wait times – The OM Blog by Heizer, Render, & Munson

OM in the News: The High Cost of Long ER Waits

Crowded emergency rooms have long been a problem in the U.S., writes The Wall Street Journal (June 9, 2020), In our discussions of queuing theory in Module D, we typically focus on the many attributes of the waiting line–length, time, cost–and on occasion we add the cost of adding multiple servers. However, a recent study by a S. Carolina prof shows that when a new ER opens, crowding at nearby facilities instantly falls an average of 10%. When comparing mortality rates at the older ERs before and after the change, the research found that a 10% drop in patient volume leads to a 24% reduction in mortality rates in the first 30 days and a 17% reduction over 6 months.

In ERs across the U.S., many patients wait for hours to be seen, and about one in 50 leaves before receiving treatment. ER patients awaiting admission to the hospital often have to wait in hallways on gurneys, while ambulances may be turned away from busy facilities. Researchers have long sought to quantify these costs of crowding.

The drop in mortality rates could be attributed to fewer people leaving against medical advice. Ten percent less patients in the ER reduced the number of patients walking out by about 51%. That is important because about 46% of people who leave the ER without being seen still need immediate medical attention. In fact, 11% are hospitalized in the next week. Since patients often come back for care soon after they leave, that could help explain why the drop in mortality rate was most significant in the first 30 days.

The study also examined whether the drop in patent volume affected “boarding”—that is, when patients wait on stretchers, sometimes for hours, before being admitted into the hospital. But patients from the ER tend to generate less profit and consequently often have to wait anyways for beds, so the study concluded that boarding is not impacted by ER crowds.

Classroom discussion questions:

Why is this study important?
What OM issues are faced on a daily basis in ERs?

OM in the News: Why Hospital ER Wait Times Are Often Wrong

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.

OM in the News: Can You Help TSA Shorten Security Queues?

In anticipation that more fliers will be eager to pay for expedited checkpoint screening, the Transportation Security Administration has promised to award $15,000 in cash prizes to whomever can design a faster waiting line system, reports Nextgov (July 18, 2014). The competition is on InnoCentive, a website for crowdsourcing solutions to problems, which enables individuals and teams to submit proposals. “There is a guaranteed award,” the contest overview states. “The total payout will be $15,000, with at least one award being no smaller than $5,000.”

The challenge aims to solve expected problems with TSA PreCheck, a program where passengers who undergo a background check and pay $85 get access to fast lanes that don’t require removing shoes, coats, liquids and laptops. “Current queue layouts at TSA Pre✓ airports will need to adapt to support the increasing population of TSA Pre✓ passengers,” the competition states. “TSA is looking for the Next Generation Checkpoint Queue Design Model to apply a scientific and simulation modeling approach to meet queue design and configuration needs of the dynamic security screening environment with TSA Pre✓.” TSA also is asking for approaches that would help speed standard, “free” waiting lines.

Competitors must supply a proposal that considers physical logistics, peak hours and staffing schedules, among other constraints. The “line” extends from the point where a passenger joins the end of the queue to the metal detector or body scan machine. Wait times cannot be more than 5 minutes for PreCheck and 10 minutes for standard lines. Also, the model should enable TSA to apply a “Computer Aided Design drawing to define the physical space available for queuing.”

Competitors are required to provide an animation of a computer screen that shows passengers flowing through lines. It must display real-time reporting during the animation, and allow a user to pause a simulation run when necessary for analysis or evaluation. What a great example of a complex, real-world queuing problem to ask your students to discuss!

Classroom discussion questions:

1. Why is TSA turning to crowdsourcing?

2. What ideas do you have for speeding the lines?

OM in the News: How Technology Helps Kroger Reduce Queues

QueVision monitors tell how many lanes need to be open now and in 30 min.

Supermarket giant Kroger, reports The Wall Street Journal (May 2, 2013), is winning the war against lengthy checkout lines with a powerful weapon: infrared cameras long used by the military and law-enforcement to track people. These cameras, which detect body heat, sit at the entrances and above cash registers at most of Kroger’s roughly 2,400 stores. Paired with in-house software that determines the number of lanes that need to be open, the technology has reduced the customer’s average wait time to 26 seconds. That compares with an average of four minutes before Kroger began installing the cameras in 2010.

Reducing wait times is becoming a top priority for retailers, from high-end department stores to hardware chains to fast-food outlets. Battling both online rivals that offer at-home convenience and intensifying competition among fellow brick-and-mortar outlets, many companies see enhancing the shopping experience as a way to build loyalty. Kroger’s system, dubbed QueVision, is now in about 95% of its stores. The system includes software developed by Kroger’s IT department that predicts for each store how long those customers spend shopping based on the day and time. The system determines the number of lanes that need to be open in 30-minute increments, and displays the information on monitors above the lanes so supervisors can deploy cashiers accordingly.

The company says surveys show customer perception of its checkout speed has improved markedly since 2010. “The bottom line is we want our checkout experience to be the best, and it’s our goal that our customers will enjoy the experience so much that they’ll want to return,” says Kroger’s senior VP.

Discussion questions:

1. What other new technologies are being used in service industries to speed up checkout lines (see the WSJ article and Chapter 7)?

2. How can QueVision help boost orders?