Central Statistical Monitoring Copilot (RBQM)
Detects site-level data anomalies early and targets monitoring where quality risk is highest Evidence basis: A 2024 multi-study analysis covering 1111 sites reported quality metric improvement in most flagged sites after statistical monitoring actions; FDA guidance endorses centralized risk-based monitoring over blanket SDV
The Problem
“Central Statistical Monitoring Copilot (RBQM)”
Organizations face these key challenges:
Detects site-level data anomalies early and targets monitoring where quality risk is highest
Impact When Solved
The Shift
Human Does
- •Review site data and quality metrics manually on a periodic basis
- •Coordinate monitoring priorities through spreadsheets and status updates
- •Investigate suspected anomalies and decide which sites need follow-up
- •Plan and document monitoring actions after retrospective quality review
Automation
- •No AI-driven analysis is used in the baseline workflow
- •No automated prioritization of site quality risk is performed
- •No continuous anomaly detection or signal triage is available
Human Does
- •Review prioritized site risk signals and confirm monitoring actions
- •Approve escalation, follow-up, and resource allocation decisions
- •Investigate exceptions and determine whether signals reflect true quality issues
AI Handles
- •Monitor site-level data for unusual patterns and emerging quality signals
- •Prioritize sites based on relative quality risk and action urgency
- •Generate standardized risk summaries and monitoring worklists
- •Surface early anomalies for targeted review and follow-up
Operating Intelligence
How Central Statistical Monitoring Copilot (RBQM) runs once it is live
AI surfaces what is hidden in the data.
Humans do the substantive investigation.
Closed cases sharpen future detection.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Scan
Step 2
Detect
Step 3
Assemble Evidence
Step 4
Investigate
Step 5
Act
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.
The Loop
6 steps
Scan
Scan broad data sources continuously.
Detect
Surface anomalies, links, or emerging signals.
Assemble Evidence
Pull related records into a working case file.
Investigate
Humans interpret evidence and make case judgments.
Authority gates · 1
The system must not approve escalation, follow-up, or resource allocation decisions without review by clinical operations or central monitoring leads. [S1]
Why this step is human
Investigative judgment involves ambiguity, legal considerations, and stakeholder impact that require human expertise.
Act
Carry out the human-directed next step.
Feedback
Closed investigations improve future detection.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Central Statistical Monitoring Copilot (RBQM) implementations: