Introduction
Combining machine translation and human review accelerates large projects while retaining quality. For regulatory documents, this hybrid approach requires strict rules for quality levels, traceability and auditing. This article explains when to use machine translation plus post-editing, a recommended operational flow, audit practices and the minimum documentation needed to demonstrate compliance.
1. When to use machine translation plus post-editing
Adopt hybrid workflows when operational benefits are clear and risks are controlled. Appropriate uses:
- Internal reference materials, training content and non-regulatory summaries.
- High-volume projects needing speed, with planned expert validation.
- Tight-deadline projects where full post-editing is feasible without compromising clinical review.
- Content that will feed translation memories for future reuse.
Avoid relying solely on machine output for:
- Informed consent forms, labels and technical data where errors carry clinical or regulatory risk.
- Documents requiring signed certification without expert validation.
- Patient-facing materials unless validated by clinical reviewers.
2. Define post-editing quality levels
Clear levels set expectations and governance:
- Level A, fluency post-edit: minimal edits for naturalness, not guaranteeing technical accuracy. Good for internal drafts.
- Level B, full post-edit: thorough edits to ensure terminological accuracy and coherence, suitable for materials that will undergo technical review.
- Level C, technical validation: includes full post-edit plus clinical or regulatory expert sign-off and documented approvals.
For regulatory content, start at Level B and add Level C when clinical or legal consequences exist.
3. Recommended operational flow
- Scope and acceptance criteria: define post-edit level and pass/fail criteria.
- Preprocessing: clean source text, normalize terms and segment for traceability.
- Machine translation: record engine version and configuration.
- Post-editing: apply agreed level, log edits per segment.
- Technical review: subject-matter expert validates clinical/regulatory accuracy.
- Final QA: automated checks and manual verification of terminology, numbers, units and formats.
- Documentation: produce a report with logs, metrics and reviewer sign-offs.
- Integration: update TMs and glossaries with approved segments.
4. How to audit post-editing and the workflow
Auditing demonstrates control and supports continuous improvement:
- Sampling approach: stratified random sampling by document type and criticality.
- Error taxonomy: classify errors by category, e.g., terminology, omission, numeric, style, compliance.
- Audit metrics: percent of segments unchanged after post-edit, critical error rate per 1,000 words, average post-edit time per 1,000 words, share of critical issues found in technical review.
- Tool evaluation: capture model version, date and parameters.
- Traceability checks: ensure each segment links to source, MT output, editor and technical reviewer.
- Findings and remediation: document corrective actions, owners and deadlines.
5. What to document for regulatory readiness
Documentation must reconstruct the workflow and support decisions. At minimum retain:
- Source file and version.
- Machine translation engine version and configuration.
- Raw MT output and post-edited output with marked differences.
- Identity and credentials of the post-editor and technical reviewer.
- Time logs: post-edit hours and technical review hours.
- Applied glossary and translation memory versions.
- QA reports and audit findings.
- Technical approver signature or confirmation.
- Metadata: dates, project IDs and cryptographic hashes or timestamps for integrity.
This package supports audits and reduces regulatory exposure.
6. Recommended KPIs for governance
- Percent of segments approved without further changes after post-edit.
- Critical errors per 1,000 words.
- Average post-edit time per 1,000 words.
- Total lead time from source submission to technical approval.
- Share of content escalated to additional technical review.
- TM reuse rate from approved segments.
Tracking these KPIs enables balancing speed and assurance.
7. Risks and mitigation controls
Common risks include loss of clinical nuance, numeric mistakes, inconsistent terminology and weak traceability. Controls:
- Maintain validated glossaries and sync them with MT workflows.
- Set criticality thresholds that trigger human escalation.
- Periodically audit MT models and their updates.
- Train post-editors in terminology and regulatory protocols.
- Log the entire workflow in a secure repository with backups and access control.
Quick checklist (decide and implement)
- Does the document require clinical or legal validation? If yes, plan Level C.
- Are glossary and translation memories current? Confirm before starting.
- Will MT engine version and configuration be recorded? Yes.
- Is sampling and an error taxonomy defined for audit? Yes.
- Will times and approver signatures be included in documentation? Yes.
- Will approved segments update translation memories? Yes.
Conclusion
AI plus human workflows deliver operational agility when guided by clear quality levels, audit sampling and full traceability. For regulatory documents, the focus must be on defined post-edit levels, representative audits and rigorous documentation. These elements ensure speed without compromising safety or compliance.
For support setting up pilot workflows, audit plans or documentation templates, expert assistance is available to tailor the approach to regulatory needs.
