0057-F), impacted payers, including Medicare Advantage organizations, must meet faster decision timeframes for prior authorization requests. Expedited requests generally require a determination within 72 hours, while standard requests must receive a decision within seven calendar days. ³

As these timelines take effect, payer review processes may increasingly occur earlier in the patient encounter.

For CDI professionals, this timing dynamic reinforces the importance of documentation clarity early in the hospitalization.

Why This Matters for Physician Leadership

For physician leaders and physician advisors, this early analytic screening stage highlights an important operational reality: payer interpretation of an encounter may begin before the clinical narrative is fully developed in the medical record.

While clinicians understand the clinical reasoning behind an admission or treatment plan, that reasoning is not always immediately visible in the structured data that payer systems first evaluate.

This creates a critical opportunity for physician leadership.

When documentation clearly communicates the patient’s risk profile, diagnostic uncertainty, and the clinical rationale for the level of care being provided, the record is better positioned to withstand both automated screening and subsequent medical review.

As payer review processes become faster and increasingly data-driven, physician engagement in documentation clarity becomes an important component of protecting both patient care decisions and organizational revenue integrity.

Looking Ahead

Understanding how payer systems select claims for review provides valuable insight for CDI programs and physician leaders alike.

It demonstrates that payer interpretation of an encounter may begin much earlier than many organizations realize. Long before a medical record is requested, analytic tools and policy rules may already be shaping how a claim is perceived.

But analytic screening is only the beginning.

In Part III of this series, we will continue pulling back the curtain on payer review by examining what happens once a claim has been selected for medical review. At that stage, physician reviewers begin evaluating the clinical documentation itself, interpreting the narrative of the record and determining whether the services provided meet the payer’s standards for medical necessity.

Understanding how those determinations are made will help organizations anticipate payer scrutiny and strengthen documentation earlier in the encounter.

References

1. McKinsey & Company. Payment Integrity in the Age of AI and Value-Based Care.
   https://www.mckinsey.com/industries/healthcare/our-insights/payment-integrity-in-the-age-of-ai-and-value-based-care
2. Healthcare Financial Management Association. AI Is a Promising Tool for Eliminating Revenue Leakage.
   https://www.hfma.org/ai/why-ai-is-such-a-promising-tool-for-eliminating-a-hospitals-revenue-leakage/
3. Centers for Medicare & Medicaid Services. Interoperability and Prior Authorization Final Rule (CMS-0057-F).
   https://www.cms.gov/newsroom/fact-sheets/cms-interoperability-and-prior-authorization-final-rule-cms-0057-f
4. Office of Inspector General. Some Medicare Advantage Organization Denials of Prior Authorization Requests Raise Concerns About Beneficiary Access to Medically Necessary Care.
   https://oig.hhs.gov/reports/all/2022/some-medicare-advantage-organization-denials-of-prior-authorization-requests-raise-concerns-about-beneficiary-access-to-medically-necessary-care/
5. Centers for Medicare & Medicaid Services. Medicare Benefit Policy Manual, Chapter 1 – Inpatient Hospital Services.
   https://www.cms.gov/regulations-and-guidance/guidance/manuals/downloads/bp102c01.pdf