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Central Nervous System

Heuristics and decision-making: what are the effects on adherence for patients with neurological disorders?

  • The human mind has evolved to make decisions and draw the most plausible conclusions regardless of the quality of available information.
  • The decision-making process is influenced by heuristics, or cognitive “short-cuts”, which can have significant influence when relevant information is limited.
  • Heuristics can therefore significantly influence patient adherence.

As described in previous articles, the mind has two systems of thinking: System 1 and System 2. The first one is immediate and spontaneous. The second is reflective and conscious but requires cognitive efforts and is thus used less frequently. System 1, our immediate, impulsive reaction, is therefore responsible for 95% of all the decisions we make. It is driven by heuristics (cognitive short-cuts), which make it faster, less taxing… and subject to error.1 This article will describe some of these heuristics.

People make decisions even when the relevant information is unavailable

A human mind is “set” to react to the environment it lives in. This process is run by System 1, which pops up automatically and effortlessly. However, in many cases, the information that comes from the environment is only partial or even irrelevant to making a “rational” decision. According to the latest behavioral science theories, the mind is “programmed” to react to its environment and make decisions automatically and without effort most of the time. This is very useful when we immediately need to get out of harm’s way, but in many cases, the immediately available information is incomplete or irrelevant to the issue at hand. Even when the environment is unknown and relevant information is limited, System 1 continues to draw conclusions and make decisions, largely automatically. In order to do so, System 1 uses mental proxies based on experience and previous learnings of System 2. These are adapted to the situation and used, within the confines of cognitive “shortcuts”, or heuristics. These heuristics significantly influence attitudes and behaviors, including patient adherence.2

Patients process information and behave according to a narrative understanding 

Daniel Kahneman describes the mind as a “machine for jumping to conclusions”. Evolution has led us to develop a “narrative” understanding of our environment based on available data. The amount and the quality of the data is irrelevant to System 1, which will choose the easiest cognitive conclusion possible2 according to the existing narrative. For example, a patient suffering from epilepsy is asked the following question: “Do you want Dr. Brown to be your doctor? He studied medicine at the best university in the country and successfully managed more than 10 000 patients during his career.” The patient’s quick System 1 answer to this question will be “yes”, but it will be based only on partial information. For example, what if the continuation of the statement on Dr. Brown is “he is an accomplished oncologist”? These conclusions result from the heuristics inherent to System 1. 

Similarly, consider the question: “Is Dr. Brown nice to his patients?”. The initial reaction is somehow different compared to the question: “Is Dr. Brown mean to his patients”? Finding the right answer to these questions would require analysis by System 2 of relevant information, which may be unavailable; in this case, System 1 tends to seek data that would confirm the immediate (sometimes induced) belief. This heuristic is referred to by Kahneman as the confirmation bias, which can lead to exaggerated emotional coherence, known as the halo effect. For example, a patient is very likely to assess the clinical skill of his physician as a function of the doctor’s interpersonal skills, since the patient is familiar with interpersonal skills and knows nothing of medicine.2 Likewise, the halo effect might have a very strong influence on the patient’s adherence, as a poor relationship with the doctor is one of the major drivers of non-adherence.3 Therefore, a patient who likes the prescribing doctor is more likely to be adherent than a patient who dislikes his doctor.

Understanding patient adherence behavior requires a thorough understanding of cognitive heuristics

Understanding these heuristics or “Rules of Thumb”, allows for further understanding of how people make their judgements and in which way these judgements are biased. On top of confirmation bias and the halo effect, already discussed, these heuristics include:

  • Anchoring – this is a tendency to make decisions with respect to a reference point. Consider the following experiment: three groups of people are asked one of the following questions:
    • Would you consider giving 5 USD to charity?
    • What would you consider giving to charity? (no anchor)
    • Would you consider giving 400 USD to charity?

The results of this experiment were that people were willing to give respectively 20 USD (anchor 5 USD), 64 USD (no anchor) and 143 USD (anchor 400 USD).2 The participants were strongly influenced by the mentioning of an initial amount. This heuristic is often used in negotiation but can be readily applied in healthcare. In a recent study in dermatology for a drug injected once a month, a group of patients (intervention group) was asked to rank their desire to take a daily (anchor) injection for psoriasis. The result showed that the patients in the intervention group anchored to daily injection were more willing to start a monthly injection treatment when compared to control group, which received no anchoring (median score 7.5 to 2).4

  • Availability (salience) heuristic– we assess probability not via an understanding of actual probability, but rather by the degree to which it is easy to imagine a given outcome.2 For example, a person who personally experienced an earthquake is more likely to overestimate its frequency. For that matter, someone who has simply recently seen images of an earthquake in a movie is more likely to overestimate the probability of an earthquake occurring. This heuristic implies that this person will be keener to buy an earthquake insurance. However, it also implies that once the memory of the earthquake disappears, the effect disappears.2 The same principle could be applied to adherence in neurological disease (ex. epilepsy): a patient who has recently had an epileptic seizure might be more likely to be adherent to a treatment, but his adherence may decrease over time as the symptoms diminish. A recent study done on HIV patients showed that the percentage of patients with mean adherence rates of 90 % or greater increases from 31.1 to 48.3 % for those who recently received positive feedback (salient information) about the HIV medication from other patients.5
  • Representativeness– this also contributes to the perceived likelihood of an event. This heuristic is very often associated with stereotypes that people might have of other people.2 For example, consider someone with a master’s degree in anthropology, passionate about environmental protection, a devoted feminist, politically on the left. Which of these two occupations is more likely for this person: working in an environmental charity or an accountant? The representativeness bias would lead you to imagine the environmental charity as the first choice, as this is how System 1 imagines her. However, there are far more accountants than employees of environmental charities, regardless of their opinions. Simply math indicates that it is more likely this person is an accountant. Similarly, people tend to develop ideas about how people in certain roles should behave. A farmer, for example, might be seen as hard-working, outdoorsy, and tough. A librarian, on the other hand, might be viewed as being quiet, organized, and reserved.6 This heuristic could be very important to understand adherence. In a study on pain management in arthritis, researchers provided conflicting information to the patients on whether the arthritis management drug should be taken with or without food. Several patients chose to take the drug with food because it is standard practice to take arthritis treatment with food.7 Taking a drug with food appeared to be representative for these patients. 
  • Loss aversion / endowment– people generally feel worse about losing something than they are happy when they earn the same thing: losing 100 USD is more painful in intensity than joy of being given 100 USD. Thaler provides the example of a group of students who were split in two sub-groups. One sub-group received a mug with the university insignia and the other did not. After that, each student was asked at which price they would consider buying or selling the mug, respectively. The sellers of the mug valued it twice as much as the buyers.8 How can this be exploited in the case of adherence? Keep in mind that when people give up something, they are hurt more than the joy they have when acquiring.2 As such, a healthcare professional can stress that every time medication is taken, or the patient engages in positive behavior, such as exercise, they are gaining in health and by abandoning that behavior they will be losing the advances they’ve made.
  • Optimism / Over-optimism – people tend to think that they assess a situation and act better than the others (roughly speaking, they are above average). In “Nudge”, Richard Thaler provides the example of people starting their own small business being asked two questions:1. What is the rate of success for business similar to yours? 2. What are your chances for success? To the first question, the answer is 50% and to the second it is 90%.8 This bias might be responsible for misconceptions that doctors might have of their patients’ adherence: while they may acknowledge that adherence is a problem, they are confident that their patients are adherent.9 A recent study showed that over-optimist HIV patients (those who believe that they will do better than other patients in the clinic, defined by an ability to fully adhere over the following month) are less likely to achieve the desired adherence rates than other patients (29.4% vs. 38.8%, respectively).5 Physicians should keep in mind that the patients’ self- assessments in themselves are not to be relied upon for ensuring adherence.

Heuristics drive decisions and behavior, including attitudes towards and adherence to treatments for chronic diseases such as Parkinson’s disease or epilepsy. A thorough understanding of these heuristics is informative for physicians, hospital authorities and HCPs to improve adherence. Future articles will discuss other behavioral theories and corresponding interventions for improving patient adherence.

References

  1. Jordanov P. Thinking fast? Slow down. Available at: https://neurofied.com/thinking-fast- slow-down/ [Accessed March 2021].
  2. Kahneman D. Thinking Fast and Slow. Penguin Books Ltd, November 2011. Available to purchase as eBook at: https://www.ebooks.com/en-gb/book/794540/thinking-fast-and- slow/daniel-kahneman/ [Accessed March 2021].
  3. Stavropoulou C. Non-adherence to medication and doctor-patient relationship; Evidence from a European survey. Patient Education and Counseling 2011;83(1):7–13.
  4. Oussedik E, et al. An anchoring-based intervention to increase patient willingness to use injectable medication in psoriasis. JAMA Dermatology 2017;153:932–4.  
  5. Linnemay S and Stecher C. Behavioral Economics Matters for HIV Research: The Impact of Behavioral Biases on Adherence to Antiretrovirals (ARVs). AIDS Behav 2015;19(11):2069–75.
  6. Cherry K. Representativeness Heuristic and Our Judgments. Available at: https://www.verywellmind.com/representativeness-heuristic-2795805 [Accessed March 2021].
  7. Elstad E, et al. Patient decision making in the face of conflicting medication information. International Journal of Qualitative Studies on Health and Well-being 2012;7:1–11.
  8. Thaler RH and Sunstein CR. Nudge: Improving Decisions About Health, Wealth and Happiness. Yale University Press; 2008.
  9. DiMatteo M.R., et al. Improving patient adherence: a three-factor model to guide practice. Health Psychology Review 2012;6(1):74–91.