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Legal Drugs in the Energy Workplace — What Could Go Wrong? – Geoffrey Cann

These translations are done via Google Translate

legal drugs in the workplace — what could go wrong geoffrey cann

By Geoffrey Cann

Heavy industry has long known that intoxication and some kinds of work simply don’t mix. Digital technologies are helping overcome the risk of hard-to-detect intoxicants on the job site.

Many years ago, I was travelling for work and my company credit card was declined at an airport for exceeding its very healthy credit limit. Being stranded on the road with no credit facilities can be bad. You can’t eat, can’t get a hotel room, can’t get on the plane.

All this was most unusual—I had put arrangements in place at work for my travel expenses to be settled with the help of one of our newer employees, a process which seemed to be working fine.

In a panic, I called my spouse, who looked after our banking, to discover that there had been no expense reimbursements for some months.

That pointed to the employee who opened the mail and captured the expenses in the expense system. While the employee was out for lunch, the office manager opened the desk drawer to discover an accumulation of months of unopened mail, including my and several of my co-worker’s unpaid credit card bills, increasingly shrill notes from various banks, complaint letters, unpaid utility bills, and stacks of receipts that hadn’t been processed.

In the discovery meetings that followed, we learned that the employee had a daily lunchtime routine of enjoying a fine doobie before returning to the office. In their subsequent impaired judgement, the employee concluded that opening the mail was all too difficult, and it piled up for months.

This was my first work exposure to the risks of cannabis impairment on the job. What was particularly baffling was how the worker managed to appear for months as a perfectly competent person of sound judgement in the world’s largest firm of professional accountants.

While serious for me, this situation was pretty light. Personal safety was not jeopardized. No heavy equipment was involved (although sometimes accountants press pretty hard with their pencils).

This is certainly not true in oil and gas workplaces.

Alcohol — The Employer’s Drug of Choice

Worker safety is a VERY BIG DEAL in oil and gas. Moving equipment, pressurized vessels, high voltage, dangerous gases, hot surfaces, sharp objects, dark and wet conditions, drops from height, slippery surfaces, falling objects… the risks in the industrial workplace are manifestly greater. Employees need to have their wits about them at all times.

Relative to other substance challenges, alcohol is easier for employers to manage. Most workers know better than to come to work inebriated as alcohol is easy to detect, you create an unsafe workplace for yourself and others, and you will likely lose your job. Your employment contract makes the consequences to you abundantly clear. Workplace posters reinforce the message. Collecting a driving-under-the- influence (DUI) conviction is life altering.

Cannabis — Legal and Hard to Detect

Other impairing substances, such as cannabis, are much more difficult to pick up. Cannabis doesn’t behave like alcohol in the body. It binds to fat cells, and is detectable for weeks, well after the intoxication effect has vanished, whereas alcohol washes out of your system relatively quickly. Unlike alcohol, the amount of cannabis you consume, and the resulting level of THC in your body, is not correlated to your level of impairment.

Societal changes in many jurisdictions now mean that cannabis consumption is a legal right. In the same way that employers cannot forbid their workers from drinking alcohol while off the clock, they can no longer bar their employees from consuming cannabis after work. The social stigma associated with consuming what was once an illegal product has vanished.

Legalization of cannabis and the creation of a competitive marketplace for cannabis products has unlocked the innovative impulses of the supplying industry. Cannabis strength has intensified 600% in the past decade, such that even tiny quantities can have outsized impacts. As a product, it now comes in many formats—as a liquid, as a vapour, as a tablet, as a gel, as food. It is consumed more frequently, in more places, by more people.

Law enforcement has few legally acceptable mechanisms to detect if a driver is impaired. Tools include the roadside breathalyzer, oral fluid tests, sobriety tests, and the Drug Recognition Expert Evaluation or DRE. These tools and tests are beyond the reach of most companies because they require specialized training and medical equipment to administer.

One reliable sobriety test involves measuring the eye’s reaction to light and movement.  Administered by a specially trained law enforcement officer, the test can detect impairment from cannabis, but the test takes time to run, and the trained professionals are scarce. By the time an enforcement professional can show up to do the test, impairment may have dissipated. And the presence of cannabis in the body does not mean you’re impaired.


What’s an employer to do?

My personal situation was revealing. Our employment contracts forbade on-the-job impairment, and the consequences were clear. Yet we were unable to tell if someone was cannabis impaired, and we only discovered it once we had an incident.

That’s just not acceptable in oil and gas. And yet, that’s exactly the situation we’re in.

Oil and gas operations often offer conditions ripe for drug and substance abuse. The jobs are well paying, job sites are remote and isolated, the work is hard, and the work force is young and risk taking. There are few alternatives for workers to fill their idle time.

A Digital Sobriety Test

What if we were able to supplement the industry standard law enforcement eye exam, normally administrated by a highly trained professional, but using digital technologies such as a camera, to capture eye behaviour in response to stimuli, and an algorithm that interprets the eye behaviour?

Sounds easy enough, but this is a hard problem to solve:

  • The human eye is not a standardized organ. Eyes vary dramatically in colour and shape. Eyes are not uniformly located—some are slightly offset, recessed, or protruding. Eyes are not perfectly round. Eyelids get in the way.
  • Pupils do not uniformly react to bright lights and darkness. Speed of focus or dilation varies. Eyes track moving objects in different ways, tracking slowly or quickly. Movements can be reflexive, extremely subtle, and involuntary.
  • Different people react differently to chemical stimulus. Some kinds of natural body conditions (lack of sleep, long hours of intense activity, the after effects of Thanksgiving turkey dinner) can even make you look like you’re impaired.

It’s clear why this is a test that typically only humans can administer, and that’s after a lot of training and experience.

To build an algorithm that supplements the efforts of a law enforcement officer, you’d first need to collect the data. For that you’d need thousands of willing cannabis consumers, covering a wide swathe of society, to participate in the testing, both before and after cannabis exposure.

A suitable digital enhancement to the human test administrator would also need to work correctly in the real life conditions of testing.

Conditions where and when the tests are administered can vary dramatically (warm and cold climates, indoor and outdoor, dry and wet). A high quality network connection is likely unavailable. Testing equipment needs to be off the shelf, low cost, and easily used without much training. Tests need to be fast enough so as to not create bottlenecks when used in mass settings. Tests need to be accurate and reliable — too many false positives will be viewed with suspicion, too many false negatives will create risk. Test subjects may try various tactics to evade detection, and may well be under the influence of multiple substances at the same time (alcohol and cannabis—a potent combination).

A good algorithm would be self-training all the time so that it improves with age. It would be able pick out the presence of other recreational drugs and narcotics, such as cocaine, ketamine, PCP, LSD, magic mushrooms, and meth. It should be able to recognize the use of legal non-impairing drugs, like blood pressure medication.

Sounds altogether too hard, frankly.

Yet, one digital entrepreneur has pioneered just such a solution. Gaize (yes, the AI refers to the use of a machine learning algorithm) has been in development for several years but benefitted from the legalization of cannabis consumption in Canada (finally, something Canada is good at). Legalization allowed for large scale clinical trials of cannabis consumption and specifically afforded researchers the opportunity to capture detailed data about the impacts of cannabis consumption.

A recent trial customer experience is telling. An employer was concerned that one of their employees was under the influence of cannabis, but they could not be sure. Rather than singling out that one worker for a sobriety test, they administered the Gaize test to the full team of 12, only to discover that 7 others, in addition to the one suspected of cannabis impairment, were also impaired.

I interviewed the founder behind Gaize for my podcast this week. Check it out to learn more.

Artwork courtesy of Geoffrey Cann


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