Saama Unveils New Capabilities for Award-Winning Life Science Analytics Cloud at SCOPE

Technologies, Inc.
, a leading data analytics company, today unveiled
three new, machine learning-based capabilities that extend the existing
functionality of its award-winning Life
Science Analytics Cloud (LSAC)
. Saama announced the addition of
these state-of-the-art programs infused with artificial intelligence
(AI) — Virtual Assistant/AI, Operational and Financial Risk Mitigation,
and Drug Efficacy and Patient Safety Analytics — during the 10th
Annual Summit for Clinical Ops Executives (SCOPE) in Orlando.

“This trio of new LSAC proficiencies demonstrates Saama’s commitment to
strategic, targeted and pragmatic deployment of AI to continually
advance clinical operations,” said Malaikannan Sankarasubbu, Vice
President of AI Research at Saama. “These exciting new features are the
first in a series that will be launched throughout 2019 to exponentially
enhance the value of our LSAC platform for Saama’s life science
partners. These new features translate into clinical trial time and cost
savings and, ultimately, safer and more effective drugs.”

Virtual Assistant/AI

Saama’s 2018 introduction of its Deep Learning Intelligent Assistant
(DaLIA), a context and domain-aware conversational user interface for
LSAC, shifted the human-computer interaction paradigm. Saama has
expanded DaLIA’s capabilities even further, broadening its capacity for
identifying the intent (what you would like to do or know) of the query
and catapulting the Virtual Assistant to an enhanced level of
conversational user engagement. When DaLIA replies to a researcher’s
question about study conduct, whether from an operational or clinical
perspective, it is now enabled to factor in key parameters, such as the
names of persons, organizations, and locations, as well as expressions
of times, quantities, monetary values and percentages. DaLIA remembers
the context of previous inquiries and can seamlessly enfold new entities
into the discussion to provide rapid clinical operations insights.
Queries about various aspects of clinical development, including
start-up, enrollment, data quality and financial risk, result in
responses that factor in the intent and specificity of the questions.
This allows DaLIA to mine the data resources from an enterprise’s LSAC
deployment and provide answers.

Operational and Financial Risk Mitigation

Saama’s new Operational and Financial Risk Mitigation significantly
advances the ability to track clinical trial key performance indicators
(KPIs), managing and mitigating operational and financial risks. Saama
is implementing an auto-ML model into LSAC that provides the ability to
go beyond the current industry standard of tracking only planned and
actual KPIs. This novel feature predicts when critical KPIs, such as
first site activated, first and last patient enrolled, etc., will be
achieved, empowering researchers to make subsequent, in-flight decisions
about and modifications to a clinical trial. Saama’s Operational and
Financial Risk Mitigation eliminates the need for clinical teams to run
their own labor-intensive analyses to approximate these important
milestones. LSAC will use historical data from various trial sites and
automatically apply the appropriate machine learning algorithm so
customers seamlessly see site-related predictions. Operational and
Financial Risk Mitigation provides researchers with the power of
information, enabling them to make decisions and course corrections
before obstacles delay a trial.

Drug Efficacy and Patient Safety Analytics

Saama’s LSAC is now also informed by an ML-based Drug Efficacy and
Patient Safety Analytics feature that significantly streamlines the time
and effort traditionally required to correlate patient profiles with
data variables. With effective clinical data management and
standardization, upwards of 50 variables can now be analyzed
simultaneously by LSAC for immediate identification of patient outliers,
versus the current, time-intensive process of trial staff manually
examining only a few variables at a time. The new feature fundamentally
changes clinical trial medical monitoring, enabling researchers to
identify previously undetectable patient deviations, as well as
potential corresponding safety and efficacy issues, sooner than ever
before. Saama estimates that the new Drug Efficacy and Patient Safety
Analytics capability will result in an approximately 30 percent savings
in clinical trial staff time and effort by rendering the need to rely on
manual data analysis obsolete.

Saama experts are available to discuss these new LSAC capabilities
in-person at booth #314 during the February 18-21 SCOPE meeting in
Orlando. Additionally, Amit Gulwadi, Senior Vice President of Clinical
Innovations at Saama, will talk about Looking at your Data with a New
Lens on Wednesday, February 20 at 12:30pm. Malaikannan Sankarasubbu,
Vice President of AI Research at Saama, will also discuss Practical
Applications of Natural Language Processing on Thursday, February 21 at

About Saama

Saama Technologies is the advanced clinical data and analytics company,
unleashing wisdom from data to deliver better business outcomes for the
life sciences industry. Saama’s unified, AI-driven clinical data
analytics platform seamlessly integrates, curates, and animates
unlimited sources of structured, unstructured, and real-world data to
deliver actionable insights across all therapeutic areas. The
award-winning platform gives unprecedented real-time visibility into
clinical data, enabling sponsors to file New Drug Applications (NDAs)
more efficiently to bring drugs to market faster and at lower costs. For
more information, visit


Crystal Black
Saama Technologies

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