1. Communicating Risk

2. Why is it important?

3. Communicating Risk
Context
Consultation skills
Knowledge we need…

4. Communicating Risk Context Consultation skills Knowledge we need…
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

5. 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

6. Where do patients get information about risks?
Family, friends, TV, magazines, on line, stories, press, health care professionals, alternative/complementary therapists, pharmacists

7. What influences responses to information on risk?
Previous experience, cultural/religious beliefs, whether the information giver is trusted/respected: thoughts of another agenda, relevant to pt/family. How it’s presented . Is it a high/low chance. Is it important. Relation to other perceived risks.

8. 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

9. 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

10. Statistics
Bad presentation of medical statistics can lead to patients making poor decisions on treatment.

11. 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

12. 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’
Mori Poll: asked to convert 20% to a fraction.. 1/3rd got it wrong. 6% didn’t know what a fraction was.
Double blind trial: taste test between 2 colas. (cracker /water in between) 35% thought pepsi tasted most like real cola… subsequently 65% agree with statement that “most people in a double blind taste test thought pepsi tasted most like real cola”

13. Conditional Probabilities e. g
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)?
Often confused with ‘if a woman has a positive result on mammography the probability that she has breast cancer is 90%’
Only 7 of the 77 women who test positive actually have breast cancer, which is one in 11 (9%).

14. 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?

17. 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%’

18. 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
In all these examples the ultimate source of confusion is the reference class. Single event probabilities leave the reference class open to interpretation. Conditional probabilities refer to different classes (the class of people with and without illness – sensitivity and specificity). Relative risks often refer to reference classes that differ from those to which the patient belongs, such as the class of patients who die rather than those who participate in screening. Using transparent representations such as natural frequencies clarifies the reference class.

19. What is “common” Verbal Frequency Probability Very common Common
Uncommon
Rare

20. 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 %
1 in to 1 in 1000

21. 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

22. 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

23. 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

24. Visual Aids Paling Perspective Scales Paling Palettes
National Prescribing Centre patient decision aids

25. 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”

26. 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

27. 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):
How can doctors communicate information about risk more effectively? Andy Alaszewski, Tom Horlick-Jones. BMJ 2003;327:728–31