Answer-only grading
A polite final message can hide skipped identity, eligibility, or documentation steps.
Healthcare AI agent evaluation
Check the steps your healthcare admin agent takes.
Review scheduling, intake, benefits, claims support, and prior-authorization preparation. We check required verification, tool use, documents, and handoffs, then test proposed changes on similar cases.
Generic LLM scores miss healthcare agent risk.
A fluent answer can hide skipped verification, invented coverage details, or a missed handoff. Review the actions leading to the reply too.
Missing policy versions
Without the policy version, reviewers may not know which rules applied when the agent acted.
Unsafe automation
Agents continue when the correct outcome is escalation to a human specialist.
Lost reviewer edits
Corrections from clinical operations and admin teams stay in tickets instead of becoming approved test cases or examples.
Record corrections alongside the test results
We review logs for one admin task, score them against agreed rules, and record approved corrections. Then we compare the existing and changed agent.
Check every step
Review tool calls, available information, required checks, error handling, and handoffs alongside the reply.
Review against the current policy
Record which task and policy version each score and correction applies to.
Test one task first
Compare results on one admin task before recommending a wider rollout.
Healthcare evaluation design
Use the checks your staff need to complete safely, rather than relying on a general model benchmark.
Workflow success criteria
Define pass/fail for identity, eligibility, documentation, disclosures, and handoff completeness.
Failure categories
Wrong tools, invented coverage, skipped verification, late handoffs, and tasks the agent should have stopped.
Correction ownership
Decide who can approve reusable examples and when an answer must remain human-only.
Pair with LLM evaluation services for release gatesSpecialize further on claims and prior auth review
Healthcare evaluation package
A record of the actions your admin or patient-access agent took, the mistakes found, and the approved corrections.
Workflow rubric
Identity, eligibility, documentation, disclosures, tools, and escalation completeness.
Failure categories
Invented coverage, skipped verification, tasks the agent should have stopped, and late handoffs.
Scored trace sample
Use the agent evaluation rubric CSV as the starting scorecard.
Approved corrections
Corrections for one recurring mistake, with a record of the policy they follow.
Release recommendation
Versioned test cases and a recommendation on whether to deploy the tested change.
Free template
Download the AI agent evaluation rubric
CSV scoring template for task outcome, tools, state, recovery, escalation, safety, cost, and latency.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
Who this is for
- Health systems and vendors shipping admin or patient-access agents
- Quality and compliance teams that need a record of each action
- Ops leaders comparing prompt changes vs retrieving approved corrections
- Teams preparing claims, prior-auth, or intake automation with human review
Healthcare agent evaluation sprint
Review
Review sample logs, group recurring mistakes, and agree scoring rules for the task.
Build
Approve corrections for one task and policy. Keep separate cases for testing.
Evaluate
Compare with the existing setup and recommend whether and how to deploy.
Evidence before broader automation
We compare the existing and changed agent on one admin task. The results guide the next step; improvement isn't guaranteed.
Limitations
- Not a substitute for clinical validation of diagnostic systems.
- Answer-only graders miss unsafe trajectories with fluent endings.
- Policy versions must be pinned or scores become uninterpretable.
- A designated policy owner must resolve disagreements before an example is approved for retrieval.
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 logs for one admin task, score them against agreed rules, and record approved corrections. Then we compare the existing and changed agent.
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 healthcare agent evaluation.
Choose one admin task to review. We'll agree access and data handling before you share logs.