Medical Decision Making最新文献

IntroductionBreast cancer survivors have a higher risk of interval cancers relative to the screening population. Patient characteristics including features of the primary cancer and its treatment can help predict interval second breast cancer risk, but patient and physician perspectives on how risk prediction tools might enhance surveillance decision making are not well characterized.DesignWe conducted a qualitative study of women with breast cancer who had completed primary treatment and multispecialty physicians recruited through Breast Cancer Surveillance Consortium registries. We conducted semi-structured focus groups with 5 to 7 breast cancer survivors and individual physician interviews. All participants were presented with information about an interval cancer risk prediction tool. We elicited participant perspectives on aspects of the tool's design, relevance, and use for surveillance decision making. Data coding, thematic analysis, and interpretation were guided by the principles of theoretical thematic analysis.ResultsForty physician interviews and 4 focus groups involving 23 breast cancer survivors were analyzed. Two prominent areas of focus emerged: 1) perspectives on how a risk prediction tool would enhance and add value to patient-centered care and 2) risk prediction tools can be a means to improve communication about risk of in-breast recurrence or new breast cancer.ConclusionsThis study provides data on breast cancer survivor and physician perceptions of a new risk prediction tool to support surveillance imaging decisions among breast cancer survivors.ImplicationsAn interval second breast cancer risk prediction tool may promote evidence-based care across an array of physicians and different clinical settings. Future research should identify care delivery settings and features that promote adoption and support use in ways that improve shared decision making and patient outcomes.HighlightsThis qualitative study of breast cancer survivors and physicians found that risk prediction tools to support surveillance decisions were perceived positively when positioned as a supplement to the patient-physician relationship.Both patients and physicians said that a tool supported by strong evidence and accessible outputs would be valuable for shared decision making.

BackgroundAlthough in vitro fertilization (IVF) has enhanced fertility opportunities for many people, it also comes with considerable burden. Concerns have been raised about patients holding unrealistic expectations and continuing treatment indefinitely. This study aimed to investigate whether anecdotes of IVF success affect hypothetical intentions to continue treatment despite very low chances of success.DesignOnline randomized controlled trial with a parallel 3-arm design, conducted in May 2022. After viewing a clinical vignette depicting 6 unsuccessful IVF cycles with less than 5% chance of subsequent treatment success, 606 females aged 18 to 45 years in Australia were randomized to receive either 1) an anecdote of IVF success despite limited chances, 2) the anecdote of success and an anecdote of failure, or 3) no anecdote. Outcomes were intention to undergo another IVF cycle, worry, likelihood of success, and narrative transportation.ResultsThere was a main effect of anecdote condition on intention to have another IVF cycle, with participants randomized to the positive and negative anecdote having higher intention than those given no additional information (mean difference = 0.65, 95% confidence interval [CI] = 0.12-1.18, P = 0.017). There were no differences between conditions regarding worry, likelihood of success, or narrative transportation. In adjusted analyses accounting for prior IVF experience, the main effect of anecdotes on intention was no longer statistically significant. Those with prior IVF experience reported a statistically higher likelihood of success and narrative transportation than those without prior IVF experience (mean difference [MD] = 34.28, 95% CI = 27.26-41.30, P < 0.001, and MD = 1.35, 95% CI = 0.96-1.74, P < 0.001, respectively).ConclusionHearing anecdotes may encourage continuation of IVF despite extremely low chances of success. These findings, along with our sample's overestimation of IVF success, illustrate the importance of frequent and frank discussions about expected treatment outcomes.Trial registration:ACTRN12622000576729.HighlightsThe presence of IVF anecdotes increased the intention to undergo another IVF cycle despite extremely low chances of success.Balancing an anecdote of success with an anecdote of failure had no attenuating effect on intention.IVF providers should be wary of the potential impact of success stories on patients' decision making.In the vignette depicting overuse of IVF, participants with previous IVF experience greatly overestimated the likelihood of success with another IVF cycle, supporting previous research finding that patients often have unrealistically high expectations about their own chance of success.

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.

BackgroundBioethicists have advocated lotteries to distribute scarce health care resources, highlighting the benefits that make them attractive amid growing health care challenges. During the COVID-19 pandemic, lotteries were used to distribute vaccines within priority groups in some settings, notably in the United States. Nonetheless, limited evidence exists on public attitudes toward lotteries.MethodsTo assess public support for vaccine allocation by lottery versus expert committee, we conducted a survey-based experiment during the pandemic. Between November 2020 and May 2021, data were collected from 15,380 respondents across 14 diverse countries. Respondents were randomly allocated (1:1) to 1 of 2 hypothetical scenarios involving COVID-19 vaccine allocation among nurses: 1) by lottery and 2) prioritization by a committee of expert physicians. The outcome was agreement on the appropriateness of the allocation mechanism on a scale ranging from 0 (strongly disagree) to 100 (strongly agree), with differences stratified by a range of covariates. Two-sided t tests were used to test for overall differences in mean agreement between lottery and expert committee.FindingsMean agreement with lottery allocation was 37.25 (95% confidence interval [CI] 34.86-39.65), ranging from 21.1 (95% CI 15.07-27.13) in Chile to 62.33 (95% CI 54.45-70.21) in India. In every country, expert committee allocation received higher support, with mean agreement of 61.19 (95% CI: 60.04-62.35), varying from 51.25 in Chile to 69.77 in India. Greater agreement with lotteries was observed among males, higher-income individuals, those with lower education, and those identifying as politically right leaning.ConclusionsDespite arguments for lottery-based allocation of medical resources, we found low overall public support, albeit with substantial variation across countries. Successful implementation of lottery allocation will require targeted public engagement and clear communication of potential benefits.HighlightsThis study surveyed 15,380 respondents from 14 diverse countries during the COVID-19 pandemic, analyzing international agreement with the appropriateness of using lottery allocation for scarce health care resources.There was universal preference for allocating vaccines by expert committee rather than by lotteries, but there was significant variation in agreement between countries, indicating the need for region-specific policy approaches.Successful implementation of lottery allocation requires targeted public engagement and communication of their benefits, especially with groups less supportive of lotteries.

BackgroundMessaging strategies hold promise to reduce breast cancer overscreening. However, it is not known whether they may have differential effects among medical maximizers who prefer to take action about their health versus medical minimizers who prefer to wait and see.MethodsIn a randomized controlled survey experiment that included 2 sequential surveys with 3,041 women aged 65+ y from a US population-based online panel, we randomized participants to 1) no messages, 2) single exposure to a screening cessation message, or 3) 2 exposures over time to the screening cessation message. We assessed support for stopping screening in a hypothetical patient and intention to stop screening oneself on 7-point scales, where higher values indicated stronger support and intentions to stop screening. We conducted stratified analyses by medical-maximizing preference and moderation analysis.ResultsOf the women, 40.7% (n = 1,238) were medical maximizers; they had lower support and intention for screening cessation in all groups compared with the medical minimizers. Two message exposures increased support for screening cessation among medical maximizers, with a mean score of 3.68 (95% confidence interval [CI] 3.51-3.85) compared with no message (mean score 2.20, 95% CI 2.00-2.39, P < 0.001). A similar pattern was seen for screening intention. Linear regression models showed no differential messaging effect by medical-maximizing preference.ConclusionsMedical maximizers, although less likely to support screening cessation, were nonetheless responsive to messaging strategies designed to reduce breast cancer overscreening.HighlightsIt is not known if a message on rationales for stopping breast cancer screening would have differential effects among medical maximizers who prefer to take action when it comes to their health versus medical minimizers who prefer to wait and see.In a 2-wave randomized controlled survey experiment with 3,041 older women, we found that medical maximizers, although less likely to support screening cessation compared with medical minimizers, were nonetheless responsive to the messaging intervention, and the magnitude of the intervention effect was similar between maximizers and minimizers.Medical maximizers reported higher levels of worry and annoyance after reading the message compared with the minimizers, but the absolute levels of worry and annoyance were low.Our findings suggest that messaging can be a useful tool for reducing overscreening even in a highly reluctant population.

BackgroundAffective forecasting errors (i.e., errors in people's predictions about future emotions) are common in health decision making and can negatively affect health outcomes. Although narrative interventions have been used to mitigate these errors, many studies did not clearly identify the specific errors targeted or examine the impact of different narrative types on affective forecasting. We applied the narrative immersion model (NIM) to capture the nuances of narratives on mitigating specific affective forecasting errors in health decision making.MethodsUsing a narrative review of existing narrative affective forecasting interventions, we investigated the potential of experience, process, and outcome narratives to reduce specific affective forecasting errors (e.g., focalism, immune neglect).ResultsDifferent narrative types-experience, process, and outcome-may play distinct roles in mitigating affective forecasting errors. Experience narratives may reduce affective forecasting errors by describing what people most likely (targeted) or might (representative) experience, process narratives by modeling optimal decision making, and outcome narratives by broadening people's understanding of possible emotional outcomes. We further discuss how narrative characteristics related to content and structure (e.g., perspective taking, transportation, etc.) may advance narrative effects on affective forecasting.ConclusionsOur findings have implications for intervention design as they facilitate the selection of narrative types tailored to specific affective forecasting errors (e.g., framing, misconstruals, or impact bias).HighlightsSpecific affective forecasting errors may be reduced through different types of narratives, but greater understanding is needed regarding the exact mechanisms.The narrative immersion model is a useful framework to investigate the potential of experience, process, and outcome narratives to reduce specific types of affective forecasting errors.We describe the pathways through which narrative types most likely influence affective forecasting and facilitate the choice of narrative message type for a specific affective forecasting error.Narratives designed for affective forecasting interventions should include detailed and realistic descriptions of people's emotional health care experiences.Other narrative characteristics (e.g., realism, perspective taking, transportation) might affect a person's ability to imagine future emotional health states, and future research should consider their effects on affective forecasting.

ObjectiveTo study the effects of exposure to a prior diagnosis (PD) on second opinions in breast pathology.Materials and MethodsPathologists interpreted digital breast biopsy cases in 2 phases separated by a washout. Phase 2 interpretations were randomly assigned to PD or no PD. When presented, PD was always more or less severe than a participant's phase 1 diagnosis. Viewing behaviors, including zoom level, were recorded during all interpretations. Twenty pathologists yielded 556 interpretations of 32 different cases.ResultsPathologists were 71% more likely to give a less severe diagnosis when exposed to a less severe PD than with no PD (RR 1.71, 95% CI 1.33-2.20, P < 0.001). In comparison, when exposed to a more severe PD than with no PD, pathologists were 27% more likely to give a more severe diagnosis, but the effect was not significant (RR 1.27, 95% CI 0.87-1.86, P = 0.223). Compared with no PD, viewing behavior shifted toward more focus on critical image regions with exposure to a less severe PD and toward higher zoom levels with exposure to a more severe PD.DiscussionResults indicate anchoring and confirmation biases from PD exposure, such that second opinions after PD exposure are not independent assessments. Viewing behaviors illustrated how PD alters the interpretive process, including increased zooming when exposed to a more severe PD. Results have implications for best practices for computer-aided diagnosis tools.ImplicationsWhen giving a second opinion, exposure to a PD can sway diagnostic classifications and alter interpretive behavior, highlighting a need for protocols that encourage independent assessments.HighlightsIn pathology diagnosis, second opinions are systematically influenced by prior diagnostic information.Less severe prior diagnoses shift pathologists' visual attention toward clinically critical regions of a pathology image, whereas more severe prior diagnoses tend to elicit increased magnification during case interpretation.Specific viewing behaviors partially mediate the effect of prior diagnoses on second opinion diagnoses.When prior diagnoses are disclosed to pathologists, anchoring and confirmation biases undermine the independence of second opinion decisions.

BackgroundMedication adherence is a critical factor in hypertension management, which remains a challenge for public health systems.MethodsGraded-pair questions were used to quantify the perception of how much nonadherence to antihypertensives increases the risk of serious cardiovascular events. A discrete-choice experiment was used to quantify the relative importance of medication outcomes (e.g., reduction in cardiovascular event risk and medication side effects). Rating questions were used to assess perspectives of the effect of treatment nonadherence on treatment side effects. Results were combined to assess how preferences and outcome expectations influence adherence.ResultsPatients perceived treatment adherence as the most significant contributor to cardiovascular event risk. A reduction in cardiovascular risk was the most significant consideration when choosing medication. Missing consecutive (v. alternate) doses was associated with greater perceived cardiovascular risk and fewer side effects. The differences between complete adherence and any level of nonadherence were significantly larger for side effects than for changes in the risk of cardiovascular events, suggesting that side effects are perceived to be more sensitive to nonadherence than treatment efficacy.LimitationsOur study relied on hypothetical scenarios, which may not fully capture real-world decision making. While our findings shed light on the relationship between adherence patterns and treatment perceptions, it is essential to recognize the complexity of adherence behavior.ConclusionsPatients believe that they can manage medication side effects by skipping doses without compromising the efficacy to the same degree and that they can offset compromises in efficacy by avoiding missing consecutive doses for prolonged periods.ImplicationsHealth care providers should understand the importance of patient education and counseling to address misconceptions and promote realistic expectations regarding treatment efficacy and the consequences of nonadherence.HighlightsThe average patient believes that they can manage medication side effects by skipping doses without compromising the efficacy to the same degree.There is a belief that patients can offset some of the impact of nonadherence on their cardiovascular event risk, particularly if they avoid missing consecutive doses for prolonged periods of time.This highlights the importance of patient education and counseling to address misconceptions and promote realistic expectations regarding treatment efficacy and the consequences of nonadherence.

Background. Awareness of shared decision making (SDM) is growing, but its integration into clinical practice guidelines (CPGs) remains challenging. We sought expert insights to identify strategies for more successfully integrating SDM and decision support tools into CPGs. Specifically, our objectives were to determine 1) how to identify CPG recommendations where SDM is most relevant and 2) what factors in CPG development hinder or facilitate the consideration of SDM and the development of decision support tools. Methods. We conducted semi-structured interviews with experts on CPGs and SDM. We analyzed the data using Mayring's qualitative content analysis. Results. The 16 interviewed participants proposed several determinants of and strategies for identifying SDM-relevant recommendations. The most frequently mentioned determinant was "multiple options with benefits and harms where choices depend on individual preferences." The most frequently mentioned strategy was prioritization, similar to the CPG scoping phase. Participants highlighted the role of patient partners in facilitating the consideration of SDM in CPG development but noted that a supportive culture toward both patient and public involvement and SDM is needed. The absence of standardized methods and inadequate resources hinder the consideration of SDM and the combined development of CPGs and decision support tools. The current format of CPGs was deemed overwhelming, while the inclusion of choice awareness in CPG recommendations could facilitate SDM. Conclusions. The identified strategies provide a starting point for CPG organizations to explore ways for integrating SDM and decision support tools into CPGs while considering context-specific barriers and facilitators. Implications. Further research is needed to assess the usefulness and feasibility of the proposed strategies. New policies and stronger collaboration between CPG and SDM communities appear to be needed to address identified barriers.HighlightsWe explored expert knowledge and experience on how to successfully integrate shared decision making (SDM) and decision support tools into clinical practice guidelines (CPGs).A combined development of CPGs and decision support tools was deemed essential; however, development processes often remain separate, with the CPG development group unaware of the decision support tool development group, and vice versa.In addition to stating choice awareness in CPGs, participants highlighted the critical role of patient partners in considering SDM in CPG development, but resource issues and a culture that neglects patient involvement and SDM remain.For CPG development groups to consider SDM and for health care professionals to practice it, things need to be as easy as possible.

Health state values, often in the form of value sets that list values applied to particular health states, are used in cost-effectiveness analyses of health care to calculate gains in quality-adjusted life-years. These values are subject to several sources of uncertainty, arising from the fact that values are not constants but variables and are of different types including variability, heterogeneity, statistical uncertainty, and methodological variation. Currently, these sources are not fully documented and are not fully accounted for when creating and analyzing economic evaluation models. This may provide to users of such models a false sense of the precision of quality-adjusted life-year gain estimates and therefore of cost-effectiveness. This article provides a comprehensive account of such sources of uncertainty and how they interact. It also provides a more detailed account of how uncertainty arises in studies that elicit and model value sets. Its aim is to encourage research to measure and report uncertainty around health state values so it can be better accounted for in cost-effectiveness analyses.HighlightsHealth state values (HSVs) used in cost-effectiveness analysis are subject to multiple types of uncertainty, including variability, heterogeneity, statistical uncertainty, and methodological variation.Current reporting and guidelines often fail to fully document or address all sources of uncertainty in HSVs, which can mislead users about the precision of QALY and cost-effectiveness estimates.Valuation studies should report measures of uncertainty (such as standard errors or variance/covariance matrices) for HSVs, not just point estimates.Researchers, decision modellers, and guideline developers should recognise, measure, and report HSV uncertainty more thoroughly to improve the reliability of cost-effectiveness analyses.