Four short links: 7 August 2019
Checklists, Farewells, De-Risking, and Statistical Complexity of Brain Activity
- Why Checklists Fail (Nature) — After the NHS mandated the WHO checklist, researchers at Imperial College London launched a project to monitor the tool’s use, and found that staff were often not using it as they should. In a review of nearly 7,000 surgical procedures performed at 5 NHS hospitals, they found that the checklist was used in 97% of cases, but was completed only 62% of the time. When the researchers watched a smaller number of procedures in person, they found that practitioners often failed to give the checks their full attention, and read only two-thirds of the items out loud. In slightly more than 40% of cases, at least one team member was absent during the checks; 10% of the time, the lead surgeon was missing. If you give a checklist that ensures X to workers who don’t value X, you get workers who half-arse their way through a checklist. And, in this case, unnecessarily hurt and/or killed patients.
- Rowboats and Magic Feathers: Reflections on 13 Years of Museum 2.0 (Nina Simon) — popular social media productions twist the creators’ perceptions and become burdens. I kept to a rigorous schedule and never took a week off. Even weeks when I was giving birth, on vacation, or exhausted from challenges at work, I blogged. My attitude was, “readers don’t care what’s going on with me. They want the content.” This blog became like Dumbo’s feather. I loved it, but I also let it overpower my sense of self. As long as I was holding it — as long as I was pumping out content — I could soar. But I was terrified to let it drop. Without the blog, I presumed I could not fly. Compare Overly-Attached Girlfriend’s video on leaving YouTube. It’s hard stuff.
- De-Risking Custom Technology Projects (18F) — sweet advice.
- Distinguishing States of Conscious Arousal using Statistical Complexity — how can you tell whether someone is awake or sedated, just from their brain activity? By analysing signals from individual electrodes and disregarding spatial correlations, we find that statistical complexity distinguishes between the two states of conscious arousal through temporal correlations alone. In particular, as the degree of temporal correlations increases, the difference in complexity between the wakeful and anaesthetised states becomes larger. Uses an “epsilon machine,” which I’d not heard of before but which is a “minimal, unifilar presentation of a stationary stochastic process” (particular type of hidden Markov model). The entropy of the epsilon machine’s states yields a measure of statistical complexity, which this paper shows maps to sedated/wake states.