UCLA Health deploys Microsoft Azure to accelerate medical research, precision medicine at the point of care

entrance of UCLA Medical Center
UCLA Health opened its Institute for Precision Health in 2017 and is looking to accelerate that work by deploying cloud computing services along with artificial intelligence and machine learning tools. (Mark Citret)

UCLA Health is collaborating with technology giant Microsoft to deploy its cloud computing services to more rapidly gain insights from clinical and medical research data with the aim of delivering more personalized healthcare to patients.

The University of California, Los Angeles health system, along with the David Geffen School of Medicine, is adopting Microsoft Azure as a standard platform to more rapidly analyze its big data through the use of artificial intelligence and machine learning tools and then act on the insights gleaned to accelerate medical research and improve patient care. The vision is to bring big data insights from the "bench to the bedside," Mohammed Mahbouba, M.D., chief data officer for UCLA Health Sciences, told FierceHealthcare.

"What this platform enables us to do is bridge that gap from the research all the way to patient care," he said.

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Analyzing large data sets to make scientific discoveries is a race against time, Mahbouba said. “Using machine learning to analyze 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," he said.

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Michael Pfeffer, M.D., assistant vice chancellor and chief information officer for UCLA Health Sciences, said the organization's data capabilities with the cloud computing services will bring more medical discoveries and effective therapies to patients faster.

"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," he said.

In 2017, UCLA Health and the David Geffen School of Medicine launched the UCLA Institute for Precision Health to bring together faculty across multiple disciplines to make large-scale genetic and genomic research actionable for patient care. The David Geffen School of Medicine also partnered with the UCLA Samueli School of Engineering to establish a department of computational medicine to use data sciences for new approaches to analyzing health data.

UCLA Health’s move to cloud computing is intended to advance the health system’s delivery of precision health, according to executives. "Precision medicine is the interaction of big data and machine learning, and applying machine learning techniques to big data is the challenge," Mahbouba said. "Historically we have mostly focused on structured data and now increasingly with precision medicine we have to analyze a more broad perspective of the patient and that could include clinical data, structured and unstructured, genomics data, images, and other data and then apply analytics techniques and tailor interventions to patients at the point of care."

UCLA scientists will use the cloud computing tools to more efficiently analyze a variety of data sources. The AI embedded in the tools enables speedy processing of data, and the resulting information can then be used by physicians and researchers. Machine learning enables the software to recognize and act on important data patterns without the need for human instruction, according to the health system.

A few years ago, health IT leaders were hesitant to move to the cloud, Mahbouba said. "There was a psychological barrier to cloud adoption, what I call the 'hugging factor,' people are more comfortable with something they can see, such as having the data center in the basement."

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There has been significant progress with privacy and security, with most major cloud services platforms now compliant with Health Insurance Portability and Accountability Act requirements, he said. The Azure platform will protect and secure sensitive data, and patient data in UCLA Health’s platform will not be shared with Microsoft as part of this agreement, according to health system leaders.

“Another advantage of cloud computing is the way it enables UCLA researchers to more efficiently and securely work with their peers,” Paul Boutros, Ph.D., director of cancer data science at the UCLA Jonsson Comprehensive Cancer Center, said in a statement.

“Cloud computing will allow researchers from different fields and institutions to collaborate, joining data sets and software from different formats that could not previously be integrated in a simple way,” Boutros said. “We’re bringing together new communities of experts—including computer scientists, engineers, material scientists, and others—to solve the biggest health care questions. This platform allows us to provide our research collaborators with secure access to important data in one place, without moving sensitive, private health information.”

The platform’s capabilities will also enable UCLA Health to use predictive analytics, or the analysis of historical data to model and assess what might happen in the future, to aid in disease prevention.

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