Welcome to Patient-Centered Care, Communicating Health Risk. This is Lecture b. The objectives for this unit, communicating health risk are, one, define risk and its importance in patient centered care and decision making. Two, describe the challenges in communicating risk. And three, describe methods of overcoming those challenges through structured communication and IT. Exactly how should numeric risk estimates be represented in order to maximize patient understanding? What are the important elements of effective risk communication and what are the best practices for representing and communicating numeric risk estimates? While the science continues to evolve, there are guiding principles based on current evidence to inform how best to present risk information to improve understanding. There are also adverse practices to avoid or, in other words, common practices, which are known to cause problems and biases. Determining how best to communicate risk information is an important choice, that should be made with care, using an evidence-based approach. This stands whether you are a provider communicating with a patient, or a user or developer of a patient decision aid. Personal health portal, online health information resource, or other sources. Over the past years papers have reviewed best practices to adopt when presenting numeric information to maximize informed decision making, and they have come to many of the same conclusions. In this lecture, we will summarize some of the main communication themes and recommend ways to nudge individuals, including those with lower numeracy towards better comprehension of risk information. The summary recommendation shown on this slide represent a set of practices that have been empirically shown to improve patients' understanding of risk and benefit information, and or their decision making. In the following slides, we will review some of these recommendations in more detail. Additional references are provided at the end of this lecture to learn more. Plain language refers to materials that are written in a simplified manner so that people of low literacy, eighth grade or lower level of education, can read them and process information. The National Institutes of Health, NIH, has established the Clear Communication Initiative that focuses on achieving health literacy objectives. Their page on plain language has information about training and links to plain language resources. Another strategy is to provide absolute risks, not just relative risks. Risk reduction can be presented using relative risk reduction or RRR. Absolute risk reduction, or ARR, or numbers needed to treat, or NNT. The relative risk reduction is the reduction of risk in the intervention group relative to the risk in the control group. The absolute risk reduction is the difference in risks between two groups. The number needed to treat is the number of patients who need to be treated or screened to prevent one additional adverse outcome. Patients are unduly influenced when risk information is presented using the relative risk approach. This can result in sub optimal decisions and should be avoided. The NNT was found to be the most difficult for patients to understand and it is recommended that this should never be the soul way information is presented. Here is an example of how to provide absolute risks, not just relative risks. Let's say there is a clinical trial evaluating a new drug X that will prevent some type of cancer. And 200 participants have signed up. In the control group, 100 participants received a placebo pill, and two developed breast cancer. In the treatment group, 100 people received the drug, and only one person developed cancer. The two groups are compared. Two developed cancer in the control group, versus one in the treatment group, resulting in a 50% reduction in cancer. That sounds pretty good. People who want to avoid cancer might consider taking this drug even if there are side effects. But the reality is that the absolute risk reduction was much smaller. If the risk of developing cancer at all was 2%, taking the drug may lower the risk to 1%. Expressed as absolute risk, this is a 1% reduction. So you can see that how risk is communicated will influence a person's deliberation about taking drug X. Research has shown that changes in risk appear larger when presented using relative risk than when using an absolute risk. And that the treatments were viewed more favorably when presented in terms of relative risk. This use of relative risk information can inappropriately lead patients to believe that a treatment is more effective than what has been empirically proven. Also, several studies have found that medical students and physicians were more likely to recommend treatment if the information was presented using relative risks. Thus, an absolute risk format should be used. A third strategy for communicating health risk is to keep denominators constant for comparisons. It is difficult for patients to compare across treatments when different denominators are used. Therefore, a consistent denominator such as 1 in 10,000 and 250 in 10,000 should always be used when the task is to compare the chance of occurrence of two or more independent events. Also it's easier for patients to understand whole numbers such as 1 in 10,000 rather than fractions or decimals like 0.01 in 100. Thus if risks are very small, larger denominators will be necessary. A fourth best practice is to consider time frames. When considering the time frame to use when presenting risk or benefit information, it is important to consider the time frame for which the best statistics are currently available. The time frame over which events occur, and the time frame that is most understood by patients. The time span chosen can influence both knowledge and risk perceptions. People often failed to adjust their risk perceptions to account for longer time spans. For example, people are more likely to increase their use of seatbelts if told they have a 33% lifetime risk of serious injury without seatbelts. Compared with being told the much smaller risk of injury, in a single trip. An example from screening illustrates some of the potential biases that may result when presenting statistics over time. It is also a good example of why selecting and communicating a meaningful outcome is critically important, since this can have a major impact of risk perceptions. Over the past few years, conversations about the risks and benefits of cancer screening have started to change. Be it prostate, breast, or ovarian cancer, the trend is to communicate both harms and benefits of screening and recommend less routine screening not more. These recommendations are based on an understanding that more screening does not necessarily translate into fewer cancer deaths. And that some screening may actually do more harm than good. Much of the confusion surrounding the benefits of screening comes from interpreting the statistics that are often used to describe the results of screening studies. An improvement in survival, defined as how long a person lives after a cancer diagnosis among people who have undergone a cancer screening, is often taken to imply that the test saves lives. But survival cannot be used accurately for this purpose because of several sources of bias. Lead time bias occurs when screening finds a cancer earlier than that cancer would have been diagnosed because of symptoms. But the earlier diagnosis does nothing to change the course of the disease. And example is shown on this slide. A man experiencing a persistent cough and weight loss is diagnosed with lung cancer at age 67, and he dies of his cancer at age 70. Five year survival for a group of patients like this man is 0%. If this man is screened and his cancer was detected earlier, say at 60, but he still dies at age 70, his life has not been extended but the measure of five year survival for a group of patients like this is 100%. So you see that people could be misinformed rather than informed without careful attention to understanding both the importance of time span and outcome when communicating both the harms and benefits of screening and other treatment options. Another helpful strategy is to use icon arrays and other visual aids when possible. Visual displays, such as bar charts and icon arrays, can improve understanding, especially among the less numerate. A growing body of research has shows that when communicating individual statistics, icon arrays are more quickly and better comprehended than other graphical formats. They can also help to prevent patients from being biased by other factors, such as denominator neglect, framing effects, and the influence of antidotes. Part of the appeal of the icon array is that they visually represent the risk as a frequency rather than a probability, while simultaneously conveying both the numerator and the denominator. Person-like icons may evoke stronger recognition that small risk are still significant to the individual people who are affected. Because icon arrays are made up of a matrix of unique elements representing individual units. For example, people as shown in this slide, within that at risk population. They communicate percentages, while simultaneously conveying gist impressions derived from the relative proportion of colored, versus uncolored area in the graph. On this slide, you see an icon array in an incremental risk format designed to make the differences between baseline and treatment risks clear. As most treatments have side effects, it is important for patients to understand the likelihood they will experience one. And make clear the differences between the baseline risk of a side effect or risk that is present without treatment and the incremental risk experienced due to the treatment. The icon array you see helps to facilitate comprehension by visually separating baseline risk from treatment risk. The people in blue represents the patient's baseline risk. And the people in green represents the additional people who would experience the side effect due to treatment. Another best practice is to use natural frequencies. Suitable formats for presenting numeric chances, depend on the nature of the task. When the task is to present the chance of a single event, simple frequency formats that include a number and time interval, such as, quote, every year 10 in 100 people with pre-diabetes develop diabetes, end quote, are more transparent than formats. Such as, quote, the chance of developing diabetes is 10%, end quote. Simply saying, 10% is problematic because it does not specify the time span nor the denominator. Also, there is good evidence to show that clinicians and patients alike find natural frequencies easier to understand than probabilities. Suggesting that decisions based on frequencies are more informed than those based on probabilities. There is also growing evidence to support the use of icon arrays to present natural frequencies with evidence suggesting that these are well understood and that they effectively support communication about individual statistics. Another way to help communicate health risk is to reduce the amount of information shown as much as possible. Information is provided to respect consumer and patient autonomy, and to help them make better informed decisions. Cognitive drawbacks exist, however, to providing more information. Studies have shown that presenting more information can be distracting and prevent people from focusing on the key pieces of information that is needed for decision making. Health information providers are faced with a challenge to communicate important content to patients and consumers through patient portals, decision aids, and mobile apps. At the same time they are not to communicate too much content since extraneous information appears to confuse those who are less numerate. Therefore it is critical that providers of information think carefully about which information is key and exclude non-critical information. An additional best practice is to provide the positive and negative frames. Gain and loss framing refers to how one describes risks and benefits. For example, the number of people who survive or die respectively research has shown that decisions are sensitive to this information framing. Positive framing, or framing in terms of gains rather than losses, has shown to be associated with lessor perception of harm and increased acceptance of harmful interventions such as high risk surgery. Framing effects can be offset by preparing participants with questions to help them identify factors relevant to their decision making. The addition of visual aids to natural frequency information has also shown to reduce the effect of possible framing. Whenever possible, describe the risks and benefits using both frames. For instance, 60% of men who have surgery to treat their prostate cancer will be impotent. This means that 40% of men will not experience impotence. The last strategy to be discussed is to provide context and evaluate labels. Risk information providers should provide contextual information when feasible. Context is particularly important for patient decision aids about disease prevention or cancer screening, in which the benefit is a reduction in disease specific mortality. One way to provide context is to provide the chance of death over the next ten years from the disease under consideration where possible according to age, smoking status, and other reliable risk factor information. As well as the chance of dying from other major causes and from all causes combined. Interpreting the meaning of numeric information, such as telling patients how good or bad a 9% risk is, can also have a substantial influence on how patients use that information. This is because patients often do not understand the meaning of unfamiliar numbers without additional help. And without meaning, information tends not to be used in subsequent decision making. In one series of studies, providing evaluative labels for numeric quality of care information, for example telling decision makers that the numbers represented poor or excellent quality of care resulted in greater use of this information in judgments and less reliance on an irrelevant emotional state among the less numeric. In another study, evaluative labels for test results that a patient's test was positive or abnormal induced larger changes to risk perceptions, and behavioral intentions than did numeric results alone. The appropriateness of these changes can be unclear in health contexts and therefore, evaluative labels should be applied carefully. To close this lecture on risk communication, it is important to emphasize that many of the strategies reviewed can be aided through the use of carefully selected patient decision aids, and other technology resources. Risk communication and attention to numeracy are important in the era of precision medicine, which uses a data-driven approach to understand patients as individuals rather than group averages. With new healthcare legislation, we are giving patients access to increasing amounts of information. And the promise is to engage them as partners in discussions and decisions about their healthcare. As we have reviewed, consumers and patients will vary considerably in numeracy skills. Some lacking basic arithmetic to understand cumulative risk, and many more lacking emergent decision based numeracy skills, which is seeking out numeric information to derive effective meaning from it. Healthcare providers can and should apply best practices and risk communication science to help consumers and patients alike make informed decisions and maximize their health and well-being. This concludes Unit 6, Communicating Health Risk. The summary of this unit is that risk information is information about the probability of future outcomes. We encounter risk information in our daily lives, not just in healthcare. However, an important goal of effective health risk communication is to facilitate in forum shared decision making. There are challenges to communicating health risk, such as low numeracy. But there are evidence based best practices that could facilitate communication and informed shared decision making. Evidence-based best practices to communicate health risk can be aided through the use of carefully selected patient decision aides and other technology resources.
