Speech analytics for pharma:
the compliance checklist.

Recording visits of medical representatives is the highest-compliance-stakes use of speech analytics there is: the conversation may contain patient stories, adverse-event mentions and promotional claims that regulators care about. Below is the checklist we recommend a compliance team walk through before any pilot — with the reasoning behind each item.

1. Consent, enforced by the product

Policy is not enough; the mechanics must enforce it. If the other party does not consent, recording of their side must stop — and that state must be logged. Verify what the rep sees, what the doctor is told, and what the audit trail shows for a consent-declined visit.

2. PII masking before storage, not after

The critical word is before. If names and patient details are masked by a later processing step, unmasked data exists in the database — and in backups — for some window. The defensible architecture masks in the processing pipeline so that personal data never reaches storage. Ask the vendor to show where masking sits in their data flow diagram.

3. Adverse-event detection with an SLA

Pharmacovigilance is the item that turns recording from a risk into an asset. A doctor mentioning a possible adverse reaction in passing starts a regulatory clock whether or not the rep noticed. The system should:

• flag suspected adverse-event mentions automatically, on every visit;
• check the fragment against the four validity criteria (identifiable patient, identifiable reporter, a suspect product, an event);
• route valid suspicions to the pharmacovigilance team the same day, inside the 24-hour SLA, with the transcript fragment attached.

4. Off-label and promotional-claim monitoring

The same pass that scores the visit should flag: indications outside the label, unsupported head-to-head comparisons with competitor products, promises of incentives, and events offered in exchange for prescriptions. These are exactly the behaviours a company currently cannot see between audits — and the ones that end up in warning letters.

5. Data residency and deployment model

Health-adjacent voice data attracts the strictest local rules in most jurisdictions. The clean answer is deployment inside the company’s own perimeter (on-premise or a private in-country cloud), so recordings, transcripts and scores never cross a border and never depend on an external service’s availability. If the vendor is cloud-only, residency is a contract clause; if it is on-premise, residency is architecture.

6. A local LLM option

Scoring with a large language model normally means sending transcripts to a model API. For companies whose policy forbids any data leaving the boundary, the vendor should offer a locally deployed model — with the accuracy trade-off stated honestly rather than discovered later.

7. Role-based access with an audit log

The blast radius of a recording is defined by who can open it. The defensible default: a rep sees their own visits; a first-line manager sees their team; brand and compliance see aggregates and flagged fragments, not browsing access to everything; and every access is written to an immutable audit log.

8. Retention you control

Raw audio, transcripts and scores have different useful lives. Verify that retention is configurable per artefact type, that deletion is real (including backups, on a stated schedule), and that a legal-hold exception exists for fragments tied to a pharmacovigilance case.

9. Coaching-only mode for the rollout

Compliance is also about how the team experiences the system. A launch where scores feed straight into performance management invites gaming and resistance; a coaching-only start — scores visible to the rep and their manager, not to HR — is both change management and a genuine privacy control. The field accepts a tool that helps them earn more; it fights a surveillance system.

How NUMU answers these nine for its own product is on the NUMU Pharma FF Intelligence page and in its FAQ; the industry context is on the pharmaceutical companies page.