Data Quality Analytics

Workspace-level analytics for data quality, call volume, event distribution, and review pipeline performance.

The platform provides workspace-level analytics covering data quality, call activity, event distribution, and review pipeline performance. These metrics give operations teams visibility into how data flows through the system and where attention is needed.

Data Quality

The data quality dashboard tracks confidence distribution across all events in the workspace:

Bucket	Confidence Range	What It Means
Rejected	0.0	Events that failed review or were explicitly contradicted
Raw	0.1-0.3	Unverified data from agent inference or initial extraction
Uncertain	0.4-0.5	Voice-extracted data awaiting review
Verified	0.6-0.7	Data that passed automated LLM review
Human-approved	0.8-0.95	Data approved by a human reviewer
Authoritative	1.0	Data from authoritative system integrations (direct EHR API)

The dashboard also shows confidence breakdown by data source, so you can see which sources produce the most reliable data and which generate the most review queue items.

Review Pipeline

Review pipeline metrics track how the automated and human review stages are performing:

• Auto-approved - Events that passed automated review without human involvement

• LLM-approved - Events verified by the LLM judge

• Rejected - Events that failed review

• Pending review - Events waiting in the human review queue

• Human-approved - Events approved by an operator

• Corrected - Events where an operator provided corrected data

• Review rate - Percentage of events that required any form of review

A daily confidence timeseries shows low-confidence and high-confidence event counts over time, making it easy to spot trends (improving data quality as STT accuracy improves, or degrading quality after a configuration change).

Call Statistics

For voice deployments, call analytics include:

• Call volume (total calls over 30-90 day windows)

• Call duration distribution

• Daily call breakdown

Event Distribution

Event analytics show how data enters the system:

• Event counts by type (patient, appointment, practitioner, etc.)

• Event counts by source (EHR sync, voice extraction, manual entry, etc.)