Medical Decision Making最新文献

ObjectivesDiscrete choice experiment (DCE) methods that account for nonlinear time preferences have been tested in adult EQ-5D instruments but have yet to be tested for the valuation of EQ-5D-Y instruments. The aims of this study were to test the feasibility of using DCE methods that model nonlinear time preferences for the valuation of the EQ-5D-Y-5L as well as to explore the impact of the perspective adult respondents are asked to take.MethodsA representative Australian adult general population sample completed an online survey that included 15 DCE split triplet tasks. Depending on arm assignment, respondents were asked to imagine themselves or a 10-y-old when choosing between health states. A Bayesian efficient design was used to construct DCE tasks; the design was updated 3 times. Data were analyzed using correlated mixed logit models with exponential discounting.ResultsThere were 955 and 947 respondents in the "self" and "10-y-old" arms, respectively. When nonlinear modeling is used, there is evidence of discounting in the "self" (17%) and "10-y-old" (15%) perspective. Avoiding the experience of pain and discomfort were most important in both arms. When imagining a 10-y-old, rather than "self," respondents considered being worried, sad, or unhappy to be more important. Sensitivity analysis revealed that nonparents considered a higher number of health states to be worse than dead when imagining themselves.ConclusionsThis is the first study to use a nonlinear DCE approach in the valuation of the EQ-5D-Y-5L and in pediatric health-related quality of life more generally. Nonlinear modeling methods were found to be suitable for the valuation of the EQ-5D-Y-5L.HighlightsThere is evidence that modeling for nonlinear time preferences is suitable for the valuation of adult health-related quality of life (HRQoL). It is unknown how time preferences affect the valuation of pediatric instruments, such as the EQ-5D-Y-5L, and whether this differs when adults are asked to imagine "self" versus a "10-y-old."There was evidence of nonlinear time preferences when adult respondents value health states for a 10-y-old using a discrete choice experiment (DCE) that included a duration attribute. Perspective was a strong driver of estimating states worse than dead: 42% of health states were considered worse than dead for a 10-y-old as opposed to 26% when respondents imagined themselves.Nonlinear DCE methods are feasible for the valuation of the EQ-5D-Y-5L and have advantages compared with the use of time tradeoff in valuing child HRQoL. Future studies can test whether nonlinear modeling methods are suitable for other pediatric HRQoL instruments.

PurposeWe examined how different narrative aspects related to the COVID-19 pandemic influenced unvaccinated individuals' willingness to vaccinate (WTV) against a future virus. We tested whether the stories focused on the perspective of the actor (who chose to vaccinate or not) versus the affected (affected by that decision), framing the outcome as death versus survival, and presenting an identified individual versus an unidentified group.MethodsA total of 1,545 respondents read scenarios depicting individuals' (actors') decisions to either vaccinate against COVID-19 or refuse vaccination, alongside the framing of the consequences for the affected individuals: death versus survival. The protagonists were either identified by name and photo or described as a group of unidentified people. Participants reported their emotions, perceived risk from the virus and the vaccine, and their future WTV against a new virus. They also reported their past vaccination decisions.ResultsWhen the narrative focused on affected individuals, framing outcomes in terms of death increased WTV by heightening the perceived threat of the virus. Conversely, when the focus was on the actor, the lifesaving frame was more effective, especially when the actor was identified. A concrete case of someone vaccinated who saved others evoked positive emotions, boosting WTV.LimitationsOur hypothetical scenarios and the cross-sectional methodology might limit understanding of the long-term effects.ConclusionsScenarios highlighting a person who died increase the perceived threat of the virus and enhance WTV. Conversely, information about a person who was vaccinated and saved others boosts positive emotions and increases WTV.ImplicationsPublic health campaigns can boost vaccination by sharing stories of vaccinated individuals who saved lives, evoking positive emotions. Highlighting the virus's dangers can also raise the perceived threat and motivate uptake.HighlightsVariations in narratives influence unvaccinated individuals' willingness to vaccinate.Emphasizing the death of those affected evokes greater threat perception of the virus, enhancing vaccine intent.Personal stories of vaccinated individuals saving others can boost positive emotions and vaccination willingness.

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.

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.

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.

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.

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.