Claims Optimization

What is claims optimization?

Claims optimization refers to the ongoing effort health plans undertake to improve the speed, accuracy, and cost-efficiency of processing claims from submission through final payment. The core lever is increasing the auto-adjudication rate, the share of claims that a rules-based or AI-driven system can approve or deny automatically without manual review, since manually processed claims cost significantly more, take substantially longer, and carry a higher risk of error than claims that clear automatically. Optimization efforts typically target the specific factors that push a claim into manual review, such as incomplete or inconsistent data, missing coordination of benefits information, or claim characteristics the existing rules engine cannot confidently resolve, and work to eliminate those triggers at the source rather than adding more staff to handle the manual volume. Because claims processing sits under strict regulatory timeframes and Medical Loss Ratio cost constraints, claims optimization works as both an operational efficiency initiative and a compliance and margin management discipline for health plans at the same time.

Why it matters

The gap between manual and automated claims processing cost is large enough that even a modest shift in auto-adjudication rate produces a meaningful financial effect at scale, since health plans process claim volumes in the hundreds of thousands or millions per month. Manual review also extends processing time significantly compared with automated adjudication, which affects provider payment timelines, provider relationships, and member satisfaction with how quickly their claims resolve. Because manual claims review is more error-prone than automated processing, optimization efforts that reduce manual touchpoints tend to improve accuracy and reduce downstream rework at the same time they cut cost, rather than trading one benefit for another. That combination of lower cost, faster payment, and fewer errors is why claims optimization ranks among the highest-return operational programs a plan can run. It also compounds over time, because each recurring exception a plan removes at the source keeps clearing automatically in every future cycle rather than consuming reviewer time again and again. For a plan operating under tight Medical Loss Ratio limits, that steady reduction in administrative cost directly protects the margin available for member benefits and premium stability.

Callout stat: Industry data shows that 15% to 20% of health plan claims still require manual processing under legacy systems, extending processing time by one to two weeks compared with claims that are automatically adjudicated, per HealthEdge.

How claims optimization works

• Auto-adjudication rules: A rules engine evaluates each claim against defined criteria and automatically approves or denies claims that meet clear-cut conditions.
• Exception triage: Claims that cannot be confidently auto-adjudicated are routed to manual reviewers, ideally with the specific exception reason flagged to speed review.
• Root cause analysis: Recurring reasons claims fall into manual review are analyzed to identify whether a rule, data source, or upstream process can be improved to reduce future exceptions.
• Coordination of benefits automation: Automated checks for other active coverage reduce the manual investigation otherwise required to confirm payer responsibility.
• Continuous rule refinement: The auto-adjudication rules engine is updated over time as new claim patterns and payer policy changes emerge, so automation keeps pace with real claim behavior.

Key metrics and benchmarks

Health plans track claims optimization progress through a small set of core metrics. Auto-adjudication rate measures the percentage of claims processed without manual intervention. Average processing cycle time compares automated versus manually reviewed claims. Cost per claim typically differs substantially between the two paths. Industry benchmarks generally target auto-adjudication rates of 80% or higher for a well-optimized health plan, though achieving that consistently requires clean upstream data, since a plan burdened by frequent eligibility or coordination of benefits gaps will see more claims fall into manual review regardless of how sophisticated its adjudication rules are. Plans that track exception reasons at the same level of granularity as they track the headline auto-adjudication rate tend to make faster optimization progress, since a generic manual review bucket obscures which specific upstream fix, whether a data source, a rule gap, or a coordination of benefits check, would move the most claim volume back into automated processing.