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Health IT Needs Moneyball Technique To Succeed

Big data in healthcare should copy the movie's analytics strategy, says eHealth Initiative Annual Conference keynote speaker.

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The healthcare industry needs to "professionalize" its analysis of big data; in other words, create a "moneyball technique," said Andrew Slavitt, group executive VP of Optum, at the eHealth Initiative Annual Conference in Orlando, Fla. As care shifts from traditional outlets to non-traditional settings, such as at-home services, the industry needs a better big data strategy to manage the change.

"…[T]he urgent needs of today are well beyond the four walls of the hospital," Slavitt said, but the industry continues to build IT for the old world of care. This new world, he said, or the "new care," involves a large portion of the population that he called "dual eligible," patients who are eligible for both Medicare and Medicaid. "The average dual eligible consumes $36,000 [of healthcare services] per year," he said. "That's $360 billion [nationwide]."

As health disparities between dual eligibles and the rest of the population grow, Slavitt noted the cost burden is shifting to those taking care of the dual eligibles. Driving down healthcare costs to address this trend is where better healthcare analytics and a moneyball strategy come in.

[ Not everyone is impressed with healthcare's latest trend. Read Big Data Analytics: Where's The ROI> ]

The Michael Lewis book Moneyball laid out how the Oakland Athletics used deep analytics to win two-thirds of its games in 2001. Baseball always had reams of data and statistics, but the A's figured out which data matters most.

"They eliminated noise and clutter from data," Slavitt said. "…They tested baseball in simple terms, and anything that didn't create an 'out' had value. … They built consistent winning teams by selecting players who excelled at the right stats."

Applying the same concept to healthcare, said Slavitt, means focusing on "how do we get value out of data?" Slavitt said a good role model is Tucson Medical Center in Arizona. The small, rural center built an accountable care organization and professionalized how it looked at data. After launching its ACO, the center selected groups of patients and used data analysis to determine where it could drive down costs.

Within a group of 230 patients, Slavitt said the organization invested in ways to create "unique and proactive ways to take care of them," such as increased home visits and primary care visits among other initiatives. As a result, the center saw a decrease of 5% in readmission rates and a 5% increase in market share due to primary care referrals.

"To get IT on top of your CEO's agenda, you need to speak two things: margin and market share," he said. "As soon as IT and data speak that, knowledge and movement is accelerated."

Slavitt closed his keynote by asking if big data is hype, fad or trend. Although most attendees responded with "fad," Slavitt insisted on "hype," and maintained there is potential for big analytics to have a lasting impression in healthcare.

Today, there's a key piece missing from the big data discussion, he said. "Our lack of interoperability and standards hurts patient care and drains usefulness out of data," he said. "We need to do this with a money ball view -- we need to link data. The verdict is big data is hype, until collaboration comes in."

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Jonathan Handler
Jonathan Handler,
User Rank: Apprentice
2/19/2013 | 6:05:37 PM
re: Health IT Needs Moneyball Technique To Succeed
I love the idea of using analytics to support decision-making like they did in Moneyball, and with true Big Data techniques we donG«÷t need to convert all our data into interoperable standards to make it G«£usable.G«• Google and Amazon did not succeed by forcing interoperability and data standards onto every web page or product description. Quite the opposite G«Ű they succeeded because they adopted a G«£take anythingG«• approach to data. They used Big Data approaches to find high-value associations and meaning, even in the G«£Wild WestG«• of free-text documents. In fact, the richness of the raw data was the fuel that powered their ability to handily beat their competitors (e.g. Yahoo) who mistakenly believed the key to success lay in careful data curation. The transformation of raw data into interoperable standards enables classic Little Data systems to use the data, but it also strips out critical information. Big Data is designed to address this problem as long as we actually use Big Data techniques. Applying Little Data techniques to a database table with lots of rows is not G«£Big Data.G«• The article notes that most Conference attendees labeled Big Data as a fad G«Ű and thatG«÷s really the closest analogy we have in healthcare to Moneyball, where Brad PittG«÷s characterG«÷s biggest challenge was not the success of his system but convincing his colleagues to use it. I went into these issues a bit more last month over at Wired:
User Rank: Apprentice
2/18/2013 | 6:19:33 PM
re: Health IT Needs Moneyball Technique To Succeed
I agree with Slavitt when he says "Our lack of interoperability and standards hurts patient care and drains usefulness out of data." We need provider systems to collaborate together in order to increase efficiency, quality of care, and to create meaning from the data collected. Once we are able to generate useful data, we can begin using healthcare analytics to drive down healthcare costs, which is one of the main goals of Health IT.

Jay Simmons
Information Week Contributor
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