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ENSEMBLE · DECENTRALIZED TRIALS

Patient intelligence is more critical
when there is no site coordinator.

DCT removes the human safety net. Site coordinators catch early disengagement signals in person. In remote trials, those signals have nowhere to land unless the system is designed to receive them.

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THE PROBLEM

DCT infrastructure is not the bottleneck. Patient behavior with that infrastructure is.

TECH FRICTION

App onboarding, log-ins, Bluetooth pairing, and notification fatigue quietly erode completion and visit adherence. Patients don't report the friction. They simply stop engaging.

The dropout signal is behavioral, not technical. It exists before a single patient enrolls.

SEGMENT-SPECIFIC DROPOUT

Digital literacy, caregiving burden, language, device access, and income all shift how patients experience the same DCT workflow. A design that retains urban, high-literacy adults fails outright for older, low-income, or caregiving-heavy segments.

Average retention projections mask the segments that will actually ghost the trial.

SLOW AND EXPENSIVE UX RESEARCH

Human factors labs and small UX pilots surface some friction, but they are expensive, slow, and rarely segment-representative at scale. By the time findings are available, the protocol is already locked.

Behavioral simulation before protocol lock is faster and cheaper than fixing dropout after it occurs.

FEASIBILITY TOOLS MISS BEHAVIOR

Current DCT planning tools capture protocol complexity and logistics. None of them model behavioral friction with the tech stack itself.

The protocol is feasible on paper. The DCT platform is robust. Patients still ghost the trial.

NO HUMAN SAFETY NET

In site-based trials, coordinators catch early disengagement in person. In remote trials, those signals have nowhere to land unless the system is designed to read them.

Ensemble is that system.

ENSEMBLE FOR DCT

Simulate how your patient population will behave with your DCT stack. Before you lock the protocol.

Ensemble generates synthetic patient populations calibrated to real behavioral and clinical data, then simulates how those patients respond to your specific onboarding flows, ePRO schedules, device requirements, and remote visit cadence.

Onboarding Friction Prediction

Identify which patient segments will abandon onboarding and at which specific step, such as app login, Bluetooth pairing, consent flow, or the first ePRO. Model the friction before the protocol is locked.

Simplify the flow for the segments that need it. Before a single patient is enrolled.

ePRO Adherence Forecasting

Predict completion curves by segment for your specific ePRO or eCOA schedule. Identify whether late-evening tasks, assessment frequency, or notification cadence will drive abandonment in specific demographic or socioeconomic groups. Spanish-preferring caregivers show significantly lower predicted completion under standard schedules; Ensemble models what changes matter.

Adjust the schedule before the data gap is real.

Device and Wearable Continuation

For trials requiring wearables or home monitoring devices, predict what proportion of patients will maintain device use past 30 days by age, digital literacy, and income segment. Model whether device complexity warrants human support at first visit for specific segments.

Device dropout is behavioral, not technical. Ensemble treats it accordingly.

Remote Recruitment Optimization

Patients who will engage with a fully remote protocol have different behavioral profiles than patients who require in-person support. Optimize recruitment messaging and channel strategy for the population that will actually complete, not the broadest eligible population.

Match recruitment to retention profile. Not eligibility criteria alone.

USE CASES

The questions Ensemble is built to answer.

Which patient segments are most likely to fail the current assessment schedule? Ensemble models completion curves by segment for your specific ePRO design, identifying whether late-evening tasks, assessment frequency, or notification cadence drives abandonment. Output is a segment-level retention forecast with specific protocol modification options and predicted lift per change.
Protocol decisions made with retention data, not intuition.
What proportion of patients will realistically maintain device use past 30 days by age and digital literacy? Is it worth introducing nurse home visits for a specific high-risk segment versus accepting higher dropout? Ensemble models device continuation by segment and quantifies the retention impact of support interventions before any resource commitment is made.
Device and visit design decisions grounded in predicted segment behavior.
Where in the actual onboarding flow do we see predicted abandonment spikes? Do we need dual-mode access for certain segments to avoid losing them on Day 1? Which notification cadence and channel mix moves adherence for the specific segments in this trial? Ensemble answers each of these before a single patient downloads the app.
Design the app experience around patients who will actually complete it.

DIFFERENTIATION

Why this is different from existing DCT tools.

Behavior
Not logistics.
Existing DCT platforms handle data capture, site activation, and logistics. Ensemble focuses on the behavioral failure points that sit on top of that stack, the layer those tools were never designed to model.
Healthcare
Not consumer data.
Synthetic populations are calibrated to healthcare-specific behaviors, including trial participation, tech tolerance under illness and income constraints, adherence under caregiving burden, and protocol fatigue.
Segment
Not average patient.
All Ensemble outputs are segmented. You see that a design which works for urban, high-literacy adults fails outright for older, low-income, or caregiving-heavy segments, and you see specifically what changes matter for each group.
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