By Michael J. Armstrong, Kenneth J. Klassen, and Reena Yoogalingam
Patients often wait weeks or months for medical appointments. The Fraser Institute recently reported that Canadians typically wait 10 weeks to see specialists; long wait times are one reason that Canada ranks behind other developed countries in health-care quality.
When they finally arrive at physicians’ offices, patients often face further waits. They may spend substantial periods in the waiting room, despite having specific appointment times. This in-office waiting occurs for many reasons. Perhaps those patients arrived early. Perhaps earlier patients put the physicians behind schedule, or urgent calls interrupted them.
But sometimes clinics purposely schedule appointments before physicians expect to be ready. They do this to ensure physicians don’t run out of work. So your appointment time might be the moment your physician really expects to start seeing you. Or it might merely be when they want you to begin standing by in the waiting room.
Appointment schedules aim to maximize physician productivity in order to avoid wasting valuable health care resources. But they should also try to minimize patients’ in-office waiting.
Real and Simulated Waiting
Researchers at Brock University aim to improve appointment scheduling by better balancing these competing goals under various conditions. One study examined the impact of emergencies that interrupt physicians. Another involved patients arriving earlier or later than planned.
Each study began by recording patient arrival, waiting, and treatment times at several clinics. Collectively, the two studies gathered that data for 664 patients; clinic staff also completed surveys about their experiences.
In those samples, about half of appointments started later than scheduled. One third began early, and the rest were on time. Most late starts occurred because physicians were not yet available, while a few were due to patients arriving late.
The next step involved loading the data into computer software that simulates patient flow through a clinic. The simulation estimates waiting by patients and physicians, and can compare different schedules to see what works best.
Of course, scheduling would be easy if no one ever ran late; simply spread out the appointments evenly across the day. If treatments always take 10 minutes, then schedule one patient every 10 minutes. Patients would show up on time and walk right in.
Late is Bad, Unpredictable is Worse
Alas, reality rarely runs so smoothly. Some patients or physicians run late, and waiting occurs. But the simulation confirmed that the type of lateness matters.
It’s easy to compensate for consistent lateness. If patients habitually arrive late, the clinic should schedule the appointments slightly earlier in the day. If physicians always start late, set the appointments a bit later. But patient and physician timings often vary randomly.
There are ways for physicians’ offices to minimize wait times, but for the time being, patients may need to show patience. Image courtesy of Shutterstock.
If half of patients arrive early and half are late, it’s harder to compensate (“no-show” patients who don’t arrive at all had relatively little effect on scheduling in these particular clinics). In such less-predictable situations, evenly-spaced appointments don’t work well. The physicians periodically run out of patients.
Instead, physicians do better by scheduling appointments earlier and closer together. Ideally, they’d have all patients arrive the instant the clinic opens, to ensure always having some ready. But that “ideal” would mean very long patient wait times. They’d arrive at 8 a.m. and possibly wait for hours.
Smarter Schedules
Instead of such simplistic extremes, our research tested more creative scheduling approaches. It found two that handled variation well.
The first method puts appointments closer together near the start and end of the work session. This helps keep physicians busy and spreads appointments farther apart in between, thus reducing patient waiting.
Suppose a physician works from 8 a.m. until the noon lunch break. Their appointments in early morning and just before noon might be eight or nine minutes apart. Mid-morning appointments instead could be every 11 or 12 minutes. The average would remain 10 minutes.
The second approach puts appointments closer together in clusters of two or three, while spreading the clusters farther apart. Both the closeness within clusters and distances between clusters increase as the day unfolds. The clusters keep physicians busy, and spaces between clusters reduce patient waiting.
With this method, early-morning appointments might alternate between five and 15 minutes apart. Later bookings could alternate between zero and 20 minutes apart (zero meaning that two patients are scheduled simultaneously),
The simulation indicated the clustering method best balances physician productivity and patient waiting. But the other approach works almost as well, and some clinics might prefer its simpler pattern.
Small Detail Helps Big Picture
Patients often wait weeks for their appointments. They may find it frustrating to wait even a few minutes more at physicians’ offices. Ironically, however, the latter wait can help reduce the former one. By keeping physicians busy, effective appointment scheduling helps them see more patients per day. That increased capacity reduces the number of days patients must wait for their appointments.
Improved scheduling systems are therefore one small way of increasing healthcare system efficiency as the population ages and physicians face greater demands on their time. That’s better for us than cutting the time physicians spend with each patient. And cheaper than paying to add more physicians or more hospital beds.
So, for the foreseeable future, we patients need patience.
This article is republished with permission from The Conversation.
Michael J. Armstrong is an associate professor of operations research in the Goodman School of Business at Brock University. He uses mathematical models to analyze historic battles, such as Gettysburg, Balaclava, and Coral Sea; and to understand modern missile combat, as in naval warfare and Israeli rocket defenses. Kenneth J. Klassen is a professor of operations management at Brock University. Reena Yoogalingam is an associate professor of operations management at Brock University.