Pharmaceutical Outsourcing Q2 2024 - 13

CLINICAL TRIALS
SECTION TITLE
the industry to discuss and distinguish clinical development data and
these can vary between organizations. While we have yet to settle
on industry-wide definitions, it is imperative within a partnership to
ensure each data point and metric is clear and understandable to
all partners, both in application and how it relates to other metrics,
actions and the overall strategy.
Ensuring alignment in definitions of both data and metrics is part of
a wider consideration for improved data and metrics literacy. Data
literacy should be part of an ongoing development program within
and across teams to ensure the right data and information is used by
the right people at the right time to drive appropriate operational
actions and decisions. With the recent data processing innovations
within artificial intelligence (AI), we have invested in an enterprisewide
AI literacy program to ensure all our teams share a consistent
baseline to enable transformational implementation of these
innovations for sponsors.
When teams are aligned on definitions and prioritize transparency
and shared access to real-time data, they are better able to track
meaningful, actionable metrics. They can more easily drive change
and course-correct with collaborative problem-solving because they
are working from a shared perspective.
Driving Action with
Intelligent Governance
The trust built through transparency is essential to high-performing
partnerships. Backing this cultural alignment with formal governance
can ensure shared success and mitigate potential risks. Governance
that clearly defines roles, responsibilities and metrics can streamline
operations and refine control across organizations, which translates
into time and cost efficiencies.
Accurate data is a foundational building block of developing a trusted
partnership. By leveraging innovations in automation partners can
reduce data errors and increase data quality. For example, automation
can rapidly identify missing, incomplete or incongruous data and
immediately flag potential issues for corrective action. Clearly defined
roles, responsibilities and escalation pathways are crucial to ensure
rapid action in relation to both data quality and the metrics generated
from these data. Over time, we can extrapolate automated data
analysis to identify larger patterns within a CTMS or other systems,
leading to a higher-level analysis for quality control and action-driven
insights across systems and teams.
Accurately Measuring Success
Selecting the right metrics is key to translating data into actionable
insight. Dialing in to the best-fit metrics can become complicated
when so much data is available, which is why establishing a
foundation of shared definitions, expectations and responsibilities
is essential. This enhances cross-organizational understanding of the
metrics that truly drive change. All partners are able to understand
their role in relation to the metric and clearly see the impact of their
actions on a project, team and program level.
Measuring partnership success with meaningful metrics can
benchmark progress and offer more control. At ICON, we leverage
four foundational categories for meaningful metrics that produce
robust and actionable insights: finance, quality, operations and
relationship health. Individual metrics within these categories can flex
to match the inherent complexity as outsourcing models evolve and
as strategic partnerships deepen. Ideally, continuous direct access
to operational data would be available in multiple forms through
a single interface, allowing for views of unfiltered data or custom
analytics to support strategic decision-making and clearly illustrate
the correlation between strategy and operational achievements.
Expanding Innovation to
Leverage Potential
Digital innovations, including AI and Machine Learning (ML), are
creating new ways to ingest, manage and interpret data for faster
insights that inform the clinical development process. These tools
offer opportunities to not just measure success but drive it - enabling
efficiencies at various stages and across organizations.
AI is utilized to optimize our partnerships in multiple ways. For
example, ICON's One Search tool optimizes the site identification and
selection process. It improves timelines, quality and performance
across key metrics by identifying patterns across a wide network of
sites to prioritize the best-fit options for a certain study or program.
We can also generate realistic and reflexive targets for patient
enrollment with real-time data that allows for strategic adjustments
and resource reallocation as needed to mitigate potential risks to
target achievement or timelines.
AI innovation is also being used more predictively to identify clearer
long-term objectives and align organizations around them for
more efficient end-to-end. Cassandra is a ML technology trained
on FDA and EMA post-marketing requirements and commitments
databases to predict the probability that regulators will require
post-marketing studies for the therapy. This offers invaluable insight
into long-term strategy and facilitates a more robust mitigation
strategy early in the drug development cycle, defining objectives
and pathways to achievement.
Foundations for Future Success
Data is abundant and use-cases are expanding as pharma, CROs
and other stakeholders collaborate to optimize its potential. Shared
frameworks, transparency and common data literacy are effectively
addressing challenges as the industry incorporates and defines more
data and methodologies evolve to be increasingly cooperative and
integrative. Intelligent governance will continue to support accurate
measurement and actionable insight while serving as a strong
foundation to leverage novel innovations to improve study and
partnership success.
pharmoutsourcing.com | 13 | April/May/June 2024
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Pharmaceutical Outsourcing Q2 2024

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