[Reblog] Will Getting More Granular Help Doctors Make Better Decisions?
Excerpt (longish post)
But, there are many things that data will never do well. For certain things, physician heuristics may lead to better decisions than any predictive model.
Heuristics are shortcuts, based on experience and training that allow doctors to solve problems quickly. They are pattern maps that physicians are trained to recognize. But, heuristics have a reputation for leading to imperfect answers: Wikipedia notes that heuristics lead to solutions that “(are) not guaranteed to be optimal, but good enough for a given set of goals…. (they) ease the cognitive load of making a decision.” Humans use them because we simply can’t process information in sequential binary fashion the way computers do.
It would be a mistake to call heuristics a sad substitute for big data. Some cognitive scientists have made the argument, and I think they’re right, that heuristics aren’t simply a shortcut for coming to good-enough answers. For the right kinds of problems, heuristically generated answers are often better than the those generated by computers.
How can this be?
I often think of the following cartoon in Randall Munroe’s superb recent book, What If? Serious Scientific Answers to Absurd Hypothetical Questions. In trying to compare human and computer thinking, he rightly notes that each excels at different things. In this cartoon, for example, humans can quickly determine what they thought happened. Most people can tell you that the kid knocked over the vase and the cat is checking it out, without going through millions of alternate scenarios. Monroe notes that most computers would struggle to quickly come to the same conclusion.
So, from the perspective of an emergency doctor, here are the three leading problems with the applied use of complex analytics in the clinical setting:
- 1. The garbage in, garbage out problem. In short, humans regularly obfuscate their medical stories and misattribute causality. You need humans to guide the patient narrative and ignore red herrings.
- 2. If we want to be able to diagnose, screen and manage an ER full of runny-nosed kids with fevers, we simply can’t afford the time it takes for computers to sequentially process millions of data points. The challenge is at one simple and nuanced: allowing 99% of uncomplicated colds to go home while catching the one case of meningitis. It’s not something that a computer does well: it’s a question of balancing sensitivity (finding all true cases of meningitis among a sea of colds) and specificity (excluding meningitis correctly) and doctors seem to do better than computers when hundreds of cases need to be seen a day.
- 3. There is a problem with excess information, where too much data actually opacifies the answer you’re looking for. Statisticians call this “overfitting” the data. What they mean is that as you add more and more data points to an equation or regression model, the variability of random error around each point gets factored in as well, creating “noise”. The more variables, the more noise.
The paradox is that ignoring information often leads to simpler and ultimately better decisions.
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