UCLA Health chooses Microsoft Azure cloud to accelerate medical research

UCLA Health chooses Microsoft Azure cloud to accelerate medical research
Cloud will enable the organisation to more quickly interpret insights and enhance research collaboration

Elly Yates-Roberts |

US-based academic medical centre UCLA Health has chosen the Microsoft Azure cloud to help it accelerate medical research. The organisation will use the cloud platform to more quickly interpret insights and enhance research collaboration. 

Azure will enable UCLA Health to synthesise huge amounts of clinical and research data to ultimately improve patient care. The platform’s artificial intelligence (AI) and machine learning tools can recognise patterns and react to them without human instruction. 

“Our data capabilities with Microsoft Azure will bring more medical discoveries and effective therapies to patients faster,” said Michael Pfeffer, assistant vice chancellor and chief information officer at UCLA Health. “The integration of information from structured data, like lab results and medication information, with unstructured data, like documentation, genomics and medical images, creates an incredibly powerful big-data learning platform for discovery.”

As well as speeding up and improving overall healthcare, UCLA Health’s move to the cloud computing also aims to use individual’s data to create a more tailor-made treatment. 

“We are committed to creating better patient outcomes by providing UCLA Health with Microsoft Azure cloud and AI solutions to improve treatments and lives,” said Peter Lee, corporate vice president of Microsoft Healthcare. “By connecting health data and systems in the cloud in an interoperable way, we’re excited we can help advance health care data for more efficient and personalized care.”

Microsoft Azure will protect the medical centre’s sensitive data, ensuring compliance with the Health Insurance Portability and Accountability Act. Using the platform will also enable UCLA Health to use predictive analytics and analyse historical data to model future health risks and aid in disease prevention. 

“Analysing large data sets to make scientific discoveries is a race against time,” said Mohammed Mahbouba, chief data officer at UCLA Health. “Using machine learning to analyse a combination of clinical and genomics data can provide critical insights but doing so with a traditional computing infrastructure can require significant processing time. Azure enables us to quickly deploy and scale high-performance computing environments that can reduce the required processing time — sometimes from months to days — to make discoveries.”

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