Determinants of Physicians' Referrals for Suspected Cancer Given a Risk-Prediction Algorithm: Linking Signal Detection and Fuzzy Trace Theory.

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Olga Kostopoulou, Bence Pálfi, Kavleen Arora, Valerie Reyna
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引用次数: 0

Abstract

BackgroundPrevious research suggests that physicians' inclination to refer patients for suspected cancer is a relatively stable characteristic of their decision making. We aimed to identify its psychological determinants in the presence of a risk-prediction algorithm.MethodsWe presented 200 UK general practitioners with online vignettes describing patients with possible colorectal cancer. Per the vignette, GPs indicated the likelihood of referral (from highly unlikely to highly likely) and level of cancer risk (negligible/low/medium/high), received an algorithmic risk estimate, and could then revise their responses. After completing the vignettes, GPs responded to questions about their values with regard to harms and benefits of cancer referral for different stakeholders, perceived severity of errors, acceptance of false alarms, and attitudes to uncertainty. We tested whether these values and attitudes predicted their earlier referral decisions.ResultsThe algorithm significantly reduced both referral likelihood (b = -0.06 [-0.10, -0.007], P = 0.025) and risk level (b = -0.14 [-0.17, -0.11], P < 0.001). The strongest predictor of referral was the value GPs attached to patient benefits (b = 0.30 [0.23, 0.36], P < 0.001), followed by benefits (b = 0.18 [0.11, 0.24], P < 0.001) and harms (b = -0.14 [-0.21, -0.08], P < 0.001) to the health system/society. The perceived severity of missing a cancer vis-à-vis overreferring also predicted referral (b = 0.004 [0.001, 0.007], P = 0.009). The algorithm did not significantly reduce the impact of these variables on referral decisions.ConclusionsThe decision to refer patients who might have cancer can be influenced by how physicians perceive and value the potential benefits and harms of referral primarily for patients and the moral seriousness of missing a cancer vis-à-vis over-referring. These values contribute to an internal threshold for action and are important even when an algorithm informs risk judgments.HighlightsPhysicians' inclination to refer patients for suspected cancer is determined by their assessment of cancer risk but also their core values; specifically, their values in relation to the perceived benefits and harms of referrals and the seriousness of missing a cancer compared with overreferring.We observed a moral prioritization of referral decision making, in which considerations about benefits to the patient were foremost, considerations about benefits but also harms to the health system or the society were second, while considerations about oneself carried little or no weight.Having an algorithm informing assessments of risk influences referral decisions but does not remove or significantly reduce the influence of physicians' core values.

基于风险预测算法的医生转诊疑似癌症的决定因素:连接信号检测和模糊追踪理论。
以往的研究表明,医生倾向于转诊疑似癌症的患者是他们决策的一个相对稳定的特征。我们的目的是在风险预测算法的存在下确定其心理决定因素。方法我们向200名英国全科医生提供了描述可能患有结直肠癌的患者的在线小插图。根据小插曲,全科医生指出转诊的可能性(从极不可能到极有可能)和癌症风险水平(可忽略/低/中/高),收到算法风险估计,然后可以修改他们的回答。在完成小短文后,全科医生回答了关于他们对不同利益相关者的癌症转诊的危害和益处的价值观,感知错误的严重程度,接受假警报以及对不确定性的态度。我们测试了这些价值观和态度是否能预测他们早期的转诊决定。结果该算法显著降低了转诊可能性(b = -0.06 [-0.10, -0.007], P = 0.025)和风险水平(b = -0.14 [-0.17, -0.11], P < 0.001)。转诊的最强预测因子是全科医生对患者利益的价值(b = 0.30 [0.23, 0.36], P < 0.001),其次是对卫生系统/社会的利益(b = 0.18 [0.11, 0.24], P < 0.001)和危害(b = -0.14 [-0.21, -0.08], P < 0.001)。未发现癌症的严重程度与-à-vis过度转诊也能预测转诊(b = 0.004 [0.001, 0.007], P = 0.009)。该算法并没有显著降低这些变量对转诊决策的影响。结论:转诊可能患有癌症的患者的决定可能受到医生如何感知和评估转诊的潜在利益和危害,以及错过癌症与-à-vis过度转诊的道德严重性的影响。这些值有助于行动的内部阈值,即使在算法通知风险判断时也很重要。医生是否倾向于转诊疑似癌症的病人,不仅取决于他们对癌症风险的评估,还取决于他们的核心价值观;具体来说,他们的价值观与转诊的感知利益和危害以及错过癌症的严重性相比,过度转诊。我们观察到转诊决策的道德优先性,其中对患者利益的考虑是最重要的,其次是对卫生系统或社会的利益和危害的考虑,而对自己的考虑很少或根本没有权重。采用算法评估风险会影响转诊决策,但不会消除或显著降低医生核心价值观的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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