After data is ingested from the devices and wearables
used in the clinical trial, AWS Lambda is used to store
the raw data copy on Amazon Simple Storage Service
(Amazon S3), where it can be used for historical
analysis and pattern prediction. Using Amazon S3
life cycle policies, customers can periodically move their
data to Amazon S3 Glacier, to further optimize their
storage costs.
Amazon S3 offers a highly available and durable,
infinitely scalable data storage infrastructure;
simplifying most data processing, backup, and
replication tasks. Customers can also choose to
encrypt their data at rest and in motion using various
encryption options available on Amazon S3.
After collecting and storing a raw copy of the data,
Amazon S3 is configured to publish events to AWS
Lambda and invoke a Lambda function by passing
the event data as a parameter. The Lambda function
is used to extract key information, like adverse event
notifications, medication adherence, treatment schedule
management, and more from the incoming data.
Lambda is used to process this information and store
it in Amazon DynamoDB, along with encryption at
rest, which powers a clinical trial status dashboard. This
dashboard alerts clinical trial coordinators in real time so
that appropriate interventions can take place.
For historical analysis and pattern prediction, the staged
data (stored in Amazon S3), is processed in batches. Using
AWS Batch, an easy and efficient batch computing service,
current and historical data is mined to derive actionable
insights, which is stored on Amazon S3. From there,
data is loaded into Amazon Redshift, a cost-effective,
petabyte-scale data warehouse offering. Customers may
also leverage Amazon Redshift Spectrum to extend data
warehousing out to exabytes without loading any data
to Amazon Redshift, as detailed in this blog post. This
allows trial coordinators to get an all encompassing picture
of the clinical trial, enabling them to react and respond
faster. Optionally, this data can also be fed to an Artificial
Intelligence/Machine Learning component to further
automate the analytics, helping to optimize costs and
improve the quality of clinical trial management.
Once the data is processed and ready to be consumed,
customers can leverage a host of Business Intelligence
(BI) tools like Amazon QuickSight, a cloud-native
business intelligence service from AWS that offers
Amazon Redshift connectivity. Amazon QuickSight
is serverless and can be rolled out to your audience
in hours. Customers can also use a host of third party
reporting tools, such as TIBCO Spotfire Analytics,
Tableau Server, Qlik Sense Enterprise, and others,
which can use a Java Database Connectivity (JDBC) or
Open Database Connectivity (ODBC) connection with
Amazon Redshift. The real-time data processing (step
3) combined with historical-view batch processing (step
4), empowers Contract Research Organizations (CROs),
study managers, trial coordinators, and other entities
involved in the clinical trial journey to make effective and
informed decisions at a speed and frequency which was
previously unavailable.
Using Amazon Simple Notification Service (Amazon
SNS), real-time feedback based on incoming data
and telemetry, along with notifications from study
managers/coordinators, is sent to patients via text
messages, mobile push notifications, and/or emails.
Amazon SNS provides fully-managed pub/sub
messaging for microservices, distributed systems,
and serverless applications; and is designed for high-
throughput, push-based, many-to-many messaging.
These alerts and notifications can be based on current
STORE DATA
DATA PROCESSING – FAST LANE
DATA PROCESSING – BATCH
VISUALIZE AND ACT ON DATA
For more information on Pharma and Biotech or other ways AWS can help your organization visit us at: https://aws.amazon.com/health/biotech-pharma/
© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
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