Nightingale
Nightingale
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Pre-EDC infrastructure for clinical trials

Prevent data loss before it reaches the EDC.

Nightingale sits between device/PRO collection and your EDC. It normalizes incoming data, captures transformation lineage automatically, and preserves partial datasets so attrition doesn’t erase usable evidence.

Catch drift early
Attrition-safe partials
Audit-ready lineage
Schedule Demo
15-25%
unusable post-collection data
30-40%
participant dropout
40-60%
time spent on reconciliation
$20-75M
typical Phase I-II cost
PARTNERS & AFFILIATIONS

Built by a team with deep roots across leading research institutions.

Our team comes from, and is supported by, partners and organizations advancing clinical research and healthcare innovation.

Emory University
Georgia Institute of Technology
Rush Medical
University of Alabama at Birmingham
National Institutes of Health
AUDIENCE

Built for CROs, AROs, sponsors, and device-heavy trials.

Upstream of EDCs. Focused on normalization, integrity, and traceable transformations.

CROs
Clinical ops + data ops teams running vendor-heavy studies.
AROs
Academic research orgs managing heterogeneous device inputs.
Sponsors
Teams accountable for integrity, timelines, and audit readiness.
Device-Heavy Trials
Wearables, sensors, and high-frequency PRO streams.
PROBLEM

Collection succeeds. The data breaks before analysis.

Not because devices fail, but because normalization, drift, and missingness are discovered too late.

The Failure Mode
The failure happens after collection, before analysis.
Multiple vendors → multiple formats → per-study pipelines rebuilt from scratch.
Device updates + protocol changes → schema drift mid-study.
Missingness is found weeks later, not at the point of ingestion.
Why it’s Expensive
The failure happens after collection, before analysis.
Reconciliation expands as participant dropout breaks longitudinal continuity.
Teams spend 40-60% of data time on cleanup instead of analysis.
Partial datasets get discarded because lineage isn’t reproducible.
SOLUTION

A pre-EDC normalization and lineage layer.

Standardize device + PRO data into reusable schemas and document every transformation automatically.

Positioning
A pre-EDC layer between collection and your EDC.
Pre-EDC layer
Sources
WearablesSensorseCOA/ePROVendor exports
Nightingale
NormalizeDocument lineagePreserve partials
EDC
MedidataOracleVeevaREDCap
What Nightingale does
  • • Standardizes device + PRO data into reusable schemas
  • • Logs every transformation with full lineage
  • • Preserves partial datasets (attrition-safe)
  • • Flags missingness + schema drift early
  • • Outputs audit-ready datasets for EDC ingestion
What Nightingale does not do
  • • Replace your EDC
  • • Interpret clinical meaning or generate endpoints
  • • Change study conduct or monitoring
HOW IT WORKS

Three steps. No workflow disruption.

Integrate once, then reuse across studies and devices.

STEP 1
Ingest device + PRO data

Accept wearables, sensors, ePRO/eCOA systems, and vendor exports. Keep raw inputs intact.

STEP 2
Normalize + document transformations

Apply reusable schemas and transformation rules while capturing full lineage automatically.

STEP 3
Output audit-ready datasets

Deliver consistent EDC-ready outputs and surface drift/missingness early, not at lock.

ECONOMIC IMPACT

Math-driven downside protection.

Nightingale targets avoidable cost from unusable post-collection data and late-cycle reconciliation.

Phase I example
~$6M
Avoidable loss when unusable post-collection data and late reconciliation drive rework, delays, and re-collection.
Phase II example
~$17M
Avoidable loss under the same assumptions at Phase II scale, where device volume and schema drift amplify reconciliation burden.
Pricing constraint

Nightingale is designed to cost less than 2% of trial budget.

WHY NOW

Device-heavy trials are becoming standard.

The pre-EDC gap is growing: more sources, more formats, and higher expectations for integrity and traceability.

Hybrid and decentralized adoption

Remote capture increases ingestion paths and normalization overhead.

Wearables and sensors are proliferating

More vendors and updates create schema drift within and across studies.

Integrity and lineage expectations

Traceability and reproducibility matter more as novel data streams become common.

CONTACT

Make your pre-EDC pipeline painless.

We help teams reduce reconciliation churn, catch drift early, and keep device + PRO data audit-ready, before it hits the EDC.

Lower the stress between collection and EDC.
We’ll review your device and PRO ingestion flow, pinpoint where drift and missingness are introduced, and outline a cleaner path to consistent, audit-ready outputs.
Schedule Demo
Note: Nightingale does not replace EDCs and does not generate endpoints.