Life Sciences

AWS Clinical Trials Architecture

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Improving clinical trials using AWS Services and clinical trials can account for up to two-thirds of the total research & development costs. The traditional clinical trial process that was designed for mass-marketed blockbuster drugs no longer meets the emergent needs of life science companies. These companies have shifted to more targeted therapeutics and precision medicine, which are focused on smaller, geographically distributed patient segments. The 'Clinical Trials 2.0' program at AWS is geared towards facilitating wider adoption of cloud-native services to assist with data ingestion from disparate sources, provide cost-optimized and reliable storage, and enable analytics. The program also provides the granular access control, end-to-end security, global scalability, and utilization of artificial intelligence and machine learning (AI/ML) needed to advance clinical trials more efficiently. In 2018, the Clinical Trials Transformation Initiative (CTTI) provided recommendations regarding leveraging mobile technologies for capturing holistic, high quality, attributable patient data and submission to the U.S. Food and Drug Administration (FDA). Using mobile technologies can help increase trial participation and reduce the length and cost of clinical trials. Pioneering companies are turning to the Amazon Web Services (AWS) Cloud to modernize their clinical trial process by utilizing analytics and artificial intelligence to optimize studies, securely collaborate and share data, and incorporate mobile technologies. AWS offers HIPAA-eligible capabilities that can help life science organizations accelerate trial timelines, optimize processes, and reduce overall trial costs by: 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. $2 billion The current cost to bring a new drug to market is estimated to exceed Predicting lack of adherence Detecting adverse events Facilitating global data management Utilizing remote or in-patient site monitoring Finding and recruiting patients using smart analytics

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