Intro Flashcards
Twomey
Clinical decision making: combination of implicit and learned characteristics
Wainwright
directive vs informative factors
Sackett/Guyatt
Evidenced based practice, vs evidence informed practice
EBP
Deduction: CDM based on studies designed to answer clinical questions, but diminishes clinical experience, patient values, anecdotal/empirical evidence
EIP
induction, abdication, deduction, clustering
Sackett: 3 legged stool
clinical experience, research, patient characteristics
Pathways of thinking
Sackett
Algorithms
step by step protocol, arboization: A+B=C
Exhaustion
gathering all patient info before sifting through
Hypothetico-deductive reasoning
forming a short lift of potential diagnoses and then performing tests to rule in or out based on subjective
Pattern recognition
recognizing condition from past patient scenarios: quick, but can be wrong
Sense making and small wins
breaking down complex problems into smaller parts to address individually
Occam’s Razor
the simpler explanation, all things equal, is better than a more complex one.
Cognitive Biases: clinical delusional
overconfident in your judgements
Cognitive biases: sunk-cost effect
following a course of action after a lot of investment in it.
Cognitive biases: recency effect
over emphasis on info most readily available to use of what we’ve seen most often lately
Cognitive biases: confirmation bias
gathering and believing in info that confirms or existing views and not considering what challenges those views (most common)
Cognitive biases: anchoring bias
allowing an initial reference point to distort our estimations
Cognitive biases: illusory correlation
drawing a relationship between 2 variables that doesn’t exist
cognitive biases: hindsight bias
looking at past outcomes as easily predictable when they were not easy to foresee. over crediting ourselves with an outcome
Prognosis
Qualitative (options), Quantitative (probability), temporal (time)
Likelihood ratio
tells us how the odds of having a disorder change with either a positive or negative test. Mathematically combines sensitivity and specificity of a test. Spin and Snout
Fagan-Baye’s Theorem
Describes the probability of an event based on the conditions that might be related to that event - why clustering is important.