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.
