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Investing in Big Data-Driven
Risk-Based Monitoring
Benefits of Big Data include managing risk and improving operational efficiency
E
very digital process and social media exchange produces Big Data. It
is being generated around us all the
time, and we have seen the knowledge
derived from Big Data positively impact
almost every industry. Market leaders are
using Big Data to gain a better understanding of actionable customer insight, supply
chain optimization and how to improve
operational efficiencies. In the banking and
insurance industries, Big Data trends, risk
performance and measurement, and regulatory compliance are the driving market
forces behind investments in business analytics. Organizations are using advanced
statistical algorithms to understand risk
and manage it. So, in this era—where the
benefits of Big Data are being capitalized
on, where regulatory bodies pose increasingly complex questions, and where the
volume of data being collected is growing
exponentially—why are we not embracing
Big Data to manage risk and improve the
operational efficiency in every clinical trial?
It is widely known that clinical trial
sponsors still rely heavily on 100% source
data verification (SDV) and on-site monitoring. Today, however, as the cost of drug
development continues to rise, this intensive SDV method is losing its relevance.
In a recent study, Transcelerate Biopharma
used operational data available through
the Medidata Clinical Cloud to explore the
relative contribution of SDV—the process
Kyle Given
Principal, Strategic Consulting,
Medidata
As strategic consulting services
principal at Medidata, Kyle is responsible for the
risk-based monitoring consulting practice.
by which data within a case report form
(CRF or electronic CRF) is compared to the
original source of information—to overall
clinical data quality. Results revealed that
only 1.1% of total site-entered eCRF data
are corrected as a result of SDV, confirming
that SDV has a minimal impact on overall
data quality. This finding supports the conclusion that SDV should not be the primary
data quality control method used in trials.
Nonetheless, clinical teams continue to
depend on 100% SDV to review all data
points. This shows us that clinical teams
don’t know where to look, so they look
at everything. That’s why it’s important
to draw attention to another concept introduced in TransCelerate’s paper: source
data review (SDR), a process that checks
the quality of the source data, reviews
protocol compliance and ensures critical
processes and source documentation are
adequate. When SDR is targeted at specific areas of risk, it has a more important
impact on the quality of trial conduct by
resolving the primary source of the issue.
TransCelerate’s paper on risk-based
monitoring (RBM), published in November 2014, indicates that a shift away from
conventional on-site monitoring methods
is warranted. This shift is predicated on the
belief that centralized, dedicated resources
will be better able to identify emerging risks
through Big Data analytics. A centralized
process for risk detection is more effective
because resources look at real-time data
rather than on site traditional monitoring
activities. This function will be responsible
for identifying key areas of risk and working with field monitors to adjust activities
for SDV and SDR based on identified risks.
Through the use of Big Data, we can
now isolate the truly relevant data and not
only spot erroneous or poor quality data
documented in a trial, but also use predic-
Reprinted from the May 2015 Issue of Contract Pharma tive models to alert ourselves to the clinical trial sites or protocol design elements
that are most likely to generate poor data
in the future. Also, with predictive modeling, Big Data allows us to intervene in
certain cases before questionable data is
collected and while processes can still be
adjusted or reversed, thus improving the
overall quality of clinical trial outcomes.
Using robust statistical algorithms, we
can conduct a comprehensive scan of a clinical trial database for inconsistencies across
data domains, sites and patients. Automated
processes can be setup so that every clinical
trial can have a quality score calculated for
data and other important study parameters.
The quality score can be a measure of overall
data consistency, and be compared across
sponsors, studies, indications and disease
areas. Individual site scores can be used to
measure data quality within the site and
identify studies and sites at high risk for procedural problems and data errors.
While compliance, quality, visibility
and insight are key benefits, the monetary
return on such an investment cannot be
overlooked. Big Data-driven RBM holds
the promise of reducing the time it takes to
analyze large volumes of data—without reducing quality or accuracy—thereby distinguishing itself as the cost-effective solution.
So what are we waiting for? Why not
benefit from real-time, Big Data by routing
clinical trial information to a centralized,
cloud-based repository that can be used to
effectively conduct RBM? The advantages
are compelling—a reduction in staffing
and travel costs, the ability to assess and
prioritize risks for maximum safety and
data quality, and source data errors caught
early, before they negatively impact trials.
It’s time to embrace technology in pursuit of enabling safer, faster and more insightful and cost-effective clinical trials. CP
contractpharma.com