Communicating Risk and Benefit to Consumers

Communicating Risk and Benefit to Consumers

Communicating Risk and Benefit to Consumers

Key messages

  • Consumers think about risks and benefit differently to health professionals.
  • Clinicians often give patients numerical information about risk but many patients cannot understand this information.
  • Patients often make decisions based on the ‘gist’ of this information.
  • You can support consumers to make informed health decisions by simplifying numerical information and making it relevant to the patient. Use the simple techniques described below.

Explaining risk

One of the hardest things to explain to consumers is the risk vs benefit of different health options. However this is a critical part of informed consent and shared decision making.

Health consumers perceive risk and benefit differently to clinicians. To make informed decisions, consumers need to understand the risks and benefits of behaviours, treatments and preventive options, and how this information relates to them specifically.

Explaining risk is hard, especially in the area of health where:

  • negative outcomes can have big impacts on individuals
  • patients expect a high degree of certainty, but
  • outcomes are uncertain, complex and often hard to predict for individuals.

Numeracy and Understanding Risk

Numeracy skills vary from person to person, and for individuals can vary from day to day. Even highly educated patients (and health professionals) may have basic mathematical understanding.

Low numeracy makes it hard to read, listen to, talk about, and evaluate quantitative information. As a result, people with low health numeracy may be less likely to understand risk and make choices that help them get or stay well.

Many health-related tasks, such as reading food labels, taking medications, interpreting blood sugar or other clinical data, and understanding health risks, rely on numeracy. These tasks often require patients to work out which mathematical skills to use and then to use them in multi-step processes. Patients who had difficulty learning maths skills during their education may be too overwhelmed, intimidated or simply unable to call upon these skills[i].


When it comes to choosing treatments, many patients have difficulty understanding statistical information about the possible outcomes of treatments, such as probabilities and risks.

In order to make informed health decisions, patients need to understand and know how to use information presented to them.

Gist-based understanding

Most patients think about risk informally. This means they unconsciously use cognitive shortcuts, or simplifications, that reduce complex and demanding cognitive tasks to make them simpler and easier to understand.[ii], [iii]. For example, people infer risk from their emotional responses such as fear or dread, and from how easily they can remember information about a risk[iv].

Health professionals generally focus on giving consumers all the information and details they believe will help the consumer understand and make decisions. Unfortunately, patients often don’t have the background knowledge and numeracy skills to absorb the details. The more complex the decision, the more people simplify it by using simple decision making approaches and gist based assessments[v].

Patients need to know what the facts and details boil down to. If they can understand the gist, they can make an informed decision. What does this mean for this for me and my individual circumstances and preferences?

Patients’ perception of risk is affected by:

  • their health literacy and numeracy
  • previous experiences – either their own or others.
  • cultural understanding about the disease
  • negative perceptions about the disease or treatments
  • emotions like fear or uncertainty.

Quick Tips and Techniques

Use these techniques with all your patients. Clearer is always better. You can’t always tell which patients have the numeracy skills they need, at the time they need them.

Make numbers clear

  • Avoid explaining risk in descriptive terms, such as “low risk”. Use numbers and explain them clearly. For example, say:  “out of every 100 people who have a stent, 1 to 2 people may develop a blood clot at the stent site.”
  • Use frequencies, not percentages. For example, say: “12 out of 100” instead of “12%.”
  • Keep denominators and timeframes the same when you compare numbers. For example, don’t say: “1 in 5 compared to 1 in 4”. Do say: “20 out of 100 compared to 25 out of 100”.
  • Many people have trouble understanding probability estimates, for example they think that a risk of 1 in 200 is greater than a risk of 1 in 25[vi].
  • Use absolute risk instead of relative risk. Absolute risk gives a better sense of personal or individual risk. For example, say, “3 out of 1,000 non-smokers may have a stroke in their lifetime, compared to 6 out of 1,000 smokers” instead of “Smokers have twice the risk of having a stroke.”
  • Frame outcomes in both positive and negative terms. For example, “With this medication, 2 out of 10 people get side effects, and 8 out of 10 people do not get side effects.”

Make numbers meaningful

  • Provide context.  For example, what a patient’s cholesterol level is this year versus last year, or compared to others of the same age and with a comparable health history.
  • It can help to add context to numbers, for example you could say “1 person in 100, which is about 1 person in your street, or 1 in 1000, which is about 1 person in your suburb”.
  • Use everyday words. For example, say, “about half” instead of “49 percent.”
  • Do the maths for them. For example, tell them what their risk is rather than expecting them to calculate it from a graph.
  • Use the teach-back technique. Check for understanding when you explain risk or use a graph or table.
  • Supplement risk explanations with good visual aids. This can help patients see the risk numbers in context, meaning you are giving them information and not just data. For example, icon arrays.


Dig Deeper

In addition to numeracy levels and gist based approaches, there are a range of general information processing factors that can influence how people process quantitative data[vii]. For example:

Satisficing. Satisficing is a decision-making strategy that aims for a satisfactory or adequate result. People tend to limit the amount of mental energy they spend obtaining information to what they believe is “enough” for their purposes. Instead of putting in a large amount of time and energy searching for information, satisficing focuses on a pragmatic approach to save time, energy and resources.

Optimising bias.  People commonly see risks as being higher for other people than for themselves.

Processing risk information. Many people misunderstand concepts related to risk, such as absolute risk, lifetime risk, and cumulative risk.

  • People commonly misunderstand percentages.
  • People struggle to convert proportions to percentages
  • Most people do not recognize that the repetition of low-risk behaviour — such as not wearing a seat belt — increases a person’s cumulative risk of adverse outcomes during their lifetime.

Framing. The way in which risks are presented or “framed” can affect people’s perceptions. Patient choices can be strongly influenced by whether risks are presented in terms of survival data (e.g. 90% of people will survive the immediate postoperative period and 34% will survive 5 years) or mortality data (e.g. 10% of people will die as a result of the operation and 66% will be dead within 5years)[viii]

Scanning. People often do a quick scan of written or visual material to work out what the major points might be, and to identify the bottom line. Highlight the points you want them to focus on or that are relevant to them.

Use of contextual cues. People tend to look for cues to help them understand information, especially in cases where the data presented is complex, detailed, or in an unfamiliar format.

Resistance to persuasion. People have a natural resistance to persuasion and often engage in defensive processing, an approach that undermines messages that are inconsistent with their current behaviour. People who smoke cigarettes may be less responsive to messages emphasising that smoking is harmful since those messages are inconsistent with the smoker’s own attitude toward tobacco use.

Representative heuristic. People sometimes use their pre-existing implicit understanding and beliefs about a subject to make judgments. For example, people see cancer as a highly aggressive, lethal disease. As a result, it could be hard to explain that many types of cancer are slow-growing, easily detectable, responsive to treatment and may not be fatal.


Fuzzy Trace Theory

Fuzzy Trace Theory is one theory about how people learn and make health decisions. It has been widely tested and supported in health settings. This model proposes that there are two modes of thinking when it comes to processing information – verbatim and gist-based[ix]. Verbatim thinking is when a person stores and recalls exact facts and details (such as numbers). It is deliberate and precise. Gist-based thinking is vague – it is more relational and categorical. It is quicker and intuitive.

The vast majority of people rely on gist-based thinking to make most decisions. It is the default mode of “tracing” information in the brain[x].

The “fuzzy” part is that we rarely remember every detail of a conversation or experience. Instead we retain the gist of it and our brains construct the rest of the context from a mixture of emotions, past experience, and our understanding of how things work.

When we are given a lot of information, particularly complex information like statistics and medical information, our brain automatically tries to extract a simple, overarching meaning. Patients tend to fully accept or fully reject a notion based on their first impression combined with their gut feeling. Sometimes the patient might arrive at a different bottom-line message than the one you are trying to get across. The patient might be able to recall the numbers and details of a conversation correctly, yet still miss the intended meaning.

We interpret information as we are receiving it, pulling together what we know, our experiences, our biases and contextual clues to arrive at the gist of what we are being told. If the gist touches on a core understanding or belief (such as: cancer is scary and deadly), we are more likely to act in accordance with the information[xi].



Winton Centre for Risk and Evidence Communication, University of Cambridge (UK).  The Winton Centre works with institutions and individuals to improve the way that important evidence is presented to all of us.

Icon Array and Clinical Icon Array – simple online tools to help clinicians develop icon arrays for use in their practice.

Paling Pallets are another type of icon array infographic for describing risk to patients,  You can find out more and create your own visual aid online at The Risk Communication Institute website.

 Online Article: 

Strategies to Enhance Numeracy Skills, 2016, by Andrew Pleasant, Megan Rooney, Catina O’Leary, Laurie Myers, and Rima Rudd.


You can access free e-learning courses produced by the Australian Commission on Safety & Quality in Healthcare, the Winton Centre for Risk & Evidence Communication and the Academy of Medical Royal Colleges in the UK. Each has been accredited by the relevant Royal Colleges in the UK and you will receive a certificate on completion of the course for your records.


[i] Rothman, R.L et al Perspective: The Role of Numeracy in Health Care, ,J Health Commun. 2008 September ; 13(6): 583–595. doi:10.1080/10810730802281791

[ii] Broadstock M, Michie S. Processes of patient decision-making: theoretical and methodological issues. Psychol Health.2000;15:191-204.

Gigerenzer G, Gaissmaier W. Heuristic decision-making. Annu Rev Psychol. 2011; 62:451-482.

De Vries M, Fagerlin A, Witteman HO, Scherer LD. Combining deliberation and intuition in patient decision support. Pat Educ Counsel. 2013; 91:154-160.

Reyna VF. How people make decisions that involve risk : a dual-process approach. Curr Direct Psychol Sci.200 4;13:60-66.

[iii] Brown, Stephen & Salmon, Peter. (2018). Reconciling the theory and reality of shared decision‐making: A “matching” approach to practitioner leadership. Health Expectations. 22. 10.1111/hex.12853

[iv] Brown, Stephen & Salmon, Peter. (2018). Reconciling the theory and reality of shared decision‐making: A “matching” approach to practitioner leadership. Health Expectations. 22. 10.1111/hex.12853


[vi] Paling, J. Strategies to help patients understand risks, British Medical Journal, 2003; 327: 745-748

[vii] National Cancer Institute, 2011, Making Data Talk: A workbook, National Institutes of Health, available at

[viii] Lloyd, AJ. The extent of patients’ understanding of the risk of treatments. Quality in Health Care 2001;10(Suppl I):i14–i1

[ix] Reyna V. A theory of medical decision making and health: Fuzzy-trace theory. Medical Decision Marking. 2008 p. 850-2

[x] Reyna V. A theory of medical decision making and health: Fuzzy-trace theory. Medical Decision Marking. 2008. p. 850-2


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Last Updated on 3 December, 2020.