DRG Review After Payment

How Payers Can Turn “Pay-and-Chase” Into Lasting System Integrity

Diagnosis-Related Groups (DRGs) sit at the center of inpatient reimbursement—and at the center of many payer headaches. When DRGs are miscoded or not clinically supported, dollars leak.

Post-payment DRG review is where many health plans first identify the leak, quantify it, and attempt to recover the losses. But recovery alone isn’t the win.

The real value is turning every post-pay finding into upstream prevention—policy logic, edits, and provider feedback that harden the system.

Why DRG review still matters

Improper payments remain stubborn. CMS’s own measurement of Medicare Fee-for-Service accuracy puts the FY 2024 improper payment rate at 7.66% (≈$31.7B)—a reminder that error, ambiguity, and process gaps persist even in mature programs.

Inpatient DRGs are assigned based on the principal diagnosis, comorbidities, complications, and procedures, as well as for a handful of DRGs, factors such as age, sex, and discharge status. A single MCC (complication/comorbidity) can shift a case into a materially higher-paid group. That is precisely why sepsis, respiratory failure, malnutrition, and encephalopathy appear repeatedly in plan audits.

The sepsis family is emblematic. MS-DRGs 870/871/872 cover septicemia or severe sepsis, split by mechanical ventilation >96 hours and presence of MCC. These codes are legitimate—and also frequently disputed.

Clinical nuance adds fuel: definitions of sepsis (Sepsis-2 vs. Sepsis-3), inconsistent indicators in documentation, and variability in hospital coding create fertile ground for denial/appeal cycles. Scholarly reviews note sepsis DRGs (e.g., 871) were among the most frequently billed and scrutinized; clinical validation denials often trace back to misaligned criteria or thinly supported documentation.

Even trade press tracking CMS’s CERT program has flagged projected improper payments in MS-DRGs 871/872 due to coding issues, specifically illustrating the signal that plans are reacting to. And the HHS-OIG has placed inpatient sepsis billing squarely in its work plan, comparing cost impacts across definitional thresholds.

Post-pay DRG review is a Sensor, Not a Strategy

Most health plans already conduct post-payment DRG reviews, which include sample selection, medical record retrieval, coding/clinical validation, provider outreach, and recovery.

The problem isn’t that these workflows don’t exist; it’s that they often live outside the productized edit and policy layer.

Findings get resolved in isolation, but the underlying policy–contract–edit drift remains.

A durable post-payment program does four things well:

1. Precision Targeting: Employ predictive models and indicators—including DRG, LOS outliers, MCC combinations, readmission correlations, facility patterns, and prior audit findings—to strategically select a smaller number of charts with a higher likelihood of overpayment. Emphasize targeted chart selection across inpatient DRGs, short-stay settings, SNF/IRF, and additional segments.
2. Clinical Validation Depth: Integrate coding expertise with comprehensive clinical criteria resources and evidence packages, including laboratory results, vital signs, timing, and treatment response, to substantiate principal diagnoses, CC/MCC, and procedures. This is particularly pertinent for high-priority DRGs, such as sepsis and respiratory failure. Prioritize collaboration between coders and clinicians, and facilitate claim reviews, either pre-pay or post-pay, against relevant medical records.
3. Provider-Grade Transparency: Maintain clarity in rationale, provide authoritative citations (for example, Coding Clinic and IPPS guidelines), and enforce defined timelines to minimize process friction. Policy measures, such as DRG validation protocols that designate review partners and communicate case specifics to providers, help set documentation benchmarks.
4. System Feedback Loop: Ensure that outcomes from resolved cases inform updates to pre-pay rules, provider education materials, and policy or contract checks. The industry is increasingly focused on transitioning “from pay-and-chase to prevention”, a methodology that succeeds only when insights gained from post-pay audits are systematically integrated into pre-pay processes.

What we learned from the market to build differently

We reviewed public material from several categories of solutions:

1. Chart targeting and clinical validation at scale (payer-oriented). Machine learning is used to identify charts with a high likelihood of DRG overpayment. It provides resources for expediting DRG review through cross-claim analysis, helping payers allocate nurse or coder time efficiently.
2. Comprehensive claim-review suites (pre and post). Claim review includes all reimbursement methodologies, with clinician review against medical records both before and after payment, aligning with blended payer portfolios.
3. Provider-side CDI/CDV platforms. Provider documentation integrity and code audit workflows, primarily designed for providers, demonstrate expected automation and document-linkage capabilities from a clinical perspective.
4. DRG validation and CDI technologies. DRG-based analytics and CMI-driven case prioritization support pre-bill provider reviews; though focused on providers, these tools reflect the types of algorithms and evidence payers may encounter.

The drumbeat is unmistakable: move left (pre-pay) when possible. We agree. But the reality for many plans is that post-payment is where the signal is cleanest today.

Our job as product builders is to (1) extract that signal with high clinical confidence and (2) pipe it forward into pre-pay edits, policy logic, and provider feedback—closing the loop.

The nēdl Labs approach: evidence-first and “policy-aware” DRG review

Here’s how we’re designing Nedl Pulse for DRG post-payment analysis, actually to reduce future leakage:

1. Smart case discovery. We combine DRG-specific features (e.g., MCC bundles, MV timing, principal/secondary diagnosis relationships) with learned patterns from prior reviews to rank likely overpayments. For sepsis, this includes the timing of antibiotics/fluids, organ dysfunction laboratory results, and ventilator duration relative to admission. (MS-DRG grouping is updated annually by CMS; we version our logic by fiscal year.)
2. Clinically defensible “evidence packs.” Every case receives an automatically assembled dossier, which includes clinical indicators, code paths, DRG switch scenarios (e.g., 871 • 872), and citations. Regarding sepsis, we outline criteria alignment and conflicts (Sepsis-2 vs. Sepsis-3 triggers) in a plain-language narrative.
3. Policy-contract-edit alignment. Findings don’t die in a PDF. They update a live drift ledger that maps to the edit that should have fired, the policy text (with version and effective date), and, when relevant, the contract clause. This is how post-pay stops being a scoreboard and becomes an engineering input.
4. Provider-ready communication. We produce templated outreach with the exact data elements your provider teams want to see—Who, What, When, Why, and How to Appeal—mirroring the transparency standards exemplified in published plan policies.
5. Closed-loop prevention. Confirmed error patterns turn into pre-pay checks and provider education packets. We also simulate the impact before going live to avoid false positives and provider abrasion. (Market narratives from payers and vendors are consistent: the savings flywheel spins fastest when post-pay informs pre-pay.)

Metrics that matter

If your post-payment DRG program is working, three curves should bend:

1. Recovery → Prevention. The ratio of dollars recovered post-payment to dollars averted pre-pay should flip over time—more prevention, fewer chases. (We track a “drift-closed” metric: how often a post-pay root cause gets an upstream fix.)
2. Cycle time. Days from case selection → provider outreach → resolution. Faster cycles with stable overturn rates mean your packets and criteria are tight.
3. Provider abrasion. Appeal rates and education effectiveness. As templates mature and policy references become clearer, both appeal volume and cycle time decrease.
4. False-positive rate (pre-pay). After you promote patterns upstream, simulate them on historical claims and monitor the live results. The goal is to minimize unnecessary provider interactions while identifying the root causes of issues.

Where nēdl Labs fits

At nēdl Labs, our vision is payment integrity that pays for itself—turning unstructured policy, contract, and clinical evidence into defensible, automated decisions. Nedl Pulse’s DRG post-payment module focuses on:

• High confidence targeting for inpatient DRGs (starting with sepsis and respiratory failure),
• Evidence packs aligned to clinical criteria and IPPS rules,
• Provider-ready packets that reduce abrasion, and
• A drift ledger that pushes proven patterns into pre-pay edits and provider education—with versioned policy/contract citations.

The outcome we’re aiming for isn’t just recovered dollars. It’s fewer disputed claims, fewer reversals, and higher trust—because the same evidence that justifies a post-pay decision is teaching your system how to prevent it next time. That’s how payers get off the hamster wheel.