Monthly Archives: August 2015

“The power of the pivot”

I have just been analysing 330,000 genital swab results with regards to trichomonas prevalence in different patient cohorts. (such fun!)

How long did that take me?

Well, about 20 minutes for IT to set up the LIS search, about 30 minutes to tidy the data, and another 30 minutes to create the necessary pivot tables and graphs.

330,000 samples, 1 hour of work, with very useful results. Absolutely ridiculous when you step back and think about it.

Microsoft Excel and its counterparts were fairly rudimentary when I was a trainee 20 years ago. Now the newer versions are extremely powerful tools, very user friendly and with the capacity to produce results within minutes that only a generation ago would have taken research students several months .

I would be far more interested in prospective employees having a sound understanding (and awareness) of this sort of software than being able to recall the biochemical reactions of Klebsiella pneumoniae. As microbiology becomes increasingly automated over the next decade, data analysis will become an increasingly important part of the job description….


“Trouble shooting microbiology complaints”

I have had a few complaints to deal with this week. I can’t say it is my favourite thing to do, but not the worst by any means.

There are five principles I try to adhere to when dealing with complaints:

1) Address: Ignoring complaints is the worst approach of all. No matter how small or trivial, ignored complaints simply fester, and inevitably come back to bite you. Address them and as soon as possible.

2) Visualise: Try and view it from the complainant’s point of view. Even if you are convinced it is not your fault or the fault of your institution, it is important not to get overly defensive. Such an approach just builds walls instead of breaking them down. Instead concentrate on point number 3.

3) Connect: Connect with the complainant. Building a relationship with the complainant is possibly the most important element of dealing with a complaint. The more personal your interaction, the better. In this respect, phoning or meeting with the complainant is far, far better than a formal email or letter. By building a relationship with the complainant, you reduce the risk of further complaints from the same source, and any that you do get are likely to be tempered by the fact that the complainant now understands both you, and your side of the story.

4) Apologise: Don’t be afraid to say sorry, we will try and improve things for next time. This creates empathy, which is really what you are aiming to achieve. Say sorry, but don’t feel sorry for yourself. Move on.

5) Analyse: Look at your systems. Never ever seek a scapegoat to blame. This achieves little and is often counterproductive. Instead look at your policies and audit trails. Look at how you can minimise the risk of a similar error happening again. But at the same time accept that you can never reduce the risk to zero, and try and make the complainant understand this also. We live in the real world, and we are all human.

And finally, accept that you are always going to receive a few complaints. Because if you don’t, you should be having a think about why this is…… (see this article on the Paradox of the Error Free Laboratory)


“The Hit Parade”

It is really useful to know your “hit rate” for any particular laboratory test. I.e. the average number of tests required to produce one positive result in your tested population.

So what is an acceptable hit rate?

Well, this depends on the severity of the disease you are looking for, how easy it is to treat, and also how easy it is to clinically suspect based on the symptomatology.

Low hit rates are generally more acceptable for serious diseases. e.g. our hit rate for gonorrhoea based on our NAAT is roughly one in a hundred. For HIV it is approximately one in several hundred. But the consequences of missing these diseases are very serious, so the low hit rate can be rationalised.

Other low hit rates are not so easy to explain away. Our hit rate for Hepatitis A is approximately 1 in a 1000. This is a disease which is rarely serious and also has some distinctive epidemiological and clinical characteristics.

After rotavirus vaccination commenced in NZ last year the rotavirus hit rate has dropped to less than 1 in a 100. Rotavirus generally does not cause particularly serious illness in the relatively affluent NZ population.

Detection of Trichomonas in genital specimens also has a local hit rate of approximately one in a hundred. Move into the older age groups and this hit rate drops to 1 in a 1000. Trichomonas infection is not a particularly severe disease in the bigger scheme of things.

Hit rates are not just important from an economic/efficiency point of view. There is an important quality issue here as well. Unless you are using a highly specific test, low hit rates often lead to poor positive predictive value, and the dreaded false positives….

So what can we do about it?

If the hit rate is unjustifiably low, one can try and focus the testing on patients more likely to have the disease. For example, Hepatitis A testing could be restricted to patients with a significant increase in liver function tests. Along the same lines routine Trichomonas testing could be restricted to certain age categories most likely to have the disease.

With more sophisticated IT capability these days, one can often determine hit rates for different tests within a few minutes with a simple computer search. Thus looking at hit rates for different tests is becoming an increasingly important aspect of laboratory management.

 Have a think about a few of the tests that you perform in your local laboratory. What are your hit rates like? And is there anything that you can do about them?