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Big data: how predictive analytics is taking over the public sector

13/06/2014 by


Few workplaces might seem more unpredictable in their workload than the emergency department of a major hospital. But even the random circumstances that send patients to the ER start to yield patterns when subjected to new methods of data analysis.

For Dr James Lind, director of access and patient flow at Gold Coast Health, clever analysis of admission records by the CSIRO means he can now predict the Gold Coast hospital’s emergency admissions on any day of the year with an accuracy of up to 93%.

That means the hospital can schedule elective procedures with greater confidence, with less chance that elective beds will need to be cleared to make room for emergencies. That means happier patients, and happier staff.

“There is a cost saving in response to not having reactive staffing patterns and having to put people on overtime shifts,” Dr Lind says. “People like working in a system that is proactive rather than reactive. When we are expecting a patient load everyone knows what their jobs, and you are more efficient with your time.”

The Patient Admission and Prediction Tool (PAPT) developed by the CSIRO is just one example of how public sector organisations are using new techniques in data analytics (also known as big data) to find useful patterns in the vast volumes of data they collect.

Current initiatives include the Australian Taxation Office trawling through records to find evidence of the use of tax havens, and data-matching to identify small online retailers that are not meeting their compliance obligations.

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