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Errors in medical labs can be extremely costly, causing physical and mental harm to patients who receive inaccurate test results. Mistakes in research labs are also important to avoid, as they can skew results and waste reagents and samples—some of which may be irreplaceable—as well as years of careful work.

 

Dealing with errors in medicine

 

There exist two primary methods of resolving errors in medicine. The first method, called the “bad apple” approach, necessitates squaring the blame on those directly responsible for the error.1 This method supposes that human errors should never occur; thus, the staff are reprimanded or fired outright. In research, the blame for misidentified specimens can also rest solely on the student or staff. This method to resolving errors has more recently been substituted with a “systems” methodology, where errors are the consequences of the protocols and conventions of the institution that ultimately lead to a certain percentage of mistakes. A certain number of mistakes are anticipated, and the staff are taught to identify mistakes quickly and figure out how to minimize them.2 Having an open environment is necessary, where staff can divulge the errors they may have made. This avoids people dodging blame by covering up mistakes and makes it easier to implement strategies to negate errors.1

 

The “Swiss Cheese” model, developed by James T. Reason and Dante Orlandella, is the most well-known system used to manage errors in medicine. This model uses barriers, representative of layers of swiss cheese that have many holes. When each barrier is placed together with another, the holes, which signify the possibility for error, are covered, averting mistakes from passing through. However, when an error lines up with all the holes of the swiss cheese layers, the effect is felt by the patient, who may suffer harm from the error.2

 

Translating knowledge from medicine to the lab

 

Like the barriers that have been implemented in medicine, the lab can employ “swiss cheese layers” to reduce the frequency of errors. Using labels is the easiest ways to avoid error when handling samples; you need a way of accurately identifying and tracking your samples, particularly when there are hundreds of them to process. Applying the appropriate label depending on the environment your samples will be stored in (e.g. cryogenic labels for storage in liquid nitrogen) ensures that the labels won’t fail. Barcodes can also track your specimens during processing. Employing a laboratory information management system (LIMS) can coordinate your workflow and increase the productivity of your lab; barcoded samples can be scanned and recorded by your LIMS, which will verify that the correct samples are treated at the proper stage of the process. Finally, it’s necessary to supervise your laboratory’s infrastructure, including the temperature, air pressure, humidity, and CO2 and O2 levels of your lab, incubators, refrigerators, freezers, as well as your HVAC and backup generators. XiltriX™ can uninterruptedly diagnose all your essential equipment, leveraging predictive maintenance and predictive analytics to prevent you from losing valuable lab assets.

 

Why do people make mistakes anyway?

 

Not all errors occur for the same reasons. What’s important is to define why the mistake occurred and to integrate solutions that remedy problems at the root.3 The most widespread causes of human error are tiredness, nutritional deprivation or dehydration, and emotional stress. These can result in fatigue and, eventually, to poor judgement and performance.4 Scientists encounter similar circumstances, though to a lesser extent than doctors or nurses. There are many situations where a lab technician is inundated with samples to process, or a new assistant professor is reaching his or her grant submission deadline and needs to complete their experiments hastily. Lab procedures can also be monotonous (e.g. pipetting and labeling) or overly familiar, making it more likely that the healthcare professional or researcher might lose awareness of the situation and slip up.5

 

Attitude is another factor to consider that determines how well errors are handled. There are specific kinds of mindsets that are related to diminished judgement, which increases the probability of error. Personnel can be imprudent, anti-authoritative, feel invincible (mistakes won’t happen to me!), too eager to assume control at inappropriate times, or believe that their efforts won’t matter.6

 

It’s crucial to identify methods that help avoid mistakes from resulting in harm—to both the patient and the scientist. There are many strategies that healthcare institutions can implement to decrease the chance for error, but with so many moving parts in the lab, no single answer exists. Everyone needs to consider that errors may occur and ensure that they take the appropriate steps to avoid them as much as possible.

 

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References:

1. Kennedy D. Analysis of sharp-end, frontline human error: Beyond throwing out “bad apples.” J Nurs Care Qual. 2004;10(2):116-122.

2. Reason J. Human error: Models and management. West J Med. 2000;320(7237):768-770.

3. Etchells E, Juurlink D, Levinson W. Medication errors: The human factor. CMAJ. 2008;178(1):63-64.

4. Brennan PA, Mitchell DA, Holmes S, Plint S, Parry D. Good people who try their best can have problems: Recognition of human factors and how to minimise error. Br J Oral Maxillofac Surg. 2016;54:3-7.

5. De Los Santos MJ, Ruiz A. Protocols for tracking and witnessing samples and patients in assisted reproductive technology. Fertil Steril. 2013. doi:10.1016/j.fertnstert.2013.09.029

6. Renouard F, Perrault-Pierre E. [Is human behavior the leading cause of complications in medical practice?]. Orthod Fr. 2016;87:3-11.