Rethinking Markets

Via the Washington Post comes an interesting article on data mining and the airline industry. Apparently, airplanes are not crashing enough for the airlines to be able to determine the sources and causes of accidents. That is, there is not enough variability in the outcomes (the last crash was August 2006) to do forensic analysis.

Instead, airlines are turning to ‘precursor’ anlayses, data mining a whole slew of events that have not led to accidents: unstabilized approaches, pitch rates at takeoff, pilot scheduling. The article suggests but does not detail the sheer number of variables and flights being analyzed, saying that Southwest has ‘mined data on more than 1 million flights’, but not really talking about what that means.

Organizationally, this is fascinating because so much more often we see organizations respond to events rather than trying to predict them. Or rather, as the vice chairperson of the NTSB put it, mining for precursors is like “‘reading tea leaves’ because it can require imagination to tie together incidents that don’t seem hazardous at first blush.” Arguably, it’s the imagination part that is so tricky in seeing what to make of precursors to mistakes and accidents. Even if you find them, often precursors only matter when they happen in conjunction (ie. in systems that are tightly coupled). So you can actually imagine a series of events that still would not result in a crash unless those events were temporally and organizationally tied together.

I would say that this is what we’re seeing now in the finance world, but it’s not. It’s worth another post, but there we’re seeing deliberate profit-seeking and many (though not nearly all or homogeneously) firms knowing that things could blow-up but not really caring.

h/t: Paul Kedrosky

The New Yorker has a great article on the effects of technology in emergency medical care, with findings that are worth drawing out more carefully. In particular, the article is about intensive care units, where extraordinary measures are taken to keep patients alive. The question the author asks is, what happens when increased organizational complexity leads to errors? And what do we do about it.

A familiar answer is specialization. And in this world, specialization works. Research findings suggest that putting an intensive care specialist on staff (in ICUs in Maryland at least) had the effect of reducing death rates in intensive care units by a third. But the more effective solution seems to be a rather mundane, analog technology: the check list.

The main proponent of checklists in ICU care is Peter Pronovost, and the article details a single arena of innovation, the IV line. The checklist here consists of: “(1) wash their hands with soap, (2) clean the patient’s skin with chlorhexidine antiseptic, (3) put sterile drapes over the entire patient, (4) wear a sterile mask, hat, gown, and gloves, and (5) put a sterile dressing over the catheter site once the line is in. Check, check, check, check, check.” This has two effects: 1) it helps with memory recall; and 2) it provides a minimum set of standards in a complex process.

The introduction of checklists in IV line procedures was pretty miraculous:

In December, 2006, the Keystone Initiative published its findings in a landmark article in The New England Journal of Medicine. Within the first three months of the project, the infection rate in Michigan’s I.C.U.s decreased by sixty-six per cent. The typical I.C.U.—including the ones at Sinai-Grace Hospital—cut its quarterly infection rate to zero. Michigan’s infection rates fell so low that its average I.C.U. outperformed ninety per cent of I.C.U.s nationwide. In the Keystone Initiative’s first eighteen months, the hospitals saved an estimated hundred and seventy-five million dollars in costs and more than fifteen hundred lives. The successes have been sustained for almost four years—all because of a stupid little checklist.

This begs the questions, why and in what circumstances is something like a checklist a useful organizational tool. What is it? Alex Pang thinks it’s about predictability, and the solidification of practices and standards in the form of a predictable document. I’m tempted to see this as standardization and to start to tease out when and where standardization works and doesn’t. My old friend commensuration seems not really to apply here.

Incidentally, the author Atul Gawande also wrote a great piece a year or so back on the Apgar score and its effect on childbirthing practices. Similar scene, but there the issue is only sort of a checklist – it was a quantification issue.