Behavioual Science & Health: Links and Resources
I spoke recently at several venues on behavioural economics, behavioural science, and health. Below is a sample of useful papers on these areas, again intended to stimulate some discussion in the Irish context. The interplay between disciplines such as health psychology, public health, behavioural medicine, and behavioural economics is a particularly interesting discussion to have. Furthermore, it would be good to discuss further the extent to which behavioural research and teaching should be embedded into medical training in Ireland. Thanks again to Sarah Breathnach who is helping on compiling resources for this blog.
General Papers
Behavioural Economics & Health: Kessler & Zhang (2014)
Behavioral Economics combines the insights of Economics and Psychology to identify how individuals deviate from the standard assumptions of economic theory and to build systematic deviations into improved models of human behavior. These models allow researchers to better describe and predict individual behavior. Lessons from Behavioral Economics can be leveraged to design large-scale public health interventions and achieve policy goals. This chapter begins with a broad overview of Behavioral Economics and identifies settings in which policy makers may wish to intervene in health decisions. The rest of the chapter explores four major topic areas within Behavioral Economics — reward incentives, information and salience, context and framing, and social forces — and investigates their influence on health behaviors including medication adherence, obesity and weight control, and medical donation. Within each of the four topic areas we discuss the relevant predictions of standard economic theory, we provide evidence of the behavioral forces that lead individuals to deviate from these predictions, and then we describe various public health interventions that have leveraged the lessons of Behavioral Economics to achieve policy goals.
Kessler, J. B., & Zhang, C. Y. (2014). Behavioral Economics and Health. Paper for Oxford Textbook of Public Health. Available at: http://assets.wharton.upenn.edu/~juddk/papers/KesslerZhang_BehavioralEconomicsHealth.pdf
Behavioral Economics and Health Economics. Frank (2014)
The health sector is filled with institutions and decision-making circumstances that create friction in markets and cognitive errors by decision makers. This paper examines the potential contributions to health economics of the ideas of behavioral economics. The discussion presented here focuses on the economics of doctor-patient interactions and some aspects of quality of care. It also touches on issues related to insurance and the demand for health care. The paper argues that long standing research impasses may be aided by applying concepts from behavioral economics.
Frank, R. G. (2004). Behavioral economics and health economics (No. w10881). National Bureau of Economic Research. Available from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.314.817&rep=rep1&type=pdf
The Behavioral Economics of Health and Health Care (2013)
People often make decisions in health care that are not in their best interest, ranging from failing to enroll in health insurance to which they are entitled, to engaging in extremely harmful behaviors. Traditional economic theory provides a limited tool kit for improving behavior because it assumes that people make decisions in a rational way, have the mental capacity to deal with huge amounts of information and choice, and have tastes endemic to them and not open to manipulation. Melding economics with psychology, behavioral economics acknowledges that people often do not act rationally in the economic sense. It therefore offers a potentially richer set of tools than provided by traditional economic theory to understand and influence behaviors. Only recently, however, has it been applied to health care. This article provides an overview of behavioral economics, reviews some of its contributions, and shows how it can be used in health care to improve people's decisions and health.
Rice, T. (2013). The behavioral economics of health and health care. Annual review of public health, 34, 431-447.
Asymmetric Paternalism to Improve Health Behaviors (2007).
Individual behavior plays a central role in the disease burden faced by society. Many major health problems in the United States and other developed nations, such as lung cancer, hypertension, and diabetes, are exacerbated by unhealthy behaviors. Modifiable behaviors such as tobacco use, overeating, and alcohol abuse account for nearly one-third of all deaths in the United States.1,2 Moreover, realizing the potential benefit of some of the most promising advances in medicine, such as medications to control blood pressure, lower cholesterol levels, and prevent stroke, has been stymied by poor adherence rates among patients.3 For example, by 1 year after having a myocardial infarction, nearly half of patients prescribed cholesterol-lowering medications have stopped taking them.4 Reducing morbidity and mortality may depend as much on motivating changes in behavior as on developing new treatments.5
Loewenstein, G., Brennan, T., & Volpp, K. G. (2007). Asymmetric paternalism to improve health behaviors. Jama, 298(20), 2415-2417. Available from http://192.70.175.129/clics/clics2008a/commsumm.nsf/b4a3962433b52fa787256e5f00670a71/853e394f84ba01f8872573ef006ec053/$FILE/080214%20Attach%20H.pdf
Health-Related Behaviour Change Papers
Some current dimensions of the behavioral economics of health-related behavior change (2016).
Health-related behaviors such as tobacco, alcohol and other substance use, poor diet and physical inactivity, and risky sexual practices are important targets for research and intervention. Health-related behaviors are especially pertinent targets in the United States, which lags behind most other developed nations on common markers of population health. In this essay we examine the application of behavioral economics, a scientific discipline that represents the intersection of economics and psychology, to the study and promotion of health-related behavior change. More specifically, we review what we consider to be some core dimensions of this discipline when applied to the study health-related behavior change. Behavioral economics (1) provides novel conceptual systems to inform scientific understanding of health behaviors, (2) translates scientific understanding into practical and effective behavior-change interventions, (3) leverages varied aspects of behavior change beyond increases or decreases in frequency, (4) recognizes and exploits trans-disease processes and interventions, and (5) leverages technology in efforts to maximize efficacy, cost effectiveness, and reach. These dimensions are overviewed and their implications for the future of the field discussed.
Bickel, W. K., Moody, L., & Higgins, S. T. (2016). Some current dimensions of the behavioral economics of health-related behavior change. Preventive medicine, 92, 16-23. Available from https://www.researchgate.net/profile/Warren_Bickel/publication/303829918_Some_Current_Dimensions_of_the_Behavioral_Economics_of_Health-Related_Behavior_Change/links/577e820a08aeaa6988b0cbc1.pdf
‘Nudging’ behaviours in healthcare: insights from behavioural economics (2015).
Since the creation of the Behavioural Insight Team (BIT) in 2010, the word “nudge” has become a popular one in social and public policy. According to policy makers and managers, applications of behavioural economics to public sector management results in increased policy efficiency and savings. In the present article, we offer a critical perspective on the topic and discuss how the application of behavioural economics can foster innovative healthcare management. We first review behavioural economics principles, and show how these can be used in healthcare management. Second, we discuss the methodological aspects of applying behavioural economics principles. Finally, we discuss limitations and current issues within the field.
Voyer, B. G. (2015). ‘Nudging’behaviours in healthcare: Insights from behavioural economics. British Journal of Healthcare Management, 21(3), 130-135. Available from: http://eprints.lse.ac.uk/61744/1/Voyer_%E2%80%98Nudging%E2%80%99%20behaviours%20in%20healthcare%20insights%20from%20behavioural%20economics.pdf
Decision-Based Disorders: The Challenge of Dysfunctional Health Behavior and the Need for a Science of Behavior Change. (2017)
Dysfunctional health behavior is a contemporary challenge, exemplified by the increasingly significant portion of health problems stemming from people’s own behavior and decision making. The challenge not only includes the direct consequences of unhealthy behavioral patterns but also their origins and the creation of policies that effectively decrease their frequency. A framework rooted in behavioral economics identifies the processes and mechanisms underlying poor health. Two behavioral economic processes, economic demand and delay discounting, are discussed in detail. Through continued development, this behavioral economic framework can guide improved outcomes in treatment and policies related to dysfunctional health behavior. Approaches are evolving to alter demand and discounting. Current and prospective policies aimed at decreasing unhealthy behavior may profit from such research.
Bickel, W. K., Pope, D. A., Moody, L. N., Snider, S. E., Athamneh, L. N., Stein, J. S., & Mellis, A. M. (2017). Decision-Based Disorders: The Challenge of Dysfunctional Health Behavior and the Need for a Science of Behavior Change. Policy Insights from the Behavioral and Brain Sciences, 2372732216686085.
Health Behavior Change: Moving from Observation to Intervention (2017).
How can progress in research on health behavior change be accelerated? Experimental medicine (EM) offers an approach that can help investigators specify the research questions that need to be addressed and the evidence needed to test those questions. Whereas current research draws predominantly on multiple overlapping theories resting largely on correlational evidence, the EM approach emphasizes experimental tests of targets or mechanisms of change and programmatic research on which targets change health behaviors and which techniques change those targets. There is evidence that engaging particular targets promotes behavior change; however, systematic studies are needed to identify and validate targets and to discover when and how targets are best engaged. The EM approach promises progress in answering the key question that will enable the science of health behavior change to improve public health: What strategies are effective in promoting behavior change, for whom, and under what circumstances?
Sheeran, P., Klein, W. M., & Rothman, A. J. (2017). Health behavior change: Moving from observation to intervention. Annual Review of Psychology, 68, 573-600.
Behavioral economic incentives to improve adherence to antiretroviral medication (2017).
Objective: Fixed incentives have been largely unsuccessful in improving adherence to antiretroviral medication. Therefore, we evaluate whether small incentives based on behavioral economic theory can increase adherence to antiretroviral medication among treatment-mature adults in Kampala, Uganda.
Design: A randomized control trial design tests whether providing small incentives based on either attending timely clinic visits (intervention group 1) or achieving high medication adherence (intervention group 2) can increase antiretroviral adherence. Antiretroviral adherence is measured by medical event monitoring system (MEMS) caps.
Methods: Overall, 155 HIV-infected men and women age 19-78 were randomized into one of two intervention groups and received small prizes of US $1.50 awarded through a drawing conditional on either attending scheduled clinic appointments or achieving at least 90% antiretroviral adherence. The control group received the usual standard of care.
Results: Preliminary results based on pooling the intervention groups showed individuals receiving incentives were 23.7 percentage points more likely to achieve 90% antiretroviral adherence compared with the control group [95% confidence interval (CI), 6.7-40.7%]. Specifically, 63.3% (95% CI, 52.9-72.8%) of participants in the pooled intervention groups maintained at least 90% mean adherence during the first 9 months of the intervention, compared with 39.6% (95% CI, 25.8-54.7%) in the control group.
Conclusion: Small prize incentives resulted in a statistically significant increase in antiretroviral adherence. Although more traditional fixed incentives have not produced the desired results, these findings suggest that small incentives based on behavioral economic theory may be more effective in motivating long-term adherence among treatment-mature adults.
Linnemayr, S., Stecher, C., & Mukasa, B. (2017). Behavioral economic incentives to improve adherence to antiretrovirals: early evidence from a randomized controlled trial in Uganda. AIDS.
A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-RE taxonomy (2010)
Background: Current reporting of intervention content in published research articles and protocols is generally poor, with great diversity of terminology, resulting in low replicability. This study aimed to extend the scope and improve the reliability of a 26-item taxonomy of behaviour change techniques developed by Abraham and Michie [Abraham, C. and Michie, S. (2008). A taxonomy of behaviour change techniques used in interventions. Health Psychology, 27(3), 379–387.] in order to optimise the reporting and scientific study of behaviour change interventions. Methods: Three UK study centres collaborated in applying this existing taxonomy to two systematic reviews of interventions to increase physical activity and healthy eating. The taxonomy was refined in iterative steps of (1) coding intervention descriptions, and assessing inter-rater reliability, (2) identifying gaps and problems across study centres and (3) refining the labels and definitions based on consensus discussions. Results: Labels and definitions were improved for all techniques, conceptual overlap between categories was resolved, some categories were split and 14 techniques were added, resulting in a 40-item taxonomy. Inter-rater reliability, assessed on 50 published intervention descriptions, was good (kappa = 0.79). Conclusions: This taxonomy can be used to improve the specification of interventions in published reports, thus improving replication, implementation and evidence syntheses. This will strengthen the scientific study of behaviour change and intervention development.
Michie, S., & Abraham, C. (2004). Interventions to change health behaviours: evidence-based or evidence-inspired? Psychology & Health, 19(1), 29-49.
Testing whether decision aids introduce cognitive biases: Results of a randomized trial (2010).
Objective: Women at high risk of breast cancer face a difficult decision whether to take medications like tamoxifen to prevent a first breast cancer diagnosis. Decision aids (DAs) offer a promising method of helping them make this decision. But concern lingers that DAs might introduce cognitive biases. Methods: We recruited 663 women at high risk of breast cancer and presented them with a DA designed to experimentally test potential methods of identifying and reducing cognitive biases that could influence this decision, by varying specific aspects of the DA across participants in a factorial design. Results: Participants were susceptible to a cognitive bias – an order effect – such that those who learned first about the risks of tamoxifen thought more favorably of the drug than women who learned first about the benefits. This order effect was eliminated among women who received additional information about competing health risks. Conclusion: We discovered that the order of risk/benefit information influenced women's perceptions of tamoxifen. This bias was eliminated by providing contextual information about competing health risks. Practice implications: We have demonstrated the feasibility of using factorial experimental designs to test whether DAs introduce cognitive biases, and whether specific elements of DAs can reduce such biases.
Ubel, P. A., Smith, D. M., Zikmund-Fisher, B. J., Derry, H. A., McClure, J., Stark, A., ... & Fagerlin, A. (2010). Testing whether decision aids introduce cognitive biases: results of a randomized trial. Patient education and counseling, 80(2), 158-163.
Overconfidence as a Cause of Diagnostic Error in Medicine (2008).
The great majority of medical diagnoses are made using automatic, efficient cognitive processes, and these diagnoses are correct most of the time. This analytic review concerns the exceptions: the times when these cognitive processes fail and the final diagnosis is missed or wrong. We argue that physicians in general underappreciate the likelihood that their diagnoses are wrong and that this tendency to overconfidence is related to both intrinsic and systemically reinforced factors. We present a comprehensive review of the available literature and current thinking related to these issues. The review covers the incidence and impact of diagnostic error, data on physician overconfidence as a contributing cause of errors, strategies to improve the accuracy of diagnostic decision making, and recommendations for future research.
Berner, E. S., & Graber, M. L. (2008). Overconfidence as a cause of diagnostic error in medicine. The American journal of medicine, 121(5), S2-S23.
A Meta-analysis of the Effects of Presenting Treatment Benefits in Different Formats (2007)
Purpose: The purpose of this article is to examine the effects of presenting treatment benefits in different formats on the decisions of both patients and health professionals. Three formats were investigated: relative risk reductions, absolute risk reductions, and number needed to treat or screen. Methods: A systematic review of the published literature was conducted. Articles were retrieved by searching a variety of databases and screened for inclusion by 2 reviewers. Data were extracted on characteristics of the subjects and methodologies used. Log-odds ratios were calculated to estimate effect sizes. Results: A total of 24 articles were retrieved that reported on 31 unique experiments. The meta-analysis showed that treatments were evaluated more favorably when the relative risk format was used rather than the absolute risk or number needed to treat format. However, a significant amount of heterogeneity was found between studies, the sources of which were explored using subgroup analyses and meta-regression. Although the subgroup analyses revealed smaller effect sizes in the studies conducted on physicians, the meta-regression showed that these differences were largely accounted for by other features of the study design. Most notably, variations in effect sizes were explained by the particular wordings that the studies had chosen to use for the relative risk and absolute risk reductions. Conclusions: The published literature has consistently demonstrated that relative risk formats produce more favorable evaluations of treatments than absolute risk or number needed to treat formats. However, the effects are heterogeneous and seem to be moderated by key differences between the methodologies used.
Covey, J. (2007). A meta-analysis of the effects of presenting treatment benefits in different formats. Medical Decision Making, 27(5), 638-654.
Designing and implementing behaviour change interventions to improve population health (2008).
Improved population health depends on changing behaviour: of those who are healthy (e.g. stopping smoking), those who are ill (e.g. adhering to health advice) and those delivering health care. To design more effective behaviour change interventions, we need more investment in developing the scientific methods for studying behaviour change. Behavioural science is relevant to all phases of the process of implementing evidence-based health care: developing evidence through primary studies, synthesizing the findings in systematic reviews, translating evidence into guidelines and practice recommendations, and implementing these in practice. 'Behaviour change: Implementation and Health', the last research programme to be funded within the MRC HSRC, aimed to develop innovative ways of applying theories and techniques of behaviour change to understand and improve the implementation of evidence-based practice, as a key step to improving health. It focused on four areas of study that apply behaviour change theory:defining and developing a taxonomy of behaviour change techniques to allow replication of studies and the possibility of accumulating evidence; conducting systematic reviews, by categorizing and synthesizing interventions on the basis of behaviour change theory; investigating the process by which evidence is translated into guideline recommendations for practice; developing a theoretical framework to apply to understanding implementation problems and designing interventions. This work will contribute to advancing the science of behaviour change by providing tools for conceptualizing and defining intervention content, and linking techniques of behaviour change to their theoretical base.
Michie, S. (2008). Designing and implementing behaviour change interventions to improve population health. Journal of health services research & policy, 13(suppl 3), 64-69.
Medical Decision Making Papers
Making better decisions: From measuring to constructing preferences. Johnson, Steffel & Goldstein (2005).
The authors examine how a constructive preferences perspective might change the prevailing view of medical decision making by suggesting that the methods used to measure preferences for medical treatments can change the preferences that are reported. The authors focus on 2 possible techniques that they believe would result in better outcomes. The 1st is the wise selection of default options. Defaults may be best applied when strong clinical evidence suggests a treatment option to be correct for most people but preserving patient choice is appropriate. The 2nd is the use of environments that explicitly facilitate the optimal construction of preferences. This seems most appropriate when choice depends on a patient's ability to understand and represent probabilities and outcomes. For each technique, the authors describe the background and literature, provide a case study, and discuss applications.
Johnson, E. J., Steffel, M., & Goldstein, D. G. (2005). Making better decisions: from measuring to constructing preferences. Health Psychology, 24(4S), S17. Available from: https://www.researchgate.net/profile/Daniel_Goldstein3/publication/7701098_Making_Better_Decisions_From_Measuring_to_Constructing_Preferences/links/0deec51791ede6e7d3000000/Making-Better-Decisions-From-Measuring-to-Constructing-Preferences.pdf
Transplantation at the Nexus of Behavioral Economics and Health Care Delivery (2012).
The transplant surgeon's decision to accept and utilize an organ typically is made within a constrained time window, explicitly cognizant of numerous health-related risks and under the potential influence of considerable regulatory and institutional pressures. This decision affects the health of two distinct populations, those patients receiving organ transplants and those waiting to receive a transplant; it also influences the physician's life and their institute's productivity. The numerous, at times nonaligned, incentives established by the complex clinical and regulatory environment, have been derived specifically to influence physicians’ behaviors, and though well intended, may lead to responses that are nonoptimal when considering the myriad stakeholders being influenced. This may compromise the quality of care provided to the population at risk, and has potential to influence the physician–patient relationship. A synergistic collaboration between transplant physicians and economists that is focused on this decision environment may help to alleviate these strains. This viewpoint discusses behavioral economic principles and how they might be applied to transplantation. Specifically, the previous medical decision-making literature on transplantation will be reviewed and a discussion on how a behavioral model of physician decision making can be utilized will be explored. To date this approach has not been integrated into transplantation decision making.
Schnier, K. E., Cox, J. C., McIntyre, C., Ruhil, R., Sadiraj, V., & Turgeon, N. (2013). Transplantation at the nexus of behavioral economics and health care delivery. American Journal of Transplantation, 13(1), 31-35. Available From: http://onlinelibrary.wiley.com/doi/10.1111/j.1600-6143.2012.04343.x/full
The Psychology of Medical Decision Making (2004)
Good decision making is an essential part of good medicine. Patients have to decide what symptoms warrant seeking medical attention and whether to accept the medical advice received. Physicians have to decide what diagnosis is most likely and what treatment plan to recommend. Health policy makers have to decide what health behaviors to encourage and what medical interventions to pay for. The study of the psychology of decision making should therefore have much to offer to the field of medicine. Conversely, medicine should provide a useful test bed for the study of decisions made by experienced decision makers about high-stakes outcomes. The current chapter reviews six intersections between the psychology of decision making and medicine.
Chapman, G. B. (2004). The psychology of medical decision making. 2004). Blackwell Handbook of Judgment and Decision Making. Malden (MA), Blackwell Publishing Ltd, 585-603. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.603&rep=rep1&type=pdf#page=596
How numeracy influences risk comprehension and medical decision making (2009).
We review the growing literature on health numeracy, the ability to understand and use numerical information, and its relation to cognition, health behaviors, and medical outcomes. Despite the surfeit of health information from commercial and noncommercial sources, national and international surveys show that many people lack basic numerical skills that are essential to maintain their health and make informed medical decisions. Low numeracy distorts perceptions of risks and benefits of screening, reduces medication compliance, impedes access to treatments, impairs risk communication (limiting prevention efforts among the most vulnerable), and, based on the scant research conducted on outcomes, appears to adversely affect medical outcomes. Low numeracy is also associated with greater susceptibility to extraneous factors (i.e., factors that do not change the objective numerical information). That is, low numeracy increases susceptibility to effects of mood or how information is presented (e.g., as frequencies vs. percentages) and to biases in judgment and decision making (e.g., framing and ratio bias effects). Much of this research is not grounded in empirically supported theories of numeracy or mathematical cognition, which are crucial for designing evidence-based policies and interventions that are effective in reducing risk and improving medical decision making. To address this gap, we outline four theoretical approaches (psychophysical, computational, standard dual-process, and fuzzy trace theory), review their implications for numeracy, and point to avenues for future research.
Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy influences risk comprehension and medical decision making. Psychological bulletin, 135(6), 943.
Rationality in medical decision making: a review of the literature on doctors’ decision-making biases (2001).
The objectives of this study were to describe ways in which doctors make suboptimal diagnostic and treatment decisions, and to discuss possible means of alleviating those biases, using a review of past studies from the psychological and medical decision-making literatures. A number of biases can affect the ways in which doctors gather and use evidence in making diagnoses. Biases also exist in how doctors make treatment decisions once a definitive diagnosis has been made. These biases are not peculiar to the medical domain but, rather, are manifestations of suboptimal reasoning to which people are susceptible in general. None the less, they can have potentially grave consequences in medical settings, such as erroneous diagnosis or patient mismanagement. No surefire methods exist for eliminating biases in medical decision making, but there is some evidence that the adoption of an evidence-based medicine approach or the incorporation of formal decision analytic tools can improve the quality of doctors’ reasoning. Doctors’ reasoning is vulnerable to a number of biases that can lead to errors in diagnosis and treatment, but there are positive signs that means for alleviating some of these biases are available.
Bornstein, B. H., & Emler, A. C. (2001). Rationality in medical decision making: a review of the literature on doctors’ decision‐making biases. Journal of evaluation in clinical practice, 7(2), 97-107.
The Beguiling Pursuit of More Information (2001).
Background: The authors tested whether clinicians make different decisions if they pursue information than if they receive the same information from the start. Methods: Three groups of clinicians participated (N = 1206): dialysis nurses (n = 171), practicing urologists (n = 461), and academic physicians (n = 574). Surveys were sent to each group containing medical scenarios formulated in 1 of 2 versions. The simple version of each scenario presented a choice between 2 options. The search version presented the same choice but only after some information had been missing and subsequently obtained. The 2 versions otherwise contained identical data and were randomly assigned. Results: In one scenario involving a personal choice about kidney donation, more dialysis nurses were willing to donate when they first decided to be tested for compatibility and were found suitable than when they knew they were suitable from the start (65% vs. 44%, P =0.007). Similar discrepancies were found in decisions made by practicing urologists concerning surgery for a patient with prostate cancer and in decisions of academic physicians considering emergency management for a patient with acute chest pain. Conclusions: The pursuit of information can increase its salience and cause clinicians to assign more importance to the information than if the same information was immediately available. An awareness of this cognitive bias may lead to improved decision making in difficult medical situations.
Redelmeier, D. A., Shafir, E., & Aujla, P. S. (2001). The beguiling pursuit of more information. Medical Decision Making, 21(5), 376-381.
Problems for clinical judgement: 5 Principles of influence in medical practice (2002)
THE BASIC SCIENCE OF PSYCHOLOGY HAS IDENTIFIED specific ingrained responses that are fundamental elements of human nature, underpin common influence strategies and may apply in medical settings. People feel a sense of obligation to repay a perceived debt. A request becomes more attractive when preceded by a marginally worse request. The drive to act consistently will persist even if demands escalate. Peer pressure is intense when people face uncertainty. The image of the requester influences the attractiveness of a request. Authorities have power beyond their expertise. Opportunities appear more valuable when they appear less available. These 7 responses were discovered decades ago in psychology research and seem intuitively understood in the business world, but they are rarely discussed in medical texts. An awareness of these principles can provide a framework for physicians to help patients change their behaviour and to understand how others in society sometime alter patients' choices.
Redelmeier, D. A., & Cialdini, R. B. (2002). Problems for clinical judgement: 5. Principles of influence in medical practice. Canadian Medical Association Journal, 166(13), 1680-1684.
The role of decision analysis in informed consent: Choosing between intuition and systematicity (1997).
An important goal of informed consent is to present information to patients so that they can decide which medical option is best for them, according to their values. Research in cognitive psychology has shown that people are rapidly overwhelmed by having to consider more than a few options in making choices. Decision analysis provides a quantifiable way to assess patients' values, and it eliminates the burden of integrating these values with probabilistic information. In this paper we evaluate the relative importance of intuition and systematicity in informed consent. We point out that there is no gold standard for optimal decision making in decisions that hinge on patient values. We also point out that in some such situations it is too early to assume that the benefits of systematicity outweigh the benefits of intuition. Research is needed to address the question of which situations favor the use of intuitive approaches of decision making and which call for a more systematic approach.
Ubel, P. A., & Loewenstein, G. (1997). The role of decision analysis in informed consent: choosing between intuition and systematicity. Social science & medicine, 44(5), 647-656.
Medical Decision Making in Situations That Offer Multiple Alternatives (1995).
Objective. —To determine whether situations involving multiple options can paradoxically influence people to choose an option that would have been declined if fewer options were available. Design. —Mailed survey containing medical scenarios formulated in one of two versions. Participants. —Two groups of physicians: members of the Ontario College of Family Physicians (response rate=77%; n=287) and neurologists and neurosurgeons affiliated with the North American Symptomatic Carotid Endarterectomy Trial (response rate=84%; n=352). One group of legislators belonging to the Ontario Provincial Parliament (response rate=32%; n=41). Intervention. —The basic version of each scenario presented a choice between two options. The expanded version presented three options: the original two plus a third. The two versions otherwise contained identical information and were randomly assigned. Outcome Measures. —Participants' treatment recommendations. Results. —In one scenario involving a patient with osteoarthritis, family physicians were less likely to prescribe a medication when deciding between two medications than when deciding about only one medication (53% vs 72%; P<.005). Apparently, the difficulty in deciding between the two medications led some physicians to recommend not starting either. Similar discrepancies were found in decisions made by neurologists and neurosurgeons concerning carotid artery surgery and by legislators concerning hospital closures. Conclusions. —The introduction of additional options can increase decision difficulty and, hence, the tendency to choose a distinctive option or maintain the status quo. Awareness of this cognitive bias may lead to improved decision making in complex medical situations.
Redelmeier, D. A., & Shafir, E. (1995). Medical decision making in situations that offer multiple alternatives. Jama, 273(4), 302-305.
Understanding Patients' Decisions: Cognitive and Emotional Perspectives (1993)
Objective. —To describe ways in which intuitive thought processes and feelings may lead patients to make suboptimal medical decisions. Design. —Review of past studies from the psychology literature. Results. —Intuitive decision making is often appropriate and results in reasonable choices; in some situations, however, intuitions lead patients to make choices that are not in their best interests. People sometimes treat safety and danger categorically, undervalue the importance of a partial risk reduction, are influenced by the way in which a problem is framed, and inappropriately evaluate an action by its subsequent outcome. These strategies help explain examples where risk perceptions conflict with standard scientific analyses. In the domain of emotions, people tend to consider losses as more significant than the corresponding gains, are imperfect at predicting future preferences, distort their memories of past personal experiences, have difficulty resolving inconsistencies between emotions and rationality, and worry with an intensity disproportionate to the actual danger. In general, such intangible aspects of clinical care have received little attention in the medical literature. Conclusion. —We suggest that an awareness of how people reason is an important clinical skill that can be promoted by knowledge of selected past studies in psychology
Redelmeier, D. A., Rozin, P., & Kahneman, D. (1993). Understanding patients' decisions: cognitive and emotional perspectives. Jama, 270(1), 72-76.
Using Behavioral Economics to Design Physician Incentives That Deliver High-Value Care (2016).
Behavioral economics provides insights about the development of effective incentives for physicians to deliver high-value care. It suggests that the structure and delivery of incentives can shape behavior, as can thoughtful design of the decision-making environment. This article discusses several principles of behavioral economics, including inertia, loss aversion, choice overload, and relative social ranking. Whereas these principles have been applied to motivate personal health decisions, retirement planning, and savings behavior, they have been largely ignored in the design of physician incentive programs. Applying these principles to physician incentives can improve their effectiveness through better alignment with performance goals. Anecdotal examples of successful incentive programs that apply behavioral economics principles are provided, even as the authors recognize that its application to the design of physician incentives is largely untested, and many outstanding questions exist. Application and rigorous evaluation of infrastructure changes and incentives are needed to design payment systems that incentivize high-quality, cost-conscious care.
Emanuel, E. J., Ubel, P. A., Kessler, J. B., Meyer, G., Muller, R. W., Navathe, A. S., ... & Sen, A. P. (2016). Using behavioral economics to design physician incentives that deliver high-value carebehavioral economics, physician incentives, and high-value care. Annals of internal medicine, 164(2), 114-119.
Promising Approaches From Behavioral Economics to Improve Patient Lung Cancer Screening Decisions (2016).
Lung cancer is a devastating disease, the deadliest form of cancer in the world and in the United States. As a consequence of CMS’s determination to provide low-dose CT (LDCT) as a covered service for at-risk smokers, LDCT lung cancer screening is now a covered service for many at-risk patients that first requires counseling and shared clinical decision making, including discussions of the risks and benefits of LDCT screening. However, shared decision making fundamentally relies on the premise that with better information, patients will arrive at rational decisions that align with their preferences and values. Evidence from the field of behavioral economics offers many contrary viewpoints that take into account patient decision making biases and the role of the shared decision environment that can lead to flawed choices and that are particularly relevant to lung cancer screening and treatment. This article discusses some of the most relevant biases, and suggests incorporating such knowledge into screening and treatment guidelines and shared decision making best practices to increase the likelihood that such efforts will produce their desired objectives to improve survival and quality of life.
Barnes, A. J., Groskaufmanis, L., & Thomson, N. B. (2016). Promising approaches from behavioral economics to improve patient lung cancer screening decisions. Journal of the American College of Radiology, 13(12), 1566-1570.
Health-Policy Papers
Do Defaults Save Lives? Johnson & Goldstein (2003).
Default options can lead to striking differences in preferences, with significant economic impact. The authors of this Policy Forum use natural and experimental data to examine the impact of simple policy defaults on the decision to become an organ donor, finding large effects that significantly increase donation rates.
Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives?. Science, 302(5649), 1338-1339. Available from https://www.researchgate.net/profile/Daniel_Goldstein3/publication/8996952_Medicine_Do_defaults_save_lives/links/0deec51791ed6cdf2c000000.pdf
Behavioural Insights in Health Care: Nudging to reduce inefficiency & waste (2015)
‘Behavioural insights’ has been described as the ‘application of behavioural science to policy and practice with a focus on (but not exclusively) “automatic” processes’.1 Nudges are a behavioural insights. Nudge-type interventions – approaches that steer people in certain directions while maintaining their freedom of choice2 – recognise that many decisions – and ensuing behaviours – are automatic and not made consciously.3 Nudges have been proposed as an effective way to change behaviour and improve outcomes at lower cost than traditional tools4,5 across a range of policy areas. With health care spending rising and the NHS facing a significant funding gap, it is important to consider ways in which health care might be made more efficient and less wasteful. Given this backdrop, Ipsos MORI was commissioned by the Health Foundation to undertake a quick scoping review, supported and guided by expert interviews, to consider the evidence of and potential for the application of nudge-type interventions to health care for the purpose of improving efficiency and reducing waste.
Perry, C., Chhatralia, K., Damesick, D., Hobden, S., & Volpe, L. (2015). Behavioural insights in health care. London: The Health Foundation, 18-29.Available from http://www.health.org.uk/sites/health/files/BehaviouralInsightsInHealthCare.pdf
Applying behavioral insights simple ways to improve health outcomes (2016).
Applying new insights about behavior can lead to better health outcomes at a lower cost. This report gives an overview of these insights and shows how they can be applied in practice. It has four key messages: 1. In order to improve health outcomes, we need a better understanding of behavior. 2. Behavioral insights offer new solutions to policy problems. 3. Behavioral insights can improve health and healthcare. 4. Trialing interventions brings important advantages. There are many opportunities to improve health and healthcare worldwide by applying behavioral insights. Many of these opportunities can be realized by applying simple tools to make practical changes. We encourage policymakers to use these tools.
Hallsworth, M., Snijders, V., Burd, H., Prestt, J., Judah, G., Huf, S., & Halpern, D. Applying behavioral insights simple ways to improve health outcomes. Available from: http://38r8om2xjhhl25mw24492dir.wpengine.netdna-cdn.com/wp-content/uploads/2016/11/WISH-2016_Behavioral_Insights_Report.pdf
Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial (2016).
Background: Unnecessary antibiotic prescribing contributes to antimicrobial resistance. In this trial, we aimed to reduce unnecessary prescriptions of antibiotics by general practitioners (GPs) in England. Methods: In this randomised, 2 × 2 factorial trial, publicly available databases were used to identify GP practices whose prescribing rate for antibiotics was in the top 20% for their National Health Service (NHS) Local Area Team. Eligible practices were randomly assigned (1:1) into two groups by computer-generated allocation sequence, stratified by NHS Local Area Team. Participants, but not investigators, were blinded to group assignment. On Sept 29, 2014, every GP in the feedback intervention group was sent a letter from England's Chief Medical Officer and a leaflet on antibiotics for use with patients. The letter stated that the practice was prescribing antibiotics at a higher rate than 80% of practices in its NHS Local Area Team. GPs in the control group received no communication. The sample was re-randomised into two groups, and in December, 2014, GP practices were either sent patient-focused information that promoted reduced use of antibiotics or received no communication. The primary outcome measure was the rate of antibiotic items dispensed per 1000 weighted population, controlling for past prescribing. Analysis was by intention to treat. This trial is registered with the ISRCTN registry, number ISRCTN32349954, and has been completed. Findings: Between Sept 8 and Sept 26, 2014, we recruited and assigned 1581 GP practices to feedback intervention (n=791) or control (n=790) groups. Letters were sent to 3227 GPs in the intervention group. Between October, 2014, and March, 2015, the rate of antibiotic items dispensed per 1000 population was 126·98 (95% CI 125·68–128·27) in the feedback intervention group and 131·25 (130·33–132·16) in the control group, a difference of 4·27 (3·3%; incidence rate ratio [IRR] 0·967 [95% CI 0·957–0·977]; p<0·0001), representing an estimated 73 406 fewer antibiotic items dispensed. In December, 2014, GP practices were re-assigned to patient-focused intervention (n=777) or control (n=804) groups. The patient-focused intervention did not significantly affect the primary outcome measure between December, 2014, and March, 2015 (antibiotic items dispensed per 1000 population: 135·00 [95% CI 133·77–136·22] in the patient-focused intervention group and 133·98 [133·06–134·90] in the control group; IRR for difference between groups 1·01, 95% CI 1·00–1·02; p=0·105). Interpretation: Social norm feedback from a high-profile messenger can substantially reduce antibiotic prescribing at low cost and at national scale; this outcome makes it a worthwhile addition to antimicrobial stewardship programmes.
Hallsworth, M., Chadborn, T., Sallis, A., Sanders, M., Berry, D., Greaves, F., ... & Davies, S. C. (2016). Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial. The Lancet, 387(10029), 1743-1752.
The Role of Behavioral Science Theory in Development and Implementation of Public Health Interventions (2010).
Increasing evidence suggests that public health and health-promotion interventions that are based on social and behavioral science theories are more effective than those lacking a theoretical base. This article provides an overview of the state of the science of theory use for designing and conducting health-promotion interventions. Influential contemporary perspectives stress the multiple determinants and multiple levels of determinants of health and health behavior. We describe key types of theory and selected often-used theories and their key concepts, including the health belief model, the transtheoretical model, social cognitive theory, and the ecological model. This summary is followed by a review of the evidence about patterns and effects of theory use in health behavior intervention research. Examples of applied theories in three large public health programs illustrate the feasibility, utility, and challenges of using theory-based interventions. This review concludes by identifying cross-cutting themes and important future directions for bridging the divides between theory, practice, and research.
Glanz, K., & Bishop, D. B. (2010). The role of behavioral science theory in development and implementation of public health interventions. Annual review of public health, 31, 399-418. Available from: https://pdfs.semanticscholar.org/37c1/2b54a222d381f31bb784d6e9162e36fc3276.pdf
Beyond carrots and sticks: Europeans support health nudges (2017).
All over the world, nations are using “health nudges” to promote healthier food choices and to reduce the health care costs of obesity and non-communicable diseases. In some circles, the relevant reforms are controversial. On the basis of nationally representative online surveys, we examine whether Europeans favour such nudges. The simplest answer is that majorities in six European nations (Denmark, France, Germany, Hungary, Italy, and the UK) do so. We find majority approval for a series of nudges, including educational messages in movie theaters, calorie and warning labels, store placement promoting healthier food, sweet-free supermarket cashiers and meat-free days in cafeterias. At the same time, we find somewhat lower approval rates in Hungary and Denmark. An implication for policymakers is that citizens are highly likely to support health nudges. An implication for further research is the importance of identifying the reasons for cross-national differences, where they exist.
Reisch, L. A., Sunstein, C. R., & Gwozdz, W. (2017). Beyond carrots and sticks: Europeans support health nudges. Food Policy, 69, 1-10.
Applying Behavioral Economics to Public Health Policy: Illustrative Examples and Promising Directions (2016)
Behavioral economics provides an empirically informed perspective on how individuals make decisions, including the important realization that even subtle features of the environment can have meaningful impacts on behavior. This commentary provides examples from the literature and recent government initiatives that incorporate concepts from behavioral economics in order to improve health, decision making, and government efficiency. The examples highlight the potential for behavioral economics to improve the effectiveness of public health policy at low cost. Although incorporating insights from behavioral economics into public health policy has the potential to improve population health, its integration into government public health programs and policies requires careful design and continual evaluation of such interventions. Limitations and drawbacks of the approach are discussed.
Matjasko, J. L., Cawley, J. H., Baker-Goering, M. M., & Yokum, D. V. (2016). Applying behavioral economics to public health policy: illustrative examples and promising directions. American journal of preventive medicine, 50(5), S13-S19.
Behavioural Insights and Healthier Lives (Halpern, 2016)
Discursive Articles
Voyer, B (2015). Behavioral Economics and Healthcare: A Match Made in Heaven. Available from: https://www.behavioraleconomics.com/behavioural-economics-and-healthcare-a-match-made-in-heaven/.
Loewenstein, G., Asch, D. A., Friedman, J. Y., Melichar, L. A., & Volpp, K. G. (2012). Can behavioural economics make us healthier? BMJ, 344(7863), 23-25. Available from http://www.cmu.edu/dietrich/sds/docs/loewenstein/CanBEHealthier.pdf
Marteau (2011). Judging nudging: can nudging improve population health? Br. Med. J, 342, 263. Available from: http://www.bmj.com/bmj/sectionpdf/186202?path=/bmj/342/7791/Analysis.full.pdf
Additional Resources
Volpp, K., Loewenstein, G., & Asch, D. (2015). Behavioral economics and health. Health Behavior: Theory, Research, and Practice, 389.
Sola, D., & Couturier, J., Voyer, B.G. (2015), Unlocking patient activation: Coupling e-health solutions coupled with gamification. British Journal of Healthcare Management, 21 (5), pp 223-228
Glanz, K., Rimer, B. K., & Viswanath, K. (Eds.). (2008). Health behavior and health education: theory, research, and practice. John Wiley & Sons. Available online from: http://202.74.245.22:8080/xmlui/bitstream/handle/123456789/362/Health%20behavior%20and%20health%20education%20by%20Karen%20Glanz.pdf?sequence=1
Behavioural Insights Team Blog Health Section: http://www.behaviouralinsights.co.uk/category/health/
Chapman, G. B., & Elstein, A. S. (2000). Cognitive processes and biases in medical decision making. Decision making in health care: Theory, psychology, and applications, 183-210.
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