Tips for reading Big Data results correctly

Tips for reading Big Data results correctly

Tips for reading Big Data results correctly

MIT healthcare economist Joseph Doyle spends his time measuring the returns on healthcare spending and outcomes with the goals of identifying value and waste in the $3 trillion U.S. healthcare system along with helping to create a higher-quality, more cost-effective approach to healthcare. And data drives Doyle’s empirical, evidence-based approach for answers.

One area Doyle has been studying is whether hospitals that spend more money on healthcare achieve greater outcomes. The answer is not a simple one to uncover, said Doyle, Erwin H. Schell professor of management and professor of applied economics at the MIT Sloan School of Management.

“There have been a lot of studies that look at the correlation between spending and outcomes, and the literature says hospitals that spend more do not have better outcomes – but I am discovering different things,” Doyle said. “This is because of a concern with data usage. There is a lot of interest in Big Data methods that get you robust correlations. But I argue in my work that these correlations can be misleading. In this case, where studies show that higher spending hospitals do not achieve greater outcomes, you cannot interpret these results as money wasted.”

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In his research, which digs deeper into Big Data, Doyle has found that higher spending hospitals do indeed achieve greater health outcomes. Doyle cautions healthcare executives and caregivers using Big Data methods simply to find correlations not to “over-interpret” these results.

“One example is the relationship between spending on newborns and health outcomes,” he explained.


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