Medical Decision Making最新文献

BackgroundCancer diagnosis causes emotional distress, which can influence patients' treatment choice. This study aimed to investigate the effect of increased emotionally supportive surgeon communication in a virtual setting on treatment preference for thyroid cancer.DesignThis randomized trial (NCT05132478), conducted from November 2021 to February 2023, enrolled adults with ≤4-cm thyroid nodules not requiring surgery. Participants were randomized 1:1 to watch a virtual clinic visit depicting a patient-surgeon treatment discussion for low-risk thyroid cancer. Control and intervention videos were identical except for added emotionally supportive communication in the intervention. The primary outcome was treatment preference for total thyroidectomy or lobectomy. Secondary outcomes were perceived physician empathy, physician trust, decisional confidence, and disease-specific knowledge. An intention-to-treat analysis was performed using conditional regression to account for stratification by sex. Qualitative content analysis evaluated participants' open-ended responses about treatment choice and surgeon communication.ResultsOf 208 eligible patients, 118 (56.7%) participated. Participants were 85.6% female and 88.1% White. Overall, 89.0% (n = 105) of participants preferred lobectomy, which was similar between the intervention and control groups (90.0% v. 87.9%, respectively, P = 0.72). Compared with control, participants who viewed the consultation with enhanced communication perceived higher levels of physician empathy (34.5 ± 5.8 v. 25.9 ± 9.1, P < 0.001) and reported increased trust in the physician (12.0 ± 2.6 v. 10.4 ± 3.1, P < 0.001). The groups were similar in decisional confidence (7.6 ± 2.1 v. 7.7 ± 1.9, P = 0.74) and disease-specific knowledge. Prominent qualitative themes among participants choosing thyroid lobectomy included desire to avoid daily thyroid hormone (n = 53) and concerns about surgical complications (n = 25).ConclusionsIn this randomized controlled study, a significant proportion of participants preferred thyroid lobectomy if diagnosed with low-risk thyroid cancer. Participants perceived increased empathy when provided even in the virtual setting, which was associated with increased trust in the physician.HighlightsIn this single-site, randomized controlled trial, enhanced emotionally supportive surgeon communication had no effect on hypothetical treatment preference for low-risk thyroid cancer.Participants who experienced enhanced emotionally supportive surgeon communication perceived higher physician empathy and reported greater trust in the physician.The incorporation of empathetic communication during surgical consultation for low-risk thyroid cancer promotes patient trust and perception of empathy.

The effective sample size (ESS) measures the informational value of a probability distribution in terms of an equivalent number of study participants. The ESS plays a crucial role in estimating the expected value of sample information (EVSI) through the Gaussian approximation approach. Despite the significance of ESS, except for a limited number of scenarios, existing ESS estimation methods within the Gaussian approximation framework are either computationally expensive or potentially inaccurate. To address these limitations, we propose a novel approach that estimates the ESS using the summary statistics of generated datasets and nonparametric regression methods. The simulation experiments suggest that the proposed method provides accurate ESS estimates at a low computational cost, making it an efficient and practical way to quantify the information contained in the probability distribution of a parameter. Overall, determining the ESS can help analysts understand the uncertainty levels in complex prior distributions in the probability analyses of decision models and perform efficient EVSI calculations.HighlightsEffective sample size (ESS) quantifies the informational value of probability distributions, essential for calculating the expected value of sample information (EVSI) using the Gaussian approximation approach. However, current ESS estimation methods are limited by high computational demands and potential inaccuracies.We propose a novel ESS estimation method that uses summary statistics and nonparametric regression models to efficiently and accurately estimate ESS.The effectiveness and accuracy of our method are validated through simulations, demonstrating significant improvements in computational efficiency and estimation accuracy.

BackgroundIn cases of surgical necrotizing enterocolitis (NEC), the choice between laparotomy (LAP) or comfort care (CC) presents a complex, ethical dilemma. A behavioral artificial intelligence technology (BAIT) decision aid was trained on expert knowledge, providing an output as "x percentage of experts advise laparotomy for this patient." This retrospective study aims to compare this output to clinical practice.DesignVariables required for the decision aid were collected of preterm patients with NEC for whom the decision of LAP or CC had been made. These data were used in 2 BAIT model versions: one center specific, built on the input of experts from the same center as the patients, and a nationwide version, incorporating the input of additional experts. The Mann-Whitney U test compared the model output for the 2 groups (LAP/CC). In addition, model output was classified as advice for LAP or CC, after which the chi-square test assessed correspondence with observed decisions.ResultsForty patients were included in the study (20 LAP). Model output (x percentage of experts advising LAP) was higher in the LAP group than in the CC group (median 95.1% v. 46.1% in the center-specific version and 97.3% v. 67.5% in the nationwide version, both P < 0.001). With an accuracy of 85.0% by the center-specific and 80.0% by the nationwide version, both showed significant correspondence with observed decisions (P < 0.001).LimitationsWe are merely examining a proof of concept of the decision aid using a small number of participants from 1 center.ConclusionsThis retrospective study demonstrates that treatment choices by artificial intelligence align with clinical practice in at least 80% of cases.ImplicationsFollowing prospective validation and ongoing refinements, the decision aid may offer valuable support to practitioners in future NEC cases.HighlightsThis study assesses the output of behavioral artificial intelligence technology in deciding between laparotomy and comfort care in surgical necrotizing enterocolitis.The model output aligns with clinical practice in at least 80% of patient cases.Following prospective validation, the decision aid may offer valuable support to physicians working at the neonatal intensive care unit.

BackgroundHealth-state utility values (HSUVs) are key inputs into cost-utility analyses. There is debate over whether they are best derived from the community or patients, with concerns raised that community-derived preferences may devalue benefits to ill, elderly, or disabled individuals. This tutorial compares the effects of using patient-derived HSUVs versus community-derived HSUVs on incremental cost-effectiveness ratios (ICERs) and shows their implications for policy.DesignWe review published studies that compared HSUVs derived from patients and the community. We then present equations for the gains in quality-adjusted life-years (QALYs) that would be estimated for an intervention using patient versus community HSUVs and discuss the implications of those QALY gains. We present a numerical example as another way of showing how ICERs change when using patient versus community HSUVs.ResultsPatient HSUVs are generally higher than community HSUVs for severe health states. When an intervention reduces mortality, patient ratings yield more favorable ICERs than do community ratings. However, when the intervention reduces morbidity, patient ratings yield less favorable ICERs. For interventions that reduce both morbidity and mortality, the effect on ICERs of patient versus community HSUVs depends on the relative contribution of each to the resulting QALYs.ConclusionsThe use of patient HSUVs does not consistently favor treatments directed at those patients. Rather, the effect depends on whether the intervention reduces mortality, morbidity, or both. Since most interventions do both, using patient HSUVs has mixed implications for promoting investments for people with illness and disabilities. A nuanced discussion of these issues is necessary to ensure that policy matches the intent of the decision makers.HighlightsThe debate about whether health state utility values (HSUVs) are best derived from patients or the community rests in part on the presumption that using community values devalues interventions for disabled persons or those with chronic diseases.However, we show why the effect of using patient HSUVs depends on whether the intervention in question primarily reduces mortality or morbidity or has substantial effects on both.If the intervention reduces mortality, using patient HSUVs will make the intervention appear more cost-effective than using community HSUVs, but if it reduces morbidity, using patient HSUVs will make the intervention appear less cost-effective.If the intervention reduces both morbidity and mortality, a common situation, the effect of patient versus community HSUVs depends on the relative magnitudes of the gains in quality and length of life.

ObjectivesDiscrete choice experiments (DCEs) as a valuation method require preferences to be anchored on the quality-adjusted life-year scale, usually through tasks involving choices between immediate death and various impaired health states or between health states with varying durations of life. We sought to determine which anchoring approach aligns best with the composite time tradeoff (cTTO) method, with a view to informing a valuation protocol that uses DCEs in place of the cTTO.MethodsA total of 970 respondents from Trinidad and Tobago completed a DCE with duration survey. Tasks involved choosing between 2 lives with identical durations, followed by a third option, representing either full health for a number of years or immediate death. Data were analyzed using mixed logit models, both with and without exponential discounting for time preferences.ResultsAssuming linear time preferences, the estimated utility of immediate death was -2.1 (95% credible interval [CrI] -3.2 to -1.2) versus -0.28 (95% CrI -0.47, -0.10) when allowing for nonlinear time preferences. Under linear time preferences, the predicted health-state values anchored on duration had range (-1.03, 1) versus (0.34, 1) when anchored on immediate death. The ranges under nonlinear time preferences were (-0.54, 1) versus (-0.22, 1). The estimated discount parameter was 23% (95% CrI 22% to 25%).ConclusionsThe nonzero discount parameter indicates that time preferences were nonlinear. Nonlinear time preferences anchored on duration provided the closest match to the benchmark EQ-VT cTTO values in Trinidad and Tobago, whose range was (-0.6, 1). Thus, DCE with duration can provide similar values to cTTO provided that nonlinear time preferences are accounted for and anchoring is based on duration.HighlightsTime preferences for health states in Trinidad and Tobago were nonlinear.In discrete choice tasks, we show that immediate death has a utility less than zero.DCE utilities under nonlinear time preferences with anchoring on duration agreed well with cTTO utilities.

BackgroundPatient decision aids (PtDAs) are effective interventions to help people participate in health care decisions. Although there are quality standards, PtDAs are complex interventions with variability in their attributes.PurposeTo determine and compare the effects of PtDA attributes (e.g., content elements, delivery timing, development) on primary outcomes for adults facing health care decisions.Data SourcesA systematic review of randomized controlled trials (RCTs) comparing PtDAs to usual care.Study SelectionEligible RCTs measured at least 1 primary outcome: informed values choice, knowledge, accurate risk perception, decisional conflict subscales, and undecided.Data AnalysisA network meta-analysis evaluated direct and indirect effects of PtDA attributes on primary outcomes.Data SynthesisOf 209 RCTs, 149 reported eligible outcomes. There was no difference in outcomes for PtDAs using implicit compared with explicit values clarification. Compared with PtDAs with probabilities, PtDAs without probabilities were associated with poorer patient knowledge (mean difference [MD] -3.86; 95% credible interval [CrI] -7.67, -0.03); there were no difference for other outcomes. There was no difference in outcomes when PtDAs presented information in ways that decrease cognitive demand and mixed results when PtDAs used strategies to enhance communication. Compared with PtDAs delivered in preparation for consultations, PtDAs used during consultations were associated with poorer knowledge (MD -4.34; 95% CrI -7.24, -1.43) and patients feeling more uninformed (MD 5.07; 95% CrI 1.06, 9.11). Involving patients in PtDA development was associated with greater knowledge (MD 6.56; 95% CrI 1.10, 12.03) compared with involving health care professionals alone.LimitationsThere were no direct comparisons between PtDAs with/without attributes.ConclusionsImprovements in knowledge were influenced by some PtDA content elements, using PtDA content before the consultation, and involving patients in development. There were few or no differences on other outcomes.HighlightsThis is the first known network meta-analysis conducted to determine the contributions of the different attributes of patient decision aids (PtDAs) on patient outcomes.There was no difference in outcomes when PtDAs used implicit compared with explicit values clarification.There were greater improvements in knowledge when PtDAs included information on probabilities, PtDAs were used in preparation for the consultation or development included patients on the research team.There was no difference in outcomes when PtDAs presented information in ways that decrease cognitive demand and mixed results when PtDAs used strategies to enhance communication.

BackgroundDiabetes is a rapidly growing global health issue, with the hidden burden of undiagnosed cases leading to severe complications and escalating health care costs.MethodsThis study investigated the potential of integrated behavioral frameworks to predict health-seeking behaviors and improve diabetes diagnosis timelines through the development of an agent-based model. Focusing on Narromine and Gilgandra in New South Wales, Australia, the model captured the integrative influence of 3 social theories-theory of planned behavior (TPB), health belief model (HBM), and goal framing theory (GFT)-on health care decisions across behavioral and nonbehavioral variables, providing a robust analysis of temporal diagnostic patterns, health care utilization, and costs.ResultsOur comparative experiments indicated that this multitheory framework improved predictive accuracy by 15% to 30% compared with single-theory models, effectively capturing the interplay of planned, belief-driven, and context-based health behaviors. Spatial-temporal analysis highlighted key regional and demographic variations in diagnosis behaviors. While early, planned medical visits were prevalent in regions with better access (Gilgandra), areas with limited infrastructure saw a reliance on hospital-based diagnoses (Narromine). Health care cost analysis demonstrated a nonlinear expenditure pattern, suggesting that these theories defy conventional linear cost trends. Scenario analysis demonstrated the impact of targeted interventions. Gender-specific awareness initiatives in Gilgandra reduced late-diagnosis rates among men by approximately 15%, while enhanced access to care in Narromine decreased hospital-based late diagnoses from a baseline of 80% to around 60%.ConclusionsThis study contributes an empirically grounded, policy-oriented decision support tool to inform targeted interventions, offering novel insights to improve diabetes management.HighlightsWe explored the delay in diabetes diagnosis, particularly within remote Australian communities, through looking into the health care-seeking behavior of individuals displaying diabetes symptoms.We developed an innovative agent-based model to craft a dynamic decision support tool for policy makers by providing unique insights into the health behaviors of diabetes patients.Our study contributes significantly to the understanding of public health management with particular concerns around diabetes, as well as equips the New South Wales Ministry of Health with impactful insights into the consequences of their decisions.

BackgroundCancer simulation models can answer research and policy questions when prospective evidence is incomplete or not feasible. However, such models require incorporating unmeasureable inputs for which there is often not strong evidence, and model utility is limited if assumptions lack face validity or if the model is not clinically relevant. We systematically incorporated formal advisory input to mitigate these challenges as we developed a microsimulation model of papillary thyroid cancer (PApillary Thyroid CArcinoma Microsimulation model [PATCAM]).MethodsWe used a participatory action research approach incorporating focus group techniques and using principles of bidirectional learning.ResultsWe assembled a formal standing advisory group with representation by perspective (medical, patient, and payor), geography, and local practice culture to understand current and historical clinical beliefs and practices about thyroid cancer diagnosis and treatment. The group provided input on critical modeling assumptions and decisions: 1) the role of nodule size in biopsy decisions, 2) trends in provider biopsy behavior, 3) specialty propensity to biopsy, 4) population prevalence of thyroid cancer over time, 5) proportion of malignant tumors showing regression, and 6) cancer epidemiology and diagnostic practices by sex and age. Advisory group questions and concerns about model development will inform future research questions and strategies to communicate and disseminate model results.ConclusionsWe successfully used our advisory group to provide critical inputs on unmeasurable assumptions, increasing the face validity of our model. The use of a standing advisory group improved model transparency and contributed to future research plans and dissemination of model results so they can have maximum impact when guiding clinical decisions and policy.HighlightsUnfamiliarity with simulation modeling poses a threat to its acceptability and adoption. The effectiveness of these models is contingent on end-users' willingness to accept and adopt model results. The effectiveness of the models is further limited if they lack face validity to potential users or do not have clinical relevance.Several approaches to overcoming validity challenges have been advanced, such as collaborative modeling, which involves developing multiple models independently using common data sources. However, when only a single model exists, another approach is needed. We used an Advisory Group and "participatory modeling," which has been used in other settings but has not been previously reported in cancer modeling. We describe the methods used for and results of incorporating a formal advisory group into the development of a cancer microsimulation model.The use of a formal, standing advisory group (as opposed to one-off focus groups or interviews) strengthened our model by rigorously vetting modeling assumptions and model inputs with subject matter experts. The formal, ongoing structur

Background. Delineation of historically marginalized populations in decision models can identify strategies to improve equity but requires assumptions in both model structure and stratification of input data. Purpose. We sought to characterize alternative methodological approaches for incorporating marginalized populations into human papillomavirus (HPV) vaccine decision-support models. Data Sources. We conducted a systematic search of PubMed, CINAHL, Scopus, and Embase from January 2006 through June 2022. Study Selection. We identified simulation models of HPV vaccination that refine any model input to specifically reflect a marginalized population. Data Extraction. We extracted data on key methodological decisions across modeling approaches to incorporate marginalized populations, including stratification of inputs, model structure, attribution of prevaccine disparities, calibration, validation, and sensitivity analyses. Data Synthesis. We identified 30 models that stratified inputs by sexual behavior (i.e., men who have sex with men), HIV infection status, race, ethnicity, income, rurality, or combinations of these. We identified 5 common approaches used to incorporate marginalized groups. These included models based primarily on differences in sexual behavior (k = 6), HPV cancer incidence (k = 10), cancer screening and care access (k = 4), and HPV natural history (through either direct incorporation of data [k = 10] or calibration [k = 5]). Few models evaluated sensitivity around their conceptualization of the marginalized group, and only 5 models validated outcomes for the marginalized group. Limitations. Evaluated studies reflected a variety of settings and research questions, making it difficult to evaluate the implications of differences across modeling approaches. Conclusions. Modelers should be explicit about the assumptions and theory driving their model structure and input parameters specific to key marginalized populations, such as the causes of prevaccination differences in outcomes. More emphasis is needed on model validation and rigorous sensitivity analysis.HighlightsWe identified 30 unique HPV vaccination models that incorporated marginalized populations, including populations living with HIV, low-income or rural populations, and individuals of a marginalized race, ethnicity, or sexual behavior.Methods for incorporating these populations, as well as the assumptions inherent in the modeling structure and parameter selections, varied substantially, with models explicitly or implicitly attributing prevaccine differences to alternative combinations of biological, behavioral, and societal mechanisms.Modelers seeking to incorporate marginalized populations should be transparent about assumptions underlying model structure and data and examine these assumptions in sensitivity analysis when possible.

BackgroundSimulation modeling is a promising tool to help policy makers and providers make evidence-based decisions when evaluating integrated care programs. The functionality of such models, however, depends on 2 prerequisites: 1) the analytical segmentation of populations to capture both health and health-related social service (HASS) needs and 2) the precise estimation of transition probabilities among the various states of need.MethodsWe took a validated instrument for segmenting the population by HASS needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population older than 50 y. We then estimated the transition probabilities across all 10 need states and death using multistate modeling. A need state was defined as a combination of any of the 5 ordinal global impression segments and a complicating factor status.ResultsKaplan-Meier survival curves, log-rank tests, and c-indices were used to assess predictive validity in relation to mortality. The Markov traces, using the estimated transition probability to replicate 2 closed cohorts, resembled the proportion of individuals per health state across subsequent waves well enough to indicate adequate fit of the estimated transition probabilities.ConclusionsThis article provides a population segmentation approach that incorporates HASS needs for the US population and 1-y transition probabilities across HASS need states and death. This is the first application of HASS segmentation that can estimate transitions between all 10 HASS need states, facilitating novel analysis of policy decisions related to integrated care.ImplicationsOur results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.HighlightsWe took a validated tool for segmenting the population according to health and health-related social service (HASS) needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population over the age of 50 y.We estimated the 1-y transition probabilities across all 10 HASS segments and death.This is the first application of a version of this HASS segmentation tool that includes HASSs in the various need states when estimating transition probabilities.Our results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.