Eclipse Trials

Anonymize Your Clinical Trial Data with Eclipse Trials

Turn clinical trial data into business value. Quickly, safely and at scale.

Eclipse Trials is purpose-built for life-science organizations and optimized for clinical trial data anonymization. It’s a powerful add-on module to Eclipse Risk, our enterprise platform for data anonymization.

Eclipse Trials is perfect for:

Scaling a transparency program

Clearing a large backlog of clinical studies not yet published

Building an internal innovation platform for drug development

Streamlining field classification and automating the setup process for clinical trial data sets

Leveraging built-in functions such as PhUSE date shifting and auto data classification for SDTM and ADaM standards

Providing auditable proof that your data is no longer identifiable

How Eclipse Trials Works

 

Watch this short product video to see Eclipse Trials in action.

“With clients reporting up to 400% more throughput and 22% lower costs due to improved process efficiency, Eclipse Trials is a powerful new way to anonymize patient trial data at scale.”

Michael McTaggart, CTT Business Leader
Read Michael’s bio and learn how he can help you >

Client Success

Anonymized data fuels AI-driven drug differentiation

  • A global pharma company is leveraging Eclipse Trials to safely
    automate the anonymization of thousands of clinical studies.
  • They’re now well positioned to transform the way medicines are developed.

Safely innovate with trial data.
Ensure compliance.

If you're...

Then Eclipse Trials will help you…

If you're...

Anonymizing patient-level trial data for internal use and innovation

Then Eclipse Trials will help you…

If you're...

Anonymizing patient-level trial data for external data sharing

Then Eclipse Trials will help you…

Find out what Eclipse Trials software can do for you

Together with Eclipse Risk, the Trials add-on accelerates enterprise data
anonymization to support transparency initiatives, data sharing and internal R&D, while remaining compliant with international regulations.

Contact us to learn more about this robust product extension and if it is a fit for your business.

How does Eclipse Trials stack up against other options?

Unlike homegrown methods, Eclipse Trials ensures consistency and scalability, and provides evidence your trial data is no longer identifiable.

With options to install on premise or in the cloud, you can ensure enterprise-grade quality and security in your information technology environment.  Plus, Eclipse Trials API and built-in features enable high automation for your internal data uses.

Unlike other rules-based products, Eclipse Trials provides statistical proof of anonymization.  Data utility is maximized based on your context of use, with flexibility to ensure your internal data scientists drive meaningful results from the data.

Unlike other trial-specific risk-based products, the Eclipse Trials add-on extends the versatile and defensible core of Eclipse Risk, providing flexibility to link to other data sets – such as omics and DICOM headers – and has been proven across environments and health data types globally.

Eclipse Trials provides high-utility data offerings that are compliant across global regulatory environments including GDPR, HIPAA and CCPA.

Unlike other product vendors, our team of over 100+ privacy experts can support your evolving privacy needs over time.  From SDTM and ADaM data today to imaging and omics data tomorrow, our privacy experts work together with your team, making ongoing investments in the product.

Talk to an expert

Find out if Eclipse Trials will meet your business needs. Our experts are ready to help you make the right choice.

Sign me up for marketing communications on safely leveraging my sensitive data.

Archiving / Destroying

Are you unleashing the full value of data you retain?

Your Challenges

Do you need help...

OUR SOLUTION

Value Retention

Client Success

Client: Comcast

Situation: California’s Consumer Privacy Act inspired Comcast to evolve the way in which they protect the privacy of customers who consent to share personal information with them.

Evaluating

Are you achieving intended outcomes from data?

Your Challenge

Do you need help...

OUR SOLUTION

Unbiased Results

Client Success

Client: Integrate.ai

Situation: Integrate.ai’s AI-powered tech helps clients improve their online experience by sharing signals about website visitor intent. They wanted to ensure privacy remained fully protected within the machine learning / AI context that produces these signals.

Accessing

Do the right people have the right data?

Your Challenges

Do you need help...

OUR SOLUTION

Usable and Reusable Data

Client Success

Client: Novartis

Situation: Novartis’ digital transformation in drug R&D drives their need to maximize value from vast stores of clinical study data for critical internal research enabled by their data42 platform.

 

Maintaining

Are you empowering people to safely leverage trusted data?

Your Challenges

Do you need help...

OUR SOLUTION

Security / compliance efficiency

CLIENT SUCCESS

Client: ASCO’s CancerLinQ

Situation: CancerLinQ™, a subsidiary of American Society of Clinical Oncology, is a rapid learning healthcare system that helps oncologists aggregate and analyze data on cancer patients to improve care. To achieve this goal, they must de-identify patient data provided by subscribing practices across the U.S.

 

Acquiring / Collecting

Are you acquiring the right data? Do you have appropriate consent?

Your Challenge

Do you need help...

OUR SOLUTIONS

Consent / Contracting strategy

Client Success

Client: IQVIA

Situation: Needed to ensure the primary market research process was fully compliant with internal policies and regulations such as GDPR. 

 

Planning

Are You Effectively Planning for Success?

Your Challenges

Do you need help...

OUR SOLUTION

Build privacy in by design

Client Success

Client: Nuance

Situation: Needed to enable AI-driven product innovation with a defensible governance program for the safe and responsible use
of voice-to-text data under Shrems II.

 

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