Samsung Medical Center Uses AWS to Develop Global Medical Research Platform
South Korean medical facility and research center is using AWS to accelerate medical research and personalize patient care worldwide
Today, Amazon Web Services (AWS), an Amazon.com company, announced that South Korea’s Samsung Medical Center (SMC) is building a medical research platform on AWS to accelerate research at healthcare institutions worldwide and help hospitals better cope with new infectious diseases like COVID-19. SMC is one of the nation’s leading healthcare institutions comprising the Samsung Seoul Hospital, Kangbook Samsung Hospital, Samsung Changwon Hospital, and the Samsung Life Sciences Research Center. Using the breadth and depth of AWS’s portfolio of cloud services—including machine learning (ML), compute, networking and content delivery, security, identity, and compliance services—SMC is enabling pharmaceutical companies and hospitals to collaboratively share and analyze medical data, optimize clinical decision making by ensuring healthcare providers have the right information at the right time, and predict disease outcomes to improve the health of patients worldwide while providing personalized care.
SMC set out to build a secure, scalable platform on which medical practitioners and researchers from pharmaceutical companies globally could share treatment findings and strategies, analyze patient data, and interpret treatment outcomes to advance medical science. Using Amazon SageMaker, a fully managed ML service to build, train and deploy machine learning models quickly, SMC will train machine learning models to help researchers better understand vast quantities of medical disease research data from various research projects and identify new treatment options. For example, SMC is developing an AI-powered solution that detects pressure ulcers in bedridden patients through scanned images, allowing clinicians to scan areas where pressure ulcers are likely to form on patients who are unable to reposition themselves, classify risk levels of pressure ulcers through ML technology, and take preventive measures. Amazon Virtual Private Cloud (VPC), a service that enables users to launch AWS resources in a secure, logically isolated virtual network that they define, helps SMC assure complete data protection and network security when researchers work with confidential patient data, enabling the institution to completely protect research data. Using AWS Identity and Access Management (IAM), a service that helps users securely control access to AWS resources, SMC simplifies complex processes, including user identification, authentication, and behavior analysis, and maintains consistent security practices with identity management and authentication tools like multi-factor authentication to provide highly privileged users the right level of access to tools and information in the research environment.
“Building a medical research platform on AWS will help researchers and clinicians accelerate medical research to save lives,” said Pung-Ryeol Lee, Head of Office of Data Innovation at SMC. “AWS’s unmatched suite of cloud services enables our teams to seamlessly and securely work with other domestic and overseas healthcare institutions on Samsung Medical Center’s platform to fuel the global understanding of diseases and improve patient outcomes worldwide.”
“Leading healthcare institutions worldwide are benefitting from the agility and scalability of AWS to further improve the quality of patient care using AWS’s unmatched capabilities in data and analytics at scale,” said Jeongwon Yoon, Country Manager, Korea, Worldwide Public Sector, AWS. “AWS has the broadest and deepest set of analytics and ML capabilities on a network architected to protect patient information and identity. Together, these advanced capabilities enable healthcare organizations to increase their pace of innovation and securely personalize the healthcare journey for patients by finding better treatments faster. We are delighted to also help Samsung Medical Center advance research using ML.”