Medical Decision Making最新文献

Background: General practitioners (GPs) make numerous care decisions throughout their workdays. Extended periods of decision making can result in decision fatigue, a gradual shift toward decisions that are less cognitively effortful. This study examines whether observed patterns in GPs' prescribing decisions are consistent with the decision fatigue phenomenon. We hypothesized that the likelihood of prescribing frequently overprescribed medications (antibiotics, benzodiazepines, opioids; less effortful to prescribe) will increase and the likelihood of prescribing frequently underprescribed medications (statins, osteoporosis medications; more effortful to prescribe) will decrease over the workday.

Methods: This study used nationally representative primary care data on GP-patient encounters from the Bettering the Evaluation and Care of Health program from Australia. The association between prescribing decisions and order of patient encounters over a GP's workday was assessed with generalized linear mixed models accounting for clustering and adjusting for patient, provider, and encounter characteristics.

Results: Among 262,456 encounters recorded by 2,909 GPs, the odds of prescribing antibiotics significantly increased by 8.7% with 15 additional patient encounters (odds ratio [OR] = 1.087; confidence interval [CI] = 1.059-1.116). The odds of prescribing decreased significantly with 15 additional patient encounters by 6.3% for benzodiazepines (OR = 0.937; CI = 0.893-0.983), 21.9% for statins (OR = 0.791; CI = 0.753-0.831), and 25.0% for osteoporosis medications (OR = 0.750; CI = 0.690-0.814). No significant effects were observed for opioids. All findings were replicated in confirmatory analyses except the effect of benzodiazepines.

Conclusions: GPs were increasingly likely to prescribe antibiotics and were less likely to prescribe statins and osteoporosis medications as the workday wore on, which was consistent with decision fatigue. There was no convincing evidence of decision fatigue effects in the prescribing of opioids or benzodiazepines. These findings establish decision fatigue as a promising target for optimizing prescribing behavior.

Highlights: We found that as general practitioners progress through their workday, they become more likely to prescribe antibiotics that are reportedly overprescribed and less likely to prescribe statins and osteoporosis medications that are reportedly underprescribed.This change in decision making over time is consistent with the decision fatigue phenomenon. Decision fatigue occurs when we make many decisions without taking a rest break. As we make those decisions, we become gradually more likely to make decisions that are less difficult.The findings of this study show that decision fatigue is a possible target for improving guideline-compliant prescribing of pharmacologic medications.

Background: Concordance between a person's values and the test or treatment they ultimately receive is widely considered to be an essential outcome for good decision quality. There is little research, however, on why patients receive "discordant" care. A large, randomized trial of decision aids for colorectal cancer (CRC) screening provided an opportunity to assess why some patients received a different test than the one they preferred at an earlier time point.

Methods: Of 688 patients who participated in the trial, 43 received a different CRC screening test than the one they selected after viewing a decision aid 6 mo prior. These patients answered 2 brief, open-ended questions about the reasons for this discordance. The research team analyzed their answers using qualitative description.

Results: Patient responses reflected 6 major categories: barriers or risks of initially favored test, benefits of alternative test, costs or health insurance coverage, discussion with family or friends, provider factors or recommendation, and health issues.

Conclusions: Some of the patients' explanations fit well with the informed concordance approach, which infers poor decision quality from the existence of discordant care, since in these cases it appears that the patient's values and preferences were not adequately respected. Other statements suggest that the patient had an informed rationale for changing their mind about which test to undergo. These cases may reflect high-quality decision making, despite the existence of discordance as measured in the trial. This analysis highlights a major challenge to a popular approach for assessing decision quality, the difficulty of normatively assessing the quality of decision making when apparent discordant care has been provided, and the need to assess patient values and preference over time.

Highlights: Value-choice concordance is an accepted measure for assessing decision quality in decision aid trials, but greater exploration of apparently discordant care challenges key assumptions of this method; this study provides evidence that discordance as typically measured may not always reflect low-quality patient decision making.Researchers evaluating decision aids and assessing decision quality should consider the use of qualitative methods to supplement measures of decision quality and consider assessing patient preferences at multiple time points.

Background: Icon arrays have been shown to be an effective method for communicating medical risk information. However, in practice, icon arrays used to visualize personal risks often differ in the type and color of the icons. The aim of this study was to examine the influence of icon type and color on the perception and recall of cardiovascular risk, as little is known about how color affects the perception of icon arrays.

Methods: A total of 866 participants aged 40 to 90 years representative of the German population in terms of gender and age completed an online experiment. Using a 2 × 2 between-subjects design, participants were randomly assigned to 1 of 4 experimental groups. They received their hypothetical 10-year cardiovascular risk using an icon array that varied by icon type (smiley v. person) and color (black/white v. red/yellow). We measured risk perception, emotional response, intentions of taking action to reduce the risk (e.g., increasing one's physical activity), risk recall, and graph evaluation/trustworthiness, as well as numeracy and graphical literacy.

Results: Icon arrays using person icons were evaluated more positively. There was no effect of icons or color on risk perception, emotional response, intentions of taking action to reduce the risk, or trustworthiness of the graph. While more numerate/graphical literate participants were more likely to correctly recall the presented risk estimate, icon type and color did not influence the probability of correct recall.

Conclusions: Differences in the perception of the tested icon arrays were rather small, suggesting that they may be equally suitable for communicating medical risks. Further research on the robustness of these results across other colors, icons, and risk domains could add to guidelines on the design of visual aids.

Highlights: The use of different icons and colors did not influence the perception and the probability of recalling the 10-year cardiovascular risk, the emotional response, or the intentions to reduce the presented risk.Icon arrays with person icons were evaluated more positively.There was no evidence to suggest that the effectiveness of the studied icon arrays varied based on individuals' levels of numerical or graphical literacy, nor did it differ between people with or without a history of CVD or on medication for an increased CVD risk.

Background: Health care interactions may require patients to share with a physician information they believe but is incorrect. While a key piece of physicians' work is educating their patients, people's concerns of being seen as uninformed or incompetent by physicians may lead them to think that sharing incorrect health beliefs comes with a penalty. We tested people's perceptions of patients who share incorrect information and how these perceptions vary by the reasonableness of the belief and its centrality to the patient's disease.

Design: We recruited 399 United States Prolific.co workers (357 retained after exclusions), 200 Prolific.co workers who reported having diabetes (139 after exclusions), and 244 primary care physicians (207 after exclusions). Participants read vignettes describing patients with type 2 diabetes sharing health beliefs that were central or peripheral to the management of diabetes. Beliefs included true and incorrect statements that were reasonable or unreasonable to believe. Participants rated how a doctor would perceive the patient, the patient's ability to manage their disease, and the patient's trust in doctors.

Results: Participants rated patients who shared more unreasonable beliefs more negatively. There was an extra penalty for incorrect statements central to the patient's diabetes management (sample 1). These results replicated for participants with type 2 diabetes (sample 2) and physician participants (sample 3).

Conclusions: Participants believed that patients who share incorrect information with their physicians will be penalized for their honesty. Physicians need to be educated on patients' concerns so they can help patients disclose what may be most important for education.

Highlights: Understanding how people think they will be perceived in a health care setting can help us understand what they may be wary to share with their physicians.People think that patients who share incorrect beliefs will be viewed negatively.Helping patients share incorrect beliefs can improve care.

Background: The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses.

Methods: We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation.

Results: Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion).

Conclusion: Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses.

Highlights: This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

Background: Willingness-to-pay (WTP) estimates are useful to policy makers only if they are generalizable beyond the moment when they are collected. To understand the "shelf life" of preference estimates, preference stability needs be tested over substantial periods of time.

Methods: We tested the stability of WTP for preventative dental care (scale and polish) using a payment-card contingent valuation question administered to 909 randomized controlled trial participants at 4 time points: baseline (prerandomization) and at annual intervals for 3 years. Trial participants were regular attenders at National Health Service dental practices. Participants were randomly offered different frequencies (intensities) of scale polish (no scale and polish, 1 scale and polish per year, 2 scale and polishes per year). We also examined whether treatment allocation to these different treatment intensities influenced the stability of WTP. Interval regression methods were used to test for changes in WTP over time while controlling for changes in 2 determinants of WTP. Individual-level changes were also examined as well as the WTP function over time.

Results: We found that at the aggregate level, mean WTP values were stable over time. The results were similar by trial arm. Individuals allocated to the arm with the highest scale and polish intensity (2 per year) had a slight increase in WTP toward the latter part of the trial. There was considerable variation at the individual level. The WTP function was stable over time.

Conclusions: The payment-card contingent valuation method can produce stable WTP values in health over time. Future research should explore the generalizability of these results in other populations, for less familiar health care services, and using alternative elicitation methods.

Highlights: Stated preferences are commonly used to value health care.Willingness-to-pay (WTP) estimates are useful only if they have a "shelf life."Little is known about the stability of WTP for health care.We test the stability of WTP for dental care over 3 y.Our results show that the contingent valuation method can produce stable WTP values.

Introduction: Detection of colorectal cancer (CRC) in the early stages through available screening tests increases the patient's survival chances. Multimodal screening policies can benefit patients by providing more diverse screening options and balancing the risks and benefits of screening tests. We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies.

Methods: We developed a Monte Carlo simulation framework to model CRC dynamics. We proposed an innovative calibration process using machine learning models to estimate age- and size-specific adenomatous polyps' progression and regression rates. The proposed approach significantly expedites the model parameter space search.

Results: Two multimodal proposed policies (i.e., 1] colonoscopy at 50 y and fecal occult blood test annually between 60 and 75 y and 2] colonoscopy at 50 and 60 y and fecal immunochemical test annually between 70 and 75 y) are identified as efficient frontier policies. Both policies are cost-effective at a willingness to pay of $50,000. Sensitivity analyses were performed to assess the sensitivity of results to a change in screening test costs as well as adherence behavior. The sensitivity analysis results suggest that the proposed policies are mostly robust to the considered changes in screening test costs, as there is a significant overlap between the efficient frontier policies of the baseline and the sensitivity analysis cases. However, the efficient frontier policies were more sensitive to changes in adherence behavior.

Conclusion: Generally, combining stool-based tests with visual tests will benefit patients with higher life expectancy and a lower expected cost compared with unimodal screening policies. Colonoscopy at younger ages (when the colonoscopy complication risk is lower) and stool-based tests at older ages are shown to be more effective.

Highlights: We propose a detailed Markov model to capture the colorectal cancer (CRC) dynamics. The proposed Markov model presents the detailed dynamics of adenomas progression to CRC.We use more than 44,000 colonoscopy reports and available data in the literature to calibrate the proposed Markov model using an innovative approach that leverages machine learning models to expedite the calibration process.We investigate the cost-effectiveness of a wide variety of multimodal CRC screening policies and compare their performances with the current in-practice policies.

Background: A risk-stratified breast screening program could offer low-risk women less screening than is currently offered by the National Health Service. The acceptability of this approach may be enhanced if it corresponds to UK women's screening preferences and values.

Objectives: To elicit and quantify preferences for low-risk screening options.

Methods: Women aged 40 to 70 y with no history of breast cancer took part in an online discrete choice experiment. We generated 32 hypothetical low-risk screening programs defined by 5 attributes (start age, end age, screening interval, risk of dying from breast cancer, and risk of overdiagnosis), the levels of which were systematically varied between the programs. Respondents were presented with 8 choice sets and asked to choose between 2 screening alternatives or no screening. Preference data were analyzed using conditional logit regression models. The relative importance of attributes and the mean predicted probability of choosing each program were estimated.

Results: Participants (N = 502) preferred all screening programs over no screening. An older starting age of screening, younger end age of screening, longer intervals between screening, and increased risk of dying had a negative impact on support for screening programs (P < 0.01). Although the risk of overdiagnosis was of low relative importance, a decreased risk of this harm had a small positive impact on screening choices. The mean predicted probabilities that risk-adapted screening programs would be supported relative to current guidelines were low (range, 0.18 to 0.52).

Conclusions: A deintensified screening pathway for women at low risk of breast cancer, especially one that recommends a later screening start age, would run counter to women's breast screening preferences. Further research is needed to enhance the acceptability of offering less screening to those at low risk of breast cancer.

Highlights: Risk-based breast screening may involve the deintensification of screening for women at low risk of breast cancer.Low-risk screening pathways run counter to women's screening preferences and values.Longer screening intervals may be preferable to a later start age.Work is needed to enhance the acceptability of a low-risk screening pathway.

Background: Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases.

Framework: A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis.

Design: Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease.

Results: When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment.

Conclusions: A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself.

Highlights: Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased "evidence" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.