Alex Porter—Worldwide GreenLake GTM & Sales, Healthcare and Life Sciences, HPE
Brad Genereaux—Global Lead, Healthcare Alliances, NVIDIA
Travis Richardson—Chief Strategist, Flywheel
This is a new age in healthcare and life sciences (HLS), where explosive data growth and distributed operations are transforming the quality of patient care. Data is now at the heart of everything. Researchers are putting biomedical data to work, using information from a variety of sources like diagnostic imaging, surgical procedures, clinical trials, and monitoring equipment to create a more complete picture of human health.
Biomedical data is a key resource for HLS initiatives, which enables researchers to visualize and therefore better understand the human body, disease, the role of genetics, and the impact of drugs and therapies. However, many organizations struggle to capture and share insights from all of the available data. Up to 80% of their time can be spent cleaning and prepping data repositories, and properly securing these data sets is a major challenge. Such roadblocks can prolong clinical study timelines, limit collaboration between experts, and lead to poor outcomes.
Developing a comprehensive data management strategy is critical to uncover timely insights and build a secure, connected environment to conduct biomedical research. The right solution should make it easy to capture, access, and operationalize complex biomedical data (including medical imaging studies) from any location, using a combination of data management tools and artificial intelligence (AI) capabilities to speed up analysis workloads and deliver life-saving results.
Accelerating clinical insights, everywhere
Hewlett Packard Enterprise understands the importance of real-time data access. Legacy infrastructure, IT complexity, and security threats all become barriers to healthcare innovation. That’s why we help organizations go hybrid with an agile, scalable cloud experience on-premises to accelerate scientific discovery.
HPE, in collaboration with NVIDIA, offers industry-leading technologies that deliver high performance, faster intelligence, and consumption flexibility. Now, we are excited to announce a new offering in collaboration with Flywheel that will expand the possibilities of biomedical research.
Together, we have developed the Biomedical Research Data Platform for AI at Scale that extends real-time clinical insights from edge to cloud. The solution is built on cloud-based research data management capabilities from Flywheel to deliver a platform that is centralized and secure, deployed on HPE systems powered by the latest NVIDIA GPUs and NVIDIA AI solutions.
Flywheel is redefining how biomedical data is managed using purpose-built software to orchestrate and scale computation, analysis, and workflows to utilize highly complex biomedical datasets including medical imaging files.
The platform features extensive HLS tools, software, and cloud services to enable agile and connected work environments—all the tools needed to empower groundbreaking research:
- Streamlining capture for a variety of data types
- Curating cohorts for search and reuse
- Automating data pre-processing workflows and analytical pipelines
- Enabling collaboration in compliance with healthcare privacy standards and data protection regulations
HLS organizations can deploy their solution as a pay-per-use model through HPE GreenLake. HPE GreenLake cloud services provide flexibility to scale up resources and capacity to handle larger biomedical workloads or scale down to reduce overhead expenses.
Enabling real-time insights and decisions
Image-based methods are essential to the work of today’s researchers and clinicians. The Biomedical Research Data Platform for AI at Scale includes MLOps solutions and domain-optimized frameworks to help them gain more value from thousands of information-rich images.
NVIDIA AI Enterprise is an end-to-end, secure, cloud-native suite of AI software, enabling organizations to solve new challenges while increasing operational efficiency. It accelerates the data science pipeline and streamlines the development and deployment of predictive AI models to automate essential processes and gain rapid insights from data. The possibilities are endless with an extensive library of full-stack software including AI workflows, frameworks, pretrained models, and infrastructure optimization.
MONAI, a medical AI framework included in NVIDIA AI Enterprise, leverages best practices of AI development for researchers using medical imaging, making the technology simple to deploy and manage. Standardized and user friendly, MONAI can be used by researchers to quickly begin training and adapting AI models, then finalize deployments in a single location or across multiple locations.
Expanding the possibilities of biomedical research
HPE is working with NVIDIA and Flywheel to deliver cloud experiences that simplify and optimize HLS operations. Whatever your requirements, the Biomedical Research Data Platform for AI at Scale provides an automated and flexible solution with all the tools to run your applications.
To learn more about what HPE, NVIDIA, and Flywheel are doing to supercharge data science in healthcare and life sciences, check out our at-a-glance paper.
To talk with our experts please click here.
Alex Porter is the Worldwide Sales strategy lead for HPE GreenLake cloud services, focused on the Healthcare and Life Sciences solutions. She is responsible for the HLS Go to Market strategy as well as assisting customers in their digital transformation journey leveraging HPE’s as-a-service portfolio.
Brad Genereaux is Global Lead, Healthcare Alliances at NVIDIA, where he is responsible for developer relations with a focus in medical imaging to accelerate artificial intelligence and deep learning, visualization, virtualization, and analytics solutions. Twitter—@IntegratorBrad LinkedIn—https://www.linkedin.com/in/integratorbrad/
Travis Richardson is Chief Strategist of Flywheel. He brings over 25 years of executive leadership experience in product development, strategy, and innovation at both large and small companies.
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