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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