“There can be only one true root cause!” Let’s examine this oft-made statement with an example of a root cause analysis. Many patients in a study have been found at database lock to have been mis-stratified – causing difficulties with analysis and potentially invalidating the whole study. We discover that at randomization, the health professional is asked “Is the BMI ≤ 25? Yes/No”. In talking with CRAs and sites we realise that at a busy site, where English is not your first language, this is rather easy to answer incorrectly. If we wanted to make it easier for the health professional to get it right, why not simply ask for the patient’s height and weight. Once those are entered, the IXRS could calculate the BMI and determine whether it is less than or equal to 25. This would be much less likely to lead to error. So, we determine that the root cause is that “the IXRS was set up without considering how to reduce the likelihood for user error.” We missed an opportunity to prevent the error occurring. That’s definitely actionable. Unfortunately, of course, it’s too late for this study but we can learn from the error for existing and future studies. We can look at other studies to see how they stratify patients and whether a similar error is likely to occur. We can update the standards for IXRS for future studies. Great!
But is there more to it? Were there other actions that might have helped prevent the issue? Why was this not detected earlier? Were there opportunities to save this study? As we investigate further, we find:
- During user acceptance testing, this same error occurred but was put down to user error.
- There were several occasions during the study where a CRA had noticed that the IXRS question was answered incorrectly. They modified the setting in EDC but were unable to change the stratification as this is set at randomization. No-one had realized that this was a systemic issue (i.e. had been detected at several sites due to a special cause).
Our one root cause definitely takes us forward. But there is more to learn from this issue. Perhaps there are some other root causes too. Such as “the results of user acceptance testing were not evaluated for the potential of user error”. And “issues detected by CRAs were not recognised as systematic because there is no standard way of pulling out common issues found at sites.” These could both lead to additional actions that might help to reduce the likelihood of the issue recurring. And notice that actions on these root causes might also help reduce the likelihood of other issues occurring too.
In my experience, root cause analysis rarely leads to one root cause. In a recent training course I was running for the Institute of Clinical Research, one of the delegates reminded me of the “Swiss Cheese” model of root causes. There are typically many hazards, such as a user entering data into an IXRS. These hazards don’t normally end up as issues because we put preventive measures in place (such as standards, user acceptance testing, training). You can think of each of these preventive measures as a slice of swiss cheese – they prevent many hazards becoming issues but won’t prevent everything. Sometimes, a hazard can get through a hole in the cheese. We also put detection methods in place (such as source data verification, edit checks, listing review). You can think of each of these as additional slices of cheese which prevent issues growing more significant but won’t prevent everything. It’s when the holes in each of the layers of prevention and detection line up that a hazard can become a significant issue that might even lead to the failure of a study. So, in our example, the IXRS was set up poorly (a prevention layer failed), the user acceptance testing wasn’t reviewed considering user error (another prevention layer failed), and CRA issues were not reviewed systematically (a detection layer failed). All these failures led to the study potentially being lost.
So if, in your root cause analysis, you have only one root cause, maybe it’s time to take another look. Are there maybe other learnings you can gain from the issue? Are there other prevention or detection layers that failed?
Do you need help in root cause analysis? Take a look at DIGR-ACT training. Or give me a call.
Text: © 2019 DMPI Ltd. All rights reserved.