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Fast or frugal, but not both: Decision heuristics under time pressure.

ABSTRACT: Heuristics are simple, yet effective, strategies that people use to make decisions. Because heuristics do not require all available information, they are thought to be easy to implement and to not tax limited cognitive resources, which has led heuristics to be characterized as fast-and-frugal. We question this monolithic conception of heuristics by contrasting the cognitive demands of two popular heuristics, Tallying and Take-the-Best. We contend that heuristics that are frugal in terms of information usage may not always be fast because of the attentional control required to implement this focus in certain contexts. In support of this hypothesis, we find that Take-the-Best, while being more frugal in terms of information usage, is slower to implement and fares worse under time pressure manipulations than Tallying. This effect is then reversed when search costs for Take-the-Best are reduced by changing the format of the stimuli. These findings suggest that heuristics are heterogeneous and should be unpacked according to their cognitive demands to determine the circumstances a heuristic best applies. (PsycINFO Database Record

SUBMITTER: Bobadilla-Suarez S

PROVIDER: S-EPMC5708146 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Fast or frugal, but not both: Decision heuristics under time pressure.

Bobadilla-Suarez Sebastian S Love Bradley C BC

Journal of experimental psychology. Learning, memory, and cognition 20170529 1

Heuristics are simple, yet effective, strategies that people use to make decisions. Because heuristics do not require all available information, they are thought to be easy to implement and to not tax limited cognitive resources, which has led heuristics to be characterized as fast-and-frugal. We question this monolithic conception of heuristics by contrasting the cognitive demands of two popular heuristics, Tallying and Take-the-Best. We contend that heuristics that are frugal in terms of infor ...[more]

PMID: 28557503

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Project description:Background. Previous research has examined the individual effects of uncertainty, time pressure, perceived stress, and team support on decision making. However, scant research has investigated how team support and perceived stress collectively influence providers' perception of decision conflict and satisfaction with decision. Objectives. The present study aims to fill this void by examining the potential mediating role of perceived stress and team support in the relationship between time pressure, uncertainty, decision satisfaction, and decision conflict. Methods. Obstetrics and gynecology (Obs and Gynae) physicians (N = 347) working in tertiary care hospitals were approached through snowball and purposive convenient sampling. Self-reported data were collected in the form of questionnaires. Results. Structural equation modeling was used to uncover the complex linkages. Perceived stress was found to be a significant mediator between uncertainty and decision conflict (b = -0.033, P < 0.05). In addition, team support was also found to be a significant mediator between uncertainty and decision satisfaction (b = 0.082, P < 0.05) as well as between time pressure and decision satisfaction (b = 0.086, P < 0.05). Conclusion. Team support acts as a bridge between uncertainty and decision satisfaction and also between time pressure and decision satisfaction, underscoring its critical role in provider perceptions of decision making in the Obs and Gynae context. Implications. This study highlights the significance of managing stress, enhancing team support, and giving priority to patient-centered care. These findings provide insights into risk and uncertainty management in medical decision making, advancing patient-centered care, and optimizing health care outcomes.HighlightsStressors in hospital settings such as the complexity and uncertainty of tasks create stress among physicians, potentially leading to decision conflicts.Team support plays a fundamental role in mitigating the negative effects of stressors such as time pressure and uncertainty.Implementing stress management and team support interventions such as cognitive-behavioral therapy and mindfulness may enhance decision making among Obs and Gynae physicians.

Project description:Rationale, aims and objectivesCritics have charged that evidence-based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID-19 according to an algorithm based on evidence-based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion.MethodsAn observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID-19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID-19 was converted into a clinical pathway and translated into fast-and-frugal decision (FFT) tree ('algorithm'). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID-19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion.ResultsIn an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse-probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23-143), indicating that for every 40 patients (23-143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive-care unit (ICU) [NNT = 19 (95% CI: 13-40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings.ConclusionsWhen delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes.

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Fast or frugal, but not both: Decision heuristics under time pressure.

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Fast or frugal, but not both: Decision heuristics under time pressure.

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