Modernizing Claims Operations with Agentic Automation

What is this webinar about?

This webinar covers how healthcare payers can reduce claims fallout and manual rework by adding agentic automation to claims adjudication—especially after first pass adjudication when claims land in pended queues.

What problem are we solving in claims operations?

Payers are facing rising volumes, increasing claim complexity, and pressure to lower administrative costs, while 15–20% of claims can miss first pass adjudication and require manual intervention in pended queues.

Why do claims get stuck in pended queues?

Common drivers include:

• Eligibility and COB mismatches

• Missing/incorrect modifiers

• Duplicate submissions

• Provider contract/payment adjustment issues

• Unstructured inputs (attachments and notes) that don’t conform to rigid rules

• Frequent configuration changes that create exceptions and rework

We already use bots and rules—why isn’t that enough?

Rules and bots are strong for structured, repeatable tasks, but many adjudication fallouts require:

• Interpreting unstructured documents

• Adapting to changing configurations and benefit designs

• Handling exceptions and context-dependent decisions
  Agentic automation is intended to fill that gap—without discarding existing automation.

What is “agentic automation” in this context?

Agentic automation uses AI agents that can reason over context, take goal-driven actions, and coordinate tasks across systems—while still working alongside bots and human reviewers.

Does this require ripping out our current claims platform or RPA?

No. The approach is “no rip and replace.” Agentic AI adds an adaptive layer on top of your current environment (including existing bots, rules, and platforms).

What is the Unified Claims Analyzer Agent?

Emids’ Unified Claims Analyzer Agent is designed to:

• Classify and analyze each pended claim

• Determine likely root cause(s)

• Orchestrate specialized micro agents to resolve common fallout types

• Provide transparent rationale and a confidence signal

• Escalate to humans when confidence is low

What are “micro agents,” and what do they do?

Micro agents are specialized agents focused on common claim fallout categories, such as:

• Eligibility/COB verification and resolution

• Modifier fixes and coding-related corrections

• Duplicate detection to prevent reprocessing/overpayment

• Provider contract adjustments to apply correct reimbursement logic

How does this help with unstructured claim inputs?

Agentic automation is designed to handle inputs that commonly break rules-only approaches—like attachments, notes, and variations in how information is submitted—so teams spend less time manually interpreting and reworking claims.

How do you ensure accuracy and oversight?

The model keeps humans in the loop:

• When agent confidence is low, claims are routed for human review

• The system provides clear rationale to support the reviewer’s decision

• Oversight is maintained through governance patterns and phased rollout

What design patterns are covered in the webinar?

The webinar includes practical patterns for:

• Managing and reducing work in pended queues

• Applying agentic automation to pre-payment audits

• Using existing bots as reliable “doers” while agents reason and orchestrate

• Implementing monitoring/controls to identify issues earlier in the workflow

What outcomes can teams expect?

Teams are aiming for:

• Fewer manual touches

• Faster adjudication and shorter cycle times

• Reduced operational strain and burnout
  (Results vary by environment, data quality, and process maturity.)

Where does agentic AI fit in the adjudication lifecycle?

Agentic AI is most impactful after first pass adjudication, where exceptions and fallouts accumulate—especially in pended queues and audit workflows that require contextual research and multi-step resolution.