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Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey

BACKGROUND: Prospect theory suggests that when faced with an uncertain outcome, people display loss aversion by preferring to risk a greater loss rather than incurring certain, lesser cost. Providing probability information improves decision making towards the economically optimal choice in these si...

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Autores principales: Simianu, Vlad V., Grounds, Margaret A., Joslyn, Susan L., LeClerc, Jared E., Ehlers, Anne P., Agrawal, Nidhi, Alfonso-Cristancho, Rafael, Flaxman, Abraham D., Flum, David R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131551/
https://www.ncbi.nlm.nih.gov/pubmed/27905926
http://dx.doi.org/10.1186/s12911-016-0391-3
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author Simianu, Vlad V.
Grounds, Margaret A.
Joslyn, Susan L.
LeClerc, Jared E.
Ehlers, Anne P.
Agrawal, Nidhi
Alfonso-Cristancho, Rafael
Flaxman, Abraham D.
Flum, David R.
author_facet Simianu, Vlad V.
Grounds, Margaret A.
Joslyn, Susan L.
LeClerc, Jared E.
Ehlers, Anne P.
Agrawal, Nidhi
Alfonso-Cristancho, Rafael
Flaxman, Abraham D.
Flum, David R.
collection PubMed
description BACKGROUND: Prospect theory suggests that when faced with an uncertain outcome, people display loss aversion by preferring to risk a greater loss rather than incurring certain, lesser cost. Providing probability information improves decision making towards the economically optimal choice in these situations. Clinicians frequently make decisions when the outcome is uncertain, and loss aversion may influence choices. This study explores the extent to which prospect theory, loss aversion, and probability information in a non-clinical domain explains clinical decision making under uncertainty. METHODS: Four hundred sixty two participants (n = 117 non-medical undergraduates, n = 113 medical students, n = 117 resident trainees, and n = 115 medical/surgical faculty) completed a three-part online task. First, participants completed an iced-road salting task using temperature forecasts with or without explicit probability information. Second, participants chose between less or more risk-averse (“defensive medicine”) decisions in standardized scenarios. Last, participants chose between recommending therapy with certain outcomes or risking additional years gained or lost. RESULTS: In the road salting task, the mean expected value for decisions made by clinicians was better than for non-clinicians(−$1,022 vs -$1,061; <0.001). Probability information improved decision making for all participants, but non-clinicians improved more (mean improvement of $64 versus $33; p = 0.027). Mean defensive decisions decreased across training level (medical students 2.1 ± 0.9, residents 1.6 ± 0.8, faculty1.6 ± 1.1; p-trend < 0.001) and prospect-theory-concordant decisions increased (25.4%, 33.9%, and 40.7%;p-trend = 0.016). There was no relationship identified between road salting choices with defensive medicine and prospect-theory-concordant decisions. CONCLUSIONS: All participants made more economically-rational decisions when provided explicit probability information in a non-clinical domain. However, choices in the non-clinical domain were not related to prospect-theory concordant decision making and risk aversion tendencies in the clinical domain. Recognizing this discordance may be important when applying prospect theory to interventions aimed at improving clinical care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-016-0391-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-51315512016-12-15 Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey Simianu, Vlad V. Grounds, Margaret A. Joslyn, Susan L. LeClerc, Jared E. Ehlers, Anne P. Agrawal, Nidhi Alfonso-Cristancho, Rafael Flaxman, Abraham D. Flum, David R. BMC Med Inform Decis Mak Research Article BACKGROUND: Prospect theory suggests that when faced with an uncertain outcome, people display loss aversion by preferring to risk a greater loss rather than incurring certain, lesser cost. Providing probability information improves decision making towards the economically optimal choice in these situations. Clinicians frequently make decisions when the outcome is uncertain, and loss aversion may influence choices. This study explores the extent to which prospect theory, loss aversion, and probability information in a non-clinical domain explains clinical decision making under uncertainty. METHODS: Four hundred sixty two participants (n = 117 non-medical undergraduates, n = 113 medical students, n = 117 resident trainees, and n = 115 medical/surgical faculty) completed a three-part online task. First, participants completed an iced-road salting task using temperature forecasts with or without explicit probability information. Second, participants chose between less or more risk-averse (“defensive medicine”) decisions in standardized scenarios. Last, participants chose between recommending therapy with certain outcomes or risking additional years gained or lost. RESULTS: In the road salting task, the mean expected value for decisions made by clinicians was better than for non-clinicians(−$1,022 vs -$1,061; <0.001). Probability information improved decision making for all participants, but non-clinicians improved more (mean improvement of $64 versus $33; p = 0.027). Mean defensive decisions decreased across training level (medical students 2.1 ± 0.9, residents 1.6 ± 0.8, faculty1.6 ± 1.1; p-trend < 0.001) and prospect-theory-concordant decisions increased (25.4%, 33.9%, and 40.7%;p-trend = 0.016). There was no relationship identified between road salting choices with defensive medicine and prospect-theory-concordant decisions. CONCLUSIONS: All participants made more economically-rational decisions when provided explicit probability information in a non-clinical domain. However, choices in the non-clinical domain were not related to prospect-theory concordant decision making and risk aversion tendencies in the clinical domain. Recognizing this discordance may be important when applying prospect theory to interventions aimed at improving clinical care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-016-0391-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-12-01 /pmc/articles/PMC5131551/ /pubmed/27905926 http://dx.doi.org/10.1186/s12911-016-0391-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Simianu, Vlad V.
Grounds, Margaret A.
Joslyn, Susan L.
LeClerc, Jared E.
Ehlers, Anne P.
Agrawal, Nidhi
Alfonso-Cristancho, Rafael
Flaxman, Abraham D.
Flum, David R.
Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey
title Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey
title_full Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey
title_fullStr Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey
title_full_unstemmed Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey
title_short Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey
title_sort understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131551/
https://www.ncbi.nlm.nih.gov/pubmed/27905926
http://dx.doi.org/10.1186/s12911-016-0391-3
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