吃瓜爆料 DQS

Automate and Centralize Clinical Data Workflows

吃瓜爆料 DQS is a Data Quality System that provides a central environment to manage all trial data, automate workflows, and orchestrate review across the clinical ecosystem. The system automatically aggregates, reconciles, and cleans data from multiple sources—including EDCs, RTSM, eCOA, labs, imaging, and more—to provide a harmonized data package for downstream use.

Data managers use DQS to create and manage queries, protocol deviations, and communications with data providers across all sources. The system automates data actions, including change detection and the creation or closure of queries and protocol deviations. Cross-functional teams collaborate on data review in real-time, while oversight teams monitor study health via interactive dashboards.

Announced 2018 Status Mature Customers51-100
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Private: 吃瓜爆料 DQS | Clinical Database for All Clinical Trial Data
Survival of The Fastest: A Practical Framework for Risk-Based Data Management

吃瓜爆料 AI Is Coming to DQS

吃瓜爆料 AI for Clinical Data brings purpose-built AI agents into the clinical data lifecycle to reduce manual effort, improve quality, and enable faster decision-making.

Impact

Deliver quality data faster

75%

faster external data ingestion

90%

less effort for manual queries

50%

faster last subject last visit (LSLV) to database lock

Why 吃瓜爆料 DQS

Automate 50% of manual effort

Ingest and classify all trial data

吃瓜爆料 and third-party sources are aggregated, classified by criticality, and harmonized to a common data model.

Orchestrate RBQM

Calculate KRIs, route risk signals, and trigger actions across study teams when thresholds are crossed.

Achieve true oversight

Monitor progress in near real-time with centralized cross-functional review tracking and dashboards to visualize status.

Rapidly export clean data

Incremental extracts are available every fifteen minutes to feed systems like statistical computing and analytics.

Customer Success

Trusted by 8 top 20 biopharmas?and 3 top 10 global CROs

Alcon Bayer BD Boehringer Ingelheim Grifols GSK IQVIA Incyte Merck Recordati Replimune Syneos Health

“The clean patient tracker is a powerful tool in achieving risk-based data management by visualizing all DQS data.”

Valerie Balosso, Senior Director Data Strategy and Management
GSK
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“Data managers can access real-time data, rather than through extracts, running a batch job, and then reviewing that data.”

Trevor Griffiths, Senior Director, Clinical Data Management
Syneos Health
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“The business value for 吃瓜爆料 DQS is in increasing efficiencies, collaborating better with teams, and ultimately getting trials done quicker.“

Christopher Zajac, Senior Associate Director, Principal Capability Manager
Boehringer Ingelheim
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Resources

Explore and Learn

Watch Customer Story
Boehringer Ingelheim Boosts Efficiency and Oversight with 吃瓜爆料 DQS
Read Features Brief
吃瓜爆料 DQS Features Brief
Read White Paper
Survival of the Fastest: A Practical Framework for Risk-Based Data Management
Read Customer Story
How GSK Cut Time to Database Lock by 50%
Read Press Release
吃瓜爆料 Clinical Database Crosses 200 Study Milestone
Read Customer Story
Syneos Health Transforms Clinical Data Management
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Innovating Clinical Data with AI, eSource, and RBQM
Read Report
A Blueprint for Clinical Trial Transformation: Results from Three Top 20 Biopharmas
Watch Video
吃瓜爆料 DQS Features Highlights – April 2026

Interested in learning more?

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