Chapter 3 Clinical Decision Support Flashcards
Decision analysis
Practical application of how people ought to make decisions
Uses rational procedure to identify likelihood and value (positive or negative) of all possible outcomes associated with each option
Expected value is likelihood times value
Clinical decision support systems
-Systematic and comprehensive software developed to help with decision analysis
-Any electronic tool that provides structured guidance
Heuristics
-mental shortcuts, information-processing rules that brain uses to produce decisions or judgments
-Can lead to biases
Cognitive biases
tendencies to think in certain ways that can lead to systematic deviations from a standard of rationality or good judgment
Availability bias
Tendency to overestimate the probability of usual events because of recent or memorable experiences
Representativeness bias
Tendency to overestimate unusual diseases or conditions due to matching pieces of the typical picture of that disease
Anchoring bias
Tendency to rely too heavily or anchor on one trait or piece of information when making decisions (usually the first piece of information acquired on that subject)
Blue based bias
Tendency to over or under estimate the probability of an outcome based on the perceived value associated with that outcome
Confirmation bias
Tendency to search for, interpret, focus on, and remember information in a way that confirms one’s preconceptions
Automation bias
Tendency to depend excessively on automated systems which can lead to erroneous information that can override or interfere with correct decisions
Clinical reasoning (4 models)
-cognitive process that clinicians use to discard or confirm a hypothesis
-Additive model: sum of diagnostic weights (positive weight if evidence supports diagnosis, negative weight does not support diagnosis)
-Bayesian model: method to calculate the probability of a condition based on the prevalence or pretest probability of the concern and other related events
-Algorithmic model: follow an internal flow sheet with series of branching logic
-Blois’ funnel: start with large differential diagnosis that gets whittled down based on medical history, physical exam, and diagnostic testing
Expected value
In decision analysis is the summation of the independent probabilities of events
Expected utility
In decision analysis includes expected value but also takes into account mitigating factors like risk aversion, personal preferences, or circumstances
-Can be used in decision analysis to adjust the value of the outcome based on the perceived utility of that outcome for that patient
Probability notation
-conditional probability
-addition rule: P(A)+P(~A)=1; (If A related to B) P(B)=P(A and B)+P(A’ and B)
-multiplication rule: P(A and B)=P(A)*P(B|A)
-outcome rule: P(A)+P(B)+P(C)…=1
Decision tree or graph notation
-square: decision node
-circle: change node
-each branch has probability
-probability of all branches of node must add up to 1
-triangle: outcome node
-outcomes are assigned value (cost, utility, QALY, etc.)
-if life and death are outcomes, life=1, death=0
Sensitivity analysis
what if analysis to determine how projected performance is affected by changes in the underlying assumptions
Standard gamble
-Way to assess utility or preference assessment
-Ask patient which of 2 choices they want: 1) continue life with the current medical condition, 2) choose the intervention with a defined risk of death
-adjust risk of death, until get to point of indifference, where he can’t really choose between the two options
-resulting value is the utility
Time Trade Off
-Way to assess utility or preference assessment
-sick patients estimate how many years of their life they would be willing to give up to live a certain number of years in full health
-TTO utility (the indifference point) is the length of remaining life in perfect health divided by length of remaining life with the evaluated health state
Visual analog
-Way to assess utility or preference assessment
-patients are asked to rate different health states on a marked or unmarked scale where 0=death, and 100=perfect health
Cost-effectiveness analysis
-Form of economic analysis that compares relative costs and outcomes (effects) of two or more courses of action
-Typically denominator is gain in health from a measure, an enumerator is cost associated with the health gain
-Often visualized on cost-effectiveness plan consisting of four quadrants (expensive/effectiveness)
