Study oversight
Enrollment, randomization, study flow, deviations, missing tasks, and real-time quality metrics.
Real-time analytics
Real-time analytics that identifies risk early.
One dashboard for patient, site, and study health, so compliance drift and data gaps are caught before they become deviations.
< 24hFrom first signal to flagged risk
Near-real-timeData latency across wearables, ePRO, and endpoints
HourlyReadiness scoring per patient, site, and study
One dashboard. Every signal.
Wearable streams, ePRO responses, device health, compliance, and digital biomarkers in one real-time platform. No more waiting weeks for vendor exports.
- Role-based dashboards for sites, CRAs, sponsors, and data science teams
- Instant access to patient status, compliance, and data quality
- Device monitoring for battery, connectivity, and sync health
- Wearable-derived biomarkers and trend modeling
- Immediate alerts when a patient falls behind
Four views of study health.
Each one updates continuously, so the whole chain of evidence stays visible from device to dashboard.
Patient-level insights
Sensor trends, PRO history, compliance curves, symptom patterns, and physiological change.
Device monitoring
Battery health, connectivity, missed syncs, wear-time estimation, replacements, and troubleshooting.
Digital endpoints
Wearable biomarkers such as sleep, activity, HRV, gait, respiratory trends, and functional capacity.
Operational readiness, scored every hour.
Most platforms tell you something went wrong after a monitoring visit. Delve scores each patient’s data readiness continuously across seven dimensions, with On track, Watch, and Action needed thresholds.
- The same score rolls up per patient, per site, and per study
- Hourly timelines surface drift while recovery is still possible
- Watch-level signals go to Delve Concierge; only Action-level alerts reach sites
- Every score links back to its signals, algorithm version, and firmware
72/100Overall readiness in a sample Parkinson’s study (Watch)
77.0%Data coverage over the last 72 hours
71%Wear time of monitored hours
P95 8.1mData latency from device to dashboard
Compliance, data quality, and signal health.
Continuous research needs continuous insight. You see data the moment it changes, not weeks later.
- Compliance curve tracking and wear-time estimation
- Signal drop detection with automated QC rules
- Outlier removal and smoothing
- Flagged physiological anomalies and study-wide thermal maps
Designed for every role in research.
The dashboard shows only the studies, sites, and patients tied to your role.
Sponsors
Portfolio-level dashboards, risk detection, and early signals.
CRAs and monitors
Visit windows, missing tasks, and actionable site alerts.
Research sites
Patient-level insights, onboarding status, and device troubleshooting.
Data science teams
Raw data access, algorithm outputs, and endpoint evaluations.
What changes when the data is live.
Issues are visible while you can still fix them, and the dataset arrives cleaner at the end.
98%Real-time data availability
45%Increase in study adherence
60%Faster issue resolution
5XMore complete sensor data
Go deeper.
The detail behind the dashboard, for teams evaluating analytics in depth.
What each role sees
Patient view
Surfaces next steps, compliance status, and device sync health in real time: what’s due, completed, or missing; wear-time and sync feedback to prevent silent data gaps; guided reminders aligned to protocol windows; and escalation to human support when needed. It closes the loop from wearable signal to task, completion, and usable evidence.
Sponsor view
Rolls up compliance, data completeness, and operational risk across sites and cohorts: cohort-level completeness and deviation risk, site-to-site performance and bottlenecks, real-time monitoring without waiting on exports, and audit-ready visibility into what changed and when. Feedback loops get shorter and vendor friction drops.
Investigator view
Supports patient-level review aligned to visit windows and protocol expectations: drill-downs with study-day timelines, context for missingness versus physiologic change, visit window adherence and task history, and operational flags that explain data gaps. Clinical signals are paired with operational context such as battery, sync, and adherence.
The seven readiness dimensions
Each patient’s readiness is scored 0–100 from seven operational dimensions. Example values below come from a sample Parkinson’s continuous monitoring study (5 patients, last 72 hours), rated Watch overall at 72/100.
Data coverage
Minutes of data received versus expected. Sample: 77.0% (3,326 of 4,320 minutes).
Wear time
Share of monitored hours the device was worn. Sample: 71%.
Sync gaps
Longest interval without a successful sync. Sample: 13 hours.
Battery health
Time spent below 20% charge. Sample: 10.3% of time.
Disconnects
Device disconnects observed per day. Sample: 1.8 per day.
Latency
Time from capture to platform. Sample: P95 8.1 minutes, P99 13.1 minutes.
Firmware alignment
Whether devices run a consistent firmware version. Sample: fragmented across 2 active branches.
How the thresholds drive action
On track
Readiness is healthy and no action is needed.
Watch
Trending down with the recovery window still open. Delve Concierge triages these signals before sites are alerted.
Action needed
Escalation is triggered. The site receives the alert with full context and recommended next steps.
Hour by hour, not week by week
Hourly readiness timelines, hour-of-day averages, and weekday patterns reveal drift early.
An alert, from signal to prevented deviation
Compliance drift is detected in a cohort within a 48-hour window. A QC rule fires on a missing ePRO entry combined with a wearable sync gap, and the risk score rises. The suggested site action is outreach plus a device check, and the deviation is prevented. One view spans compliance, device health, data quality, and endpoint readiness, working as an early warning system for operational risk.
From the Knowledge Hub
Operational guides on monitoring and data quality in real trials.
How to Measure Compliance in Clinical Trials
A practical, vendor-neutral framework for measuring compliance across modalities.
Read →Signal Quality Control for Wearable Data
How signal QC protects endpoint integrity before, during, and after data lock.
Read →Why Clinical Trials Lose Data Continuity
Trials rarely fail loudly. They degrade quietly between visits.
Read →See your study in real time.
The dashboard designed for modern hybrid and decentralized research.
Book a demo Or try the 2-minute compliance calculator