How to Build a Data-Sharing Strategy for Pharma Innovation

How to Build a Data-Sharing Strategy for Pharma Innovation

About this webinar

Now more than ever, the eyes of the world are on sponsors to collaborate, sharing their clinical trial data to combat human health emergencies.

While individual patient-level data sharing is largely done on a voluntary basis, privacy is just as important as when clinical studies are published by regulators.

This webinar, held in collaboration with DIA, will investigate the different decisions pharma companies face and look at how these decisions impact brand reputation and public trust.

Featured Topics:

  • Data sharing as a clinical trial sponsor’s social responsibility
  • The real-world cost of not sharing safe, insight-rich data
  • A framework for safely sharing and using sensitive clinical trial data

Learning Objectives:

  • Compare qualitative and quantitative approaches to anonymization
  • Evaluate whether to build an in-house anonymization capability, buy software, or engage a vendor
  • Analyze methods for balancing patient privacy and data utility

Presenters

Niamh McGuinness, PhD, Senior Analyst, Clinical Trial Transparency, Privacy Analytics
Laura Dodd, Global Data Sharing Manager, PRA Health Sciences, Takeda
Jingyi Liu, Principal Research Scientist, Eli Lilly and Company

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About this webinar

Now more than ever, the eyes of the world are on sponsors to collaborate, sharing their clinical trial data to combat human health emergencies.

While individual patient-level data sharing is largely done on a voluntary basis, privacy is just as important as when clinical studies are published by regulators.

This webinar, held in collaboration with DIA, will investigate the different decisions pharma companies face and look at how these decisions impact brand reputation and public trust.

Featured Topics:

  • Data sharing as a clinical trial sponsor’s social responsibility
  • The real-world cost of not sharing safe, insight-rich data
  • A framework for safely sharing and using sensitive clinical trial data

Learning Objectives:

  • Compare qualitative and quantitative approaches to anonymization
  • Evaluate whether to build an in-house anonymization capability, buy software, or engage a vendor
  • Analyze methods for balancing patient privacy and data utility

Presenters

Niamh McGuinness, PhD, Senior Analyst, Clinical Trial Transparency, Privacy Analytics
Laura Dodd, Global Data Sharing Manager, PRA Health Sciences, Takeda
Jingyi Liu, Principal Research Scientist, Eli Lilly and Company

Presenter(s)

Niamh McGuinness is an expert in clinical trial document submissions and a senior member of the Privacy Analytics team, focused on Clinical Trial Transparency (CTT). She has personally managed many successful projects, with documents representing hundreds of clinical trials. Under her watch, no client has ever missed a regulatory submission deadline or a deadline to make their trial data publicly available.

Niamh’s team works with a variety of clinical trial sponsors that have turned to Privacy Analytics to meet the stringent requirements of regulations such as EMA Policy 0070, Health Canada Public Release of Clinical Information (PRCI), and EU CTR.

Niamh McGuinness, PhD

Associate Director, Safety, Regulatory & Transparency

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Archiving / Destroying

Are you unleashing the full value of data you retain?

Your Challenges

Do you need help...

  • Unleashing the full value of retained data so they can be put to work safely and responsibly in line with privacy regulation?
  • Defining and implementing effective archival and disposal processes for personal data?

OUR SOLUTION

Value Retention

  • Retain the business value in your data through anonymization.
  • Ensure compliance in your retention & destruction strategy.
  • Maximize the reuse of information by removing what is personal.

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.

Read the full story >

Evaluating

Are you achieving intended outcomes from data?

Your Challenge

Do you need help...

  • Optimizing the use / analysis of data to achieve your organization’s intended outcomes with measured success?

OUR SOLUTION

Unbiased Results

  • Reduce / minimize risk of bias in your data for machine learning.
  • Ensure transparency, fairness, and AI ethics / compliance.
  • Ensure data ethics & compliance in your data models and 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.

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Accessing

Do the right people have the right data?

Your Challenges

Do you need help...

  • Ensuring the right people have access to the right data at the right times?

OUR SOLUTION

Usable and Reusable Data

  • Enable internal access and / or external sharing of data for primary and / or secondary uses.
  • Ensure data are useful and reusable in alignment with FAIR principles.
  • Ensure data have the right level of privacy protection for the use.

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.

 

See the full story >

Maintaining

Are you empowering people to safely leverage trusted data?

Your Challenges

Do you need help...

  • Maintaining / processing data securely and efficiently to promote data enablement with auditable proof of compliance?

OUR SOLUTION

Security / compliance efficiency

  • Creating synthetic versions of data to be used for innovation / AIML.
  • Applying data minimization principles to reduce your risk exposure.
  • Transforming your data for secondary uses and access.

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.

 

Visit ASCOPubs.org to read the full story >

Acquiring / Collecting

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

Your Challenge

Do you need help...

  • Ensuring the acquisition / collection of the right data under the appropriate consent / contracts to maintain corporate integrity?

OUR SOLUTIONS

Consent / Contracting strategy

  • Developing a consent / contractual framework for collecting / sourcing.
  • Creating a comms strategy for internal / external stakeholders.
  • Ingesting and transforming data safely to earn trust.

Client Success

Client: IQVIA

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

 

Read the full story >

Planning

Are You Effectively Planning for Success?

Your Challenges

Do you need help...

  • Defining a clear data strategy to achieve desired outcomes?
  • Getting it right at the start to avoid costly privacy missteps later?

OUR SOLUTION

Build privacy in by design

  • Building privacy into your data plan / platform / pipeline upfront.
  • Establishing a trusted data strategy (ethics, compliance, privacy).

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.

 

Visit Nuance.com to read the full story >

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