Claim review

Prevention starts with a structured claim review program that evaluates accuracy and completeness before and after payment. On the front end, automated validations and medical policy checks confirm that key elements (member eligibility, provider status, coding specificity, coverage rules, pricing terms and required documentation) are in place. Rules and analytics identify inconsistencies — such as illogical procedure/diagnosis pairings, duplicate billing, up-coding/down‑coding, and non‑covered services — so issues can be corrected prior to payment.

Post-payment controls complement this by detecting residual errors for recovery and by surfacing improvement opportunities that can be “shifted left” into pre-pay prevention. Integrating these activities with SIU/FWA, utilization management, benefits and contracting teams creates a closed‑loop system: Findings inform policy updates, provider education and workflow refinements, working to steadily reduce improper payments over time.

Read more about how a comprehensive claim review strategy can help eliminate repeat errors and increase claims payment accuracy.

Accuracy improves when health plans layer pre‑adjudication edits, mid-adjudication checks and post‑adjudication reviews with consistent, up‑to‑date content (regulatory guidance, payer policies and contract terms). Applying analytics upstream can help organizations to catch high‑probability issues early and reserve document requests for only the small subset of claims that truly need deeper review — minimizing disruption and keeping provider abrasion low.

Standardizing decision criteria across teams reduces variability, while feedback from post-pay discoveries is used to tune rules, refine clinical validation logic and update contract configuration. Finally, transparency — clear rationales, citations and actionable guidance — helps providers understand what changed and why, which may decrease appeals and rework and can reinforce correct billing behavior going forward.

Learn more about how pre-pay prevention is improving the accuracy of claims submission.

AI augments claim review at several points. Risk scoring and anomaly detection can help quickly surface claims with the highest likelihood of billing error, waste or abuse, allowing reviewers to focus attention where it matters most.

Natural language processing (NLP) can extract and normalize details from notes and attachments to support clinical validation without extensive manual reading. Pattern analytics can help identify emerging trends (e.g., new coding combinations or provider-specific error patterns) to inform targeted outreach and rule updates.

Within the review itself, AI-guided workflows can route the most complex, high‑value cases to the right reviewers, auto‑assemble relevant evidence and generate draft rationales or checklists to help speed consistent decisions while preserving human oversight. The result can be faster cycle time, higher quality determinations and fewer unnecessary touches.

Yes. Effective programs tailor selection criteria, rules, documentation requirements and review depth to each claim type and context. Professional, facility, outpatient, inpatient, emergency, ancillary/DME, dental and high-dollar claims each have distinct coding patterns, clinical nuances and policy constraints.

Health plans should calibrate models and edits by line of business (commercial, Medicare, Medicaid), incorporate state‑specific requirements and apply provider-level insights (specialty, historical error trends and engagement preferences). Workflows can also be tuned by intent — pre‑pay avoidance vs. post‑pay recovery — and by risk tolerance (e.g., surgical DRGs may warrant deeper pre‑pay clinical validation than low‑risk E/M claims). This configurable approach is designed to improve precision, limit unnecessary requests and align review effort with the potential impact on payment accuracy.