5 basics of big data

At the recent HIMSS Virtual Conference and Expo, Chris Gough, solutions architect at Intel Healthcare Information Technology and Alan Stein, MD, vice president of healthcare technology Autonomy, an HP company, presented a webinar titled, "Big Data and Analytics in Healthcare."

Gough and Stein outlined five basics of big data. 

1.The main problem is the fragmentation of data. The separation of data among labs, hospital systems, and even clinical components, like financial IT and EHRs, serves as the main issue with leveraging the data, said Stein. "All of these are separate repositories for information," he said. "Their single use in nature is to provide clinical care or provide scheduling information or operational information, and this is a problem if we want systems to talk to each other." Sometimes, he added, an organization can also end up with redundant information due to a legacy system. "So we also have this normalization problem," he said. "And this is where we want to go: we want to improve quality of care and lower costs…we need a shift from best practices to a culture of best practices – if we have them available – but also best experiences and using data from various components of health IT to improve care and lower costs in a holistic way."

2.Big data is all about real or near-real time. Traditional analytics, said Gough, use ETL processes that upload data nightly or weekly to a data warehouse. Processing takes place in the warehouse, yet, the trend of big data is moving toward real or near-real time. "It's not waiting for batch processes but is driving value from data more immediately," he said. "In healthcare, it's clinical decision support, so at the point of care, being able to understand data to make a decision." With traditional analytics, Gough said, reporting focuses on the past, but with big data, "it's more predictive, and it looks forward to what may happen in the future."

[See also: Big data: opportunity and challenge.]

3.Processing is moving to the data. Another trend Gough pointed out is the processing coming to the data, instead of the other way around. "So traditionally, you move data out of a production database to a warehouse, and you pull from different repositories." At the rate data is increasing in healthcare though, he said, whether it's from medical imaging, EHRs, etc., moving this data around is becoming more of a challenge. "So the trend we're seeing is moving processing to the data," said Gough. "That's a large job, that's split up into a number of parts and split across a system. The infrastructure knows where the data resides, and processing happens as close to it as possible to improve performance."

4."Scale-up" is shifting to "scale-out." Typically, said Gough, the industry leans toward a "scale-up" mentality.  "So, [they say] 'Get me a bigger server, a more powerful server,' but instead, the trend is 'scale-out,'" he said. "So don't leave behind or get rid of older hardware nodes – just add them over time and improve performance and scalability of a system by adding nodes." The same notion is true from a storage point of view, he added. "So being able to much more easily scale with the architecture, where you can add another node to the solution and it adds to the system more memory." This makes systems more easy to manage, he said, and are," the kinds of solutions the industry is moving toward, instead of a 'rip and replace' mentality."

[See also: 6 keys to the future of analytics and big data in healthcare.]

5.For smaller organizations, it's all about software-as-a-service (SaaS.) Most of the trends Gough said he's seeing are for smaller hospitals that are leaning toward SaaS. "So an EHR vendor basically posting the solution on behalf of clients and customers, and something we're seeing is, many of those vendors are looking to add services alongside EHRs and other types of applications, more specifically, analytics," he said. A lot of those analytics solutions, he continued, focus on meaningful use and quality metrics. "I expect that trend to accelerate over time toward SaaS, especially for smaller organizations," he said. "It makes sense [for them] to look at hosted analytics solutions and hosted services."