搜索结果：Medical decision making : an international journal of the Society for Medical Decision Making

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.

BackgroundPrevious research suggests that physicians' inclination to refer patients for suspected cancer is a relatively stable characteristic of their decision making. We aimed to identify its psychological determinants in the presence of a risk-prediction algorithm.MethodsWe presented 200 UK general practitioners with online vignettes describing patients with possible colorectal cancer. Per the vignette, GPs indicated the likelihood of referral (from highly unlikely to highly likely) and level of cancer risk (negligible/low/medium/high), received an algorithmic risk estimate, and could then revise their responses. After completing the vignettes, GPs responded to questions about their values with regard to harms and benefits of cancer referral for different stakeholders, perceived severity of errors, acceptance of false alarms, and attitudes to uncertainty. We tested whether these values and attitudes predicted their earlier referral decisions.ResultsThe algorithm significantly reduced both referral likelihood (b = -0.06 [-0.10, -0.007], P = 0.025) and risk level (b = -0.14 [-0.17, -0.11], P < 0.001). The strongest predictor of referral was the value GPs attached to patient benefits (b = 0.30 [0.23, 0.36], P < 0.001), followed by benefits (b = 0.18 [0.11, 0.24], P < 0.001) and harms (b = -0.14 [-0.21, -0.08], P < 0.001) to the health system/society. The perceived severity of missing a cancer vis-&#xe0;-vis overreferring also predicted referral (b = 0.004 [0.001, 0.007], P = 0.009). The algorithm did not significantly reduce the impact of these variables on referral decisions.ConclusionsThe decision to refer patients who might have cancer can be influenced by how physicians perceive and value the potential benefits and harms of referral primarily for patients and the moral seriousness of missing a cancer vis-&#xe0;-vis over-referring. These values contribute to an internal threshold for action and are important even when an algorithm informs risk judgments.HighlightsPhysicians' inclination to refer patients for suspected cancer is determined by their assessment of cancer risk but also their core values; specifically, their values in relation to the perceived benefits and harms of referrals and the seriousness of missing a cancer compared with overreferring.We observed a moral prioritization of referral decision making, in which considerations about benefits to the patient were foremost, considerations about benefits but also harms to the health system or the society were second, while considerations about oneself carried little or no weight.Having an algorithm informing assessments of risk influences referral decisions but does not remove or significantly reduce the influence of physicians' core values.

ObjectiveTo evaluate the clinical and cost-effectiveness of SHARE TO CARE (S2C), a complex intervention for hospital-wide, systematic implementation of shared decision making.MethodsWe analyzed clinical effectiveness, health care resource utilization, and implementation costs of S2C from the statutory health insurance perspective using a quasi-experimental difference-in-differences approach with evidence from the Department of Neurology. Clinical outcomes included inpatient hospital admissions, emergency department admissions, and rates of standard and advanced imaging procedures. Implementation costs comprised those related to the conception, development, process integration, ongoing support, and auditing of S2C. Health care utilization data covered inpatient and outpatient care, pharmaceuticals, therapeutic services, assistive devices, and nursing care. We conducted sensitivity analyses to account for uncertainties.FindingsS2C was associated with a reduction in inpatient hospital admissions, emergency department admissions, and imaging rates in the intervention group. The cost analyses aligned with these findings, showing reduced total costs and health care resource utilization in the intervention group. Although none of the estimates reached the predefined thresholds for statistical significance, the primary analysis yielded weak evidence (P&#x2009;<&#x2009;0.1) of a reduction in emergency department admissions in the intervention group. Overall, savings outweighed the costs of implementing S2C, suggesting cost-effectiveness.ConclusionsS2C has the potential to reduce emergency department admissions and overall health care costs from the statutory health insurance perspective. Further research should investigate generalizability, the timing of the treatment effect, and potential biases introduced by the COVID-19 pandemic. The demonstrated effects of shared decision making (SDM) have encouraged statutory health insurances in Germany to offer additional reimbursement for clinics certified under the S2C program. The S2C model illustrates how payers and providers can collaborate to facilitate the nationwide implementation of SDM.HighlightsThe implementation of SHARE TO CARE (S2C) was associated with a statistically nonsignificant reduction in emergency department admissions after 1 y from the statutory health insurance perspective, based on data from the Department of Neurology.The cost savings from reduced health care utilization outweighed the implementation costs, and despite not reaching statistical significance, the results support the potential cost-effectiveness of S2C.S2C has the potential for nationwide implementation as a systematic form of shared decision making.Future research should investigate the generalizability of the results to other health care settings.

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&#x2009;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&#x2009;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&#x2009;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.

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

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 >80% 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 20%and100%) 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.

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.

BackgroundCorrectional facilities can act as amplifiers of infectious disease outbreaks. Small community outbreaks can cause larger prison outbreaks, which can in turn exacerbate the community outbreaks. However, strategies for epidemic control in communities and correctional facilities are generally not closely coordinated. We sought to evaluate different strategies for coordinated control.MethodsWe developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities. We parameterized it for the initial phases of the COVID-19 epidemic for 1) California communities and prisons based on community data from covidestim, prison data from the California Department of Corrections and Rehabilitation, and mobility data from SafeGraph, and 2) a small, illustrative network of communities and prisons. For each community or prison, control measures were defined by the intensity of 2 activities: 1) screening to detect and isolate cases and 2) nonpharmaceutical interventions (e.g., masking and social distancing) to reduce transmission. We compared the performance of different control strategies including heuristic and reinforcement learning (RL) strategies using a reward function, which accounted for both the benefit of averted infections and nonlinear cost of the control measures. Finally, we performed analyses to interpret the optimal strategy and examine its robustness.ResultsThe RL control strategy robustly outperformed other strategies including heuristic approaches such as those that were largely used during the COVID-19 epidemic. The RL strategy prioritized different characteristics of communities versus prisons when allocating control resources and exhibited geo-temporal patterns consistent with mitigating prison amplification dynamics.ConclusionRL is a promising method to find efficient policies for controlling epidemic spread on networks of communities and correctional facilities, providing insights that can help guide policy.HighlightsFor modelers, we developed a stochastic simulation model of an epidemic spreading across a network of communities and correctional facilities, and we parameterized it for the initial phases of the COVID-19 epidemic for California communities and prisons in addition to an illustrative network.We compared different control strategies using a reward function that accounted for both the benefit of averted infections and cost of the control measures; we found that reinforcement learning robustly outperformed the other strategies including heuristic approaches such as those that were largely used during the COVID-19 epidemic.For policy makers, our work suggests that they should consider investing in the further development of such methods and using them for the control of future epidemics.We offer qualitative insights into different factors that might inform resource allocation to communities versus prisons during future epidemics.

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.

BackgroundMedical providers often face challenges in accurately predicting the survival of critically sick patients. Optimistic forecasts can lead to the overuse of resources, while overly cautious predictions might restrict treatments. This study examines the role of specific psychological factors, analyzed realistically and holistically, in predicting survival outcomes for intensive care unit patients.MethodsThis single-center cohort study evaluated health care providers (e.g., physicians, residents and fellows, and advanced practice practitioners) using two 7-d clinical vignettes. Providers assessed the need for mechanical ventilation (MV), renal replacement therapy (RRT), a percutaneous endoscopic gastrostomy (PEG) tube, or palliative care. Psychological factors were measured using scales that assessed ambiguity tolerance, rationality versus emotional defensiveness, anxiety related to uncertainty, decision-making style, and risk taking. These psychological traits were analyzed using a more realistic and holistic approach, employing cluster techniques. Providers also determined whether they had enough information to evaluate the patient's condition and compared their survival estimates to APACHE II scores.ResultsIn general, engagement in MV and RRT was common by day 2, although physicians were significantly less likely to recommend RRT. Providers generally suggested starting a palliative care consultation by day 6, with a noticeable shift on day 4. Three distinct composite psychological groups emerged: optimistic denial individuals (ODI), optimistic providers (OP), and resilient providers (RP). While these composite psychological groups did not significantly influence engagement in mechanical therapies, they did affect palliative care decisions: RP were more likely to request palliative care, whereas ODI were much less likely to do so. In contrast, individual psychological traits had nonsignificant correlations with the decision to use therapies. Providers initially overestimated survival probabilities during the first 3 d compared with APACHE II survival estimates. However, after day 4, this trend reversed, with providers becoming significantly more pessimistic versus the predictive score and increasingly requesting palliative care involvement.ConclusionsProviders' psychological profiles, rather than their clinical experience, significantly influenced decisions about organ-support therapies and palliative care. Survival estimates showed a biphasic pattern: initially, providers overestimated survival compared with APACHE II predictions, then became more pessimistic and more likely to consult palliative care after day 4.HighlightsIntensive care unit survival predictions by providers followed a biphasic pattern: optimistic early on, then increasingly pessimistic after day 4.Psychological traits such as denial and ambiguity tolerance influenced palliative care decisions more than clinical experience did.Resilient providers were more likely to initiate timely palliative care, while denial-prone providers delayed it.Clinicians and critical care teams should be aware of how their psychological makeup can affect patient care decisions and outcomes.

BackgroundAn artificial intelligence (AI)-enabled rule-out device may autonomously remove patient images unlikely to have cancer from radiologist review. Many published studies evaluate this type of device by retrospectively applying the AI to large datasets and use sensitivity and specificity as the performance metrics. However, these metrics have fundamental shortcomings because sensitivity will always be negatively affected in retrospective studies of rule-out applications of AI.MethodWe reviewed 2 performance metrics to compare the screening performance between the radiologist-with-rule-out-device and radiologist-without-device workflows: positive/negative predictive values (PPV/NPV) and expected utility (EU). We applied both methods to a recent study that reported improved performance in the radiologist-with-device workflow using a retrospective US dataset. We then applied the EU method to a European study based on the reported recall and cancer detection rates at different AI thresholds to compare the potential utility among different thresholds.ResultsFor the US study, neither PPV/NPV nor EU can demonstrate significant improvement for any of the algorithm thresholds reported. For the study using European data, we found that EU is lower as AI rules out more patients including false-negative cases and reduces the overall screening performance.ConclusionsDue to the nature of the retrospective simulated study design, sensitivity and specificity can be ambiguous in evaluating a rule-out device. We showed that using PPV/NPV or EU can resolve the ambiguity. The EU method can be applied with only recall rates and cancer detection rates, which is convenient as ground truth is often unavailable for nonrecalled patients in screening mammography.HighlightsSensitivity and specificity can be ambiguous metrics for evaluating a rule-out device in a retrospective setting. PPV and NPV can resolve the ambiguity but require the ground truth for all patients. Based on utility theory, expected utility (EU) is a potential metric that helps demonstrate improvement in screening performance due to a rule-out device using large retrospective datasets.We applied EU to a recent study that used a large retrospective mammography screening dataset from the United States. That study reported an improvement in specificity and decrease in sensitivity when using their AI as a rule-out device retrospectively. In terms of EU, we cannot conclude a significant improvement when the AI is used as a rule-out device.We applied the method to a European study that reported only recall rates and cancer detection rates. Since there is no established EU baseline value in European mammography screening workflow, we estimated the EU baseline using data from previous literature. We cannot conclude a significant improvement when the AI is used as a rule-out device for the European study.In this work, we investigated the use of EU to evaluate rule-out devices using large retrospective datasets. This metric, used with retrospective clinical data, could be used as supporting evidence for rule-out devices.

BackgroundConcordance, or alignment of care with patients' preferences, is a key component of high-quality decision making. Some patients may not have a clear preference, and others may not receive care aligned with their preference-both situations indicating a lack of concordance. The reasons behind these situations remain poorly understood. This study explores the reasons for lack of concordance in colorectal cancer screening among older adults.MethodsInterviews were conducted with 160 older adults from the Promoting Informed Decisions About Colorectal Cancer Screening in Older Adults trial (NCT03959696) who did not meet the criteria for concordance. A thematic analysis of 152 analyzable interviews was performed to explore reasons for lack of concordance.ResultsFour themes summarize the different reasons for the lack of concordance: 1) provider discussion and the need for more guidance (e.g., patients reported very limited discussion and desire for more information), 2) age-related considerations (e.g., patients acknowledge that at their age, screening may no longer be needed), 3) changes in health condition (e.g., patients report other health issues that take priority over screening), and 4) the impact of COVID-19 and practical barriers (e.g., patients report a desire to avoid hospitals and procedures).ConclusionsThe lack of concordance stemming from limited discussion, guidance, or lack of clear preference signal low decision quality, whereas the lack of concordance from changing patient preferences over time has implications for timing of measurement. To improve concordance, patients need support to clarify their preferences as well as support to implement their preferred approach.HighlightsLimited provider discussion, age-related factors, changing health priorities, and COVID-19-related or practical challenges were identified as key contributors to lack of concordance.Achieving high concordance will require helping patients clarify their preferences, strengthening shared decision making, and providing implementation support.Researchers also need to be aware of evolving preferences and implications for timing of preference measurements.

PurposeTo compare an established benefit-estimating algorithm for recommending and prioritizing preventive services for a patient (Individualized Precision Prevention; IPP) with concordant rankings from primary care providers (PCPs).MethodsWe developed 12 realistic routine patient care scenarios focused on preventive services and recruited 40 PCPs to rank the priority of recommended preventive services. Our analysis compared the benefit-estimating algorithm's rankings of preventive services for each of the 12 patient scenarios to the PCPs' rankings using length-dependent rank-biased overlap (LDRBO) calculations. Moderate concordance would suggest that the computer algorithm presented an opportunity to improve preventive care, whereas very high or low concordance would call into question what the algorithm could contribute to clinical practice.ResultsFor all 12 patient care scenarios, comparing the benefit-estimating algorithm's output to the combined priority rankings from all PCPs yields a mean value of 0.45, corresponding to a moderate level of concordance or agreement between the numeric rankings of the algorithm and the expert provider rankings. This study illustrates the potential importance of having computed IPP recommendations readily available for point-of-care decision making by PCPs.ConclusionWe demonstrate that this approach aligned with the overall judgment of clinical experts and may help providers prioritize preventive services in time-constrained clinical contexts. The modest correlation between the benefit-estimating algorithm and expert providers suggests that, in some cases, the algorithm has the potential to provide useful advice about preventive services during care.HighlightsUsing scenarios, we compared how primary care providers and an algorithm prioritized recommendations for preventive services based on individual information about a patient.The providers' rankings of the clinical importance of preventive services were moderately concordant with rankings produced by the algorithm, suggesting that the algorithm presents an opportunity to improve the effects of preventive care.For half of the scenarios, the algorithm recommended one preventive service that was not in the PCPs' consensus top 3, suggesting that the algorithm may raise provider awareness of services that may be beneficial to specific patients.An algorithm-driven approach to individualized precision prevention that uses a patient's data to generate personalized recommendations of preventive services can help providers and patients identify and prioritize high-priority preventive services together.

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.

BackgroundWhile machine learning (ML) models are increasingly used to predict outcomes in health care, their practical effect on health care operations, such as bed capacity management, remains underexplored. There is a variety of traditionally used evaluation metrics to analyze ML models; however, decision makers in health care settings require a deeper understanding of their implications for resource management. Traditional performance measures often fail to provide this practical insight.MethodsIn this work, we conduct a simulation study to evaluate the impact of ML-driven length-of-stay (LOS) predictions on intensive care unit (ICU) bed capacity management. Two classification models differing in terms of explainability and interpretability, logistic regression (LR) and extreme gradient boosting (XGB), are applied to predict ICU-LOS. We use the HiRID dataset containing high-frequency data of more than 33,000 patients. The predictions of the ML models are integrated into a simulation framework that replicates real-world ICU bed management, allowing for the assessment of the practical implications of using these algorithms in a clinical setting.ResultsThe application of both classification models results in improved capacity control regarding the key performance indicators in the simulation study, with XGB outperforming LR. While LR leads to slight overoccupancy in the ICU, slight underoccupancy can be observed when XGB is applied.ConclusionOur study bridges the gap between predictive accuracy and practical application by emphasizing the importance of evaluating ML models within the context of ICU capacity management. The simulation-based approach offers a more relevant assessment for health care practitioners, providing actionable insights that go beyond classical performance measures and directly address the needs of decision makers in clinical practice.HighlightsWe apply multiple classification models for ICU-LOS prediction using time-series data. This approach enables an update of the initial prediction resulting in the possibility of efficiently managing intensive care capacities.We present a simulation-based approach to evaluate ML algorithms and their impact on bed capacity management in real-world clinical settings.Our work provides in-depth insights into the impact of using ML techniques as decision support systems in the ICU and can lead to increased acceptance in practice.

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

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 Synthesis. 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.

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

BackgroundThe Australian National Bowel Cancer Screening Program (NBCSP), which provides 2-yearly screening to people aged 50 to 74 y, had a phased rollout from 2006 and was fully implemented in 2020. To measure the effectiveness of the NBCSP accounting for age-specific trends, we aimed to develop a novel integrative method to project colorectal cancer (CRC) incidence rates from 2006 to 2045 in the absence of the NBCSP (referred to as "no-NBCSP projections") while addressing the challenge of complex age-specific trends in CRC incidence.MethodsWe constructed a new dataset by replacing the observed data for NBCSP-eligible individuals aged 50 to 74&#x2009;y with intermediate projections based on pre-NBCSP data from 1982 to 2005. We compared the no-NBCSP CRC incidence projected using a standard age-period-cohort (APC) model, age-stratified APC models, and the integrative modeling approach.ResultsThe integrative modeling approach captured complex age-specific trends better than the standard and age-stratified APC models did. Without the NBCSP, the overall CRC incidence rates would be expected to decline from 2005 to 2025, followed by increases from 2026 to 2045. The incidence rates for those aged <50&#x2009;y would be projected to continue increasing to 2045, and an increase in incidence rates for older age groups would be projected to occur from 2020 for ages 50 to 54&#x2009;y, from 2030 for ages 65 to 74&#x2009;y, and from 2035 for ages 75&#x2009;y and older.ConclusionsThese no-NBCSP projections provide a counterfactual benchmark against which to measure the impact of the NBCSP on CRC incidence in Australia, and they have been used as new calibration targets for a simulation model of CRC and screening in Australia. The methods developed here could be used to generate comparators to assess the impact of other public health interventions.HighlightsWe constructed counterfactual projections of colorectal cancer (CRC) incidence rates in the absence of the National Bowel Cancer Screening Program (no-NBCSP projections).To do this, we developed a new integrative modeling approach to capture complex age-specific colorectal cancer incidence trends.These no-NBCSP projections provide a counterfactual benchmark against which to measure the impact of the NBCSP on CRC incidence in Australia.These projections stress the need for ongoing assessment of the starting age for the NBCSP, to tackle the increasing incidence for people younger than 50&#x2009;y.