Saving Healthcare [Recruiting] From Itself

Griffin Mulcahey October 28, 2017
Saving Healthcare [Recruiting] From Itself

The headlines are everywhere, besides healthcare. Machine learning algorithms are radically improving recruiting.

“Algorithm Beats Recruiters in Picking the Best Employees”

“To all recruiters — use machine learning to hire better candidates”

“Algorithms Are Changing the Recruiter’s Role”

“Researchers from the National Bureau of Economic Research have created an algorithm that does a better job at selecting appropriate candidates for a job position than humans.”

Choosing the right person for a job can be challenging. The challenge and stakes get even higher for highly skilled industries like practicing medicine. The volume of applicants can be overwhelming.

And even for organizations patient enough to review each application, poor choices can arise from psychological biases ranging from racial discrimination to narrow bracketing (in which people overemphasize subsets — rather than the universe — of choices, for example, choosing the best candidate interviewed that day rather than the best candidate interviewed over the course of the search).

The tech industry, also full of unique highly skilled candidates, has fully embraced the machine learning algorithms to improve recruiting. “But healthcare is different,” is often the response. Is it really different, or is it simply an industry afraid of change? Using an algorithm to fast track recruiting does not remove the human process, it simplifies and improves it.

To see the close relationship between algorithms and hiring, consider the simple fact that hiring is essentially a prediction problem. Statistical algorithms are built for prediction problems. Algorithms can improve hiring in areas ranging from sales teams to executives to physicians.

Harvard Business Review has donethe research. Algorithms can help with some of the nation’s most challenging personnel issues. “Skeptics will rightly want to know more about the sort of data that are used. Our point is not that an algorithm alone will yield the perfect answer and replace all human interaction in the hiring process. Our point is much simpler – algorithms are an important decision aid for managers looking to make hiring and promotion decisions.  Algorithms have strengths and limitations, and need to be managed.”

“There are just so many limitations to the human part of hiring, and the way we’re doing it now isn’t working because people are unhappy with work,” saidNathalie Miller, chief executive and co-founder of Doxa.

Of course, such an exercise creates plenty of controversy in organizations entrenched in existing practices. So where do humans fit if recruiting and hiring become automated? Data is just one tool for recruiters to use. Human expertise is still necessary.

The question is not whether to use algorithms for hiring, but how to get the most out of them. The key to maximizing your recruiting is allowing experts to program the matches on the algorithm. And free up your organization’s resources to focus on hiring the best candidates tailored to your needs by the algorithm.