Medical Decision Making[JOURNAL]
Sun
200 papers
From Calculation to Communication: Using Risk Score Calculators to Inform Clinical Decision Making and Facilitate Patient Engagement.
Fakhari H, Scherr CL, Moe S … +5 more , Hoell C, Smith ME, Rasmussen-Torvik LJ, Chisholm RL, McNally EM
Med Decis Making · 2024 Nov · PMID 39377500 · Full text
BACKGROUND: Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score c...
BACKGROUND: Risk score calculators are a widely developed tool to support clinicians in identifying and managing risk for certain diseases. However, little is known about physicians' applied experiences with risk score calculators and the role of risk score estimates in clinical decision making and patient communication. METHODS: Physicians providing care in outpatient community-based clinical settings ( = 20) were recruited to participate in semi-structured individual interviews to assess their use of risk score calculators in practice. Two study team members conducted an inductive thematic analysis using a consensus-based coding approach. RESULTS: Participants referenced at least 20 risk score calculators, the most common being the Atherosclerotic Cardiovascular Disease Risk Calculator. Ecological factors related to the clinical system (e.g., time), patient (e.g., receptivity), and physician (e.g., experience) influenced conditions and patterns of risk score calculator use. For example, compared with attending physicians, residents tended to use a greater variety of risk score calculators and with higher frequency. Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool. CONCLUSIONS: The degree to which risk score estimates influenced physician decision making and whether and how these scores were communicated to patients varied, reflecting a nuanced role of risk score calculator use in clinical practice. The theory of planned behavior can help explain how attitudes, beliefs, and norms shape the use of risk score estimates in clinical decision making and patient communication. Additional research is needed to evaluate best practices in the use of risk score calculators and risk score estimates. HIGHLIGHTS: The risk score calculators and estimates that participants referenced in this study represented a range of conditions (e.g., heart disease, anxiety), levels of model complexity (e.g., probability calculations, scales of severity), and output formats (e.g., point estimates, risk intervals).Risk score calculators that are easily accessed, have simple inputs, and are trusted by physicians appear more likely to be used.Risk score estimates were generally used in clinical decision making to improve or validate clinical judgment and in patient communication to serve as a motivational tool.Risk score estimates helped participants manage the uncertainty and complexity of various clinical situations, yet consideration of the limitations of these estimates was relatively minimal.Developers of risk score calculators should consider the patient- (e.g., response to risk score estimates) and physician- (e.g., training status) related characteristics that influence risk score calculator use in addition to that of the clinical system.
Shared Decision Making Is in Need of Effectiveness-Implementation Hybrid Studies.
Pieterse AH, van Bodegom-Vos L
Med Decis Making · 2024 Nov · PMID 39344033 · Publisher ↗
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Reframing SDM Using Implementation Science: SDM Is the Intervention.
Clayman ML, Elwy AR, Vassy JL
Med Decis Making · 2024 Nov · PMID 39334566 · Publisher ↗
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The Use of Nudge Strategies in Improving Physicians' Prescribing Behavior: A Systematic Review and Meta-analysis.
Hallett MF, Kjær T, Bjørnskov Pedersen L
Med Decis Making · 2024 Nov · PMID 39297370 · Publisher ↗
BACKGROUND: Nudges have been proposed as a method of influencing prescribing decisions. PURPOSE: The purpose of this article is to 1) investigate associations between nudges' characteristics and effectiveness, 2) assess...
BACKGROUND: Nudges have been proposed as a method of influencing prescribing decisions. PURPOSE: The purpose of this article is to 1) investigate associations between nudges' characteristics and effectiveness, 2) assess the quality of the literature, 3) assess cost-effectiveness, and 4) create a synthesis with policy recommendations. METHODS: We searched health and social science databases. We included studies that targeted prescribing decisions, included a nudge, and used prescribing behavior as the outcome. We recorded study characteristics, effect size of the primary outcomes, and information on cost-effectiveness. We performed a meta-analysis on the standardized mean difference of the studies' primary outcomes, tested for associations between effect size and key intervention characteristics, and created a funnel plot evaluating publication bias. SYNTHESIS: We identified 21 studies containing 25 nudges. In total, 62 of 85 (73%) outcomes showed a statistically significant effect. The average effect size was -0.22 standardized mean difference. No studies included heterogeneity analyses. We found no associations between effects and selected study characteristics. Study quality varied and correlated with study design. A total of 7 of 21 (33%) studies included an evaluation of costs. These studies suggested that the interventions were cost-effective but considered only direct effects. We found evidence of publication bias. LIMITATIONS: Heterogeneity and few studies limit the possibilities of statistical inference about effectiveness. CONCLUSIONS: Nudges may be effective at directing prescribing decisions, but effects are small and health effects and cost-effectiveness are unclear. Future nudge studies should contain a rationale for the chosen nudge, prioritize the use of high-quality study designs, and include evaluations of heterogeneity, cost-effectiveness, and health outcomes to inform decision makers. Moreover, preregistration of the protocol is warranted to limit publication bias. HIGHLIGHTS: Nudging as a method to improve prescribing decisions has gained popularity during the past decade.We find that nudging can improve prescribing decisions, but effect sizes are mostly small, and the size of derived health outcomes is unclear.Most studies use feedback and error-stopping nudges to target excessive opioid or antibiotic prescribing, making heterogeneity analyses across nudge types difficult.Further research on the cost-effectiveness of nudges and generalizability is needed to guide decision makers considering nudging as a tool to guide prescribing decisions.
Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines.
Ali S, Li Z, Moqueet N … +11 more , Moghadas SM, Galvani AP, Cooper LA, Stranges S, Haworth-Brockman M, Pinto AD, Asaria M, Champredon D, Hamilton D, Moulin M, John-Baptiste AA
Med Decis Making · 2024 Oct · PMID 39305116 · Full text
BACKGROUND: Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social...
BACKGROUND: Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities. METHODS: To underscore the importance of considering social heterogeneity in epidemiological modeling, we systematically reviewed ID modeling guidelines to identify reasons and recommendations for incorporating social determinants of health into models in relation to the conceptualization, implementation, and interpretations of models. RESULTS: After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age ( = 11), sex and gender ( = 5), and socioeconomic status ( = 5) were the most commonly discussed social determinants. Specific recommendations were identified to consider social determinants to 1) improve the predictive accuracy of models, 2) understand heterogeneity of disease burden and policy impact, 3) contextualize decision making, 4) address inequalities, and 5) assess implementation challenges. CONCLUSION: This study can support modelers and policy makers in taking into account social heterogeneity, to consider the distributional impact of infectious disease outbreaks across social groups as well as to tailor approaches to improve equitable access to prevention, diagnostics, and therapeutics. HIGHLIGHTS: Infectious disease (ID) models often overlook the role of social determinants of health (SDH) in understanding variation in disease risk, health burden, and policy impact across social groups.In this study, we systematically review ID guidelines and identify key areas to consider SDH in relation to the conceptualization, implementation, and interpretations of models.We identify specific recommendations to consider SDH to improve model accuracy, understand heterogeneity, estimate policy impact, address inequalities, and assess implementation challenges.
Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.
Vervaart M
Med Decis Making · 2024 Oct · PMID 39305058 · Full text
The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for col...
The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.
How Much Information Is Too Much? An Experimental Examination of How Information Disclosures May Unintentionally Encourage the Withholding of Health Information.
Colby H, Popovich D, Stovall T
Med Decis Making · 2024 Nov · PMID 39291366 · Publisher ↗
INTRODUCTION: Information disclosures are used in medicine to provide patients with relevant information. This research examines whether patients are less likely to discuss medical conditions with their physicians after...
INTRODUCTION: Information disclosures are used in medicine to provide patients with relevant information. This research examines whether patients are less likely to discuss medical conditions with their physicians after seeing an insurance information disclosure. METHODS: Three experimental studies with nonprobability online samples (n = 875 US adult participants) examined the impact of information disclosures on patients' likelihood of disclosing symptoms to providers, using new symptoms and preexisting chronic conditions. The effects of insurance disclosures were also compared to those of pharmaceutical discount disclosures. RESULTS: These studies demonstrate that information disclosures can result in unintended consequences for patients and providers. Results showed that information disclosures about insurance claims significantly negatively affected willingness to discuss health information with providers. This effect was consistent for both new health concerns, b = -0.661, < 0.001 (study 1, = 250) and b = -0.893, < 0.001 (study 3, = 375), as well as chronic conditions, b = -1.175, < .001 (study 2, = 250); all studies were conducted in January 2023. Information provided to patients about pharmaceutical savings did not similarly affect willingness to discuss symptoms with providers. LIMITATIONS: These were experimental studies with hypothetical scenarios. Future research is needed to understand how patients react to information disclosures in a physician's office. Future research is also needed to examine the role of specific wording and tone used in information disclosures. CONCLUSIONS: Prior research has shown that patients prefer more information and to be involved in their medical decisions; however, these studies demonstrate that some information disclosures can discourage full communication between patients and physicians. IMPLICATIONS: This research has important implications for the potential consequences of information disclosures in health care settings. Information disclosures should be presented in a way that will not discourage candid discussions of patient symptoms. HIGHLIGHTS: This research found that information disclosures about insurance claims can negatively affect patient willingness to discuss health information with providers.Information disclosures may sometimes fall short of their intended purpose of aiding patient decisions with the goal of improved well-being.When information disclosures are focused on warning about potential new costs, patients may feel uncomfortable discussing new symptoms with their providers.Findings suggest patients may often be more concerned with costs than with addressing their ongoing health problems.
Natural Frequencies Improve Public Understanding of Medical Test Results: An Experimental Study on Various Bayesian Inference Tasks with Multiple Scoring Methods and Non-Bayesian Reasoning Strategies.
Kim S
Med Decis Making · 2024 Nov · PMID 39291336 · Publisher ↗
BACKGROUND: It is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested...
BACKGROUND: It is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested that the NF facilitation effect might be limited to specific groups of individuals. Unlike previous studies that focused on a limited number of Bayesian inference problems evaluated by a single scoring method, it was essential to examine multiple Bayesian problems using various scoring metrics. This study also explored the impact of numeracy on Bayesian inference and assessed non-Bayesian cognitive strategies using the numerical information in problem solving. METHODS: In a Web-based experimental survey, 175 South Korean adults were randomly assigned to 1 of 2 format groups (NF v. CP). After completing numeracy scales, participants were asked to estimate 4 Bayesian inference problems and document the numerical information used in their problem-solving process. Four scoring methods-strict rounding, loose rounding, absolute deviation, and 50-Split-were used to evaluate participants' estimations. RESULTS: The NF format generally outperformed the CP format across all problems, except in a chorionic villus sampling test problem when evaluated using the 50-Split method. In addition, numeracy levels significantly influenced Bayesian inference; participants with higher numeracy demonstrated better performance. In addition, participants used various non-Bayesian strategies influenced by the format and the nature of the problems. CONCLUSIONS: The NF facilitation effect was consistently observed across multiple Bayesian problems and scoring methods. Individuals with higher numeracy levels benefited more from the NF format. The use of various non-Bayesian strategies varied with the formats and nature of specific tasks. HIGHLIGHTS: The natural frequencies (NF) format is known to foster understanding of medical test results compared with the conditional probabilities (CP) format, but some studies have reported that this benefit is either nonexistent or limited to specific groups.This study aims to replicate previous empirical studies using various Bayesian problems using multiple scoring methods.The NF format fosters understanding of medical test results across all Bayesian problems by all scoring methods, except in the CVS problem when using a 50-Split scoring method.Participants with high numeracy perform better Bayesian inference than those with lower numeracy. Particularly, higher numerates benefit more in the NF format than lower numerates do. In addition, the public tend to use various non-Bayesian reasoning strategies depending on the format and the nature of the tasks.
Adaptation and Validation of the Psychological Consequences of Screening Questionnaire (PCQ) for Cognitive Screening in Primary Care.
Lovett RM, Filec S, Hurtado J … +5 more , Kwasny M, Sideman A, Persell SD, Possin K, Wolf M
Med Decis Making · 2024 Nov · PMID 39263823 · Full text
BACKGROUND: Context-specific measures with adequate external validity are needed to appropriately determine psychosocial effects related to screening for cognitive impairment. METHODS: Two-hundred adults aged ≥65 y recen...
BACKGROUND: Context-specific measures with adequate external validity are needed to appropriately determine psychosocial effects related to screening for cognitive impairment. METHODS: Two-hundred adults aged ≥65 y recently completing routine, standardized cognitive screening as part of their Medicare annual wellness visit were administered an adapted version of the Psychological Consequences of Screening Questionnaire (PCQ), composed of negative (PCQ-Neg) and positive (PCQ-Pos) scales. Measure distribution, acceptability, internal consistency, factor structure, and external validity (construct, discriminative, criterion) were analyzed. RESULTS: Participants had a mean age of 73.3 y and were primarily female and socioeconomically advantaged. Most had a normal cognitive screening result (99.5%, = 199). Overall PCQ scores were low (PCQ-Neg: = 1.27, possible range 0-36; PCQ-Pos: = 7.63, possible range 0-30). Both scales demonstrated floor effects. Acceptability was satisfactory, although the PCQ-Pos had slightly more item missingness. Both scales had Cronbach alphas >0.80 and a single-factor structure. Spearman correlations between the PCQ-Neg with general measures of psychological distress (Impacts of Events Scale-Revised, Perceived Stress Scale, Kessler Distress Scale) ranged from 0.26 to 0.37 ('s < 0.001); the correlation with the World Health Organization-Five Well-Being Index was -0.19 ( < 0.01). The PCQ-Neg discriminated between those with and without a self-reported subjective cognitive complaint ( = 2.73 v. 0.89, < 0.001) and was associated with medical visit satisfaction ( = -0.24, < 0.001) on the Patient Satisfaction Questionnaire. The PCQ-Pos predicted self-reported willingness to engage in future screening ( = 8.00 v. 3.00, = 0.03). CONCLUSIONS: The adapted PCQ-Neg is an overall valid measure of negative psychological consequences of cognitive screening; findings for the PCQ-Pos were more variable. Future studies should address measure performance among diverse samples and those with abnormal screening results. HIGHLIGHTS: The PCQ scale is an overall valid measure of psychological dysfunction related to cognitive screening in older adults receiving normal screen results.PCQ scale performance should be further validated in diverse populations and those with abnormal cognitive screening results.The adapted PCQ may be useful to both health research and policy stakeholders seeking improved assessment of psychological impacts of cognitive screening.
General Population Mortality Adjustment in Survival Extrapolation of Cancer Trials: Exploring Plausibility and Implications for Cost-Effectiveness Analyses in HER2-Positive Breast Cancer in Sweden.
Kim K, Sweeting M, Wilking N … +1 more , Jönsson L
Med Decis Making · 2024 Oct · PMID 39263806 · Full text
BACKGROUND: In economic evaluations of novel therapies, assessing lifetime effects based on trial data often necessitates survival extrapolation, with the choice of model affecting outcomes. The aim of this study was to...
BACKGROUND: In economic evaluations of novel therapies, assessing lifetime effects based on trial data often necessitates survival extrapolation, with the choice of model affecting outcomes. The aim of this study was to assess accuracy and variability between alternative approaches to survival extrapolation. METHODS: Data on HER2-positive breast cancer patients from the Swedish National Breast Cancer Register were used to fit standard parametric distribution (SPD) models and excess hazard (EH) models adjusting the survival projections based on general population mortality (GPM). Models were fitted using 6-y data for stage I and II, 4-y data for stage III, and 2-y data for stage IV cancer reflecting an early data cutoff while maintaining sufficient events for comparison of model estimates with actual long-term outcomes. We compared model projections of 15-y survival and restricted mean survival time (RMST) to 15-y registry data and explored the variability between models in extrapolations of long-term survival. RESULTS: Among 11,224 patients compared with the observed registry 15-y RMST estimates across the disease stages, EH cure models provided the most accurate estimates in patients with stage I to III cancer, whereas EH models without cure most closely matched survival in patients with stage IV cancer, in which cure assumption was less plausible. The Akaike information criterion-averaged model projections varied as follows: -8.2% to +5.3% for SPD models, -4.9% to +5.2% for the EH model without a cure assumption, and -19.3% to -0.2% for the EH model with a cure assumption. EH models significantly reduced between-model variance in the predicted RMSTs over a 50-y time horizon compared with SPD models. CONCLUSIONS: EH models may be considered as alternatives to SPD models to produce more accurate and plausible survival extrapolation that accounts for general population mortality. HIGHLIGHTS: Excess hazard (EH) methods have been suggested as an approach to incorporate background mortality rates in economic evaluation using survival extrapolation.We highlight that EH models with or without a cure assumption can produce more accurate survival projections and significantly reduce between-model variability in comparison with standard parametric distribution models across cancer stages.EH models may be a preferred modeling method to reduce model uncertainty in health economic modeling since models that would otherwise have produced implausible extrapolations are constrained by the EH framework.Reduced uncertainty in economic evaluations will enhance the application of evidence-based health care decision making.
Perceived Penalties for Sharing Patient Beliefs with Health Care Providers.
Marsh JK, Asan O, Kleinberg S
Med Decis Making · 2024 Aug · PMID 39092564 · Full text
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...
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.
Impact of Structural Differences on the Modeled Cost-Effectiveness of Noninvasive Prenatal Testing.
Salisbury A, Pearce A, Howard K … +1 more , Norris S
Med Decis Making · 2024 Oct · PMID 39092556 · Full text
BACKGROUND: Noninvasive prenatal testing (NIPT) was developed to improve the accuracy of prenatal screening to detect chromosomal abnormalities. Published economic analyses have yielded different incremental cost-effecti...
BACKGROUND: Noninvasive prenatal testing (NIPT) was developed to improve the accuracy of prenatal screening to detect chromosomal abnormalities. Published economic analyses have yielded different incremental cost-effective ratios (ICERs), leading to conclusions of NIPT being dominant, cost-effective, and cost-ineffective. These analyses have used different model structures, and the extent to which these structural variations have contributed to differences in ICERs is unclear. AIM: To assess the impact of different model structures on the cost-effectiveness of NIPT for the detection of trisomy 21 (T21; Down syndrome). METHODS: A systematic review identified economic models comparing NIPT to conventional screening. The key variations in identified model structures were the number of health states and modeling approach. New models with different structures were developed in TreeAge and populated with consistent parameters to enable a comparison of the impact of selected structural variations on results. RESULTS: The review identified 34 economic models. Based on these findings, demonstration models were developed: 1) a decision tree with 3 health states, 2) a decision tree with 5 health states, 3) a microsimulation with 3 health states, and 4) a microsimulation with 5 health states. The base-case ICER from each model was 1) USD$34,474 (2023)/quality-adjusted life-year (QALY), 2) USD$14,990 (2023)/QALY, (3) USD$54,983 (2023)/QALY, and (4) NIPT was dominated. CONCLUSION: Model-structuring choices can have a large impact on the ICER and conclusions regarding cost-effectiveness, which may inadvertently affect policy decisions to support or not support funding for NIPT. The use of reference models could improve international consistency in health policy decision making for prenatal screening. HIGHLIGHTS: NIPT is a clinical area in which a variety of modeling approaches have been published, with wide variation in reported cost-effectiveness.This study shows that when broader contextual factors are held constant, varying the model structure yields results that range from NIPT being less effective and more expensive than conventional screening (i.e., NIPT was dominated) through to NIPT being more effective and more expensive than conventional screening with an ICER of USD$54,983 (2023)/QALY.Model-structuring choices may inadvertently affect policy decisions to support or not support funding of NIPT. Reference models could improve international consistency in health policy decision making for prenatal screening.
Identifying Strategies to Improve Shared Decision Making for Pregnant Patients' Decisions about Prenatal Genetic Screens and Diagnostic Tests.
Collart C, Craighead C, Yao M … +8 more , Chien EK, Rose S, Frankel RM, Coleridge M, Hu B, Edmonds BT, Ranzini AC, Farrell RM
Med Decis Making · 2024 Aug · PMID 39082665 · Full text
PURPOSE: Prenatal genetic screens and diagnostic tests are vital components of prenatal care. The first prenatal visit is a critical time in the decision-making process when patients decide whether to use these tests in...
PURPOSE: Prenatal genetic screens and diagnostic tests are vital components of prenatal care. The first prenatal visit is a critical time in the decision-making process when patients decide whether to use these tests in addition to address a series of other essential prenatal care aspects. We conducted this study to examine the role of a shared decision-making (SDM) instrument to support these discussions. METHODS: We conducted a cluster randomized controlled trial of patients allocated to an SDM tool or usual care at their first prenatal visit. Participants completed a baseline survey to measure decision-making needs and preferences. Direct observation was conducted and analyzed using the OPTION scale to measure SDM during prenatal genetic testing discussions. RESULTS: Levels of SDM were similar across groups ( = 0.081). The highest levels of SDM were observed during screening test discussions (NEST 2.4 ± 0.9 v. control 2.6 ± 1.0). Lowest levels were observed in discussions about patients' preference for risk versus diagnostic information (NEST 1.0 ± 1.1 v. control 1.2 ± 1.3). CONCLUSION: Study findings demonstrate the need for targeted patient-focused and provider-focused efforts to improve SDM to enhance patients' informed decision making about these options. Importantly, patients' baseline knowledge and attitudes need to be considered given that patients with less knowledge may need more carefully crafted communication. HIGHLIGHTS: Choices about whether, when, and how to use prenatal genetic tests are highly preference-based decisions, with patients' baseline attitudes about these options as a major driver in health care discussions.The decision-making process is also shaped by patient preferences regarding a shared or informed decision-making process for medical decisions that are highly personal and have significant ramifications for obstetric outcomes.There is a need to develop targeted efforts to improve decision making and enhance patients' ability to make informed decisions about prenatal genetic tests in early pregnancy.
Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.
Li L, Jalal H, Heath A
Med Decis Making · 2024 Oct · PMID 39082512 · Full text
BACKGROUND: The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computat...
BACKGROUND: The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models. METHODS: Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation. RESULTS: The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods. CONCLUSIONS: The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI. HIGHLIGHTS: The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.
Identifying Decisional Needs for Adult Tracheostomy and Prolonged Mechanical Ventilation Decision Making to Inform Shared Decision-Making Interventions.
Mehta AB, Lockhart S, Lange AV … +3 more , Matlock DD, Douglas IS, Morris MA
Med Decis Making · 2024 Nov · PMID 39082480 · Full text
BACKGROUND: Decision making for adult tracheostomy and prolonged mechanical ventilation is emotionally complex. Expectations of surrogate decision makers and physicians rarely align. Little is known about what surrogates...
BACKGROUND: Decision making for adult tracheostomy and prolonged mechanical ventilation is emotionally complex. Expectations of surrogate decision makers and physicians rarely align. Little is known about what surrogates need to make goal-concordant decisions. Currently, little is known about the decisional needs of surrogates and providers, impeding efforts to improve the decision-making process. METHODS: Using a thematic analysis approach, we performed a qualitative study with semistructured interviews with surrogates of adult patients receiving mechanical ventilation (MV) being considered for tracheostomy and physicians routinely caring for patients receiving MV. Recruitment was stopped when thematic saturation was reached. We describe the decision-making process, identify core decisional needs, and map the process and needs for possible elements of a future shared decision-making tool. RESULTS: Forty-three participants (23 surrogates and 20 physicians) completed interviews. Hope, Lack of Knowledge Data, and Uncertainty emerged as the 3 main themes that described the decision-making process and were interconnected with one another and, at times, opposed each other. Core decisional needs included information about patient wishes, past activity/medical history, short- and long-term outcomes, and meaningful recovery. The themes were the lens through which the decisional needs were weighed. Decision making existed as a balance between surrogate emotions and understanding and physician recommendations. CONCLUSIONS: Tracheostomy and prolonged MV decision making is complex. Hope and Uncertainty were conceptual themes that often battled with one another. Lack of Knowledge & Data plagued both surrogates and physicians. Multiple tangible factors were identified that affected surrogate decision making and physician recommendations. IMPLICATIONS: Understanding this complex decision-making process has the potential to improve the information provided to surrogates and, potentially, increase the goal-concordant care and alignment of surrogate and physician expectations. HIGHLIGHTS: Decision making for tracheostomy and prolonged mechanical ventilation is a complex interactive process between surrogate decision makers and providers.Qualitative themes of Hope, Uncertainty, and Lack of Knowledge & Data shared by both providers and surrogates were identified and described the decision-making process.Concrete decisional needs of patient wishes, past activity/medical history, short- and long-term outcomes, and meaningful recovery affected each of the larger themes and represented key information from which surrogates and providers based decisions and recommendations.The qualitative themes and decisional needs identified provide a roadmap to design a shared decision-making intervention to improve adult tracheostomy and prolonged mechanical ventilation decision making.
Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.
Gommers JJJ, Abbey CK, Strand F … +6 more , Taylor-Phillips S, Jenkinson DJ, Larsen M, Hofvind S, Broeders MJM, Sechopoulos I
Med Decis Making · 2024 Oct · PMID 39077968 · Full text
PURPOSE: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance. METHODS: Logist...
PURPOSE: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance. METHODS: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients. The logistic regression models were subsequently used to simulate different screening programs with reader pairing based on individual true-positive rates (TPR) and/or false-positive rates (FPR). For this, retrospective results from breast cancer screening programs employing double reading in Sweden, England, and Norway were used. Outcomes of random pairing were compared against those composed of readers with similar and opposite TPRs/FPRs, with positive assessments defined by either reader flagging an examination as abnormal. RESULTS: The analysis data sets consisted of 936,621 (Sweden), 435,281 (England), and 1,820,053 (Norway) examinations. There was good agreement between the model-predicted and observed radiologists' TPR and FPR ( ≥ 0.969). Model-predicted negative-case disagreement rates showed high correlations ( ≥ 0.709), whereas positive-case disagreement rates had lower correlation levels due to sparse data ( ≥ 0.532). Pairing radiologists with similar FPR characteristics (Sweden: 4.50% [95% confidence interval: 4.46%-4.54%], England: 5.51% [5.47%-5.56%], Norway: 8.03% [7.99%-8.07%]) resulted in significantly lower FPR than with random pairing (Sweden: 4.74% [4.70%-4.78%], England: 5.76% [5.71%-5.80%], Norway: 8.30% [8.26%-8.34%]), reducing examinations sent to consensus/arbitration while the TPR did not change significantly. Other pairing strategies resulted in equal or worse performance than random pairing. CONCLUSIONS: Logistic regression models accurately predicted screening mammography assessments and helped explore different radiologist pairing strategies. Pairing readers with similar modeled FPR characteristics reduced the number of examinations unnecessarily sent to consensus/arbitration without significantly compromising the TPR. HIGHLIGHTS: A logistic-regression model can be derived that accurately predicts individual and paired reader performance during mammography screening reading.Pairing screening mammography radiologists with similar false-positive characteristics reduced false-positive rates with no significant loss in true positives and may reduce the number of examinations unnecessarily sent to consensus/arbitration.
Assessing Decision Fatigue in General Practitioners' Prescribing Decisions Using the Australian BEACH Data Set.
Maier M, Powell D, Harrison C … +3 more , Gordon J, Murchie P, Allan JL
Med Decis Making · 2024 Aug · PMID 39056336 · Full text
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 cognitive...
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.
Who Makes the Decision, How, and Why: A Fuzzy-Trace Theory Approach.
Edelson SM, Reyna VF
Med Decis Making · 2024 Aug · PMID 39056326 · Publisher ↗
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Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.
Zhang Y, Kreif N, Gc VS … +1 more , Manca A
Med Decis Making · 2024 Oct · PMID 39056320 · Full text
BACKGROUND: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients...
BACKGROUND: Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. METHODS: In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. RESULTS: We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. LIMITATIONS: This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. CONCLUSIONS: Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. IMPLICATIONS: ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments. HIGHLIGHTS: Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making.
Icon Arrays for Medical Risk Communication: Do Icon Type and Color Influence Cardiovascular Risk Perception and Recall?
Blase R, Meis-Harris J, Weltermann B … +1 more , Dohle S
Med Decis Making · 2024 Aug · PMID 39056311 · Publisher ↗
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 i...
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.
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