Answer-only grading
A polite final message can hide skipped identity, eligibility, or documentation steps.
Healthcare AI agent evaluation
Healthcare AI agent evaluation grounded in production workflows.
Score trajectory quality for scheduling, intake, benefits, claims support, and prior-auth prep—then capture approved replacements for the failures that keep repeating.
Generic LLM scores miss healthcare agent risk.
Fluent answers can still skip verification, invent coverage language, or fail to escalate. Healthcare agents need path-level evaluation.
Policy version drift
Evaluations without policy/version metadata cannot explain why a trajectory failed.
Unsafe automation
Agents continue when the correct outcome is escalation to a human specialist.
Lost reviewer edits
ClinOps and ops corrections stay in tickets instead of becoming reusable gold.
Evaluation plus an approved experience layer
We reconstruct healthcare agent traces, apply a workflow rubric, capture approved corrections, and compare before/after on one scoped path.
Trajectory-aware scoring
Judge tools, state, required checks, recovery, and escalation—not only wording.
Policy-scoped review
Tie grades and corrections to the policy version and workflow boundary in force.
Measured pilot
Prove lift on one admin workflow before expanding coverage.
Healthcare evaluation design
Buyers need release gates tied to real operational risk—not a leaderboard score.
Workflow success criteria
Define pass/fail for identity, eligibility, documentation, disclosures, and handoff completeness.
Failure taxonomy
Wrong tool, invented coverage, skipped verification, late escalation, and unsafe containment.
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
Path-level evidence for admin and patient-access agents.
Workflow rubric
Identity, eligibility, documentation, disclosures, tools, and escalation completeness.
Failure taxonomy
Invented coverage, skipped verification, unsafe containment, and late handoff.
Scored trace sample
Use the agent evaluation rubric CSV as the starting scorecard.
Approved corrections
Policy-scoped replacements for one repeating failure cluster.
Release-gate outline
Versioned scenarios and a go/no-go recommendation for the scoped workflow.
Reusable resource
Download the AI agent evaluation rubric
CSV scoring template for task outcome, tools, state, recovery, escalation, safety, cost, and latency.
Illustrative template—not client data. Adapt it to your policies, privacy controls, and approval process.
Who this is for
- Health systems and vendors shipping admin or patient-access agents
- Quality and compliance teams needing path-level evidence
- Ops leaders comparing prompt changes vs retrieving approved corrections
- Teams preparing claims, prior-auth, or intake automation with human review
Healthcare agent evaluation sprint
A — Audit
Trace sample, failure clusters, and a healthcare workflow rubric.
B — Dataset
Approved corrections for one workflow plus a retrieval prototype.
C — Proof
Baseline comparison and a rollout recommendation with controls.
Evidence before broader automation
We do not claim improvement before reviewing the workflow. The first sprint produces measured evidence on one scoped healthcare path.
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.
- Specialist disagreement requires an owner before gold enters retrieval.
Methodology by LTTAI LTD
This page describes less than three’s production-trace review method: reconstruct failures, capture human-approved corrections, store them outside model weights, and measure whether scoped retrieval improves a defined workflow.
Primary references used for terminology and risk framing:
Common questions
What does healthcare ai agent evaluation include?
We reconstruct healthcare agent traces, apply a workflow rubric, capture approved corrections, and compare before/after on one scoped path.
Do you fine-tune our model?
No. Approved corrections stay outside model weights and can be retrieved at runtime. Fine-tuning can still complement the system for stable, common behavior.
What do we need to start?
A sample of production traces or conversation exports for one workflow, plus a working definition of success for that workflow.
How is success measured?
We compare a fixed baseline against a changed agent on the same workflow using task success, edits, escalations, latency, and cost where available.
Related
Book a healthcare agent evaluation.
Bring production traces from one admin workflow. We will map failures and propose one Agent Improvement Sprint.