Using Methods of Experimental Economics to Investigate Individual Determinants of Adherence and Implications for Mechanisms to Nudge Patients‘ Behavior
Mir Djawadi, B., Fahr, R.
(University of Paderborn, Germany)
Turk, F. (Novartis Pharma AG, Basel, Switzerland)
Motivation (1)
• Efficacy + Behavior = Effectiveness
Health care delivery/ health production is about effectiveness Need to address both, efficacy & behavior
• Why do patients suddenly decide to non‐comply?
• What can we do about it ?
Take an Economic Perspective
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Motivation (2)
• So far only few studies attempt to explain medical non‐
compliance in an economic decision model:
Ellickson/Stern/Trajtenberg (2001)
Lamiraud/Geoffard (2007)
Elliot et al. (2008)
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What is missing…
• …a conceptual framework which allows an empirical investigation of the individual determinants of non‐adherence in clinical trials • Such a framework would allow eventually 1. to screen the individual disposition to non‐adherence
2. develop and evaluate mechanisms to nudge patients‘
behavior towards adherence
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Decision under risk and uncertainty is tricky: Bounded Rationality
• Time and memory is limited, knowledge imperfect and the future uncertain
• Ambition for optimization, but exposure to reasoning fallacies Misjudgment of outcomes Misperception of the degree of uncertainty Miscalculation of probabilities
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Methodological implications (1)
• Question remains …
What fallacies might drive patients‘ behavior?
How are the fallacies related to each other within the decision‐making process?
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Methodological implications (2)
• Difficult to answer, because…
fallacies are private information and need to be unravelled in a controlled way no control about the complete decision‐making process so that certain phases are unobservable
questionnaires might not be of help because of hypothetical environment that is induced, so true preferences are not elicited
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Method: Experimental Economics (1)
• Why?
Complete decision‐making process becomes observable within a controlled setting
The influence of typically strongly related institutional factors can be disentangled
Mechanisms can be tested in the laboratory first before they get implemented in the field avoiding a costly trial‐and‐error approach
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Hands‐On: Let‘s run an experiment right now !!
(takes about 15 Minutes)
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TASK 1 10
TASK 1 ‐ Solution 22
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TASK 2 Decision problem
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TASK 2 Decision problem
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TASK 2 Decision problem
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TASK 2 Decision problem
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TASK 2 Decision problem
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Method: Experimental Economics (2)
• Usual procedure:
Student subject pool
Computerized
Anonymized conditions
Payment about 11 USD per hour depending on performance 18
Approach to understand patient‘s behavior
• Take it to the Lab:
Transfer the key components of a medical treatment into an economic setting
Look for an equivalent decision‐making process regarding choices under risk and uncertainty
Use an investment setting
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Medical Treatment vs. Investment Decision (1)
Medical Treatment
Investment Decision
• Treatment is scheduled for several time periods
• Investment plan is scheduled for several time periods
• Patient takes part in treatment to improve initial health state
• Investor agrees to the plan in order to increase her initial wealth status
• Patient makes a choice how many pills she wants to take in each period (for simplicity assume binary choice)
• Investor makes a choice how much she wants to invest for specific options (for simplicity assume binary choice)
• Patient faces opportunity costs when • The outcome of each option is linked to certain probabilities that is affected taking pills, thus investing in her quality of life
by the amount of money invested
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Medical Treatment vs. Investment Decision (2)
Medical Treatment
Investment Decision
• Return is a decreased risk of suffering a relapse • Return is a decreased risk of losing money
• If fewer pills are taken, opportunity costs are less but risk of suffering a relapse is as high as in the initial state • If more risky option is taken, less money must be invested, but higher chances of losing money • If relapse occurs treatment is cancelled
• Once money is lost there is not any longer an investment opportunity
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Conceptual Framework ‐ Three Phases (1)
Income < 0
Income > 0
(1) „Phase of invasion“ (2) „High compliance phase“
(3) „Expected variations in compliance behavior“
(1) „Phase of invasion“
• Is extended over several periods
• In this phase: player invests first without getting anything in return, so the income is below 0
• The only gain: through constant investment, the player is able to enter the second phase
• Future gains in the second phase are high enough so that player has an incentive to invest in this first phase
Æ Consistent with medical treatment, where first a threshold level has to be met for the drugs being effective
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Conceptual Framework ‐ Three Phases (2)
Income < 0
Income > 0
(1) „Phase of invasion“ (2) „High compliance phase“
(3) „Expected variations in compliance behavior“
(2) „High compliance phase“
• In this phase: possibility to generate income and compensate losses from
phase 1
• Binary decision: Choice between a risky lottery A and a less risky lottery B to simulate individual decision‐making under risk and uncertainty
• Lottery B generates the same outcome but with far lower risk; but: the player has to invest a certain amount in order to be able to choose lottery B
• Assumption: Players will choose lottery B far more often in order to compensate the previous losses
Æ Consistent with patients behavior in medical treatment at that point in time
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Conceptual Framework ‐ Three Phases (3)
Income < 0
Income > 0
(1) „Phase of invasion“ (2) „High compliance phase“
(3) „Expected variations in compliance behavior“
(3) „Expected variations in compliance behavior“
• Players enter this phase as soon as they compensated completely their losses from phase 1
• Assumption: Players will now switch from lottery B to lottery A more often
Reason according to theory (Thaler/Johnson 1990, Thaler 1999) : • As soon as the losses are compensated future gains are not valued as much as before • Gambling behavior by taking now the more risky option
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Results from the baseline experiment
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Closely before (period 5 to period 6) and closely after (period 8 to period 9) losses are compensated significant drop in compliance (p=0.03; p=0.016; McNemar 2‐sided)
Strong support for the conceptual framework
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Is there more to it…?
• Develop screening tool by correlating behavioral compliance measure with subject‘s characteristics:
Individual risk preferences
Personality characteristics
Patience/Impatience (individual time preferences)
Locus of control
Illusion of control
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Possible Mechanisms
Induce higher loss aversion in „phase of invasion“
Use financial incentives to reward compliant behavior towards the end of the time horizon (evaluate which incentive scheme is the most effective one)
Tackle the cognitive biases which lead to non‐compliance
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Possible implementation of a screening device (exemplary)
Non‐compliant behavior Non‐compliant behavior observed in early stages observed in later stages
Patient A - Profile
Use financial incentives
Patient B - Profile
Risk Preferences
Risk Preferences
Time Preferences
Time Preferences
Locus of Control
Locus of Control
Impulsiveness
Impulsiveness
Openness
Openness
Conflict avoid
Conflict avoid
Extraversion
Extraversion
Patient C - Profile
Induce higher loss‐aversion
Patient D - Profile
Risk Preferences
Risk Preferences
Time Preferences
Time Preferences
Locus of Control
Locus of Control
Impulsiveness
Impulsiveness
Openness
Openness
Conflict avoid
Conflict avoid
Extraversion
Extraversion
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Conclusions
• Conventional medical treatment successfully transferred into an equivalent economic setting
• Conceptual framework based on bounded rationality explained why and when compliant behavior changes to non‐compliance
• Capturing the key components of the conceptual framework we observe in our experimental design the same course of actions as in medical treatments
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Where Next…
• Increase external validity by….
Clinical experimental economic research Supporting case study evidence for experimental design and results
Experiments with a patient subject‐pool over a longer time span and a natural setting that is more closely aligned to a medical process
• Implementing pilots to improve health care delivery
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Thank you for your attention!
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Discussion
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