Companies looking to make good on the promise of machine learning for data analysis are turning to a somewhat unlikely old friend. Business intelligence systems, largely the domain for analyzing past performance, are being retrofitted with artificial intelligence to bring predictive features to their reporting capabilities.
The Symphony Post Acute Network is one such organization. The health care company, which has 5,000 beds in 28 health care facilities in Illinois, Indiana and Wisconsin, wanted to use artificial intelligence and machine learning to improve care for up to 80,000 patients a year recovering from procedures like knee surgery, or receiving dialysis treatment. For example, buried deep in a patient’s medical core could be an indication that a patient is particularly at risk for a dangerous fall and therefore requires extra precautions.
Finding these indicators, which could be individual data points or subtle patterns of data, is a perfect use case for machine learning. But building the models isn’t a simple job.
“I got bombarded with questions about predictions,” says Nathan Patrick Taylor, director of data science and analytics at Symphony. “Even if I spend every waking moment building machine learning models, there’s no way I can do all that.”