Medical Decision Making最新文献

{"title":"So You've Got a High AUC, Now What? An Overview of Important Considerations when Bringing Machine-Learning Models from Computer to Bedside.","authors":"Jiawen Deng, Mohamed E Elghobashy, Kathleen Zang, Shubh K Patel, Eddie Guo, Kiyan Heybati","doi":"10.1177/0272989X251343082","DOIUrl":"https://doi.org/10.1177/0272989X251343082","url":null,"abstract":"<p><p>Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing.HighlightsThis tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice.Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models.Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251343082"},"PeriodicalIF":3.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Disability-Adjusted Life-Years for Policy and Decision Analysis.","authors":"Ashley A Leech, Jinyi Zhu, Hannah Peterson, Marie H Martin, Grace Ratcliff, Shawn Garbett, John A Graves","doi":"10.1177/0272989X251340077","DOIUrl":"https://doi.org/10.1177/0272989X251340077","url":null,"abstract":"<p><p>This study outlines methods for modeling disability-adjusted life-years (DALYs) in common decision-modeling frameworks. Recognizing the wide spectrum of experience and programming comfort level among practitioners, we outline 2 approaches for modeling DALYs in its constituent parts: years of life lost to disease (YLL) and years of life lived with disability (YLD). Our beginner approach draws on the Markov trace, while the intermediate approach facilitates more efficient estimation by incorporating non-Markovian tracking elements into the transition probability matrix. Drawing on an existing disease progression discrete time Markov cohort model, we demonstrate the equivalence of DALY estimates and cost-effectiveness analysis results across our methods and show that other commonly used \"shortcuts\" for estimating DALYs will not, in general, yield accurate estimates of DALY levels nor incremental cost-effectiveness ratios in a modeled population.HighlightsThis study introduces 2 DALY estimation methods-beginner and intermediate approaches-that produce similar results, expanding the toolkit available to decision modelers.These methods can be adapted to estimate other outcomes (e.g., QALYs, life-years) and applied to other common decision-modeling frameworks, including microsimulation models with patient-level attributes and discrete event simulations that estimate YLDs and YLLs based on time to death and disease duration.Our findings further reveal that commonly used shortcut methods for DALY calculations may lead to differing results, particularly for DALY levels and incremental cost-effectiveness ratios.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251340077"},"PeriodicalIF":3.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Notes Contain Limited Documentation of Shared Decision Making for Colorectal Cancer Screening Decisions.","authors":"Brittney Mancini, Joshua Siar, Kathrene Diane Valentine, Leigh Simmons, Lauren Leavitt, Karen Sepucha","doi":"10.1177/0272989X251340704","DOIUrl":"https://doi.org/10.1177/0272989X251340704","url":null,"abstract":"<p><p>BackgroundEffective shared decision making (SDM) in health care involves thorough discussions of options, pros, cons, and patient preferences. While SDM is recommended for engaging adults aged 76 to 85 y in colorectal cancer (CRC) screening decisions, the extent of SDM documentation in clinical notes remains unclear.ObjectiveThis study aimed to evaluate the current state of SDM documentation in clinical notes regarding CRC screening discussions for adults aged 76 to 85 y. It also sought to assess the impact of an SDM training intervention on documentation quality and compare documented SDM elements with physician- and patient-reported SDM.MethodsData from 465 patient participants and 58 primary care physicians in a multisite cluster randomized trial were analyzed. Physicians in the intervention arm underwent a 2-h SDM skills training and received support tools, including an electronic health record SmartPhrase. Coders analyzed clinical notes using content analysis to identify SDM elements. Linear multilevel models and multilevel partial correlations were used for analysis.ResultsOverall, SDM Note scores were low (<math><mrow><mover><mrow><mi>x</mi></mrow><mo>¯</mo></mover></mrow></math> = 0.80, <i>s</i> = 0.99). The intervention arm exhibited higher SDM Note scores than the comparator arm did (adjusted mean 1.02 v. 0.66; <i>P</i> = 0.006), with more frequent documentation of stool-based tests (52% v. 33%; <i>P</i> = 0.02) and colonoscopy cons (28% v. 8%; <i>P</i> = 0.001). No significant differences were observed in patient preference documentation. SDM Note scores correlated moderately with patient- and physician-reported SDM.ConclusionDocumentation of CRC screening discussions with older adults lacks comprehensive SDM elements. The intervention improved SDM documentation, particularly regarding alternative screening options and potential cons. Given the limited documentation of SDM even after a training intervention, attention to more robust SDM documentation, including patient preferences and discussion of stopping CRC screening, is needed.HighlightsShared decision-making (SDM) documentation in clinical notes is limited for discussions on colon cancer screening among older adults.SDM training improves SDM documentation of screening options for colorectal cancer, specifically documentation of stool-based testing and the downsides of screening options.SDM documentation in clinical notes is related to patient and provider reports of SDM.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251340704"},"PeriodicalIF":3.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Prediction to Support ICU Admission Decision Making among Very Old Patients with Respiratory Infections: A Proof of Concept on a Nationwide Population-Based Cohort Study.","authors":"Lionel Tchatat Wangueu, Arthur Kassa-Sombo, Guy Ilango, Christophe Gaborit, Mustapha Si-Tahar, Leslie Grammatico-Guillon, Antoine Guillon","doi":"10.1177/0272989X251337314","DOIUrl":"https://doi.org/10.1177/0272989X251337314","url":null,"abstract":"<p><p>BackgroundIntensive care unit (ICU) hospitalizations of very old patients with acute respiratory infection have risen. The decision-making process for ICU admission is multifaceted, and the prediction of long-term survival outcome is an important component. We hypothesized that data-driven algorithms could build long-term prediction by examining massive real-life data. Our objective was to assess machine learning (ML) algorithms to predict the 1-y survival of very old patients with severe respiratory infections.MethodsA national 2011-2020 study of ICU patients ≥80 y with respiratory infection was carried out, using French hospital discharge databases. Data for the training cohort were collected from 2013 to 2016 to build the models, and the data of patients extracted in 2017 were used for external validation. Our proposed models were developed using random forest, logistic regression (LR), and XGBoost. The optimal model was selected based on its accuracy, sensitivity, specificity, Matthews coefficient correlation (MCC), receiver-operating characteristic curve (AUROC), and decision curve analysis (DCA). The local interpretable model-agnostic explanation (LIME) algorithm was used to analyze the contribution of individual features.ResultsA total of 24,270 very old patients were hospitalized in the ICU for respiratory infection (2013-2017) with a known vital status at 1 y. The 1-y survival rate was 41.3% (median survival: 3 mo [2.7-3.3]). Of the 3 ML models tested, LR exhibited promising performance with an accuracy, sensitivity, specificity, MCC, and AUROC (95% confidence interval) of 0.65, 0.76, 0.60, 0.27, and 0.70 (0.69-0.72), respectively. LR achieved an AUROC of 0.70 (0.68-0.71) in external validation by temporal splitting. LR demonstrated higher net benefits across a range of threshold probability values in DCA. The LIME algorithm identified the 10 most influential features at an individual scale.ConclusionsWe demonstrated that a ML model has the potential to predict long-term outcomes for very old patients with acute respiratory infections. As a proof of concept, we proposed a program that acts as an \"explainer\" for the ML model. This work represents a step forward in translating ML models into practical, transparent, and reliable clinical tools to support medical decision making.HighlightsThe decision to admit a very old patient to the ICU is one of the most complex challenges faced by intensivists, often relying on subjective judgment.In this study, we evaluated the efficacy of machine learning algorithms in predicting the 1-y survival rate of critically ill very old patients (≥80 y) with severe respiratory infections, using data available prior to the admission decision.Our findings demonstrate that machine learning can effectively predict long-term outcomes in very old patients. We used an innovative approach that aims to support medical decision making about admission in ICU.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251337314"},"PeriodicalIF":3.1,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Values Clarification and Health-Literate Design in Patient Decision Aids: A Qualitative Interview Study.","authors":"Julie Ayre, Hazel Jenkins, Richie Kumarage, Kirsten J McCaffery, Christopher G Maher, Mark J Hancock","doi":"10.1177/0272989X251334356","DOIUrl":"https://doi.org/10.1177/0272989X251334356","url":null,"abstract":"<p><p>BackgroundThis study explores patient and clinician perceptions of a patient decision aid, focusing on 2 features that are often absent: a health-literate approach (e.g., using plain language, encouraging question asking) and a tool that explicitly shows how treatment options align with patient values. The aim was to gather qualitative feedback from patients and clinicians to better understand how such features might be useful in guiding future decision aid development.MethodsWe present a secondary analysis of data collected during the development of a decision aid for patients considering surgery for sciatica (20 patients with sciatica or low-back pain; 20 clinicians). Patient and clinician feedback on the design was collected via semi-structured interviews with a think-aloud protocol. Transcripts were analyzed using framework analysis.ResultsTheme 1 explored designs that reinforced key messages about personal autonomy, including an interactive values-clarification tool. Theme 2 explored how participants valued encouragement and scaffolding to ask questions. Theme 3 described how patients preferred information they felt was complete, balanced, and understandable.LimitationsFurther experimental and observational research is needed to quantitatively evaluate these decision aid features including evaluation among patients with and without low health literacy.ConclusionsA health-literate approach to decision aid design and embedding an interactive values-clarification tool may be useful strategies for increasing patient capacity to engage in key aspects of shared decision making. These features may support patients in developing an understanding of personal autonomy in the choice at hand and confidence to ask questions.ImplicationsFindings presented here were specific to the clinical context but provide generalizable practical insights for decision aid developers. This study provides insight into potential future areas of research for decision aid design.HighlightsThis qualitative study explored clinician and patient perceptions of health literacy features and an interactive values-clarification task within a decision aid for patients considering surgery for sciatica.The first theme described how patients and clinicians appreciated sections of the decision aid that reinforced the importance of personal choice. Patients and clinicians thought the interactive values-clarification task would help patients reflect on their values and support shared decision-making discussions.The second theme described how patients and clinicians appreciated strategies to encourage patients to ask questions of the surgeon.The third theme described patients' preference for information that they felt was complete, balanced, and understandable.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251334356"},"PeriodicalIF":3.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143992510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Semi-Markov Processes and Other Epidemiological Time-to-Event Models by Computing Disease Sojourn Density as Partial Differential Equations.","authors":"Joachim Worthington, Eleonora Feletto, Emily He, Stephen Wade, Barbara de Graaff, Anh Le Tuan Nguyen, Jacob George, Karen Canfell, Michael Caruana","doi":"10.1177/0272989X251333398","DOIUrl":"https://doi.org/10.1177/0272989X251333398","url":null,"abstract":"<p><p>IntroductionEpidemiological models benefit from incorporating detailed time-to-event data to understand how disease risk evolves. For example, decompensation risk in liver cirrhosis depends on sojourn time spent with cirrhosis. Semi-Markov and related models capture these details by modeling time-to-event distributions based on published survival data. However, implementations of semi-Markov processes rely on Monte Carlo sampling methods, which increase computational requirements and introduce stochastic variability. Explicitly calculating the evolving transition likelihood can avoid these issues and provide fast, reliable estimates.MethodsWe present the sojourn time density framework for computing semi-Markov and related models by calculating the evolving sojourn time probability density as a system of partial differential equations. The framework is parametrized by commonly used hazard and models the distribution of current disease state and sojourn time. We describe the mathematical background, a numerical method for computation, and an example model of liver disease.ResultsModels developed with the sojourn time density framework can directly incorporate time-to-event data and serial events in a deterministic system. This increases the level of potential model detail over Markov-type models, improves parameter identifiability, and reduces computational burden and stochastic uncertainty compared with Monte Carlo methods. The example model of liver disease was able to accurately reproduce targets without extensive calibration or fitting and required minimal computational burden.ConclusionsExplicitly modeling sojourn time distribution allows us to represent semi-Markov systems using detailed survival data from epidemiological studies without requiring sampling, avoiding the need for calibration, reducing computational time, and allowing for more robust probabilistic sensitivity analyses.HighlightsTime-inhomogeneous semi-Markov models and other time-to-event-based modeling approaches can capture risks that evolve over time spent with a disease.We describe an approach to computing these models that represents them as partial differential equations representing the evolution of the sojourn time probability density.This sojourn time density framework incorporates complex data sources on competing risks and serial events while minimizing computational complexity.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251333398"},"PeriodicalIF":3.1,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"People Living with Chronic Pain Experience a High Prevalence of Decision Regret in Canada: A Pan-Canadian Online Survey.","authors":"Florian Naye, Yannick Tousignant-Laflamme, Maxime Sasseville, Chloé Cachinho, Thomas Gérard, Karine Toupin-April, Olivia Dubois, Jean-Sébastien Paquette, Annie LeBlanc, Isabelle Gaboury, Marie-Ève Poitras, Linda C Li, Alison M Hoens, Marie-Dominique Poirier, France Légaré, Simon Décary","doi":"10.1177/0272989X251326069","DOIUrl":"10.1177/0272989X251326069","url":null,"abstract":"<p><p>Background(1) To estimate the prevalence of decision regret in chronic pain care, and (2) to identify factors associated with decision regret.DesignWe conducted a pan-Canadian cross-sectional online survey and reported the results following the Checklist for Reporting of Survey Studies guidelines. We recruited a sample of adults experiencing chronic noncancer pain. We used a stratified proportional random sampling based on the population and chronic pain prevalence of each province. We measured decision regret with the Decision Regret Scale (DRS) and decisional needs with the Ottawa Decision Support Framework. We performed descriptive analysis to estimate the prevalence and level of decision regret and multilevel multivariable regression analysis to identify factors associated with regret according to the STRengthening Analytical Thinking for Observational Studies recommendations.ResultsWe surveyed 1,649 people living with chronic pain, and 1,373 reported a most difficult decision from the 10 prespecified ones, enabling the collection of a DRS score. On a scale ranging from 0 to 100 where 1 reflects the presence of decision regret and 25 constitutes important decision regret, the mean DRS score in our sample was 28.8 (<i>s</i> = 19.6). Eighty-four percent of respondents experienced some decision regret and 50% at an important level. We identified 15 factors associated with decision regret, including 4 personal and 9 decision-making characteristics, and 2 consequences of the chosen option. Respondents with low education level and higher decisional conflict experienced more decision regret when the decision was deemed difficult.ConclusionsThis pan-Canadian survey highlighted a high prevalence and level of decision regret associated with difficult decisions for pain care. Decision making in pain care could be enhanced by addressing factors that contribute to decision regret.HighlightsWe conducted an online pan-Canadian survey and collected responses from a wide diversity of people living with chronic pain.More than 84% of respondents experienced decision regret and approximately 50% at an important level.We identified 15 factors associated with decision regret, including 4 personal and 9 decision-making characteristics, and 2 consequences of the chosen option.Our pan-Canadian survey reveals an urgent need of a shared decision-making approach in chronic pain care that can be potentiated by targeting multiple factors associated with decision regret.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"462-479"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Nonparametric Approach for Estimating the Effective Sample Size in Gaussian Approximation of Expected Value of Sample Information.","authors":"Linke Li, Hawre Jalal, Anna Heath","doi":"10.1177/0272989X251324936","DOIUrl":"10.1177/0272989X251324936","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"370-375"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992650/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of a Surgeon Communication Strategy on Treatment Preference for Thyroid Cancer: A Randomized Trial.","authors":"Catherine B Jensen, Brandy Sinco, Megan C Saucke, Kyle J Bushaw, Alexis G Antunez, Corrine I Voils, Susan C Pitt","doi":"10.1177/0272989X251325837","DOIUrl":"10.1177/0272989X251325837","url":null,"abstract":"<p><p>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% (<i>n</i> = 105) of participants preferred lobectomy, which was similar between the intervention and control groups (90.0% v. 87.9%, respectively, <i>P</i> = 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, <i>P</i> < 0.001) and reported increased trust in the physician (12.0 ± 2.6 v. 10.4 ± 3.1, <i>P</i> < 0.001). The groups were similar in decisional confidence (7.6 ± 2.1 v. 7.7 ± 1.9, <i>P</i> = 0.74) and disease-specific knowledge. Prominent qualitative themes among participants choosing thyroid lobectomy included desire to avoid daily thyroid hormone (<i>n</i> = 53) and concerns about surgical complications (<i>n</i> = 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.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"426-436"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health State Utility Values: The Implications of Patient versus Community Ratings in Assessing the Value of Care.","authors":"Risha Gidwani, Katherine W Saylor, Louise B Russell","doi":"10.1177/0272989X251326600","DOIUrl":"10.1177/0272989X251326600","url":null,"abstract":"<p><p>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 <i>mortality</i>, patient ratings yield more favorable ICERs than do community ratings. However, when the intervention reduces <i>morbidity</i>, 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.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"347-357"},"PeriodicalIF":3.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143677335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}