Published – my one man Milan Conference

Having read the consensus statement and all the papers from the Milan conference (available without subscription), I prepared my version of this for the Antwerp conference. This talk contained the following:

• A description of why the Westgard model for total error is incomplete (with of course Jim Westgard sitting in the audience)
• A description of why expanded total error models are nevertheless also incomplete
• A critique of Boyd and Bruns’ glucose meter performance simulations using the Westgard model
• A critique of the ISO and CLSI glucose meter specifications, both based on total error
• A description of what the companies with most of the market share in glucose meters did, when they started to lose market share
• How Ciba Corning specified and evaluated performance
• What I currently recommend

I submitted a written version of this talk to Clin Chem and Lab Medicine, with recommended reviewers being Milan authors with whom I disagreed. (The journal asks authors to recommend reviewers). Now I don’t know who the reviewers were, but suffice it to say that they didn’t like my paper at all. So after several revisions, I scaled back my paper to its current version, which is here (subscription required).

The influence of The Milan conference on industry – none

I just came back from the Quality in the Spotlight conference in Antwerp. Many of the presentations were about the Milan conference. After the Antwerp conference, I had an epiphany so here it is:

Regarding setting and evaluating performance goals in laboratory assays, I believe there are two worlds:

World A consists of the people who are either part of the Milan conference, previous meetings, or have a keen interest in them. These people talk about creating performance specifications based on outcome studies or biological variation, they estimate sigma values for assays or calculate measurement uncertainty. They also praise the ISO equivalent of the 9001 standard, which for labs is ISO 15189. Such discussions have been going on for a long time. World A people primarily work in hospitals.

World B consists of people who work in industry developing assays. A subset of this group collect and analyze data to determine if product performance meets the company’s performance specifications and they also prepare FDA submissions.

Most of the people in World A are not also part of World B. I belong to both worlds, which is rare.

I contend that if you put a group of World B people in a room and explain the ideas of the World A group, the World B group would listen politely and maybe ask a few questions (perhaps a sign that World A ideas are unknown to World B). After the lecture, World B people would go back to work and soon forget everything that was said.

Basically, the impact of World A on World B is zero. 

That is not to say that no one has an influence on World B. Changes to FDA regulations have an impact as do newer statistical tools such as those by Bland Altman and Passing Bablok.

Now it has been frustrating for me since I have tried to have an influence on World A by suggesting that World B methods should be considered for World A. I have been largely unsuccessful.

So maybe it’s time for World A to ask themselves why they have no influence on World B.