Know who's going to drop out before they do.

TrialPulse uses multi-modal AI to predict patient adherence and dropout risk in clinical trials, enabling proactive, personalized interventions that keep participants enrolled and your data intact.

Patient Risk Monitor 3 flagged

Patient 1042

Missed 2 ePRO submissions

87%

Patient 0887

Declining app engagement

54%

Patient 0631

All signals stable

12%

Patient 0219

Travel distance increased 40%

79%

30%

of trial participants drop out

$200M+

average Phase III trial cost

6-7yr

average trial timeline

How TrialPulse Works

From signals to interventions, before the patient is gone.

Ingest

Aggregate behavioral signals

ePRO completion rates, wearable data drift, app engagement patterns, visit attendance, travel friction, and clinical indicators feed into a unified patient profile.

Predict

Score dropout risk per patient

Multi-modal ML models generate per-patient risk scores with explainable factors. Know exactly who is at risk, why, and how urgently they need attention.

Act

Trigger personalized interventions

Automated alerts route to coordinators with specific, personalized actions: switch to home visits, adjust schedules, escalate engagement. Prevent the dropout, don't just document it.

The Problem

Most sponsors find out a patient left after they're already gone.

Existing tools verify adherence (did they take the pill?) or analyze data quality after the fact. Nobody predicts behavioral dropout from multi-modal signals in real time, then tells coordinators exactly what to do about it.

86%

of clinical trials fail to meet enrollment timelines, often due to dropout replacing recruited patients

$19B

estimated annual cost of patient dropout to the pharmaceutical industry globally

6mo

average delay per trial caused by enrollment and retention failures

Clinical trials shouldn't be a guessing game.

TrialPulse turns patient retention from a hope into a system. Every signal tracked. Every risk scored. Every intervention personalized. Built by researchers who understand both the science and the engineering.