Active Study Enrollment Risk Monitor

Detects emerging enrollment risks in active studies and continuously recalibrates forecasts to help teams prevent milestone misses and timeline slippage.

The Problem

“Prevent enrollment delays in oncology studies with continuously updated risk detection and forecast recalibration”

Organizations face these key challenges:

1

Static feasibility models fail to reflect changing site and patient recruitment conditions

2

Enrollment forecasts are often manually maintained and quickly become stale

3

Oncology protocols may be misaligned with real-world care pathways and site capabilities

4

Study teams struggle to determine the right number of sites to hit timelines efficiently

5

Traditional recruitment channels alone do not surface enough eligible patients

6

Operational data is fragmented across CTMS, EDC, recruitment platforms, and spreadsheets

7

Interventions happen too late because risk signals are detected only after visible lagging performance

8

Country and site performance varies widely, making one-size-fits-all planning ineffective

Impact When Solved

Earlier detection of enrollment risk before milestone misses become visibleMore accurate rolling forecasts for country, site, and study-level enrollmentBetter site sizing and activation recommendations for new oncology protocolsReduced over-allocation of sites and recruitment budgetImproved patient matching and conversion through digital-first recruitment channelsFaster intervention planning for underperforming studiesHigher confidence in portfolio-level timeline commitments

The Shift

Before AI~85% Manual

Human Does

  • •Review weekly enrollment reports against study milestones and static plans
  • •Combine CTMS, EDC, and spreadsheet updates into a single status view
  • •Investigate site, country, and screen failure changes through manual analysis
  • •Escalate emerging enrollment issues in meetings and status trackers

Automation

  • •No meaningful AI-driven monitoring or forecasting is used
  • •Static dashboard calculations summarize historical enrollment performance
  • •Basic threshold alerts highlight obvious plan-versus-actual gaps
With AI~75% Automated

Human Does

  • •Review ranked enrollment risks and decide where intervention is most urgent
  • •Approve forecast-driven escalation priorities for studies, countries, and sites
  • •Validate recommended actions using operational context not visible in the data

AI Handles

  • •Continuously monitor active study enrollment signals across available operational data
  • •Detect abnormal site, country, and study performance earlier than reporting cycles
  • •Recalibrate enrollment and milestone forecasts as new data arrives
  • •Score and rank enrollment risks by likely timeline impact and urgency

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence91%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

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.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Active Study Enrollment Risk Monitor implementations:

AI/machine learning toolingOther
4 mentions
Trial design analyticsOther
4 mentions
Oncology real-world data platformOther
3 mentions
GSER baseline calculationOther
2 mentions

Key Players

Companies actively working on Active Study Enrollment Risk Monitor solutions:

IQVIATempusCOTATriNetX

Real-World Use Cases

Pragmatic oncology trial design using real-world data to improve study efficiency

Genentech and Flatiron Health use real patient-care data to design cancer trials that better match how treatment happens in everyday clinics, helping studies run faster and more efficiently.

Decision support and design optimizationearly production/proposed deployment; presented as a concrete case study rather than a broad concept.
10.0

Digital-first patient matching and enrollment through Citeline Connect ecosystem

Citeline uses online tools and recruitment partners to help the right patients find trial information, learn about studies, and enroll more easily.

matching and workflow orchestrationcommercially deployed platform offering; source describes active products and services, with selective ai enhancement mentioned at portfolio level but not tied to this exact workflow.
10.0

Operational feasibility recommendation for oncology study site sizing

Use fitted enrollment equations to recommend how many sites a specific study should open so it can recruit patients as fast as practical.

prescriptive analytics based on predictive enrollment modelsapplied in a real project example described by the author, indicating practical deployment in consulting-style study planning.
10.0

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