Using Data Analytics for Better Financial Outcomes
"We think of the staff as the supply and the number of ER visits as the demand. The better we can match the supply to the demand, the better for everybody. We certainly don't want to be understaffed and not serve our patients the best we can, but we don't want to be overstaffed either. We started by asking the question, 'Is the number and pattern of emergency room visits predictable?' "
With the help of an analytics tool, he looked at four years' worth of data and identified patterns in emergency department usage. "Barring any unforeseen event that would overload the ER, it is predictable by month, by week, by day of the week," he says.
Waller says HMA invested in staff scheduling tools that "allow us to better match supply with demand" based on the available data.
HMA's goal is for a patient in its emergency room to be seen by a medical provider within 29 minutes of arrival, and Waller says there has been significant achievement of that goal where analytics have been implemented.
"We've been able to reach our goal because of our ability to predict demand. We've ended up with happier nurses and happier patients. This has real financial implications because patients will continue to use our hospitals, and we've lowered our labor costs," he says.
Overall, HMA has reduced nurse staff hours in its emergency departments by 3% to 8%, depending on the shift and where analytics are in use; although Waller notes in some cases that staffing has increased during certain times to better reflect patient demand.
"It's been an excellent, excellent investment. Moving the needle a little has resulted in big savings," he says.
This article appears in the July/August issue of HealthLeaders magazine.
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