Adaptive Trial Scenario Planner
AI-assisted adaptive trial design and scenario planning for optimizing endpoints, refining protocols, evaluating enrollment feasibility, and supporting phase advancement decisions.
The Problem
“Adaptive clinical trial design is slow, fragmented, and difficult to optimize across endpoints, protocol constraints, enrollment feasibility, and phase-gate de…”
Organizations face these key challenges:
Endpoint selection is difficult to benchmark across prior trials and evolving standards of care
Protocol complexity can unintentionally reduce site participation and patient enrollment
Enrollment feasibility estimates are often built from incomplete or stale assumptions
Scenario analysis is manual, slow, and hard to reproduce across teams
Impact When Solved
The Shift
Human Does
- •Review prior trials, publications, and internal study reports to benchmark endpoints and protocol choices
- •Build spreadsheet scenarios for eligibility criteria, visit schedules, enrollment assumptions, cost, and timelines
- •Consult cross-functional experts to assess feasibility, statistical tradeoffs, and operational risks
- •Compare design alternatives in review meetings and decide protocol revisions or phase advancement recommendations
Automation
- •No meaningful AI support; evidence gathering, scenario analysis, and comparisons are performed manually
- •Basic search and document retrieval may assist literature review without structured synthesis
- •Standalone statistical tools may run limited simulations based on manually prepared inputs
Human Does
- •Set trial objectives, decision criteria, and acceptable tradeoffs for endpoints, feasibility, cost, and timelines
- •Review AI-generated scenario scorecards, explanations, and cited evidence in design discussions
- •Approve protocol changes, endpoint selections, and phase advancement or redesign decisions
AI Handles
- •Synthesize historical trial evidence, protocol documents, publications, and enrollment data into grounded comparisons
- •Generate and compare protocol scenarios across endpoint choices, eligibility criteria, visit burden, cost, and duration
- •Predict enrollment feasibility, dropout risk, amendment likelihood, and probability of meeting phase objectives
- •Rank design alternatives, highlight tradeoffs and risks, and draft standardized decision summaries for review
Operating Intelligence
How it works
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 protocol changes, endpoint selections, or phase advancement decisions without human review and sign-off.[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 Adaptive Trial Scenario Planner implementations:
Key Players
Companies actively working on Adaptive Trial Scenario Planner solutions: