Fitness for Duty, like evaluating athletes, can now be based on objective data
It might seem nonsensical to equate the evaluation of whether an employee is fit for on-the-job duty with the evaluation of a professional baseball player’s ability. But it’s not. The similarity is instructive.
In the 2011 baseball film Moneyball, the general manager of the Oakland Athletics, Billy Beane (played by Brad Pitt), is putting in place a data-driven, statistical approach to player selection. The team’s scouts, their traditional methods being disrespected, are upset. The head scout pulls Billy aside to say:
“You don’t put a team together for the computer, Billy. Baseball isn’t just numbers. It’s not science. If it was, then anybody could do what we’re doing. But they can’t because they don’t know what we know. They don’t have our experience and they don’t have our intuition, okay?... There are intangibles that only baseball people understand. You’re discounting what scouts have done for 150 years.”
Billy responds, sharply: “Adapt or die.”
The “intangibles” and “intuition” the head scout is referring to – their subjective approach – is captured in this dialogue between several of the scouts:
Artie: I like Perez. He swings like a man.
Keough: He swings like a man who swings at too much.
Artie: There's some work needs to be done. I admit it. He needs to be reworked a little. But he's noticeable.
Grady: He's notable?
Artie: No, he's noticeable. You notice him.
Keough: He's got an ugly girlfriend.
Barry: What's that mean?
Keough: Ugly girlfriend means no confidence.
Barry: Alright. That's true.
Pittaro: I agree with Art. I like the way he walks into a room.
George: Passes the eye candy test. He's got the looks. He's ready to play the part. He just needs some playing time.
Keough: I'm just saying, his girlfriend's a six.
This scathing lampoonish dialogue makes clear the scouts’ subjective approach puts their judgments into question, to say the least.
In the workplace, the subjective approach to determining whether an employee is fit for duty has been used from time immemorial. How else to determine, other than by scrutinizing appearance and behavior, whether someone is capable of performing their duties, especially if those duties put them or others at risk for their safety?
One relatively recent advance has been drug testing. Since the bodily presence of alcohol and certain drugs is correlated with cognitive and psychomotor impairment, a workplace drug test can help identify at least some employees who are not fit for duty. Note, however, that according to several scientific studies, the mere presence of some drugs – most noticeably cannabis – is not a valid indicator of cognitive and psychomotor impairment. Also, keep in mind that drugs and alcohol are only the tip of the impairment iceberg. Most impairment is caused by fatigue, illness, injury, chronic medical conditions, stress, or environmental conditions like extreme heat or cold. The bottom line is that drug testing, often thought of as a conclusive method to determine fitness for duty, cannot, in fact, get the job done alone.
Billy Bean’s Oakland Athletics had great success and became one of the most cost-effective teams in professional baseball because he put into place (alongside a tempered scouting team) a data-driven, statistical approach, which is now in general use across professional baseball. Indeed, businesses of all types have been transformed through the use of data analytics. It promises to do the same for workplace safety.
A data-driven statistical approach using impairment detection technology (IDT) has recently become available to employers to determine and measure employee impairment. DRUID, from Impairment Science, Inc., a neuroscience-based mobile app that detects impairment from any cause is one such IDT product. The test, just one minute in duration, takes hundreds of digital measurements of hand-eye coordination, reaction time, decision-making, time estimation, and balance – all of which are statistically integrated to calculate an impairment score on a scale of 25 – 75, with higher scores indicating greater impairment. A DRUID score is data-driven and, most significantly, objective.
With the ever-increasing legalization and use of cannabis, the drug testing industry - offering a test that only detects drug use, not impairment - is well-advised to incorporate an objective data-driven approach to test for impairment.
As Billy Beane put it, “Adapt or die.”