Nov 08 2017
Data Analytics

Medical Research Gets a Boost from Higher Ed IT Transformation

University research represents a huge portion of medical breakthroughs, but storing, backing up and analyzing massive amounts of clinical research data can be a struggle for IT teams.

Tech innovation in the health field has to start somewhere, and that somewhere is often in a university lab. But as tech innovation in clinical research grows and Big Data and data analytics become evermore vital to cancer research, precision medicine initiatives, and much more, a university’s ability to enable these capabilities is becoming just as critical.

Research is changing around Big Data and analytics and, in some cases, machine learning and transforming the way that IT works and works with our researchers at our institutions,” said Steve Sears, director of cloud and virtualization services at Johns Hopkins Institutions, during a panel discussion at the Commvault GO 2017 conference at the National Harbor near Washington, D.C., on Nov. 7.

Research Data Presents Size Hurdles for University IT Teams

Moves toward IT consolidation at universities, while ultimately beneficial, can present roadblocks for research data, said Mark Penny, a systems specialist on the infrastructure team at the University of Leicester in England, speaking on the panel, entitled “How IT Transformation Makes Remarkable Research Possible.”

Penny spoke of how the University of Leicester recently embarked on a mission to consolidate all of its disparate department IT services under one roof. The consolidation helped to streamline IT efficiencies and cut costs for redundant services, but it also introduced challenges for many of the IT teams that had not previously worked with research data sets.

“Research data provides its own challenges because it can be very different from corporate data because of the types of research they are doing. We have data that are big, genetic sequence data sets,” said Penny, noting that many data sets are so large that a 12-hour backup is not uncommon. “The real challenge is, how do you back those up efficiently?

Much of the backup has to happen manually so it has become a major focus of the university.

“Something we are really keen on is that development continues to improve access to efficiently stand those file systems so that we can get our backup times down,” said Penny.

The Positive Impacts of IT Transformation on Clinical Research

While there’s still work to do in enabling the growing needs of data for medical research, an effective IT transformation can open up medical data for further use and launch ripples of positive consequences.

At Johns Hopkins, for example, Sears notes that the university set off on a mission to build a curated and prepopulated data repository around precision medicine that aims to not only aid clinical research, but also support developments of new types of clinical applications. The recently launched data repository is doing just that and encouraging immediate improvements in precision medicine initiatives as a whole.

“Immediately, the organization and especially the data scientists started turning to look at ‘Now, what can we do with this data?’ and working directly with that data repository,” said Sears. “Our interface guys, data scientists and database gurus have gotten together and are imagining a new architecture that combines our patient care records with longitudinal imaging data, physiological monitors, and all sorts of things, and they are exposing that in a way that is secure, safe and [Institutional Review Board] approved.”

Already, due to that collaboration and the emergence of the data repository, use cases are beginning to emerge. Sears pointed to a use case around prostate cancer in which a leading clinician is hoping to use the data to back away from surgeries when possible and recommend alternate treatment options more often.

“What he’s found is that through accessing large amounts of data and pulling these different studies together, they are starting to get these different results that show if we educate our patients about their continuum of care, maybe we can put surgeries off, maybe we can do less radical surgeries,” said Sears. “He is working with computer scientists and machine learning algorithms and putting in a lot of work to give patients access to this data.” The doctor wants to take the complex data and put it in a form patients can understand, Sears added.

While only a few of these use cases currently exist, Sears believes this is just a start to the types of data-backed clinical changes that effective research data management can enable.

“I think it’s a glimpse into the future of what we’re seeing around precision medicine. Now, this individual can make an informed decision with his physician based on medical science,” said Sears. “It’s an existential change effected directly by the research at Johns Hopkins.”

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