Monday, July 14, 2014
Hot Stuff about Quality Control
Reflections on Quality Control
As a general rule any time you meet a group of people thinking on a specific topic and all have (A) different ideas and strategies and (B) confidence that there way is best, it means that the subject is very complex and not yet at the point for much more than general consensus. What is interesting is that this describes today’s focus on Quality Control in the Medical Laboratory.
What is even more interesting is that whether you date this back to Shewhart, or up a couple decades to Levey and Jennings, we’re talking about a topic that dates back at least 50 years plus.
I was recently at a miniseries of industry supported workshops on the subject in China. While most of the speakers were not English speakers, through the hard work of our simultaneous translator, the messages were pretty clear, and it was evident that the audience was very happy and impressed. If customer satisfaction is a measure to monitor for situations like this, the workshops were a pretty substantial success. I was given the opportunity to present an overall perspective on the subject, and focused on the error consequences when Quality Control is done poorly or, not at all.
Usually I don’t talk about others in MMLQR, but in this case, I will tell you that the most interesting person that I met was Dr. Richard Pang, a now retired laboratorian who combined brilliant insights with incredible wit.
Right after my opening talk he zeroed in on the single most significant question on peoples mind; how many QC samples is the right amount to do, and is there a wrong amount. It is of course an impossible question to answer with a specific number. I knew it and of course so did he. It was a gotcha question.
Rather than go down the path of number picking I said it was a matter of balance that each laboratory has to figure out for itself and in its own situation. Doing too little risks the opportunity for missed error and setting the laboratory up for TEEM failure. Doing too much risks financial ruin and never getting the work done, and still having enough gaps that failures can still occur.
From my perspective, and I will preface this with the disclaimer that I don’t manage biochemistry laboratories for a living, there are a number of variables that have to be taken into consideration, like: volume of samples, complexity of assays, morbidity of the patients being cared for, the consequence of false positive or falsely elevated results, and the consequence of false negatives or falsely low results. Add it personal liability, institutional reputation and liability and you have a start. There is a guideline that talks about QC and risk and predicates decisions on severity-occurrence grids, which is correct, provided that folks understand the inherent subjectivity of the tool. This is not an evidence based tool, rather it is a reasoned opinion rationale tool.
One keeps an open mind and if they are smart, they avoid formulas and dogmatic argument. It’s not “what’s right” or “what’s wrong”, its more “what works for me today”, while reserving the right to change your mind tomorrow.
If I found one aspect of the meeting a little disappointing it was a conversation that I had on Costs of Poor Quality where the notion of trying to calculate what part of Quality Costs can be cut when the boss says “cut costs”. Throughout my career I have seen the consequence of making compromise cuts. If history teaches anything, it is that sometimes you have to stand firm. Many years ago, one of those compromise cuts included cease doing gram stains on genital swab cultures. First, they ALWAYS lead to unintended consequences. Second, they never save money, and third they always create more work and bother (ie increase stress and strain – ie TEEM). Juran pointed out way back when that failure costs are always way bigger than prevention and appraisal and cutting prevention and appraisal always increases failure.
Cutting Quality Costs always reminds me of the movie “War Games” (an early effort by Matthew Broderick), where the supercomputer learns about thermonuclear war strategies by playing tic-tac-to. “It is an interesting game” the computer says. “The only way to win is to not play the game”.