When NorthShore University Health System did an analysis of needed staffing levels, they found, to their surprise, that Tuesdays were as busy as Mondays, said Dr. Daniel Chertok, senior data scientist for the Chicago-area health system.
They decided to build a predictive model that predicts staffing needs four hours in advance so they can call nurses in to work before they find themselves understaffed.
“Not all nurses are happy about on-demand scheduling,” Chertok said.
According to Chertok, the example is just one reason why utilizing predictive analytics in healthcare providers can be a smart business investment. The data also uncovered a correlation between patient lengths of stay and staffing levels.
“If you don’t ramp up soon enough, the length of stay increases,” he told the crowd at the Predictive Analytics forum in Boston on Tuesday.
The hospital system also has made graphs of the median patient waiting time versus turnaround time, a dashboard now used by 95 percent of physicians, he said.
Drawbacks to using such models are that the data must be clean and valid for up to six weeks, he said. Inertia also plays a role in changing how things are done.
By: Susan Morse, Associate Editor, www.healthcarefinancenews.com
Originally published at www.healthcarefinancenews.com
