Neuroscience 21b – Clinical Decision Making
Anil Chopra
1. Describe why people are generally very poor at making probability judgements
2. Define the most common types of error made in estimating probabilities
3. Describe how these errors can affect health-related decisions by both patients and
doctors
4. Define “algorithms” and discuss their potential benefits and limitations in clinical
situations
5. Define the “availability”’, “representativeness” and “sickness” heuristics be able
to give examples of them
6. Appreciate that diagnosis and decisions to treat are examples of problem solving
and understand how use of heuristics and probability judgements may results in
errors being made
7. Understand how heuristics can be used by patients to support health-related
decisions
Medical consultants tend to over-diagnose their specialist disorders whereas GP’s
tend to under-diagnose them.
People are affected by irrational, extraneous factors in their decision making which
they may not consciously be aware of.
 Anchoring: this is when initial experience of something clouds any other
possibilities of estimation e.g. previous patients or previous histories.

Probability: many clinical decision involve probability e.g. diagnosis, reaction to
treatment. It is easy to fall into the gambler’s fallacy.
Common errors include:
 The gamblers fallacy
 This is where we incorrectly perceive the probability of an outcome to be
different based on previous outcomes that have happened e.g. If a coin is tossed
and lands on heads 6x times in row the belief that it is more likely to land on
heads the next time (assuming the coin is fair) is false as the chance is 0.5 – this
false belief = the gambles fallacy
 Subjective judgement of probability
 All families of six children in a city were surveyed. In 72 families, the exact
order of births of boys and girls was GBGBBG. What is your estimate of the
number of families in which the exact order of births was BGBBBB? – no
evidence for judgement
 Distraction by irrelevant information
 Linda is 31 years old, single, outspoken and very bright. She took a degree in
philosophy. As a student, she was deeply concerned with issues of discrimination
and social justice, and also participated in anti-nuclear demonstrations. Which of
the following statements about Linda is most likely to be true? a) Linda is a bank
clerk b) Linda is a bank clerk who is active in the feminist movement.
 Failing to take into account base rates
 At a conference where there are 70 engineers and 30 solicitors, you meet Dick
who is a 30 year old man. He is married with no children. He is a man of high
ability and high motivation who promises to be quite successful in his field. He is
well liked by his colleagues. What is the probability that he is an engineer?
Conditional Probabilities

Difficulty in assessing conditional probabilities
Mammogram
Result
Positive
Negative
Cancer
0.8
(sensitivity)
0.2
(false negative)
A woman presents to you with
a lump in her breast. From your
examination, her age and your
previous records of similar
cases, you estimate that the
chance of cancer is low, about
1% (p=.01).
 You
send her to the
radiologist for a mammogram
and the radiologist says the
mammogram
is
positive,
indicating cancer
 Event
though she has a
positive mammogram she is

• Baseline risk of cancer=1%
No Cancer
0.1
(false positive)
0.9
(specificity)
Given the positive mammogram, what is the
probability that your patient has cancer?
does not necessarily have cancer
Calculating Conditional Probabilities:
Framing: this is the process by which two options have the same probability on
average even though one may be more risky than they other because of the
terminology used to frame it. People are generally more risk averse if the problem is
framed as “lives saved” and risk seeking when the problem is framed as “lives lost”
Problem Solving:
Experimental studies
» Decisions are often based on the solution to a problem
» Problem solving is often studied using protocol analysis in which subjects are
asked to give a continuous verbal report of their thought processes while tackling
a problem – these though processes are then written into a computer program
» If the analysis of the way people tackle a problem is correct then the computer
will do it in the same way making the same mistakes
There are 2 broad ways to solve problems – algorithms and heuristics
Algorithms:
» A procedure that if followed correctly will lead to the right answer
» The rules of probability are examples
» Useful when the problem is well defined – excludes most day to day problems
» For the most people have to specially taught how to use them
Heuristics:
» Rules of thumb or short cuts which give an approximate or probable solution but
which are not guaranteed to be right
» Most useful in ill defined problems where algorithms are not available
» Are not taught – part of someone’s natural problem solving approach
» Some are frequently used and their unquestioning approach often leads to
judgements of error
» Commonly misapplied heuristics:
o The representative heuristic
o The availability heuristic
Representativeness: commonality between objects of similar appearance is assumed.
It can result in neglect of relevant base rates and other errors. It leads to
misconceptions about randomness and often ignores sample size.
Availability: is a rule of thumb (which can result in a cognitive bias), where people
base their prediction of the frequency of an event or the proportion within a
population based on how easily an example can be brought to mind. This results in
people overestimating the likelehood of a catastrophic event.
The Sickness Heuristic – she looks and feels fine so of course she cant be infected
(often in regards to unsafe sex) or 'If this woman was infected, she'd really be a lot
more careful about taking a risk than she's being now. The fact that she's willing to
have sex without a condom means she can't be infected' or 'This woman seems such a
nice person - she's got a lovely personality - so she can't possibly be infected'
Improving Clinical Decision Making
1) Recognising that heuristics and biases may affect our judgement unconsciously.
2) Counter-act the effect of “top-down” information processing and look for evidence
to support theories.
3) Understand and employ statistical principles.