Enrollment Velocity and Site Activation Forecasting
Predicts enrollment pace and site ramp-up risk for earlier intervention and reallocation Evidence basis: Historical trial ML models can forecast recruitment efficiency and trial duration from planned study attributes; FDA risk-based monitoring guidance supports continuous use of risk indicators when combined with human review
The Problem
“Enrollment Velocity and Site Activation Forecasting”
Organizations face these key challenges:
Predicts enrollment pace and site ramp-up risk for earlier intervention and reallocation
Impact When Solved
The Shift
Human Does
- •Collect site activation updates and enrollment counts from study teams
- •Review spreadsheets to compare actual enrollment against plan
- •Discuss slow-start sites and decide where to focus follow-up
- •Reallocate outreach and startup effort based on periodic reviews
Automation
- •No AI-driven forecasting or risk prioritization is used
- •No automated monitoring of enrollment pace or site ramp-up trends
- •No system-generated alerts for likely delays or underperforming sites
Human Does
- •Review forecasted enrollment and site ramp-up risks with operational context
- •Approve intervention plans and resource reallocation for at-risk sites
- •Handle exceptions where forecasts conflict with recent field updates
AI Handles
- •Monitor enrollment pace and site activation progress on an ongoing basis
- •Forecast likely recruitment velocity and site ramp-up risk from study attributes and performance trends
- •Prioritize sites and studies needing earlier intervention or support
- •Generate alerts and recommended focus areas for operational review
Operating Intelligence
How Enrollment Velocity and Site Activation Forecasting runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
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
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve intervention plans or resource reallocation without review and sign-off from a clinical operations lead [S1].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Enrollment Velocity and Site Activation Forecasting implementations: