Could you summarize the two paradigms for improving a system mentioned in the text? How are they different from each other?
Generate according to: I’m working on a project involving the evaluation of social service innovations, and the other day one of my colleagues remarked that in many cases, we really know what works, the issue is getting it done. This reminded me of a fascinating article by Atul Gawande on the use of checklists for medical treatments, which in turn made me think about two different paradigms for improving a system, whether it be health, education, services, or whatever.
The first paradigm–the one we’re taught in statistics classes–is of progress via “interventions” or “treatments.” The story is that people come up with ideas (perhaps from fundamental science, as we non-biologists imagine is happening in medical research, or maybe from exploratory analysis of existing data, or maybe just from somebody’s brilliant insight), and then these get studied (possibly through randomized clinical trials, but that’s not really my point here; my real focus is on the concept of the discrete “intervention”), and then some ideas are revealed to be successful and some are not (with allowances taken for multiple testing or hierarchical structure in the studies), and the successful ideas get dispersed and used widely. There’s then a secondary phase in which interventions can get tested and modified in the wild.
The second paradigm, alluded to by my colleague above, is that of the checklist. Here the story is that everyone knows what works, but for logistical or other reasons, not all these things always get done. Improvement occurs when people are required (or encouraged or bribed or whatever) to do the 10 or 12 things that, together, are known to improve effectiveness. This “checklist” paradigm seems much different than the “intervention” approach that is standard in statistics and econometrics.
The two paradigms are not mutually exclusive. For example, the items on a checklist might have had their effectiveness individually demonstrated via earlier clinical trials–in fact, maybe that’s what got them on the checklist in the first place. Conversely, the procedure of “following a checklist” can itself be seen as an intervention and be evaluated as such.
And there are other paradigms out there, such as the self-experimentation paradigm (in which the generation and testing of new ideas go together) and the “marketplace of ideas” paradigm (in which more efficient systems are believed to evolve and survive through competitive pressures).
I just think it’s interesting that the intervention paradigm, which is so central to our thinking in statistics and econometrics (not to mention NIH funding), is not the only way to think about process improvement. A point that is obvious to nonstatisticians, perhaps.
"In a 1989 Canadian study, adults were asked to imagine the death of children of various ages and estimate which deaths would create the greatest sense of loss in a parent. The results, plotted on a graph, show grief growing until just before adolescence and then beginning to drop. When this curve was compared with a curve showing changes in reproductive potential over the life cycle (a pattern calculated from Canadian demographic data), the correlation was fairly strong. But much stronger – nearly perfect, in fact – was the correlation between the grief curves of these modern Canadians and the reproductive-potential curve of a hunter-gatherer people, the !Kung of Africa. In other words, the pattern of changing grief was almost exactly what a Darwinian would predict, given demographic realities in the ancestral environment… The first correlation was .64, the second an extremely high .92."
A correlation of .92(!) This may sound suspiciously high – could evolution really do such exact fine-tuning? – until you realize that this selection pressure was not only great enough to fine-tune parental grief, but, in fact, carve it out of existence from scratch in the first place.
99.9% of the DNA [there] belonged to one bacterium, a new species. The remaining DNA was contamination from the mine and the laboratory. … A community of a single species is almost unheard of in the microbial world. … Deep-sea vent communities, for instance … use oxygen … produced by photosynthesising plankton at the surface. ….
This prompt doesn't seem malicious.