How data analysis can help docs improve patient flow, satisfaction
Unless patients have a personal doctor on-call, odds are that they spend too much time in a waiting room. According to a recent survey, 85 percent of patients now say they have to wait as long as 30 minutes past their appointment time to see their doctor—and 63 percent call this the most stressful aspect of the visit.
But long waiting room wait times aren’t just stressing patients out; they’re also driving them away. Recent studies have also found that:
- Some 20 percent of patients say that long wait times have caused them to switch doctors.
- Longer wait times translate directly into lower ratings for doctors on review sites.
- Longer waits lower the chances that a patient will recommend the practice to others.
These are serious business risks for a doctor trying to run a practice. Even worse, research also finds that a history of long waits at a particular doctor’s office leads to no-shows; 30 percent of patients have decided to leave the appointment altogether because of long waits.
Despite the challenges involved in running a practice and coordinating the many moving parts involved in improving practice efficiency, technology and the use of analytics can help practices make significant improvements.
When long waits cause patients to abandon their appointments, the effects can go beyond customer satisfaction and business-level concerns and actually start impacting patient outcomes. Imagine the cascading effect that failed early detection of a health complication can have. Even if the patient goes to see a new doctor immediately, that practitioner is a new one who is just playing “catch-up” on the patient’s medical history.
Fortunately, there are ways to try and shorten waiting room times. There are administrative fixes like scheduling fewer patients, hiring more office staff and adding more doctors to the practice. Some solutions involve introducing new technology into the front-office and patient experience. But the biggest potential driver of change is simply gathering data about the specific dimensions of the problem so that you can fix it.
For example, researchers may have a pretty good idea how long patients across the country are waiting. But what about in a specific office? Learning the exact amount of time people wait in a specific office or hospital might illuminate a trend, and maybe point to a practical solution. Research indicates that patient satisfaction drops precipitously after the 20-minute mark. So if a given office’s wait time is within striking distance of 20 minutes, this might be an achievable benchmark to strive for.
A second approach is to get more granular when it comes to how patients are waiting. After all, not all waits are the same. Which stages of the wait are taking the longest? Are there unnecessary delays or hurdles at discrete points of the process that can be managed better? This is an instance in which technology and analytics could break the logjam.
Another key question is, “What kinds of medical visits take the longest?” By breaking these out, practices can employ strategies like staggering long-wait and short-wait patients, resizing appointment slots to better “fit” a patient who is likely to take longer, and even referring patients to other providers who have more capacity at typical bottleneck times.
And in the big picture, what about simply taking a hard, statistics-driven look at what the longest wait times have in common? If it’s not a specific stage of the process, or type of consultation, it might be a completely unpredicted unknown factor, such as time of day or the day of the week. For example, appointments toward the end of the day are the most likely to be delayed because of the snowballing of earlier delays or appointments running over. Likewise, studies have found that certain days, like Tuesday, are the most popular for scheduling appointments.
In the past, gathering this kind of data would have been complex and error-prone. Today, however, medical offices can simply digitize their patient intake process and get all this data automatically. Modern wait management technologies, for example, which enable patients to get in line and wait virtually with their mobile devices (or through any other channel), can provide practices with tons of structured data detailing—not just how long each patient had to wait at each stage of their appointment, but other wait-related statistics too, such as what kind of visit that patient was in for and which doctor and other staff members they interacted with.
With that kind of insight so readily available, the only thing left for practices to do is to act on it. Practices that take time to parse data after digitizing their patient intake and analyze it with the intent of using that information to improve these practices have seen increases of 20 percent in throughput, patient satisfaction scores and demand.