Protocol Feasibility and Early-Termination Risk Scoring
Forecasts protocol risk before launch so teams can reduce avoidable trial failures Evidence basis: A Scientific Reports analysis of 420k+ trials showed interpretable ML can estimate early termination risk from design features; a separate 2000+ trial operations study showed recruitment and duration efficiency can be predicted from protocol characteristics
The Problem
“Protocol Feasibility and Early-Termination Risk Scoring”
Organizations face these key challenges:
Forecasts protocol risk before launch so teams can reduce avoidable trial failures
Impact When Solved
The Shift
Human Does
- •Review protocol design manually against feasibility and risk criteria
- •Coordinate inputs across stakeholders using spreadsheets and email
- •Assess early-termination risk based on past experience and retrospective checks
- •Document findings, recommendations, and follow-up actions in static reports
Automation
- •No AI-driven analysis is used in the legacy workflow
- •No automated prioritization of protocol risk or opportunity is available
- •No continuous monitoring or standardized risk scoring is performed
Human Does
- •Confirm final feasibility and early-termination risk decisions before protocol launch
- •Review flagged risks and approve mitigation actions or protocol changes
- •Handle exceptions, missing context, and cases that require expert judgment
AI Handles
- •Score protocol feasibility and early-termination risk from protocol characteristics
- •Prioritize high-risk design elements and surface likely operational issues early
- •Generate consistent risk summaries and recommended follow-up areas for review
- •Track scoring outputs across reviews to support standardized reporting and triage
Operating Intelligence
How Protocol Feasibility and Early-Termination Risk Scoring 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 make the final feasibility or early-termination risk decision without review and approval from clinical operations or protocol review leads [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 Protocol Feasibility and Early-Termination Risk Scoring implementations: