Your ER RN team isn’t as short-staffed as you may think

Everyone involved in running an ER thinks that they have an RN staffing problem. But what if I were to tell you that your problem isn’t entirely a staff shortage? What if I proposed that you take a breath, get off of the recruiting hamster wheel, and reconsider your actual need for more staff?

You might say this would fly in the face of logic and facts. And you’d be right. To a degree.

So let’s begin with the facts that make my suggestion seem absurd.

Even pre-pandemic, the country was suffering from a nursing shortage, caused in part by an aging workforce. According to the “2021 NSI National Health Care Retention & RN Staffing Report,” since 2016, the average hospital turned over about 90 percent of its workforce and 83 percent of its RN staff. As we all know, the pandemic only accelerated a huge churn of healthcare jobs that was already in full motion, with clinicians seeking relief from high stress, burnout, and sheer exhaustion.

Moreover, hundreds of thousands of health care workers haven’t just left their jobs – they’ve left their professions altogether. For the first time, retirement is amongst the top three reasons that RNs are resigning. And the exodus shows no sign of ending; a Health Affairs report determined that 1 million registered nurses are expected to leave the profession by 2030.

Hospital leaders know one thing for sure: if they don’t have doctors and nurses, they don’t have a hospital. So naturally, as staff turnover thins an ER’s nursing ranks, directors race to fill the empty job positions. Recruit, recruit, recruit is their daily mantra.

You may not need to replace the 4 nurses who just quit

So now let’s get back to my question: 

What if many of those jobs didn’t need to be filled at all? What if, instead, ER leaders could seamlessly cover the gaps with their existing staff? And what if they could do this without increasing that staff’s workload.

Hear me out.

It’s logical that if a given ER leader is accustomed to running their department with 12 RNs, they will instinctively look to fill those roles whenever a nurse leaves. However, the high likelihood is that your staffing hasn’t been managed as efficiently as it could be.

There’s every reason to question whether you really need 12 RNs to run your ER Department. You may only need 10. And yes, you can make these staffing adjustments without increasing the patient load of your RNs.

But here’s the catch: this only works if your staffing models are more sophisticated than most models currently are.

You need models that include reliable, AI-driven predictive analytics to determine your ER’s hourly patient volume. With this information in hand, you can use AI and machine learning to create patient-focused staffing models that optimize schedules with laser precision.

The reality is that your ER may have challenges recruiting, but your biggest problem is with your staffing models. And the good news is that it’s a lot easier and less costly to adjust your models than to hire new staff.

High stakes for smart staffing models

Most ER directors wouldn’t be surprised to hear that there’s room for improvement with their staffing models. But they don’t feel the urgency to optimize their models. Why not?

There are several reasons that ER directors are making do with staffing models that they know are flawed and are in no rush to fix them.

First, they’re tired.

They’re doing the best they can under the extra enormous strain of the past few Covid years. It’s hard enough to just find and retain good talent, and once they get those people into a schedule, it’s difficult to move around the chess pieces.

Second, their schedules may not be perfect, but they’re “ok.” Sure, anyone with eyes can see the times that their models break down, those hours when they’ve overscheduled or underscheduled their resources. They’re aware that there are some issues – but they’re getting by. And if it ain’t broke, why fix it?

Third, even if things could be better, the stakes don’t seem all that high. Yes, there may be some improvements to be had on the margins. But finessing the efficiencies of your staffing models just isn’t that big of a priority. It’s certainly not as important as recruiting. You’ll get to it later.

Modern staffing models pay immediate dividends 

Here’s the thing: the recruiting hamster wheel just keeps spinning. Meanwhile, your staffing model hasn’t gotten a fresh look in ages.  

Unlike recruiting, you can control your staffing models. And modernizing those models with AI-driven predictive analytics can pay immediate dividends.

That means that you don’t have to wait to hire more staff to address your staffing issue. You have the ability to take the guesswork out of staffing and instead apply maximum efficiency to your model. Your nurses can be staffed when they are needed most in the cadence that is directly tied to your ER’s hourly patient volume.

Yes, this is possible, right now.

Overlooking or minimizing the significance of AI tools for your staffing models just makes your work harder and more expensive. Machine learning has changed everything about the way we live. Now it’s time to apply it to the way you create your staffing schedules.