When something significant goes wrong, we all know that getting to the root cause is an important start to understanding and helping to prevent the same issue recurring. I’ve talked many times in this blog about methods of root cause analysis and, of course, I recommend DIGR-ACT®. But there are other methods too. The assumption with all these methods is that you can actually get to the root cause(s).
I was running process and risk training for the Institute of Clinical Research recently. The training includes root cause analysis. And one of the trainees gave an example of a Principal Investigator (PI) who had randomized a patient, received the randomization number and proceeded to pick the wrong medication lot for the patient. She should have selected the medication lot that matched the randomization number but picked the wrong one. This was discovered later in the trial when Investigational Product accountability was carried out by the CRA visiting the site. By this time, of course, the patient had potentially been put at risk and the results could not be included in the analysis. So why had this happened? It definitely seemed to be human error. But why had that error occurred?
The PI was experienced in clinical trials. She knew what to do. This error had not occurred before. There was no indication that she was particularly rushed or under pressure on that day. The number was clear and in large type. How was it possible to mis-read the number? The PI simply said she made a mistake. And mistakes happen. That’s true, of course, but would we accept that of an aeroplane pilot? We’d still want to understand how it happened. Human error is not a root cause. But if human error isn’t the root cause, what is?
Sometimes, we just don’t know. Root cause analysis relies on cause and effect. If we don’t understand the cause and effect relationships, we will not be able to get to true root causes. But that doesn’t mean we just hold up our hands and hope it doesn’t happen again. That would never pass in the airline industry. So what should we do in this case?
It’s worth trying to see, first, how widespread a problem this is. Has it happened before at other sites? On other studies? What are the differences between sites / studies where this has and had not happened? This may still not be enough to lead you to root cause(s). If not, then maybe we could modify the process to make it less likely to recur? Could we add a QC step such as having the PI write the number of the medication down next to the randomization number – this should highlight a difference if there is one. Or perhaps they could enter the number into a system so that it can check, Or maybe there has to be someone else at the site that checks at the point of dispensing.
A secret in root cause analysis that is rarely mentioned is that sometimes you can’t get to the root cause(s). There are occasions when you simply don’t have enough information to be able to get there. In these cases, whatever method you use, you cannot establish the root cause(s). Of course, if you do, it will help in determining effective actions to help stop recurrence. But without establishing root cause(s), there are still actions you can take to try to reduce the likelihood of recurrence.
Text: © 2020 Dorricott MPI Ltd. All rights reserved.