ConvergeHEALTH™ CognitiveSpark™
for Clinical
Using AI and machine learning in clinical trial
data management
Automate data management across the clinical
trial life cycle to help improve efficiency and
deliver breakthrough therapies to patients at the
speed of need.
Why ConvergeHEALTH CognitiveSpark for Clinical?
Speed clinical trials using AI and machine learning
For a patient awaiting a life-saving treatment, the current drug development timeline can feel like an eternity. As life sciences CIOs and R&D leaders know, that’s because the traditional flow of data across the clinical trial life cycle can be a complicated maze marked by manual effort, rework, and inefficiency.
These pain points add up, contributing to both increased trial time and cost. To help alleviate the pain and open new opportunities, life sciences companies need an advanced solution that harnesses the power of AI and machine learning to streamline the clinical trials life cycle: ConvergeHEALTH CognitiveSpark for Clinical.
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The benefits
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01
Accelerate trial cycle times
CognitiveSpark for Clinical enables the downstream use of data elements, automates data quality checks, and features user-driven Study Data Tabulation Model (SDTM) conversions to drive faster, more efficient clinical trials.
02
Improve clinical trial efficiency and data quality
With a collaborative authoring platform, metadata traceability to standards, and machine learning features, CognitiveSpark for Clinical delivers data quickly—while preserving data quality.
03
Lower trial costs
By standardizing workflows and documentation and adding studies automatically, you can limit resource needs, manual errors, and costs.
04
Scale—and customize—to your needs
Easily extend CognitiveSpark for Clinical across therapeutic areas and trial types to promote an end-to-end digital workflow.
Our solutions
Clinical Data Repository (CDR)
A scalable data foundation for raw, enriched, and analytical data sets
Metadata repository
Provides access to metadata for consumption by downstream systems
AI/ML algorithm store
A set of standard application programming interfaces focused on insight generation
Study designer
Creates comprehensive digital study specifications—including objectives, endpoints, biomedical concepts, and eligibility criteria
Data harmonization
Integrated mapping from source systems to SDTM and Analysis Dataset Model, aided by automation and machine learning
Tables, listings, and figures (TLF) creation
Automates the creation of TLF shells using standards and templates
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ConvergeHEALTH CognitiveSpark for Clinical in action
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Insights to guide your journey
