ER online check-in tools on the market today offer static, one-size-fits-all solutions that lack an understanding of consumer behavior and don’t appropriately manage expectations for patients searching online to “get care now.” Many of them allow patients to choose a specific check-in “appointment,” which disappoints patients and sets poor expectations in the process.
Once someone selects and confirms an exact time slot offered by the facility, they think of that check-in like any other reservation they’re used to making: a restaurant reservation or a hair appointment. They reasonably expect that, when they arrive, someone will see them and take care of them right away.
But when someone shows up at that pre-booked time, they’re bound to be disappointed as they will likely have to wait. Since emergency care is based on acuity and therefore isn’t linear or predictable, it’s impossible to give patients exact wait times or specific appointment slots.
When reality falls short of expectations, it’s upsetting. Prolonged waiting times are associated with worse patient experience in patients discharged from the emergency department, a 2021 NIH study found.
ER facilities will never be able to eliminate waiting or give definitive answers around wait times, but they can better manage patient expectations and improve their experience as a result.
Our team at Arriv, an intelligent online check-in tool, ran an experiment and found that one simple change in the digital experience increased the conversion rate of web traffic into patients and increased patient satisfaction ratings because it set more realistic expectations.
Pitting arrival windows against specific check-in times
Arriv designed and ran an experiment to determine how online ER check-in could balance growing patient volume while still setting appropriate expectations.
41,000 patients across 47 facilities who used Arriv’s online check-in tool were shown one of two check-in methods: Half were asked to choose a 30-minute window for when to show up at the emergency department while the others were given the traditional option of specific check-in times.
Patients who were given the option of time windows were 32% more likely to complete the online check-in process compared to the group given specific times. The rate of patients presenting at the facility of these cohorts were nearly identical, but on top of converting higher, the group given arrival time windows boasted a 98% satisfaction rating.
To put it simply, more patients converted in the check-in process that set more realistic expectations.
Realistic expectations are directly linked to higher satisfaction
How is it that this seemingly small change turned out to be quite powerful?
“Waiting in ignorance creates a feeling of powerlessness,” writes David H. Maister in his paper The Psychology of Waiting Lines.
It’s not the waiting alone that strips people’s feeling of control, he posits. The operative phrase here is “in ignorance.”
Studies have found that someone who arrives early for an appointment will be content to sit until the appointment’s scheduled time. However, once the appointment time has passed — even by as little as 10 minutes — they grow increasingly annoyed.
No one wants to experience the combination of waiting and not knowing what to expect on the other side.
People want to know what to expect, and feel satisfied when they gain that clarity. Setting better expectations, then, creates a better patient experience.
Two crucial factors in management of patient expectations are time and money
Consumers essentially have two valuable resources to spend: time and money. These tend to be sensitive topics, because consumers have expectations of the goods or services they’ll receive in exchange for their precious investments.
Sensitivity surrounding time is even more heightened when it comes to on-demand care because the patient is in a stressful, emotional state. It’s crucial that health systems manage patients’ expectations around wait times well. They can do this through the right digital front door experience.
We saw in the arrival windows vs. specific check-in time experiment how time — actually, the perception of time — can make an outsized difference in the patient’s perception of their experience.
Although ERs operate based on acuity and therefore can’t guarantee appointment times, it’s still helpful for both the facility and the patient to have a general idea of arrival time.
For the patient, checking in means they can now put the chaos of the search aside and simply focus on arriving. Not only do they feel relieved they now have a plan of action — they’re confident in the care they’re about to receive, knowing health care providers are expecting their arrival.
By helping high-intent patients lock in a plan and eliminate some of the unknowns around which ER to visit, health systems can win more patients. Arriv boosts the conversion of website traffic into patients by an average of 220% vs other solutions.
The other key expectation is cost.
ER visits are pricey. The average cost for an emergency room visit is $2,600 without insurance, according to UnitedHealthcare, and much of that cost goes to high facility fees. Patients are often caught off guard when they receive expensive ER bills because, once again, they didn’t know what to expect.
Health systems can avoid some of these mismanaged patient expectations by routing patients to the right type of facility.
Health systems with multiple urgent care and ER facilities can utilize Arriv’s Intelligent Routing technology, which automatically directs patients to the best facility for them. The majority of current ER visits are for non-emergency needs, so lower acuity patients, for example, are directed to the urgent care rather than the ER, so they can receive better care, shorter waits, and lower bills — resulting in higher satisfaction with your health system.
By routing patients to the right care setting, this technology also helps load-balance across facilities and can result in shorter wait times.
Even small factors can affect patient expectation and satisfaction
Arriv has found that small, seemingly inconsequential factors can impact how patients perceive their on-demand care. For many health systems, the hard part is finding out what those factors are.
Health systems should leverage their digital front door intentionally to better understand consumer behavior and patient satisfaction. They can use the digital experience as a testing ground to experiment, learn, and optimize different variables to find which combination works best for their audiences.
However, many online check-in tools on the market today don’t take this opportunity. Instead, they use a stale and templated approach, serving the same digital check-in experience to all patients across all of their health system customers.
Arriv does the opposite and uses AI-driven personalization to better serve patients. Arriv’s technology is continuously testing about 40-50 different variables, such as including an image of the facility, adding a disclaimer that specific appointment times cannot be guaranteed to the check-in page, even changing text size and color.
Through machine learning, Arriv continuously analyzes which combination of these variables lead to more conversion and satisfaction than others. It learns which online experience performs best for certain audience segments, and then serves the best performers to those specific users based on their geographic location, online behavior, and device.
We’ve found that personalization is a powerful way to manage patient expectations. Patient preferences vary widely, even between users of two facilities within the same health system. Leveraging a personalized digital experience to each specific user resulted in a 183% increase in conversion.
Alex Zubey is the CEO of Arriv, the industry’s highest converting online check-in solution, focused on converting web traffic into satisfied patients.
Alex has extensive experience working with health systems’ digital front door initiatives with a focus on patient experience and new patient acquisition software. Before co-founding Arriv, he worked with Philips Healthcare, Solv Health, and various other healthtech startups.
Alex is a United States Air Force Veteran and holds a bachelor of science from the United States Air Force Academy.
