Driving down the highway, you’ve undoubtedly seen a new kind of digital sign advertising local hospitals. “Current wait 5 minutes,” they say, with the wait time updating in real time to reflect the current conditions in the ER. It’s an effective form of advertising, and it gives consumers a sense of transparency about making the choice to go to the ER. Yet if you head to that nearest ER, don’t be surprised if you end up waiting longer than the sign says. “The truth behind these numbers is that they’re often wrong,” according to Insights by Stanford Business (Aug., 2017). Looking at the ERs of 4 LA hospitals and testing the effectiveness of the method for estimating wait times, the study by Stanford U. professors found the method extremely unreliable in all cases–off by as much as 1.5 hours. Drawing on queuing theory, a new model, Q-Lasso, was able to cut the margin of error by as much as 33%.
The trouble with most wait time estimates is that the models these systems use are often oversimplified compared to the complicated reality on the ground. One of the most common ways of arriving at a wait time estimate is to simply give a rolling average of the time it took for the last few patients to be seen. This works well if every patient is the same, they arrive at a steady rate, and all of their ailments take the same amount of time to diagnose and remedy. But that’s rarely the case in the real world.
So the researchers came up with a large number of potential factors to look at. Q-Lasso would then select the best of them from the data. For example, it was initially assumed that the number of nurses working would be an important criterion for assessing wait time. But the data showed this was mostly irrelevant. Q-Lasso could provide wrong times, but the model tended to overestimate wait times, rather than underestimate them, making the experience more acceptable.
Classroom discussion questions:
- Why are advertised wait times often wrong?
- Describe the Q-Lasso model.