Big data promises a health care remedy Posted on : Jan 29 - 2015

 The use of big data to rapidly analyze costs, understand public behaviors and anticipate security threats continues to attract the interest of government agencies that see the technology as a way to gain measurable insights into their most demanding problems.

Nowhere are researchers more active in exploring the uses of big data than in government health care organizations, where data scientists are working toward creating reliable tools for predicting a patient’s risk of disease or a virus’s path of infection.

To some extent health care programs are an obvious target for big data investment. Agencies already have large databases with years of information on diseases and patient health, and they have an urgent need to provide better and more productive information for researchers, doctors and nurses.

The Veterans Health Administration (VHA), for example, has created several big data analytics tools to help it improve health services to its 6.5 million primary care patients.

The VHA’s care assessments needs (CAN) score is a predictive analytic tool that indicates how a given veteran compares with other individuals in terms of likelihood of hospitalization or death. The scores are analyzed by VHA’s patient care assessment system (PCAS), which uses these scores and other data to help medical teams coordinate patient care.

The technology has changed the whole approach at the VHA from being purely reactive to one in which patients at the highest risk of being hospitalized can be identified in advance and provided services that can help keep them out of emergency rooms and other critical care facilities, according to Stephan Fihn, director of the VHA’s Office of Analytics and Business Intelligence.

While still considered fairly rudimentary tools, the CAN score and PCAS demonstrate that big data predictive analytics can work for large populations.

The agency now needs to “markedly ramp that effort up,” Fihn said, and to that end the VHA is working on dozens of predictive models that can be deployed over the next decade. The models  will show patients  that “this what we know about you, here’s what we think you need,” he said, and be able to do that in a rapid, medically relevant manner.

Big data, open data

Big data tools are also being rapidly developed by the Department of Health and Human Services, a sprawling, 90,000-person enterprise that that both creates and uses data for genomics research, disease surveillance and epidemiology studies.

“There are efforts across the department to try and leverage the data we have,” said Bryan Sivak, HHS’ chief technology officer.

“At the same time a lot of the datasets we maintain, collect, create or curate can be extended to external entities to help them understand aspects of the HHS ecosystem and try to improve on them, such as with CMS (Centers for Medicare and Medicaid Services) claims data,” he said.

One such effort is the OpenFDA project, which essentially took  three massive Food and Drug Administration datasets through an intensive cleaning process, Sivak said, and then added an application programming interface (API)  so people could access the data in machine-readable ways.

Open FDA was also linked to other data sources, so that users could access related information from the National Institutes of Health and the National Library of Medicine’s MedlinePlus .

The project, which launched as a beta program in June 2014, has already helped to create “a lot of different applications that have the potential to really help reshape that part of the (HHS) ecosystem,” Sivak said.

Also within HHS, the National Institutes of Health has committed to several big data programs, including its Big Data to Knowledge (BD2K) initiative. The program, begun in late 2013, is aimed at improving researchers’ use of biomedical data to predict who is at increased risk of conditions such as breast cancer and heart disease and to come up with better treatments. 

BD2K’s goal is to help develop a “vibrant biomedical data science ecosystem,” that will include standards for dataset description, tools and methods for finding, accessing and working with datasets stored in other locations and training biomedical scientists in big data techniques.

In October last year it announced grants of nearly $32 million for fiscal 2014 to create 11 centers of excellence for big data computing, a consortium to develop a data discovery index and measures to boost data science training and workforce development. NIH hopes to invest a total of $656 million in these projects through 2020.

While physical infrastructure for computational biomedical research has been growing for many years, the NIH said, as data gets bigger and more widely distributed, “an appropriate virtual infrastructure become vital.”  View more