Medical Decision Making[JOURNAL]
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200 papers
Social Media Health Information Formats and Endometriosis Treatment-Seeking Intentions: A Randomized Controlled Trial.
Graham A, Nickel B, McCaffery K … +3 more , Doust J, Cvejic E, Copp T
Med Decis Making · 2026 Apr · PMID 41975269 · Publisher ↗
BackgroundDespite many women learning about endometriosis on social media, posts about the condition often fail to reflect current evidence. With the content and credibility of online health messages being found to influ...
BackgroundDespite many women learning about endometriosis on social media, posts about the condition often fail to reflect current evidence. With the content and credibility of online health messages being found to influence behavioral intentions in other areas, this study aimed to explore how the format of endometriosis-related social media posts affects women's intentions to get a laparoscopy for the diagnosis and treatment of endometriosis.DesignIn this 2 × 2 × (2) online randomized controlled trial, Australian women aged 18 to 45 y who had no prior endometriosis diagnosis were randomly assigned to view 1 of 4 mock Instagram posts. Post content (personal anecdote v. nonnarrative, factual information) and source (high-credibility "World Health Organization" (WHO) account v. low credibility layperson account) varied across conditions. A within-subjects component investigated change in intention when participants were informed of new treatment guidelines.ResultsA total of 1,473 women were included in the analysis. Instagram posts featuring an anecdote produced higher treatment-seeking intentions (mean difference [MD] = 0.22, 95% confidence interval [CI] = 0.04-0.39) and more favorable attitudes toward getting a laparoscopy compared with posts containing nonnarrative information (MD = 0.13, 95% CI = 0.01-0.25). While the WHO account was perceived as more credible (MD = 0.29, 95% CI = 0.17-0.41), there were no differences in intentions, perceived norms, or self-efficacy toward laparoscopy compared with the layperson account. Advising participants of new evidence regarding the limitations of laparoscopy reduced intentions to get the procedure (MD = 0.29, 95% CI = 0.21-0.37), irrespective of condition.ConclusionsFindings demonstrate the power of anecdotes in shaping treatment preferences. Supplementing evidence-based information with personal anecdotes may ensure accurate yet engaging health information is used by online endometriosis communities to seek appropriate care. Australian New Zealand Clinical Trials Registry (https://www.anzctr.org.au/; ACTRN12624000767505p)HighlightsFraming health information on Instagram as a personal anecdote increased women's intentions to get a laparoscopy for the diagnosis and treatment of endometriosis.We found few differences between high- and low-credibility sources.Informing participants of new evidence and clinical guidelines reduced intentions across all conditions, potentially reflecting more informed decision making.
Extrapolation of Time-to-Event Survival Outcomes of Histology-Independent Therapies Using a Bayesian Hierarchical Model.
Mikelson J, Birnie R, McCarthy G … +7 more , Madin-Warburton M, Xu R, Chumbley J, Aguiar-Ibáñez R, Amonkar M, Baio G, Young K
Med Decis Making · 2026 Jul · PMID 41960684 · Full text
IntroductionHealth technology assessment of histology-independent therapies (HITs) requires statistical methods that can capture heterogeneity in outcomes while allowing borrowing of information between tumor sites to in...
IntroductionHealth technology assessment of histology-independent therapies (HITs) requires statistical methods that can capture heterogeneity in outcomes while allowing borrowing of information between tumor sites to inform cost-effectiveness analysis. In this study, we extend previous work on binary outcomes to the application of Bayesian hierarchical models (BHMs) for extrapolation of overall survival from pembrolizumab-treated patients with microsatellite instability-high/deficient mismatch repair solid tumors.MethodsWe considered BHMs based on 1- or 2-parameter distributions for extrapolation of survival outcomes. The scale or rate parameter of each model was assumed exchangeable among tumor types, and the shape parameter was assumed the same for all tumor types in the 2-parameter models. We compared overall survival (OS) and estimated mean survival time for each BHM with the corresponding nonhierarchical model.ResultsExtrapolated OS showed similar results between the BHM and standard models for colorectal, endometrial, and gastric cancers. Small intestine and biliary cancers showed higher OS estimates with a BHM than the standard models due to a combination of smaller sample sizes, information sharing in the BHM, and the use of a common shape parameter. Estimated mean survival times were similar between the BHM and equivalent standard model. However, the BHM showed reduced uncertainty in all cases.ConclusionsWe have demonstrated that BHMs provide a suitable framework to extrapolate time-to-event outcomes for HITs. The results provide extrapolated curves for OS that vary by tumor site, thus capturing and quantifying the inherent heterogeneity within the patient population. BHMs offer advantages in terms of reduced uncertainty around parameters that are often key drivers in cost-effectiveness analyses, such as estimated OS, through the borrowing of information between tumor sites.HighlightsBayesian hierarchical models (BHMs) reduced uncertainty in extrapolation of time-to-event outcomes for histology-independent treatments compared with nonhierarchical models fit to each tumor site.Reduced uncertainty around the mean survival time is a key factor of cost-effectiveness analyses of histology-independent treatments.BHMs provide a suitable framework for extrapolating histology-independent survival outcomes, effectively integrating prior knowledge and explicitly capturing heterogeneity between different tumor sites.
Visualization of Multi-indication Randomized Control Trial Evidence to Support Decision Making in Oncology: A Case Study on Bevacizumab.
Anwer S, Singh J, Bujkiewicz S … +7 more , Thomas A, Adams R, Smyth E, Saramago P, Palmer S, Soares MO, Dias S
Med Decis Making · 2026 Mar · PMID 41913717 · Publisher ↗
BackgroundAs an increasing number of oncology drugs are licensed for multiple indications, sharing information across indications may help improve the precision of estimates for a target indication where evidence may be...
BackgroundAs an increasing number of oncology drugs are licensed for multiple indications, sharing information across indications may help improve the precision of estimates for a target indication where evidence may be immature. Visualizing the accumulation of evidence and its characteristics across all indications can help inform policy makers as to whether multi-indication synthesis methods should be considered and guide expert elicitation on appropriate cross-indication assumptions.MethodsThe multi-indication oncology drug bevacizumab was selected as a case study. We used visualization methods including timeline, ridgeline, and split-violin plots to display evidence and synthesis results across 7 licensed cancer types, focusing on the evidence on overall and progression-free survival and the display of results from models with and without information sharing.ResultsThe proposed displays allow for visualization of key characteristics of the evidence to support the assessment of heterogeneity within and across indications and inform the feasibility of information-sharing models.LimitationsThe lack of consistent reporting of data in trial reports limits the visualization of some study characteristics. Tradeoffs between plot readability and the level of detail to include were required.ConclusionsClear graphical representations of the evolution and accumulation of evidence and synthesis results can provide a better understanding of the entire multi-indication evidence base, which can inform judgments regarding the appropriate use of data within and across indications. Interactive plots could help overcome some of the current limitations.ImplicationsThe proposed displays should be used to facilitate discussion with experts on the judgments required to assess the feasibility of using information-sharing methods to improve the estimation of relative treatment effects in evidence synthesis approaches and health technology assessment.HighlightsAn increasing number of oncology drugs are licensed for multiple indications; we developed visualization methods for multi-indication evidence that consider key characteristics unique to oncology.Graphical displays can be used to show the evolution of evidence within and across multiple indications.Clear evidence visualizations can be used as a tool to support evidence synthesis approaches, support policy makers, or guide expert elicitation.
Competencies for Pediatric Shared Decision Making: A Delphi Consensus Study.
Orellana-Villazon VI, deSante-Bertkau JE, Giambra BK … +1 more , Lipstein EA
Med Decis Making · 2026 Mar · PMID 41883011 · Publisher ↗
BackgroundPediatric shared decision making (SDM) involves clinicians working with children and families to make health care decisions that reflect their values and preferences. Existing pediatric SDM programs are highly...
BackgroundPediatric shared decision making (SDM) involves clinicians working with children and families to make health care decisions that reflect their values and preferences. Existing pediatric SDM programs are highly variable and rely on frameworks designed for adults. While efforts have been made to identify SDM competencies in adult care, this has not happened in pediatrics.ObjectiveTo establish consensus-based core competencies enabling clinicians to effectively implement SDM with pediatric patients and their families.MethodsWe conducted a modified Delphi study with pediatric clinicians, SDM researchers, ethicists, and medical educators. In round 1, panelists listed essential SDM competencies. In rounds 2 and 3, panelists rated the importance of each competency using a 4-point Likert scale. Consensus was defined as ≥80% of panelists rating an item as "important" or "very important," a median score ≥3.5, and interquartile range ≤1.ResultsForty professionals were invited; 22 panelists participated in round 1, suggesting 176 competencies, which were merged with 170 from prior literature. After refinement, 42 competencies were rated in round 2 ( = 21 panelists). During this round, 17 competencies reached consensus, and 8 required modifications. In round 3 ( = 20 panelists), an additional 15 competencies reached consensus. The final framework includes 32 competencies organized into 6 domains: Fundamentals of SDM, Unique Aspects of Pediatric SDM, Communication and Information-Sharing, Eliciting and Integrating Preferences and Values, Collaboration and Consensus Building, and Providing Evidence-Based Recommendations.ConclusionsThis study defines the knowledge, skills, values, and attitudes pediatric health care professionals need to engage children and families in SDM effectively. The framework supports competency-based education and guides curriculum development and assessment to strengthen SDM practice in pediatrics.HighlightsThis study is the first to develop pediatric-specific SDM competencies using a Delphi approach.The final list includes 32 competencies across knowledge, skills, attitudes, and values.These competencies provide a foundation for designing SDM training within a competency-based education approach in pediatrics.
Hypothetical Bias in the SG and TTO.
Baillon A, Bleichrodt H, Granic GD
Med Decis Making · 2026 May · PMID 41863041 · Publisher ↗
BackgroundHealth state utility measurements are central in health policy and medical decision making. Common methods are the standard gamble (SG) and the time tradeoff (TTO). They typically use hypothetical questions. It...
BackgroundHealth state utility measurements are central in health policy and medical decision making. Common methods are the standard gamble (SG) and the time tradeoff (TTO). They typically use hypothetical questions. It is unknown whether this leads to a bias.MethodsWe used the Bayesian truth serum (BTS) to incentivize choices in the SG and the TTO. We asked these choices both with and without incentives in 2 online experiments: 1 with 498 Dutch students and 1 with 1,298 members of the US general population. To give incentives their maximal possibility to work, we deliberately introduced default bias in the US sample.ResultsIncentives made no difference. Individual choices and aggregate valuations in the SG and the TTO were the same with and without incentives in both experiments. Defaults affected the TTO, but not the SG.LimitationsThe BTS assumes that respondents have a common prior and use Bayesian updating. Moreover, it is hard to explain why answering truthfully is in respondents' best interests in the BTS.Conclusions and ImplicationsIncentives did not affect the SG and TTO. Our results support the current practice of using hypothetical questions in health state utility measurement.HighlightsWe found no evidence of hypothetical bias in the choices made in the SG and TTO measurements.This was true even when we introduced a default bias.The common practice to use hypothetical questions in health state utility measurement seems valid.
Nonchoice Surgical Decision Making among Patients with Cancer: A Narrative Review.
Bechthold AC, Kennedy A, Monton O … +1 more , Kopecky KE
Med Decis Making · 2026 Mar · PMID 41854025 · Publisher ↗
BackgroundShared decision making is widely endorsed as the gold standard for patient-centered care, yet in the context of cancer, patients often describe surgery as a nonchoice. This narrative review explores the concept...
BackgroundShared decision making is widely endorsed as the gold standard for patient-centered care, yet in the context of cancer, patients often describe surgery as a nonchoice. This narrative review explores the concept of patient-perceived nonchoice decision making in cancer surgery.MethodsThis narrative review was guided by the Scale for the Assessment of Narrative Review Articles (SANRA) criteria. Studies examining nonchoice surgical decision making in adult patients with resectable solid-organ malignancies were identified through manual screening, citation searching, and a targeted PubMed search. Descriptive themes were developed through inductive analysis and iterative discussions among authors. Findings were synthesized using a structured narrative approach.ResultsSeventeen studies met inclusion criteria. Three themes emerged: 1) surgery as the only choice offered by the surgeon, 2) choosing surgery did not feel like a choice, and (3) patient preference to relinquish decision making. According to patients, surgery was often framed as the sole viable treatment option with minimal discussion of alternatives. External social and societal pressures, combined with the belief that surgery equated to survival, further reinforced this perception. Patients who felt overwhelmed or had little medical knowledge often chose to relinquish decision-making responsibility. Collectively, these dynamics limited patients' ability to engage in meaningful deliberation.ConclusionsDespite the emphasis on shared decision making in cancer care, many patients undergoing surgery for resectable solid-organ malignancy face constrained decision making shaped by clinical realities, social context, and psychological stressors. Addressing the perception of surgery as a nonchoice is critical to promote meaningful patient engagement. Future research should aim to identify and mitigate modifiable factors that contribute to nonchoice mindsets, ultimately supporting value-concordant surgical decisions.HighlightsMany patients with cancer perceive surgery as their only option, rather than an active decision.Surgeons often frame surgery for cancer as inevitable, limiting discussions of alternatives and undermining shared decision making.Even when surgeons explicitly present surgical and nonsurgical options to patients, many patients still do not perceive a choice.Some patients intentionally defer decision making to surgeons, either out of trust in clinical expertise or a desire to avoid the emotional burden of choice.Understanding how nonchoice dynamics arise can help clinicians better support informed, values-based surgical decisions.
Operationalizing and Measuring Informed Choice in Health Care: An Umbrella Review.
Sprosen H, Re C, Stewart GD … +1 more , Usher-Smith JA
Med Decis Making · 2026 May · PMID 41834478 · Full text
BackgroundInformed choice is of the highest importance in health care. However, confusion and challenges remain toward how it is conceptualized and measured.PurposeThis umbrella review aimed to establish how informed cho...
BackgroundInformed choice is of the highest importance in health care. However, confusion and challenges remain toward how it is conceptualized and measured.PurposeThis umbrella review aimed to establish how informed choice is operationalized in health care and the characteristics and performance of the most commonly used measurement instruments.Data SourcesFour electronic databases (Ovid MEDLINE, Ovid EMBASE, APA PsycINFO, and Cochrane Library) were searched up to January 29, 2024. Reference lists of included studies were hand searched for further relevant publications.Study SelectionAfter the titles and abstracts of 10,434 articles were screened by one reviewer and 10% were screened by a second reviewer for consistency, 2 reviewers independently screened 60 full-text articles for inclusion. Key eligibility criteria included systematic reviews in adult health care settings where the aim included an evaluation of measures of informed choice. Sixteen articles were included.Data ExtractionData were independently extracted by 2 reviewers using a standardized template. Data were synthesized using the summarization technique with systematic reviews as the main unit of analysis and additional subanalysis of primary measurement instruments identified.LimitationsHeterogeneous definitions complicate search strategies, and eligibility criteria may limit external validity. The ROBIS appraisal identified many reviews as high risk of bias, limiting the conclusions drawn. Due to heterogeneity, meta-analysis was not possible, and conclusions were limited to narrative reviews.ConclusionsThere remains no consensus on how informed choice should be conceptualized and measured within health care. This review attempts to bridge these gaps by presenting available concepts and instruments for clinicians, researchers, and policy makers. Future recommendations include achieving consistent definitions of informed choice and related concepts, followed by the use of standardized, validated, multidimensional instruments informed by theory in diverse populations.HighlightsInformed choice is of key importance and increasingly emphasized across health care.Despite this importance, confusion and challenges remain regarding how informed choice is conceptualized and measured in health care.Consistent definitions and the use of standardized, validated, multidimensional instruments, informed by theory and patients themselves, in diverse populations should be the first steps to improve this.These recommendations apply to all in health care, including health professionals, researchers, and policy makers.
A Pragmatic Bayesian Adaptive Trial Design Based on the Value of Information: The Value-Driven Adaptive Design.
Dymock M, Marsh JA, Jones M … +3 more , Heath A, Murray K, Snelling TL
Med Decis Making · 2026 Jul · PMID 41826246 · Full text
BackgroundClinical trial designs are typically narrowly focused on error control in hypothesis testing, but this approach is inadequate in many contexts, particularly when a decision maker intends to, or must, consider m...
BackgroundClinical trial designs are typically narrowly focused on error control in hypothesis testing, but this approach is inadequate in many contexts, particularly when a decision maker intends to, or must, consider multiple relevant clinical and health economic outcomes under uncertainty. Value-of-information (VoI) metrics can be used to estimate the monetary value of data collection to the decision maker. Adaptive trial designs use prespecified decision rules as data are collected and analyzed to modify the ongoing trial design. To date, VoI considerations have rarely been integrated into this approach, partly due to the computational burden.MethodsWe propose a value-driven adaptive design that refocuses trial design on VoI as a metric to direct trial adaptations. Specifically, a VoI analysis is performed at each interim analysis to determine whether or not the trial should proceed to the next analysis (i.e., determine whether further data collection is sufficiently valuable). We provide methods to compute the expected net benefit of perfect information, expected net benefit of sampling (ENBS) for the next analysis, and the ENBS for subsequent sequential analyses. Our approach is flexible to any statistical model, decision model, and research cost function and does not require distributional assumptions about the net benefit.ResultsWe describe our method in detail and demonstrate its implementation via a case study comparing infant immunoprophylaxis and maternal vaccination to prevent respiratory syncytial virus-related medical attendances.ConclusionsOur value-driven adaptive design aligns pragmatic clinical trial design with the requirements of decision makers. Designs with VoI-based adaptations have the potential to improve the cost-effectiveness of clinical trials.HighlightsOur value-driven adaptive design is a new method that uses the expected net benefit of sampling to define stopping rules at interim analyses (i.e., to determine if further data collection is sufficiently valuable).Our method orients trial designs to efficiently produce evidence to inform the decision maker.
A Comparison of Methods for Modeling Multistate Cancer Progression Using Screening Data with Censoring after Intervention.
Akwiwu EU, Coupé VMH, Berkhof J … +1 more , Klausch T
Med Decis Making · 2026 Jul · PMID 41821401 · Full text
BackgroundOptimizing cancer screening and surveillance frequency requires accurate information on parameters such as sojourn time and cancer risk from premalignant lesions. These parameters can be estimated using multist...
BackgroundOptimizing cancer screening and surveillance frequency requires accurate information on parameters such as sojourn time and cancer risk from premalignant lesions. These parameters can be estimated using multistate cancer models applied to screening or surveillance data. However, the performance of these models has not been thoroughly investigated in settings in which cancer precursors are treated upon detection, preventing progression to cancer. Our main goal is understanding the performance of available multistate methods in this challenging censoring setting.MethodsWe assumed progression hazards between consecutive health states in a 3-state model (healthy [HE], cancer precursor, and cancer) to be either time independent or dependent on time since state entry and compared 6 methods implemented in R software packages with varying assumptions: time-independent hazards (msm), hazards dependent on time since state entry (msm with a phase-type model, cthmm, smms, BayesTSM), and hazards dependent on time since the start of the process (hmm). Risk estimates from each method were compared in simulations and illustrated using colorectal cancer surveillance data from 734 individuals, classified into 3 health states: HE, non-advanced adenoma (nAA), and advanced neoplasia (AN).ResultsAll methods performed well with time-independent hazards in the simulation study. With hazards dependent on time since state entry, only smms and BayesTSM provided unbiased risk estimates. In the application, only msm,hmm, and BayesTSM yielded converged solutions. The nAA risk estimates were similar between hmm and BayesTSM but differed for msm, while AN risk estimates varied across methods.ConclusionsMethods for multistate cancer models, specifically with unobservable precursor-to-cancer transition, are strongly affected by the time dependency of the hazard. With time-dependent hazards since state entry, BayesTSM provided robust estimates, in both the simulation and application.HighlightsThis study presents the first comprehensive comparison of available multistate modeling options for screening and surveillance data, focusing on the specific setting of a 3-state progressive model (healthy, cancer precursor, cancer) in which cancer precursors are treated upon detection so that the transition to cancer is prevented (censoring after intervention). Sample R code and simulated data demonstrating the compared methods, along with documentation (including installation instructions, manual, and/or worked examples) for the corresponding R software packages, are available at https://github.com/EddymurphyAkwiwu/MultiStateMethods.All methods provide unbiased risk estimates for transition times when the true progression hazards are time independent. With more realistic models in which progression hazards are dependent on time since state entry, only BayesTSM and smms yield unbiased risk estimates for transition times.In situations with weakly identifiable likelihoods, the smms package may suffer from numerical and optimization problems. The BayesTSM package overcomes these problems by applying regularized parameter estimation using weakly informative priors.Methods for multistate cancer models, more specifically with unobservable precursor-to-cancer transition, are strongly affected by the time dependency of the hazard. An inappropriate choice can lead to biased parameter estimates.
Adapting Sexual Behavior Survey Data to Parameterize an Agent-Based Model of Human Papillomavirus (HPV) Transmission.
Spencer JC, Burger EA, Portnoy A … +4 more , Campos NG, Regan MC, Sy S, Kim JJ
Med Decis Making · 2026 Jul · PMID 41792911 · Full text
PurposeSexual transmission of human papillomavirus (HPV) infection is important for capturing the indirect effects of interventions in mathematical models, but limited data create challenges for reflecting sexual behavio...
PurposeSexual transmission of human papillomavirus (HPV) infection is important for capturing the indirect effects of interventions in mathematical models, but limited data create challenges for reflecting sexual behavior patterns over the lifespan of individuals and across heterogenous populations. We used nationally representative data from the United States to parameterize, calibrate, and validate a heterosexual transmission model of HPV.MethodsBased on sexual behavior data from the National Survey of Family Growth (2011-2019), we categorized respondents into 4 sexual activity categories, using their percentile of cumulative lifetime partners compared with others within their same sex and age group. We modeled probabilistic partnership acquisition and dissolution by age, sex, and sexual activity category. Partnership data were incorporated into an existing agent-based model of HPV transmission in the United States. We calibrated 1) per-partnership HPV transmission and 2) reduced risk of type-specific reinfection from natural immunity to fit age- and type-specific HPV prevalence using the National Health and Nutrition Examination Survey (NHANES 2002-2008). We validated the final model by comparing model-based projections of HPV prevalence against empirical data in the US population before and after widespread HPV vaccination.ResultsAfter calibrating to fit overall HPV prevalence, model validation exercises indicated that the distribution of prevaccine HPV prevalence across sexual activity categories closely matched NHANES estimates. Simulating vaccination rates over 10 y, the model replicated postvaccine NHANES data for prevalence of HPV16.ConclusionCapturing HPV transmission dynamics requires an understanding of sexual behavior across populations and over time. Defining sexual activity categories based on cumulative lifetime partners can capture patterns of HPV risk over a lifespan to reflect the dynamics of HPV transmission and vaccination.HighlightsUsing data from a large national survey, we developed sexual behavior inputs for an agent-based model of HPV transmission.We define 4 heterogenous risk groups using cumulative lifetime sexual behavior for males and females and find we can recreate validation data on both lifetime sexual patterns and age-specific HPV prevalence.Our calibrated model also reproduces early patterns of HPV reduction following HPV vaccine introduction.Modelers seeking to understand the long-term effects of the HPV vaccine should carefully consider the heterogeneity of sexual behavior across groups as well as changes in behavior over the lifespan.
"They Are Not Going to Be Happy": An Ethnographic Study of the Prioritization of Patients Awaiting Elective Surgery in an Academic Hospital in the Netherlands.
Mos P, de Graaff B, Heijke M … +3 more , Lingsma H, Baatenburg de Jong RJ, Reckers-Droog V
Med Decis Making · 2026 May · PMID 41721536 · Full text
BackgroundTo reduce variation in waiting time for elective surgery, a Dutch academic hospital introduced a classification system based on urgency scores to standardize decision making. Physicians, however, retain clinica...
BackgroundTo reduce variation in waiting time for elective surgery, a Dutch academic hospital introduced a classification system based on urgency scores to standardize decision making. Physicians, however, retain clinical discretion in assigning urgency scores. This facilitates the provision of personalized and efficient care but may also create variation between patients and lack of transparency. The aim of this study was to describe the prioritization of patients awaiting elective surgery, including the use of urgency scores, and to explore explanations for discrepancies between assigned scores and actual waiting times.MethodsWe conducted an ethnographic study combining interviews with physicians and observations of elective surgery planners in the academic hospital. Data were analyzed thematically, guided by 3 sensitizing concepts: professional autonomy, emotions, and traditions.ResultsThe prioritization of patients awaiting elective surgery begins with physicians' assessment of urgency and concludes with planners drafting the schedule. The assessment is guided by clinical parameters, patient- and physician-related factors, and logistical constraints. Importantly, the prioritization of patients for elective surgery is shaped by subjective and affective considerations, customary decision-making practices, as well as the considerable professional autonomy of physicians and planners.ConclusionsStandardized prioritization tools, such as urgency scores, may reduce unjustified variation in waiting times, but initial resistance to their implementation can hamper their use in decision-making practice. Moreover, such tools alone may fail to capture the complexity of clinical practice and the importance of the expertise and experience of physicians and planners therein. Rather than relying solely on stricter adherence to urgency scores, prioritization processes may be strengthened by facilitating communication and feedback exchanges to support a more integrated and context-specific approach that considers the complexity of clinical practice.HighlightsStandardized decision-making tools are implemented to standardize and support the prioritization of patients awaiting elective surgery.Prioritization decisions are made by different professionals, and nonclinical factors that include subjective perceptions and logistic constraints may guide these decisions.Standardized tools inadequately capture the complexity of clinical decision making and the professional autonomy physicians and planners.
A Novel Decision-Modeling Framework for Health Policy Analyses When Outcomes Are Influenced by Social and Disease Processes.
Cusick MM, Alarid-Escudero F, Goldhaber-Fiebert JD … +1 more , Rose S
Med Decis Making · 2026 Jul · PMID 41668628 · Full text
PurposeHealth policy simulation models incorporate disease processes but often ignore social processes that influence health outcomes, potentially leading to suboptimal policy recommendations. To address this gap, we dev...
PurposeHealth policy simulation models incorporate disease processes but often ignore social processes that influence health outcomes, potentially leading to suboptimal policy recommendations. To address this gap, we developed a novel decision-analytic modeling framework to integrate social processes.MethodsWe evaluated a simplified decision problem using two models: a standard decision-analytic model and a model incorporating our social factors framework. The standard model simulated individuals transitioning through three disease natural history states-healthy, sick, and dead-without accounting for differential health system utilization. Our social factors framework incorporated heterogeneous health insurance coverage, which influenced disease progression and health system utilization. We assessed the impact of a new treatment on a hypothetical cohort of 100,000 healthy, non-Hispanic Black and non-Hispanic white 40-y-old adults. Primary outcomes included life expectancy, cumulative incidence and duration of sickness, and health system utilization throughout a person's lifetime. Secondary outcomes included costs, quality-adjusted life years, and incremental cost-effectiveness ratios.ResultsIn the standard model, the new treatment increased life expectancy by 2.7 y for both non-Hispanic Black and non-Hispanic white adults, without affecting racial/ethnic gaps in life expectancy. However, incorporating known racial/ethnic disparities in health insurance coverage with the social factors framework led to smaller life expectancy gains for non-Hispanic Black adults (2.0 y) compared with non-Hispanic white adults (2.2 y), increasing racial/ethnic disparities in life expectancy.LimitationsThe availability of social factors data and complexity of causal pathways between factors may pose challenges in applying our social factors framework.ConclusionsExcluding social processes from health policy modeling can result in unrealistic projections and biased policy recommendations. Incorporating the social factors framework enhances simulation models' effectiveness in evaluating interventions with health equity implications.HighlightsHealth policy simulation models that ignore social processes may be biased and lead to suboptimal policy recommendations. To address this, we proposed a novel social factors framework to integrate social factors into decision-analytic models for health policy.Applying our social factors framework to a simplified example highlighted the potential bias that results from ignoring social factors. In a standard model, a hypothetical new treatment appeared to have no effect on health disparities. However, incorporating our social factors framework demonstrated that this treatment would exacerbate disparities.Incorporating a social factors framework into health policy simulation models has particular relevance for evaluating health interventions with equity implications.
Pressured or Voluntary? Motivations for Vaccination during the COVID-19 Pandemic and Future Health-Protective Behaviors.
Pittarello A, Rabinovitch H, Rubaltelli E … +2 more , Slovic P, Kogut T
Med Decis Making · 2026 May · PMID 41656570 · Publisher ↗
PurposeThis research investigates how individuals' perceived motivations for receiving the COVID-19 vaccine-specifically, feeling pressured versus vaccinating voluntarily-relate to future health-protective behaviors and...
PurposeThis research investigates how individuals' perceived motivations for receiving the COVID-19 vaccine-specifically, feeling pressured versus vaccinating voluntarily-relate to future health-protective behaviors and perceived risk of the vaccine and the virus.MethodsIn 2 studies, with a total of = 1,252 respondents, participants self-reported their past vaccination motivation and completed measures assessing willingness to receive future vaccines, engage in general health-protective behaviors, and perceived risks associated with the virus and the vaccine.ResultsFindings consistently show that individuals who felt pressured to vaccinate are positioned between unvaccinated individuals and those who vaccinated voluntarily in their perceptions and intentions. Compared with voluntary vaccinators, they reported lower willingness to receive future vaccines and engage in protective behaviors and greater perceived vaccine risk. However, their willingness to engage in these behaviors was still greater than that of unvaccinated individuals.LimitationsThe studies are mainly cross-sectional and do not track the same individuals over time.ConclusionsPerceived motivation for past vaccination significantly predicts vaccinated individuals' attitudes and future intentions related to health behaviors, even unrelated to COVID-19.ImplicationsTreating all vaccinated individuals as a uniform group can be overly simplistic. Public health messaging and interventions may be more effective when considering individuals' vaccination motivation.HighlightsTreating all vaccinated individuals the same can be simplistic.The perception of the vaccine and virus risks differ depending on whether vaccination felt voluntary or coerced.Different motivations behind vaccination can shape future medical decisions beyond the pandemic.
Shared Decision Making among Patients with Chronic Conditions in France: A Cross-Sectional Survey in the ComPaRe E-Cohort.
Busnel Y, Légaré F, Moumjid N … +4 more , Panse L, Testud A, Tran VT, Haesebaert J
Med Decis Making · 2026 May · PMID 41649074 · Publisher ↗
BackgroundShared decision making (SDM) is a cornerstone of patient-centered care; however, little information is available on how SDM is practiced in routine care. We aimed to assess the level of SDM perceived by patient...
BackgroundShared decision making (SDM) is a cornerstone of patient-centered care; however, little information is available on how SDM is practiced in routine care. We aimed to assess the level of SDM perceived by patients with chronic conditions for the most important health decision in the past 12 mo.MethodsThis was a cross-sectional online survey among ComPaRe, a nationwide e-cohort of patients with chronic conditions in France. The survey asked participants about their perception of SDM using the 9-item Shared Decision-Making Questionnaire (SDM-Q-9) regarding their most important health decision in the past 12 mo. We weighted the sample to represent French patients with chronic conditions and conducted regression models to identify factors associated with higher SDM levels, adjusting for sociodemographic and clinical characteristics.ResultsIn total, 2,087 patients were analyzed (participation rate: 34.9%). In the weighted sample, 53.0% were women, the mean (SD) age was 51.0 (15) y, and the most frequent conditions were endometriosis (27.3%), inflammatory rheumatic diseases (20.7%), and high blood pressure (19.3%). The most important health decisions in the past 12 mo were mainly about drug treatments (36.5%) or surgery (20.5%). The mean (SD) SDM-Q-9 score was 63 (27)/100 (moderate level of SDM). The highest scores were observed for cancer (70 [26]) and depression (69 [26]), whereas the lowest scores were for long COVID (54 [28]) and endometriosis (58 [25]). Decisions about surgery (71 [25]) and with specialists (64 [27]) were associated with higher scores compared with medication decisions (60 [28]) or with general practitioners (62 [27]). Multivariate analysis confirmed that a higher SDM level was associated with being a man; having higher health literacy; making decisions relating to cancer, surgery, or medical devices; and specialist involvement.ConclusionsPatients with chronic conditions in France report moderate levels of SDM, with substantial variations by condition, decision type, and patient characteristics. Findings highlight the need for tailored strategies to foster SDM in chronic care.HighlightsShared decision making (SDM) is considered a key component of the chronic disease management model.This study provides the first nationwide assessment of perceived SDM levels among patients with chronic conditions in France.Patients have a moderate overall SDM score, but significant disparities exist. Patients with less recognized conditions such as long COVID or endometriosis, low health literacy, and high treatment burden reported significantly lower SDM scores as compared with others in their care decisions.These findings underscore the need for targeted interventions to improve SDM implementation.
Forecasting Local Surges in COVID-19 Hospitalizations through Adaptive Decision Tree Classifiers.
Murray-Watson RE, Guaracha X, Bilinski A … +1 more , Yaesoubi R
Med Decis Making · 2026 Apr · PMID 41626707 · Publisher ↗
IntroductionDuring the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local hospital capacity. Some risk metrics, such as the Center for Disease Co...
IntroductionDuring the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local hospital capacity. Some risk metrics, such as the Center for Disease Control and Prevention's (CDC's) Community Levels, were developed to predict the impact of COVID-19 on the community-level health care system based on routine surveillance data. However, they had limited utility as they were not routinely updated based on accumulating data and were not directly linked to specific outcomes, such as surges in COVID-19 hospitalizations beyond local capacities.MethodsIn this article, we evaluated decision tree classifiers developed in real time to predict surges in local hospitalizations due to COVID-19 between July 2020 and November 2022. These classifiers would have provided visually intuitive and interpretable decision rules and, by being updated weekly, would have responded to changes in the epidemic. We compared the performance of these classifiers with that of logistic regression and neural network models using various metrics, including the area under the receiver-operating characteristic curve (auROC) and the area under the precision-recall curve (auPRC).ResultsDecision tree classifiers achieved an auROC of for most pandemic weeks and outperformed the CDC's Community Levels in predicting high hospital occupancy. The auPRC, sensitivity, and specificity of the classifiers varied more substantially over time (between ) and in sync with pandemic waves. Decision tree classifiers demonstrated similar performance compared with logistic regression and neural network models while presenting more interpretable classification rules.ConclusionsUsing routinely collected hospital surveillance data, decision tree classifiers can be adaptively updated to predict surges in local hospitalizations. However, the sensitivity and specificity of these classifiers could change markedly during different pandemic waves.HighlightsA major concern during the COVID-19 pandemic was the risk of exceeding local health care capacity due to COVID-19-related hospitalizations.To assess this risk and inform mitigating strategies, several risk assessment tools were developed during the pandemic. Many of these tools, however, did not predict local outcomes, were not updated as the pandemic progressed, and/or were not interpretable by decision makers.We propose an adaptive framework of decision tree classifiers to predict whether COVID-19-related hospital occupancy would exceed a given capacity threshold. These classifiers demonstrated reasonable and stable prediction performance over time. However, their sensitivity and specificity may change substantially over the course of pandemic waves.
Why Most Australians Consider It Valuable to Find Harmless Abnormalities with Diagnostic Tests: A Mixed-Methods Study.
Rozbroj T, Hoo MH, Gorelik A … +2 more , O'Connor DA, Buchbinder R
Med Decis Making · 2026 May · PMID 41618104 · Full text
BackgroundIndividuals commonly want diagnostic testing even after being informed the test is clinically unbeneficial and has risks. These preferences are poorly understood but may relate to beliefs that any testing infor...
BackgroundIndividuals commonly want diagnostic testing even after being informed the test is clinically unbeneficial and has risks. These preferences are poorly understood but may relate to beliefs that any testing information is valuable. To explore this, we examined Australian adults' attitudes toward finding harmless abnormalities using diagnostic tests and the broader beliefs related to these attitudes.MethodsData collected via survey were analyzed using mixed methods. Free text explaining attitudes to finding harmless abnormalities were analyzed using comparative content and interpretative analyses. Associations between attitudes to finding harmless abnormalities and broader beliefs and demographics were analyzed using regression.ResultsAlmost three-fifths of 655 participants considered it valuable to identify harmless abnormalities using tests. Qualitative analyses showed this attitude was driven by beliefs that identification would provide psychological reassurance, valuable biodata, and enable monitoring and management of the harmless abnormalities. These beliefs were underpinned by a skepticism that abnormalities can ever be harmless and by a range of beliefs about the broader value of diagnostic testing. Participants with negative attitudes to identifying harmless abnormalities were concerned about resultant anxiety and unnecessary health interventions. Regression showed that positive attitudes to identifying harmless abnormalities were associated with greater confidence in doctors, lesser concerns about overtreatment, and a stronger desire to know as much about their bodies as possible as well as with several demographic variables.Conclusions and ImplicationsOur study explores why people seek diagnostic tests that they know lack obvious clinical benefits. It identifies broader beliefs and psychological factors that profoundly influence testing choices. This knowledge will help overcome the limitations of existing strategies to explain the risks of tests to patients and the public.HighlightsFindings help explain why facts showing that particular diagnostic tests are ineffective or harmful fail to dissuade many Australians from seeking those tests.Many Australians value diagnostic testing for perceived reassurance, understanding one's body, and use in medical decision making.Many are skeptical that identifying incidentalomas is harmful, and are confident they can avoid unnecessarily treating them.Messages about testing risks should focus on broader beliefs and respond to psychological factors that undermine the effect of risk/benefit information.
Target Trial Emulation to Incorporate Real-World Data in the Estimation of the Clinical and Cost-Effectiveness of Biologic Treatment.
Singh J, Stevenson M, Hyrich KL … +3 more , Gillies CL, Abrams KR, Bujkiewicz S
Med Decis Making · 2026 Apr · PMID 41540820 · Full text
IntroductionIn the health technology assessment (HTA) of biologic treatments for rheumatoid arthritis (RA), there is limited randomized evidence on treatment effectiveness after first-line treatment failure. We demonstra...
IntroductionIn the health technology assessment (HTA) of biologic treatments for rheumatoid arthritis (RA), there is limited randomized evidence on treatment effectiveness after first-line treatment failure. We demonstrate how real-world data (RWD) could fill this evidence gap.MethodsTarget trial emulation (TTE) minimizes biases in the causal analysis of RWD by prespecifying a protocol for a hypothetical randomized clinical trial (RCT) that would estimate the effect of interest. The application of TTE for HTA was illustrated using RWD from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis to estimate the effectiveness of rituximab versus nonbiologic therapy (NBT) after first-line biologic failure, in terms of European Alliance of Associations for Rheumatology response achievement. The effectiveness estimates from RWD were combined with RCT estimates in a meta-analysis. The pooled estimates were entered into an economic model to estimate the incremental cost-effectiveness ratio (ICER) comparing biologic versus NBT strategies.ResultsBased on RWD, rituximab was associated with higher probabilities of achieving a moderate or good response (0.215 v. 0.174) and a good response (0.090 v. 0.066) as compared with NBT. These probabilities were lower than those estimated from RCT data (moderate or good 0.650; good 0.150). The economic model estimated less time on treatment and lower costs associated with biologics when based on RWD compared with RCT data (mean £63,500 v. £70,000). This resulted in a higher ICER based on RWD compared with RCT data (mean £46,800 v. £34,700 per quality-adjusted life-year gained).ConclusionsRWD can provide supplemental evidence on treatment effectiveness where randomized evidence is limited. This can make a meaningful difference to cost-effectiveness estimates. Our results are not intended to inform current RA management.HighlightsIn health technology assessment, real-world data (RWD) can provide supplemental evidence on treatment effectiveness where there is limited randomized evidence.Target trial emulation was applied using RWD to estimate the clinical effectiveness of biologic treatment; these estimates were combined with estimates from an RCT in a meta-analysis, and the pooled estimates were entered into an economic model for rheumatoid arthritis.Treatment effect estimates based on combining RWD and RCT data were more modest compared with the effectiveness estimates from the RCT data alone, leading to a difference in the estimate of cost-effectiveness comparing biologics with nonbiologic therapy.
On Representations and Quantifications of Uncertainty.
Iskandar R, Trikalinos TA
Med Decis Making · 2026 Apr · PMID 41527924 · Full text
Abstract loading — click title to view on PubMed.
SMDM Presidential Address: Reflecting on the Gaps between Research and Practice in Decision Making from Treatment to the End of Life.
Shaffer VA
Med Decis Making · 2026 May · PMID 41527876 · Publisher ↗
Abstract loading — click title to view on PubMed.
Valuation of the EQ-5D-Y-5L Using DCE Methods That Account for Nonlinear Time Preferences.
Yu A, Roudijk B, Jiang P … +5 more , Norman R, Viney R, Street D, Devlin N, Brendan M
Med Decis Making · 2026 Apr · PMID 41527679 · Full text
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 t...
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.
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