Communicating Risk: Part 1

Here is an abstract from my chapter on communicating risk in Grieve’s Modern Musculoskeletal Therapy 2015 (Vol 4) 

 

Risk is the probability an event will give rise to harm (Edwards et al 2001). As healthcare professionals, communicating risk is central to patient, peer, and public interactions. Manual therapy doesn’t hold the severity of risks other professions do, e.g. medicine – we rarely consider death as a risk, although there are situations where this might be the case.  Less severe risks might, for example, be transient unwanted responses to treatment. Nevertheless, we have a responsibility to consider and communicate risk as best we can to make the best clinical decision. This section summarises evidence and thought on the best ways to communicate risk to optimise shared decision-making.

Although communicating risk might seem straightforward, evidence reveals complexity, contradiction, and ambiguity. Further, we should accept that human-beings, and particularly health care professionals, are not good at understanding risk, let alone communicating it (Gigerenzer 2002).  Risk communication has become increasingly important with publication of data and evidence-based practice. In contrast to traditional ‘gut-feelings’ about risk, it is becoming possible to make data-informed judgements. Despite this numerical dimension, there is still uncertainty in understanding and communicating risk.  Paradoxically, communicating uncertain risk judgements using numerical ranges can worsen understanding, credibility, and perceptions of risk (Longman et al 2012).  This section now focuses on understanding risk; communication tools; and framing risk.

 

Understanding risk

Healthcare professionals are poor at understanding numbers (Ahmed et al 2012, Gigerenzer 2002). Gigerenzer  et al  (2007) reported only 25% of subjects correctly identified 1 in 1000 as being the same as 0.1% , coining the phrase ‘collective statistical illiteracy’ in relation to health statistics users.  Education and numeracy levels have little impact on risk judgement or understanding (Lipkus et al 2001, Gigerenzer and Galesic 2012).  Consensus on the best ways for health professionals to communicate risk is lacking (Ghosh and Ghosh 2005).  These facts create barriers to communication, and can lead to aberrant use of research-generated data (Moyer 2012).  Numerical interpretations of probability are necessary yet insufficient conditions of clinicians’ understanding of risk. Risk communication should be inclusive of the numerical probability of an unwanted event happening, together with the effect of this on a patient; importance of the effect; and the context in which the risk might occur (Edwards 2009).

“every representation of risk carries its own connotations and biases that may vary according to the individual’s perspective concerning the way the world works” (Speigelhalter 2008)

risk-bayes

Understanding probabilities

What does 5% mean? Is this the same as 0.05?  Does 5 out of 100 mean the same thing as 50 out of 1000?  Do the odds of 1:20-for say the same as 19:1-against? These are all mathematically valid expressions of the same data relating to probability judgment, but can and do mean different things. But what actually is a 5% risk? If I said you had a 5% chance of increased pain following intervention X, how do you interpret that? Does this mean you might be one of the 5 out of 100 people who’ll experience pain (in which case your probability would actually be 20%)? or that in every 100 patients I treat, 5 experience pain? Does it mean if you had 100 treatments, you’d experience pain 5 times? Does it mean that in 5% of the time, people experience pain? Or that 5 out of every 100 manual therapists induce pain to all their patients?  Is this 5% epistemological – i.e. it is already decided that you’ll have pain, but you just don’t know it yet, to the degree of 5%; or is it aleatory – i.e. a completely random notion to the degree of 5% that you will or won’t experience pain? Such variables should be considered when communicating risk.

The first stage in effective communication is establishing the reference class to which the probability relates e.g. time; location; person. In using population data for risk communication, most of the time the reference class will be historical. i.e. data from past events are used to inform the chance of the next event. Embedding a new individual event in data from a past population should carry some additional judgment, as new informative knowledge may be ignored. Spiegelhalter ’s report of pre-Obama odds on a black US president is a good example: 43/43 past US Presidents were white, indicating a statistical prediction of almost certainty of a 44th white President (Spiegelhalter 2008).

 

Part 2 will look at the relative v absolute risk; probabilities v natural frequencies; and framing of risk.

References

Ahmed H, Naik G, Willoughby H, Edwards AGK 2012 Communicating risk. British Medical Journal 344:e3996

Edwards, A, Elwyn G, Covey J et al. 2001. “Presenting risk information–A review of the effects of ‘framing’ and other manipulations on patient outcomes.” Journal of Health Communication 6(1): 61-82.

Ghosh AK, Ghosh K 2005 Translating evidence based information into effective risk communication: current challenges and opportunities. Journal of Laboratory and Clinical Medicine 145(4):171–180.

Gigerenzer G 2002 How innumeracy can be exploited. In: Reckoning with risk—learning tolive with uncertainty. 1st ed. Penguin Press, p 201-10.

Gigerenzer G, Gaissmaier W, Kurz-Milcke E 2007 Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest 8:53-96.

Gigerenzer G, Galesic M 2012 Why do single event probabilities confuse patients. British Medical Journal 344:e245

Lipkus IM, Samsa G, Rimmer BK 2001 General performance on a numeracy scale amonghighly educated samples. Medical Decision Making 21:37-44.

Longman T, Turner RM, King M et al 2012 The effects of communicating uncertainty in quantities health risk estimates. Patient Education and Counselling 89: 252-259

Moyer VA 2012 What we don’t know can hurt our patients: physician innumeracy and overuse of screening tests. Annals of Internal Medicine 156:392-393.

Speigelhalter DJ 2008 Understanding uncertainty. Annals of Family Medicine 6(3): 196-197.

Buy Grieve’s Modern Musculoskeletal Therapy here.

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