Safety is a non-negotiable aspect of running a laboratory. But, counterintuitively, lab safety often isn’t the best way to examine hazards within the lab.
“Safety is a very binary concept,” says Jonathan Klane, MSEd, CIH, CSP, CHMM, CIT, a lab safety and EHS expert and a business development, senior manager, and advisor for Draeger, Inc. “We say something is safe or isn’t safe. I have never heard, ‘This is sort of safe.’”
Deeming a situation “safe” or “not safe” results in a low-resolution view of the challenge and is not helpful in formulating a safer approach. That’s why Klane recommends looking at safety through the lens of risk. If safety is a binary switch, risk is a spectrum, which is more helpful in navigating the ambiguity and challenges that arise in every lab.
Defining risk and its equation
Klane defines risk as “the probability of a negative outcome.” This definition, he says, captures two factors that comprise risk: probability and severity—in other words, how likely is the risk and how bad will the situation be if the risk comes to fruition?” Klane also ascribes a third factor: exposure, or put another way, “How close am I to the hazard?”
Oftentimes, exposure is embedded within probability. The closer you are to the hazard, the higher the chance it has of impacting you. But delineating between probability and exposure can help you arrive at a more comprehensive evaluation of a risk.
After defining the factors, Klane lays out an equation for calculating risk:
Risk = Severity x Exposure x Probability, or R = S x E x P
It may seem unintuitive to frame an abstract concept like risk as an equation. Klane, however, believes that it can be a helpful way to make the risk more tangible: If two approaches to addressing a hazard can be quantified, even if arbitrarily, then they can be compared objectively and help highlight the better approach.
The lower the risk number, the safer the option. But it's important to note that risk factor isn’t always the only factor—labs must also balance risk tolerance with costs, timelines, and project priorities. As such, the option with the lowest risk factor isn’t always going to be the best option available.
These factors don’t map to any real-world units. Rather, they can be mapped numbers on a predefined scale—Klane recommends one to five, with one being the lowest amount of severity, exposure, or probability—which can then be multiplied together to arrive at a final product of risk. Whichever idea for approaching a situation has a lower risk number, the safer it is.
The risk equation in action
Here’s a fictitious example showcasing the risk equation in action:
Dr. Whitacre and Dr. Lee are working on synthesizing a new energetic compound in their materials research laboratory. Initially, they planned to scale up a reaction that previously worked on a small scale. But Lee points out that the compound’s molecular structure may make it highly sensitive to heat and shock—raising the risk of an explosion during scale-up.
The pair deliberate on the best way to approach the situation. They eventually land on three options:
1. Proceed with the scale-up as planned using a fume hood for standard protection
2. Use a remote-controlled setup inside a blast-proof enclosure
3. Pause the research to gather more data, performing an additional differential scanning calorimetry (DSC) test to quantify the compound’s thermal stability
Each approach has its pros and cons. Given that this research is time-sensitive, option 1 has the advantage of keeping them on track. The second option would be much safer, but reserving the containment lab and equipment necessary to carry it out would cost money. The third option could be helpful in determining the next steps, but would not be progress in itself.
As they weigh their choices, Whitacre and Lee decide to use the risk equation to guide their decision of what an acceptable level of risk is.
Lab Management Certificate
The Lab Management certificate is more than training—it’s a professional advantage.
Gain critical skills and IACET-approved CEUs that make a measurable difference.
They create the following equations:
Option 1: Proceed with the scale-up as planned
S = 5 – High severity due to the possibility of an explosion
E = 4 – Moderate to high exposure since they’ll be in close proximity to an explosion, but will have some barriers like the fume hood sash and their PPE
P = 3 – Moderate chance of an explosion that could harm those nearby. While the material’s characteristics indicate that it’s sensitive to heat and shock, it’s not guaranteed.
R = 5 x 4 x 3 = 60
Option 2: Use a remote-controlled setup inside a blast-proof enclosure
S = 5 – If an explosion occurs, the researchers expect it would be the same severity in the enclosure as it would in their own lab
E = 1 – Minimal exposure thanks to the robot carrying out the scale-up and the blast-proof enclosure
P = 2 – Lower probability of an explosion harming anyone due to engineering controls and containment measures
R = 5 x 1 x 2 = 10
Option 3: Pause and run a DSC analysis before proceeding
S = 1 – DSC analysis is low-risk
E = 1 – Minimal hazard exposure due to the nature of the technique and safety controls built into the instrument
P = 1 – Almost no chance of anything adverse occurring during analysis, so no danger to be in close proximity to
R = 1 x 1 x 1 = 1
Which option to choose?
The final risk products for each option are 60, 10, and 1, respectively. After arriving at these numbers, Whitacre and Lee use the equation as a starting point for discussion. While the DSC analysis carried the lowest risk score, they agreed it wasn’t practical for their project needs. The remote-controlled setup offered a much safer path than scaling up directly, while still aligning with client timelines and budget. In this way, the risk equation guides their choice, even if the absolute lowest score wasn’t the final decision.
By replacing the binary “safe or not safe” mindset with a structured, conversational approach to risk, scientists can make more informed decisions and keep themselves safer. The severity-exposure-proximity model opens up space for dialogue, tradeoff analysis, and shared understanding. For a deeper dive into this framework and how to apply it in your own lab, see Jonathan Klane’s webinar on the topic at LabManager.com.
