Decision Aids
A 1987 study (”Applying the acute ischemic heart disease predictive instrument“) showed that a successful predictive instrument for acute ischemic heart disease (which reduced the false positive rate from 71% to 0) was, after its use in randomized trials, all but discarded by doctors (only 2.8% of the sample continued to use it).
Most doctors do not like decision aids. And patients think less of doctors’ abilities who rely on such aids. (See “Patients Derogate Physicians Who use a Computer-Assisted Diagnostic Aid.”)
Doctors cannot outperform mechanical diagnoses because their own diagnoses are inconsistent. An algorithm guarantees the same input results in the same output, and this maximizes accuracy.
Physicians can find “exceptions” everywhere they look, and, augmenting a decision aid as they see fit, will only end up lowering its overall diagnostic accuracy — because doctors are subject to random fluctuations in diagnosis caused by judgmentally-irrelevant factors including availability, priming, recency effects, inconsistent weighting of information, fatigue, etc., all of which reduce accuracy. (See “Clinical versus mechanical prediction” and “Clinical versus Actuarial Judgment.”)
“How Doctors Think They Think,” by Charles Lambdin