There’s No Such Thing as a Good Nurse

By Myra Norton, President and COO

At Arena, we work frequently with hiring teams at hospitals and other health care organizations. Nearly every team we work with expresses a desire to hire “good” nurses and other medical professionals.

The only bad news for these teams?

There’s no such thing as a good nurse.

Quickly — before I provoke the ire of RNs around the globe — let me clarify and say that the same is true of any job. There are no good doctors, no good lawyers, no good factory workers or housekeepers or lab technicians or garbage men.

I’d go so far to say there’s no such thing as a “good” employee at all.

What does exist is the right employee — that is, the right person for a certain department at a certain organization, serving a certain population in a certain capacity at a certain point in time.

Let me explain.

Personalizing the Process

Recruiters often ask us questions like, “What are some of the key predictors you see in job applicants that indicate someone will be a high performing ER nurse?” The assumption here is that there are certain characteristics that are generally valuable in an ER nurse, and that if you can find someone who embodies those characteristics, you’ll have made a good hire.

Patient receives chemotherapy. By Rhoda Baer (Photographer), via Wikimedia Commons

But ultimately, it’s impossible to put together a definitive list of characteristics that make an effective ER nurse. For example, Arena currently works with a health care system that runs both an acute care hospital as well as a long-term care hospital. The two facilities are right across the street, and the system handles recruiting centrally.

Recently, the health care system was looking to hire Certified Nursing Assistants (CNAs) at both the acute and long-term care hospitals. The system issued the same job specification for both roles, in which they listed the desired characteristics for successful candidates.

One of those characteristics was leadership experience in community organizations. Sounds like a no-brainer, right? Of course leadership experience would lead to a better employee.

That’s what the recruiters thought too. And it turned out they were right — but only partially. Arena’s analysis of the hiring and retention data for these two positions ultimately revealed that while leadership experience was a positive predictor of success for CNAs at the acute care facility, it was a negative predictor of success for those hired at the long-term care facility.

There are many reasons this might have been the case. My point here isn’t to speculate about why. It’s also not to argue that all long-term care hospitals should strike “leadership experience” from their list of desired characteristics for potential CNA hires.

Rather, what the finding emphasizes is that there is no one arbiter of success or failure for a given role that will be applicable across all health care systems and settings (no matter whether they’re across the street or across the country). Effectiveness in a given job will depend not only on the candidate’s individual characteristics, but also on countless evolving factors related to the organization, the other employees, the patient population, etc.

Finding the Right Nurse

Given this, how can health care systems and other employers implement a more effective hiring process — one which takes into account these dynamic factors, rather than relying on a static list of job requirements?

This is where Arena’s predictive analytics platform comes into play. By taking into account massive amounts of historical and current data about an individual candidate, the organization, the specific department, and the desired role (as well as data from other similar organizations and departments), Arena can predict a candidate’s likelihood of success for a specific job, at a specific moment in time.

That said, a predictive analytics platform can’t do all the legwork. Data and analytics are powerful tools, but they aren’t a silver bullet. I’m a mathematician and a statistician by training. If anyone trusts data, it’s me. But even I will readily admit that no algorithm should ever make a hiring decision — at least not on its own.

Machine learning cannot replicate the wisdom and intuition of a human. Chatting with a potential hire on the phone, watching his or her body language during a face-to-face interview, observing the way he or she interacts with potential colleagues or patients — these are crucial factors to consider before making a hiring decision, and they’re factors that a predictive analytics platform can’t and won’t integrate into its recommendations.

What predictive analytics can do is help make the hiring process more efficient and cost-effective for recruiters and hiring managers, by allowing them to focus their energy on candidates with a high likelihood of success in a given role.

So while there may be no such thing as a “good” nurse, there is such a thing as the right nurse for your team — and predictive analytics can help you find him or her.