AI Phone Quality Review: Audit the Request From Conversation to Resolution

The rubric and sampling method below are original operational proposals. Use only recordings, transcripts and records your organization has approved for this review. Clinical, privacy and security concerns go to their responsible reviewers.

Define the administrative outcome that makes a call complete

Define the endpoint for each permitted administrative task. For a reschedule, check the approved replacement appointment, the disposition of the old appointment and the promised confirmation. For a referral-status question routed to staff, record two states: handoff accepted by the correct owner, and the caller's request resolved. Creating a task may finish the agent's assigned step while leaving the overall request open.

The Linear Health Operational AI Evaluation Kit asks buyers to "define completion in observable terms, including the final status and where it is recorded" (Linear Health evaluation kit). That was written for pre-purchase review, but it is also the right standard for ongoing quality assurance. The AHRQ Workflow Assessment for Health IT Toolkit makes the broader point that health IT changes both clinical and administrative workflow, and that once implemented it can supply information to help you reorganize that workflow (AHRQ). Your completion definitions are that information, made explicit.

Write the definitions in a short table before you pull a single call. If a call reason has no observable completion state, treat that as a finding on its own.

Choose a sample across call reasons and exceptions

A proposed sampling plan, offered as editorial guidance rather than a validated standard:

1. List every call reason the agent is configured to handle, plus a catch-all for calls outside scope.
2. For each reason, pull a fixed number of calls per review cycle. Pick a count your team can review; the point is consistency across cycles, not a particular number.
3. Within each reason, deliberately include exception calls: missing data, a caller who changes their mind mid-call, contradictory information, a transfer, and a hangup before confirmation.
4. Add a small random draw across all calls so you catch failures nobody thought to categorize.
5. Record the sample list before review begins so reviewers cannot drift toward easy calls.

The evaluation kit's first method step is to run "one representative case with normal complexity, missing data, and at least one exception" because "a scripted happy path is not enough" (Linear Health evaluation kit). This plan extends that idea to approved live review; the source does not prescribe a statistical sampling method. Keep the random sample separate from deliberately selected exceptions and retests. Report counts by group instead of presenting a targeted defect rate as the rate for all calls.

If you are also reviewing EHR write behavior before launch, that is a different exercise covered in AI voice scheduling and EHR integration. This article assumes launch is behind you.

Check summary fidelity against approved source records

Treat the call summary as a separate artifact to inspect. For each sampled call, a reviewer compares three things: the audio or transcript, the agent's summary, and the approved record of truth (the schedule, the task queue, or the patient record as your organization defines access).

Call-quality rubric

For each dimension, mark verified for this case, discrepancy found, not verifiable or not applicable with a reason. These are review findings, not the evaluation kit's procurement evidence score. Do not add them into a total that could hide a wrong-patient action.

Record severity and containment under your organization's existing rules. A correct summary cannot offset an incorrect patient link, and a task is not automatically defective if creating that task was the approved scope.

Audit the downstream booking or staff task

A downstream check is necessary to assess whether the recorded action matches the conversation. For every sampled call, confirm:

• The booking, task, or note exists in the system named in your completion definition.
• The patient identity on the record matches the identity verified on the call.
• The status is the status a staff member would expect (confirmed versus pending, assigned versus unassigned).
• Any follow-up the agent promised the caller has a corresponding record.

The evaluation kit asks vendors to "explain how every automated action is logged and how an authorized user can correct it" (Linear Health evaluation kit). Use that log during review. If you cannot trace a call to its downstream record, note it as an audit gap, not just a call failure. What any vendor writes to your EHR has to be demonstrated for your configuration rather than assumed from a logo (patient engagement platform).

If you want to pressure-test an agent's trace, logging, and correction path with your own call reasons before this review becomes routine, you can book a demo and bring the rubric with you.

Use a correction log to choose the next regression test

Each discrepancy gets an observed symptom, affected request, evidence reference, immediate recovery owner, suspected cause and verification plan. Keep the cause provisional until investigated. A prompt edit cannot correct an existing wrong appointment by itself. Record that operational repair separately from the configuration change and regression test. If the source evidence is missing, mark the finding not verifiable rather than automatically blaming the agent.

Labelled synthetic example

The following is illustrative only. It is not a Linear Health customer experience and not a measured result.

A fictional multi-site clinic samples 40 calls in a cycle: 10 new appointment requests, 10 reschedules, 10 referral-status questions, and 10 out-of-scope or transferred calls. Reviewers find three discrepancies. One reschedule summary says "Thursday" while the audio says "next Thursday"; the slot was booked for the wrong week. One referral-status question created a task with no reason code. One transfer included a promised callback, but no corresponding task was visible.

The log reads:

Keep targeted regression cases alongside, rather than instead of, the random surveillance sample. A passing retest shows the tested case worked on that version. The owner decides when to reduce enhanced review based on defect severity, deployment evidence and observed recurrence; one or two clean cycles are not a universal closure rule.

For teams building the broader program, the Operational AI Evaluation Kit provides related oversight and evidence questions, and healthcare call center automation covers how AI call handling fits with the rest of the contact center.

A practical evaluation method in five steps

1. Write completion definitions per call reason, with the system and status named.
2. Freeze a sample list across reasons, exceptions, and a random draw.
3. Record findings for each dimension, including evidence gaps; escalate material defects under existing policy without averaging them away.
4. Open the downstream system and confirm identity, status, and promised follow-ups.
5. Log discrepancies with category, owner, decision, and retest; let the log set next cycle's over-sample.

This is a proposed method. Adapt it to your governance, and route anything touching patient safety, privacy, or clinical content to the responsible reviewer rather than treating it as an operations finding.

If you would rather see how a vendor walks through a sampled call, its summary, and the downstream record in one trace, book a demo and ask for exactly that, with an exception included.

Frequently asked questions

Sources

Sources accessed October 29, 2026.

• Linear Health, Operational AI Evaluation Kit.
• AHRQ, Workflow Assessment for Health IT Toolkit.
• Linear Health, Patient engagement platform.