(Photo by Lars Ploughmann, Flikr; License)
In these strange days, when facts seem to matter less, I thought the pediment above the door of London’s Kirkaldy Testing and Experimenting Works from 1874 was rather good. Of course, with root cause analysis (RCA), we are trying to use all the facts available to get to root cause and not rely on lots of guesswork and opinion. In my last post I described a method of RCA that I called DIGR® and I explained why I think it is more effective than the oft-taught “Five Whys” method. As a reminder the steps to DIGR® are:
Define
Is – Is Not
Go Step By Step
Root Cause
When you decide you are going to carry out an RCA there are a number of hidden assumptions that you make. Being aware of these might mean you don’t fall into a trap. In the comments to my previous posts, people have mentioned some of these already and I wanted to explore five of them a little further.
1.Assuming that the effects you see are all due to the same root cause. In the example I have been using in this blog where expired vaccine was administered to several patients at two different sites, we carried out an RCA using DIGR*. In doing so, we assumed that the root cause of the different incidents was the same and the evidence we gathered in the DIGR® process seems to confirm that. But it is possible that these independent incidents have no common root cause – the issue occurred for different reasons at each site. As you review the evidence in the Is-Is Not and Root Cause parts of DIGR® it is worth remembering that the effects might be from different root causes. This is likely to show up when the analysis seems to be getting stuck and facts seem to be at odds with each other.
2. Assuming there is only one root cause. Often issues happen because of more than one root cause or ‘causal factor’. Sometimes there is benefit in focusing on just one of these but other times, there may be a benefit in considering more than one. In our example, we came to the conclusion that the root cause was that ‘the process of identifying expired batches and quarantining them has not been verified’. This is something we can tackle with actions and try to stop a recurrence of the issue. But we could have gone down the path of trying to understand why the checks in the process had failed on these occasions and tried to get to root cause on those. We would have started looking at Human Factors which I will cover in a subsequent post. You have to make a judgement on how many strands of the issue you want to focus your efforts on. In our example we have assumed that by focusing on the primary process, the pharmacists and nurses will not have expired vaccine and so their check (whilst still a good one) should never show up expired vaccine.
3. Assuming you have enough information to work out the cause and effect relationships. Frustrating though it is, it is not always possible to get to root cause with the facts you have available. You always want to use facts (evidence) to check whether your root cause is sound and if you’re really in the guessing mode. If there is no further information available you might have to put additional QC checks in place until you obtain more facts. In our example, if we carried out a RCA using DIGR® straight after the first issue occurred, we might have focused on the root cause being at that particular site on the basis it had not happened at any others (the Is-Is Not part of DIGR®). But we might simply not know enough about exactly what happened at that one site. Of course, following further cases at another site, we realised that there was a more fundamental, systemic issue.
4. Assuming all facts presented are true. I’ve mentioned Edward Hodnett’s book from 1955 “The Art of Problem Solving” previously. There is a chapter on ‘facts’ and in it he says: “Be sceptical of assertions of fact that start, ‘J. Irving Allerdyce, the tax expert, says…’ There are at least ten ways in which these facts may not be valid. (1) Allerdyce may not have made the statement at all. (2) He may have made an error. (3) He may be misquoted. (4) He may have been quoted only in part.” Hodnett goes on to list another six possible reasons the facts might not be valid. This is not to say you should disbelieve people – but rather that you should be sceptical. Asking follow up questions such as “how do you know that?” and “do we have evidence for that?” help avoid erroneous facts setting you off in the wrong direction on your search for root cause.
5. Assuming that because an issue appears to be the same as another issue, the root cause is the same. One of the challenges with carrying out a good RCA is the lack of time. When we are pressurized to get results now, we focus on containing the issue and getting to root cause comes lower down in the priorities. After all, if we get to root cause and put fixes in place, we will help the organization in the future but it doesn’t help us now. As RCA is often a low priority, it is also rushed. And to quote Tim Lister from Tom DeMarco’s book Slack, “people under time pressure don’t think faster.” One way of short-cutting thinking is to use a cognitive short-cut and just assume that the root cause must be the same as a similar issue you saw years ago. If you go down that route you really need to test the root cause against the available facts to make sure it stands up in this case too. Deliberate use of the DIGR® method of RCA can help combat this cognitive bias as it takes you logically through the steps of Define, Is-Is Not, Go step by step and Root Cause. People need time to think.
DIGR® can help with the focus on facts rather than opinion in RCA. It helps pull together all the available facts rather than leaving some to the side by focusing on ‘why’ too early.
In my next post I will go into some more detail on the G of DIGR®. How using process maps can really help everyone involved to Go step by step and start to see where a process might fail.
Text © 2017 Dorricott MPI Ltd. All rights reserved.
DIGR® is a registered trademark of Dorricott MPI Ltd.