Incomplete packets
Agents proceed without required clinical notes, codes, or attachments.
Claims and prior auth AI
Review how AI agents prepare claims and prior authorizations.
Check whether the agent gathers the required documents, applies the right payer rules, uses tools correctly, and hands off to a specialist when needed. Record approved corrections and test them on similar cases.
A complete-looking summary can still miss a required document.
A complete-looking summary can still miss required attachments, misread policy, or push an unsafe auto-decision.
Policy misapplication
Payer rules and versioned criteria are paraphrased incorrectly.
Tool blind spots
Eligibility or status tools return empty/partial results and the agent invents state.
No reusable rewrite
Specialist corrections never become approved trajectories for similar cases.
Test corrections for one type of case
We review the documents, tool calls, and decisions in each case. Specialists approve the correct actions, then we test the change on similar cases.
Case reconstruction
Request context, documents, tools, intermediate decisions, and final recommendation.
Specialist corrections
Reviewers rewrite the path that should have happened, with critique and scope.
Limit where examples apply
Restrict retrieval to the matching claim or prior-authorization task and policy.
What a claims/prior-auth review must inspect
Review the evidence required for each decision in the revenue-cycle or utilization-management process.
Documentation completeness
Required notes, codes, attachments, and chronology before a recommendation is allowed.
Policy and payer logic
Versioned criteria, exclusions, and when the agent must escalate instead of decide.
Specialist approval
Decide who can approve examples and which case types must stay with human reviewers.
Review the broader healthcare evaluation methodStore approved case paths as golden datasets for AI agents
Claims / prior-auth review package
Review records for the documents, policy decisions, and handoffs in each case.
Packet completeness checks
Required notes, codes, attachments, and chronology gates.
Policy application review
Versioned criteria, exclusions, and escalate-vs-decide rules.
Case-action scorecard
Tool use, information handling, recovery from errors, and safety of recommendations.
Approved case paths
Specialist-corrected trajectories for one claim or prior-auth family.
Audit trail fields
Reviewer, approval time, policy version, and retrieval restrictions.
Free template
Download the agent-action scorecard
Score outcome, tools, state, recovery, escalation, safety, cost, and latency for multi-step agent cases.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
Who this is for
- Payers, providers, and vendors automating claims or prior-auth prep
- Revenue-cycle and utilization management teams with repeating case failures
- Leaders who need auditability for automated recommendations
- Ops groups whose specialists already correct agent drafts
Claims / prior-auth sprint
Review
Sample cases, group document and policy mistakes, and agree scoring rules.
Build
Have specialists approve the correct actions and rules for reuse on one type of case.
Evaluate
Compare before-and-after results and agree limits for use in live review queues.
Controls for high-stakes reuse
A specialist's correction may apply only to one payer or policy version. Record those limits and remove outdated examples before expanding automation.
Limitations
- Automation recommendations remain subject to payer and provider policy owners.
- Reviewers must not invent information missing from the source documents.
- High-stakes case types may stay human-only by design.
- An improvement on one type of case may not apply to other payers' rules.
How we test changes
We review real conversations with your team and agree what the agent should do differently. Approved corrections can become training data, test cases, or examples the agent looks up. We compare each change with your best existing setup on separate test cases, then check for new failures in the software you plan to deploy.
References:
Common questions
What does the work include?
We review the documents, tool calls, and decisions in each case. Specialists approve the correct actions, then we test the change on similar cases.
Do you fine-tune our model?
If it's worth testing after we review the problem. We try your best prompt and consider better examples or tool fixes first. We test an adapter on cases it wasn't trained on and check for new failures. The contract sets delivery and usage rights; the base model's license still applies.
What do we need to start?
Logs or conversation exports for one task, examples of good results, and a reviewer who can approve corrections. We agree data handling before you send anything. Remove unnecessary personal details.
How is success measured?
We test the existing and changed agent on the same task. Where the data is available, we compare completed tasks, human edits, handoffs, response time, and cost.
Related
Book a claims and prior-auth review.
Discuss one type of case your agent helps prepare. We'll agree what specialists need to review and how to handle the data.