In a recent post I talked about Human Factors and different error types. You don’t necessarily need to classify human errors into these types but splitting them out this way helps us think about the different sorts of errors there are. This moves us on from when we get to ‘human error’ when carrying out our root cause analysis (using DIGR® or another method). Part of the problem with having ‘human error’ as a root cause is that there isn’t much you can do with your conclusion. To err is human after all so let’s move on to something else. But people make errors for a reason and trying to understand why they made the error can lead us down a much more fruitful path to actions we can implement to try to prevent recurrence. If a pilot makes an error that leads to a near disaster or worse, we don’t just conclude that it was human error and there is nothing we can do about it. In a crash involving a self-driving car we want to go beyond “human error” as a root cause to understand why the human error might have occurred. As we get more self-driving cars on the road, we want to learn from every incident.
By getting beyond human error and considering different error types, we can start to think of what some actions are that we can implement to try to stop the errors occurring (“corrective actions”). Ideally, we want processes and systems to be easy and intuitive and the people to be well trained. When people are well trained but the process and/or system is complex, there are likely to be errors from time to time. As W. Edwards Deming once said, “A bad system will beat a good person every time.”
Below are examples of each of the error types described in my last post and example corrective actions.
| Error Type | Example | Example Corrective Action |
| Action errors (slips) | Entering data into the wrong field in EDC | Error and sense checks to flag a possible error |
| Action errors (lapses) | Forgetting to check fridge temperature | Checklist that shows when fridge was last checked |
| Thinking errors (rule based) | Reading a date written in American format as European (3/8/16 being 8-Mar-2016 rather than 3-Aug-2016) | Use an unambiguous date format such as dd-mmm-yyyy |
| Thinking errors (knowledge based) | Incorrect use of a scale | Ensure proper training and testing on use of the scale. Only those trained can use it. |
| Non-compliance (routine, situational and exceptional) | Not noting down details of the drug used in the Accountability Log due to rushing | Regular checking by staff and consequences for not noting appropriately |
These are examples and you should be able to think of additional possible corrective actions. But then which ones would you actually implement? You want the most effective and efficient ones of course. You want your actions to be focused on the root cause – or the chain of cause and effect that leads to the problem.
The most effective actions are those that eliminate the problem completely such as adding an automated calculation of BMI (Body Mass Index) from height and mass, for example, rather than expecting staff to calculate it correctly. If it can’t go wrong, it won’t go wrong (the corollary of Murphy’s Law). This is mistake-proofing.
The next most effective actions are ones that help people to get it right. Drop-down lists and clear, concise instructions are examples of this. Although instructions do have their limitations (as I will discuss in a future post). “No-one goes to work to do a bad job!” (W Edwards Deming again) so let’s help them do a good job.
The least effective actions are ones that rely on a check catching an error right at the end of the process. For example, the nurse checking the expiry date on a vial before administering. That’s not to say these checks should not be there, but rather they should be thought of as the “last line of defence”.
Ideally, you also want some sort of check to make sure the revised process is working. This check is an early signal as to whether your actions are effective at fixing the problem.
Got questions or comments? Interested in training options? Contact me.
Text: © 2017 Dorricott MPI Ltd. All rights reserved.
DIGR® is a registered trademark of Dorricott Metrics & Process Improvement Ltd.
“To err is human” – Alexander Pope
