The Expectation Gap: Patient Wait Time

Image by Jason Parks

Discussions of healthcare permeate throughout our daily lives. Political battles focus on drug prices, insurance coverage, and payment structures. Popular news outlets tout the newest “wellness” programs and technological advancements. Social media highlights inspirational stories of the lives changed by medical miracles.

On a personal level, we share our most recent care experiences with friends and family. We debate how we could live healthier. We watch ads about a “better” life provided by the newest drug (and if you’re like me, marvel at the multitude of disclosed side effects). We try to learn what percent insurance (if we have it) will cover of our last visit and how much we’re going to pay out-of-pocket.

Healthcare is an unavoidable constant in our daily lives. And like any other constant, it is sometimes hard to take a step back and understand from a new (and intuitive) perspective.

I have spent my entire professional — and much of my academic — career in healthcare. I have worked in clinical settings, taught medical professionals, and conducted research (including lab, clinical, and epidemiological). I am a student of healthcare history and policy. I have even experienced the consequences of a botched surgery. Throughout this journey, I have always sought to understand why things are the way they are in our system and why things never seem to quite add up.

In my current role, I observe how organizations and the people within it deliver care to patients. I follow staff and patients around clinical settings to understand who is doing what, where, when, why, and for how long (5W1H). I ask questions and listen to what these people feel, see, and think. Above all, my job is to understand the story of how care is actually delivered in the care setting. One of the parts I enjoy most about this job is exploring how this reality differs from expectations.

Expectation Gap

Patient wait time is an increasingly visible and important metric to ambulatory practices. Studies have shown that longer wait times are harmful to Net Promoter Scores (NPS). In particular, patient NPS drop-off precipitously after waiting for 20 minutes. Further, patients in the waiting area occupy valuable physical space that could be repurposed. In sum, wait time is costly and a tangible marker of operational inefficiency.

Nearly every patient I have interviewed described their current expectations of wait time as:

“I am prepared to wait, but I hope not to.”

The sentiment underscores how unpredictable and variable wait times can be today. When asked about their expectation for controlling their wait time:

“I don’t feel like I have any control over it, frankly. It doesn’t seem to matter when I show up. I can complain, but it doesn’t seem like anyone knows or that asking changes anything. When it comes down to it, how long I wait is how long I have to wait.”

Unfortunately for patients, reality aligns with expectations. Data shows the average patient waits for a total of 32 minutes between the waiting area and exam room.* While this is a high average, the amount of wait time actually experienced by an individual varies a lot. Sixty-eight percent of patients can expect a wait time somewhere between 6–58 minutes. And ninety-five percent of patients can expect a wait time of 1–87 minutes. Interestingly, patients who arrive late for their appointments wait the least.

It’s easy then to understand patient dissatisfaction. It’s the perfect customer service storm: ill-defined expectations, little-to-no agency, and high variability of outcome. (Sign me up!)

But before we paint practices as villains, it’s important to note their perspective. The truth is, the vast majority of practices do not know how long you or any other patient is going to wait. If they did, they would share it and keep both patients and staff updated. They’re unhappy with it too.

Care teams feel responsible for wasting patients’ time. Most providers, as empathetic people, worry, rush, or both. They describe wait time as distracting. It detracts from their ability to focus on patient care.

Not ironically, care teams also feel that they have little control over wait time. They cite patient no-shows and late arrivals as the primary reason they run behind. Although the data doesn’t support these anecdotes. Early patients have a greater impact on patient wait time than late arrivals and no-shows tend to provide workflow relief.

Un(der)informed Reality

Where data does exist, data are incomplete. Practices report wait time derived from their EHR software which captures the timestamp of the first click in a patient’s chart. Consequently, this defines “wait time” as the amount of time the patient spends in the waiting area and excludes wait time in the exam room. (Note that our numbers above count both.) This definition not only downplays the severity of wait time but also veils its underlying determinant(s).

Compounding measurement flaws, practices evaluate their performance using patient satisfaction surveys. That is to say, practices judge their ability to meet patient wait time “expectations” (an undefined target) based on a subjective source. As a result, practices are trying to make improvements using an unstandardized and incomplete data picture. As Drucker famously stated, “If you can’t measure it, you can’t improve it.” Practices know this but are still struggling to identify a path forward.

Asking the Right Question

Turns out, not really. Most of us are okay waiting as long as expectations are set and we know when the appointment will be over. Overwhelmingly, patients are willing to accept waiting (up to 30 minutes) in exchange for a guaranteed “exit” time.** Moreover, this option was significantly preferred to the current setup (i.e. an appointment “start” time and our perfect storm). Take note that the 30 minutes of wait time patients are willing to accept is in-line with what they already experience today.

This type of service agreement isn’t new. Take the service commitments ride-share applications (i.e. Uber) and airline flights (i.e. Delta) make to their customers. These businesses provide: (A) estimated time of departure, (B) duration in-transit, and (C) time of arrival at the destination. They also provide updates to customers on how (A) and (B) affect (C) in real-time. If they are unable to meet the expectations set, customers actually have recourse.

Intuitively, it makes sense. If I think about the flights I’ve flown in the last year, I can remember departure delays, but the plane still landed on-time. Frustrating? Sure. Ruined my day? Nope. I could still schedule meetings, coordinate dinner, and know when I’d get home. If I think about my visits to my doctor’s office, I think about the mornings I’ve taken off for what was scheduled as a 30-minute visit.

Applying healthcare practices’ current approach to expectation setting and management of wait times to these business drives the point home. Imagine an airline offering you a projected departure time, but no information about flight duration nor arrival time. Your plane could take off on time, maybe even early, or you could wait an extra hour. Would you fly that airline?

The market has already shown the outcomes of such a choice: taxi cabs lost the battle to ride-share applications. While acknowledging this isn’t the only factor, reliable expectation setting plays a big part in that choice. NPS market research backs this up: patients are more likely to rate as “promoters” (9 or 10 out of 10) if practices set and reliably met (>95%) the guaranteed clinic exit time. For healthcare practices, this offers a clear market opportunity.

Aligning Expectations With Reality

To be able to accomplish this will take work and buy-in from stakeholders (no easy feat). But, the first step is to stop and ask, “Are we asking the right question?” Then, armed with a data set that depicts the whole and authentic story of care (5W1H), we can understand, measure, and manage the real problem.

Thanks for reading! Let me know what you think.

P.S. In upcoming posts, I’ll discuss several of the concepts briefly alluded to here. This includes the notion of a patient-practice service agreement, metric-measurement-intervention alignment, creating an authentic story of care (5W1H), and more.

For next week, I’ll be re-visiting the concept of the real-time healthcare system. As a prelude, Uber announced their goal to achieve an equivalent system in transportation: just-in-time production while guaranteeing customer service performance. It builds on the information and service commitment to their customers.


*Data captured at leading national ambulatory clinics using Beacinsight technology.

Dataset limited to only include baseline measurement of patient wait time in medical and surgical specialty clinics with linear workflow. Medical clinics and highly-coordinated care settings showed significantly longer wait times due to case mix.

Baseline measurements were conducted for a period of 20 days in each clinic prior to any workflow adjustments.

Patient wait time is defined as the total amount of time the patient was not with a clinical staff member from check-in to check-out.

**Beacinsight original market research.

Founder of Healthcare and tech nerd with sociohistorical lean. Focus on improving the value of care through better daily operations.

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