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Communicating Risk
Why is it important?
Communicating Risk
• Context
• Consultation skills
• Knowledge we need…
Communicating Risk
• Context
– Health promotion and disease prevention
– Investigations and treatments
• Consultation skills
• Communicating risk well helps shared decision
making!
• Knowledge we need…
– The concepts of incidence and prevalence
– Basic Statistics
Basic Statistics
• Absolute risk (AR) and absolute risk reduction (ARR)
• Relative risk (RR) and relative risk reduction (RRR)
• Number needed to harm (NNH) and number needed to treat
(NNT)
• Incidence and prevalence
• Positive predictive value (PPV) and negative predictive value
(NPV)
• Specificity and sensitivity
Where do patients get information
about risks?
What influences responses to
information on risk?
What influences responses to information
on risk?
• The extent to which the source of the information is
trusted
• The relevance of the information for everyday life and
decision making
• The relation to other perceived risks
• The fit with previous knowledge and experience
• The difficulty and importance of the choices and
decisions
How to improve communication
We need to:
• Build relationships of trust
• Be aware of the multiple and conflicting sources of
risk information that patients access
• Be sensitive to the psychological and social factors
that influence the ways in which patients respond to
risk information
Statistics
• Bad presentation of medical statistics can lead to
patients making poor decisions on treatment.
Statistics
• Particularly confusing:
– single event probabilities,
– conditional probabilities (such as sensitivity and
specificity),
– relative risks.
• Our challenge: to improve the presentation of
statistical information so that patients can make
well informed decisions
Communicating Risk
Presenting statistical information
Single event probabilities
• ‘you have a 20% chance of a side effect from
this drug’
• ‘2 out of every 10 patients have a side effect
from this drug’
Conditional Probabilities e.g. sensitivity,
specificity, positive predictive value
• The probability that a women has breast cancer is
0.8% (prevalence).
• If she has breast cancer, the probability that a
mammogram will show a positive result is 90%
(sensitivity).
• If a woman does not have breast cancer the
probability of a positive result is 7% (specificity).
• If a woman has a positive result what is the
probability that she actually has breast cancer (PPV)?
Natural Frequencies
• Eight out of every 1000 women have breast cancer.
• Of these eight women with breast cancer seven will
have a positive result on mammography.
• Of the 992 women who do not have breast cancer
some 70 will still have a positive mammogram.
• How many women with a positive mammogram
actually have breast cancer?
Relative Risks
• Four out of every 1000 women (>40) who do
not undergo mammography screening die of
breast cancer compared with three out of
every 1000 who are screened
• – relative risk reduction of 25%
‘Mammography reduces breast cancer mortality
by 25%’
Absolute Risk & NNT
• In every 1000 women (>40) who undergo
screening 1 will be saved from dying of breast
cancer
• To prevent one death from breast cancer, 1000
women need to undergo screening for 10
years
What is “common”
Verbal
Very common
Common
Uncommon
Rare
Frequency
Probability
What is “common”
Verbal
Frequency
Probability
Very common
>10%
More than 1 in 10
Common
1-10%
1in 100 to 1 in 10
Uncommon
0.1-1%
1 in 1000 to 1 in 100
Rare
0.01-0.1%
1 in 10000 to 1 in 1000
Framing
Putting the same information in different
ways.
• Positive and negative framing - treatment
– 97% chance of survival
– or 3% chance of dying
– …..Positive framing is more effective in
persuading people to take risky treatment
options
Framing
• Gain and loss framing – tests
– Loss framing – potential losses from not having a
test, e.g. mammography, loss of good health, life
– Gain framing – maintenance of good health
– ….Loss framing seems to influence the uptake of
screening more than gain framing
Communicating Risk
• The way doctors communicate risk can affect a
patient’s perception of risk
• Supplement verbal explanations with numerical data
• Use absolute numbers
• State the odds from a positive and negative
perspective and use a consistent denominator
• Think of the language you use
• Use visual aids wherever possible
Visual Aids
• Paling Perspective Scales
• Paling Palettes
– http://riskcomm.com/?p=0
• National Prescribing Centre patient decision
aids
– http://www.npc.nhs.uk/patient_decision_aids/
Information Giving and
Explanation: Aims
• Gauge the correct amount and type of
information to give to each individual patient
• In a way they can remember and understand
• In a way that relates to their “illness
framework”
Information Giving and
Explanation: Aims
• Use an interactive approach to ensure shared
understanding
• Involve the patient and plan together to
increase their commitment to any plans made
• Continue to build a relationship and provide a
supportive environment
References:
Kurtz SM, Silverman JD, Draper J (2005)
Teaching and Learning Communication
Skills in Medicine 2nd Edition. Radcliffe
Publishing (Oxford)
Silverman JD, Kurtz SM, Draper J (2005)
Skills for Communicating with Patients
2nd Edition. Radcliffe Publishing
(Oxford)
Kurtz S, Silverman J, Benson J, Draper
J (2003) Marrying Content and Process
in Clinical Method Teaching: Enhancing
the Calgary-Cambridge Guides
Academic Medicine;78(8):802-809
•How can doctors communicate
information about risk more effectively?
Andy Alaszewski, Tom Horlick-Jones. BMJ
2003;327:728–31