The predictive validity of prospect theory versus expected utility in health utility measurement

Abellán-Perpiñán, José-María; Bleichrodt, Han; Prades, José Luis Pinto

doi:10.1016/j.jhealeco.2009.09.002

Cited by 39 publications

(22 citation statements)

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“…Our study adds new evidence to the debate about transferability of utility between domains, which is ongoing (Abellán-Perpinán et al, 2009b;Wakker, 1994). We found significant loss aversion, with coefficients of lower magnitude than those reported in studies on loss aversion for monetary outcomes (Abdellaoui et al 2007; Booij and van de Kuilen 2009).…”

Section: Discussionsupporting

confidence: 53%

Prospect theory in the health domain: A quantitative assessment

Attema

Brouwer

l’Haridon

2013

Journal of Health Economics

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1 We are grateful to Laurens Niëns and Matthijs Versteegh for advice on the experimental design. Mohammed Abdellaoui, Aurélien Baillon, Han Bleichrodt and Peter Wakker gave helpful comments on a previous version of this manuscript. The usual disclaimer applies. ABSTRACTIt is well-known that expected utility (EU) has empirical deficiencies. Prospect theory (PT) has developed as an alternative with more descriptive validity. However, PT's full function had not yet been quantified in the health domain. This paper is the first to simultaneously measure utility of life duration, probability weighting, and loss aversion for health.We observe loss aversion and risk aversion, which for gains can be explained by probabilistic pessimism. Utility for gains is almost linear. For losses, we find less weighting of probability 1/2 and concave utility. This contrasts with the common finding of convex utility for monetary losses.However, PT was proposed to explain choices among lotteries involving small outcomes. Life years are arguably not 'small' and need not generate convex utility for losses. Moreover, utility of life duration reflects discounting, causing concave utility. These results are a first step in fitting non-EU models for health-related decisions.

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Section: Discussionsupporting

confidence: 53%

Prospect theory in the health domain: A quantitative assessment

Attema

Brouwer

l’Haridon

2013

Journal of Health Economics

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“…Several parametric specifications of the probability weighting function have been proposed in the literature accounting for that pattern (Tversky and Kahneman 1992;Prelec 1998;Gonzalez and Wu 1999). We will employ the one-parameter specification suggested by Tversky and Kahneman (1992), which is the functional form extensively used in the domain of health outcomes (Bleichrodt and Pinto 2000;Bleichrodt et al 2001Bleichrodt et al , 2007Abellan-Perpiñan et al 2009):…”

Section: Notation and Outline Of The Cpt Modelmentioning

confidence: 99%

When normative and descriptive diverge: how to bridge the difference

Prades

Abellán-Perpiñán

2012

Soc Choice Welf

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Revealed preferences are not consistent. Many anomalies have been found in different contexts. This finding leads to a divergence between normative and descriptive analyses. There are several ways of facing this problem. In this paper we argue in favour of debiasing observed choices in such a way that the "true" preferences are discovered. Our procedure is based on quantitative corrections derived from assuming the descriptive validity of prospect theory and the normative validity of expected utility theory. Those corrective formulas were first applied by Bleichrodt et al. (Manag Sci 47:1498-1514. We explain here how such formulas can be used to avoid inefficient allocation of health care resources. This approach shares the philosophy of libertarian paternalism (LP). However, it reduces some of the potential problems of LP: the definition of error (and the need to nudge people) is more clear and objective. In this sense, it reduces the chances that the regulator tries to nudge people toward behaviour based on her preferences rather than those of the people she is nudging.

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“…[10,36] Poor management of the transition from hospital to home results in a greater incidence of readmissions to hospitals and increased hospital costs. [32,33,37] Other adverse effects for hospitals include loss of government funds, and most important, the cost to the health of the patient and subsequent impact on family, friends, and other caregivers. [1][2][3] …”

Section: Discussionmentioning

confidence: 99%

Framed messages effects on readmissions

Halpin

Hodge

2016

JHA

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Objective: As the eighth leading cause of death in the US, pneumonia (PN) is relevant to the health of the elderly and young. Accountability for readmission is part of the Affordable Care Act's Hospital Readmissions Reduction Program (RRP), which levies penalties for readmissions. We examined communication using framing effects which can motivate patients' decisions collaboratively with providers for post discharge care and readmissions prevention. Communication strategies (CS) can facilitate decision-making (DM) about health care choices. The project's aims were to (1) compare CS of framing effects (positive or negative messages) on the readmission outcome 30 days post discharge; (2) assess PN readmissions decrease 30 days post discharge when CS include the patient/family in decisions about transitions; (3) determine the impact of between patients and HCPs agreement for post hospital care, and (4) examine confounding effects between framing effects and readmission rates of age, PN severity index (PSI), and the number of diagnoses. Methods: A double-blind randomized control trial (RCT) used parallel assignment of 153 PN patients to one of three arms to test the communication framing effects using power analysis, odds ratio, Fischer's exact and ANOVA. Arm A was the Intervention positive framing group (n = 44), arm B was the Intervention Negative framing group (n = 65), and arm C was the control group (n = 44). Conclusions: Findings suggest that framed messages aid in the reduction of PN readmission rates in hospitals. DM strategies incorporates education and understanding of risk by the patient, so the healthcare teams can encourage and improve readmission outcomes.

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The predictive validity of prospect theory versus expected utility in health utility measurement

Cited by 39 publications

References 40 publications

Prospect theory in the health domain: A quantitative assessment

Prospect theory in the health domain: A quantitative assessment

When normative and descriptive diverge: how to bridge the difference

Framed messages effects on readmissions

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