We’ve all been there. You walk into a restaurant and the maître d' says your wait will be 15 minutes. Twenty minutes passes and just as you’re preparing to leave, they say your table is ready. There are only two servers working that night, and yours takes forever to arrive. The Caesar salad you ordered comes so late, you suspect they were waiting for the vegetables to ripen. Everyone is so busy, you can’t even get a water refill. Frustrated, you pay and don’t leave a tip.
But you do leave a review on Google.
Now, I’m not equating restaurants with emergency departments—the seriousness of those interactions differ greatly. Regardless, it’s human nature to expect personal attention in certain situations, especially when so many factors are beyond your control. In fact, you could argue that patients are even more sensitive to poor experiences due to the stress they’re already feeling when they walk into an overcrowded ED.
This can have a negative impact on several success metrics, including left without being seen (LWBS) rates, CAHPS scores, and patient satisfaction survey results, not to mention reputation-harming online reviews. Fortunately, today’s technology can help hospital administrators and chief nursing officers avoid some of the negative consequences of overcrowding.
High ED crowding results in 5% greater odds of patient death following admission.
Hallway use and prolonged boarding are the strongest predictors of low patient satisfaction rates.
A study of 187 hospitals uncovered $17 million in costs associated with ED overcrowding.
At Vital, we pay close attention to how patients across 100+ hospitals interact with our ED-based platform, ERAdvisor. This kind of volume helps us inform better technology and gives insight into the largest pain points emergency patients are experiencing, particularly as EDs are busier than ever.
In this blog, we’d like to share three common opportunities we see that may help you address patient satisfaction rates, length of stay, throughput, and more.
Predicting wait times is difficult, especially when the ED is overcrowded. Fortunately, recent advancements in AI can easily calculate the average wait time based on the number of patients in your waiting area, the number of beds available, and current discharge-to-admit ratios. That’s a good start, but there’s really no such thing as an “average” ED patient.
To get a personalized idea of individual wait times, factors unique to each patient (such as reason for visit, age, sex, emergency severity index score, etc.) and the volumes coming in the ambulance bay should be taken into account. Assessing a baseline and comparing that to the AI algorithms is key to getting this right. For example, by training our algorithms on various ED environmental and patient-specific factors, we’re currently seeing a 97% accuracy rate in our ERAdvisor application.
Health systems learn a lot through patient surveys. Unfortunately, many are conducted days or weeks after discharge—hardly timely, specific, or actionable. That’s why more EDs are turning to intra-care prompts to capture live feedback during the patient visit, not after. This gives patients a voice and allows for more responsiveness to pain management, room cleanliness, patient education, and more.
According to thousands of real-time ED satisfaction surveys, the top indicators for “what’s going well” included a respectful environment, high quality of care, and feeling that the staff listened to the patient.
In the “room for improvement” category, the chief issues were long wait times and untreated pain. Tying for the third most common area for improvement were not feeling heard, lack of clarity about the treatment plan, and unanswered questions.
At the facility level, Vital’s customers not only see what kind of feedback was submitted during a specific time on a specific day (e.g. during overcrowding), but they can also gain insight into which of their team members were getting the most comments from appreciative patients. That also makes for an easier, real-time way of recognizing staff for their outstanding service and compassion.
We’ve seen this play out many times. We've worked with EDs that objectively offer the highest-quality clinical care available, but may have service response gaps that skew the patient’s view of the quality of care they received. Having interfaces (digital or otherwise) that can serve as a proactive “air traffic control” for service gaps is key to aligning the clinical quality with the experience quality.
Automatic routing of service requests is key to tackling this challenge, otherwise the risk of even further clinician burnout is high. Fortunately, AI is able to help.
For example, our ERAdvisor application automatically routes service requests (e.g., “I’m thirsty,” “I need help to the bathroom,” etc.) to the appropriate personnel who are able to help with that specific need. Depending on the facility’s preferences, we route the request to environmental services, concierge care, or the HUC. This means nurses can respond to more clinical needs, thereby allowing them to operate at the top of their license.
So what kind of requests are most often sent? According to tens of thousands of service requests logged, teams know when the patient is in pain, is cold, has a medical question, and is feeling nauseous.
By providing patients with personalized wait times, facilitating real-time feedback, and helping the right staff respond to service requests, your ED can facilitate a smoother throughput, fewer frustrated or angry patients and family members, and a less burdened staff.
Learn how Vital’s patient experience platform can help you avoid the negative consequences of an overcrowded ED.
Schedule a demo today or check out part 2 of this blog series.