Clinical Verification

Build clinical-safety evidence with representative simulations, customer-owned release criteria, and qualified human review.

Before an AI agent interacts with patients, teams need evidence that its configured behavior is appropriate for the intended clinical scenarios and operating boundaries. This guide covers how to use Amigo's testing framework alongside organizational release criteria and qualified human review. No finite test set proves that a model will behave correctly on every future interaction.

Simulation scores and model-graded metrics are evidence, not clinical validation, regulatory approval, or a substitute for review by qualified clinical and compliance owners.

Why Clinical Verification Matters

Healthcare AI operates under constraints that most other domains do not have. A missed safety escalation can harm a patient. A scope-of-practice violation can create liability. A subtle bias in how the agent handles certain populations can produce inequitable care.

Generic testing is not sufficient. You need to test against your specific clinical workflows, patient populations, and safety standards. A model that scores well on general medical knowledge benchmarks may still fail to follow your organization's escalation protocols correctly.

Simulation with Medical Scenarios

Build simulations that reflect the clinical situations your agent will encounter. Start with your highest-risk workflows and expand from there.

Designing Clinical Personas

Clinical personas should represent the diversity of your patient population, including the patients who are hardest to serve well:

• Complex medication regimens: Patients on multiple medications where interaction detection is critical
• Cognitive limitations: Elderly patients or those with cognitive impairment who may provide unreliable information
• Communication barriers: Patients with limited health literacy, non-native speakers, or patients who minimize symptoms
• High acuity: Patients presenting with symptoms that require urgent escalation

Each persona exercises a specific capability. A persona with cognitive impairment can probe whether the agent notices confusion and follows the authored communication policy. A persona who downplays symptoms can exercise the organization's clarifying-question and escalation criteria.

Building Clinical Scenarios

Pair personas with scenarios that test the boundaries of safe agent behavior:

• Routine follow-up that reveals a problem: A standard post-discharge check-in where the patient casually mentions a new symptom that requires escalation
• Conflicting information: The patient's self-report contradicts EHR data, and the agent must handle the discrepancy appropriately
• Scope boundary: The patient asks a question that falls outside the agent's defined scope of practice
• Emotional distress: The patient becomes upset, anxious, or frustrated during a clinical interaction

Do not only test happy paths. The scenarios that matter most for clinical safety are the ones where things go sideways: the patient who lies about taking their medication, the patient whose symptoms escalate mid-conversation, or the patient who insists on advice the agent should not give.

Metric-Based Quality Gates

Define metrics that your release process treats as required checks. The platform records evaluation evidence, but it does not automatically block a version-set update or make the release decision.

Safety Metrics (Hard Gates)

The values below are illustrative policy targets, not platform guarantees or claims that a finite evaluation set proves future behavior.

Example Metric	Example Target	Notes
Escalation accuracy	100%	Agent correctly identifies situations requiring clinical escalation
Scope-of-practice adherence	100%	No observed out-of-scope advice in the evaluated set
Privacy protocol compliance	100%	No observed violation of the tested PHI-handling requirements
Medical information accuracy	99.5%+	Factual correctness of clinical information provided
Risk disclosure completeness	99%+	Agent discloses relevant risks when appropriate

Set these thresholds with the clinical, compliance, and operational owners for the workflow. If a required check fails, keep the release out of production through your team's explicit review and promotion process.

Quality Metrics (Improvement Targets)

Example Metric	Example Target	Notes
Explanation clarity	90%+	Information presented in language appropriate to the patient
Empathy score	85%+	Agent demonstrates appropriate emotional support
Response completeness	90%+	Agent fully addresses the patient's question or concern
Goal completion	85%+	Agent accomplishes the intended purpose of the interaction

Improvement targets guide prioritization rather than making the release decision. A configuration that meets the organization's required checks but falls slightly below an empathy target may still be appropriate to release while improvement work continues.

Human Review Workflows

Automated metrics do not catch everything. Human review adds independent clinical judgment and context that a model-based evaluator may miss.

When to Use Human Review

• Initial deployment: Have clinical staff review a meaningful sample of conversations before and during early production use.
• After configuration changes: Review conversations from the first few days after any update to agent instructions, Context Graphs, tools, or model configuration.
• Selected conversations: Use run filters, metric results, recordings, and transcripts to identify borderline or representative cases for human review.
• Ongoing sampling: Regularly review a random sample of production conversations to catch issues that metrics and drift detection may miss.

Structuring Reviews

Provide reviewers with clear rubrics aligned to your metrics. Reviewers should assess:

• Did the agent stay within its scope of practice?
• Were escalation decisions appropriate?
• Was the clinical information accurate and complete?
• Was the communication appropriate for the patient's situation?
• Were there any missed opportunities or concerns?

Compare human review scores against automated metric scores. If they diverge, investigate the rubric, evidence, sampling, and reviewer calibration before changing the metric or release criteria.

Putting It Together

Clinical verification is not a one-time event. It is an ongoing process that runs in parallel with deployment:

1. Before deployment: Run clinical simulation suites, compare results with your required criteria, complete initial human review, and make an explicit release decision.
2. During early deployment: Monitor metrics daily. Select representative and concerning conversations for review. Expand simulation coverage based on production patterns.
3. In steady state: Track metrics across cohorts. Detect drift. Update simulations when clinical workflows or guidelines change. Maintain ongoing human review sampling.

For detailed guidance on phased deployment with quality gates at each stage, see Simulations. For the testing framework details, see Testing and Evaluation.