Medical Decision Making最新文献

{"title":"Use of Persuasive Language in Communication of Risk during Prostate Cancer Treatment Consultations.","authors":"Aurash Naser-Tavakolian, Rebecca Gale, Michael Luu, John M Masterson, Abhishek Venkataramana, Dmitry Khodyakov, Jennifer T Anger, Edwin Posadas, Howard Sandler, Stephen J Freedland, Brennan Spiegel, Timothy J Daskivich","doi":"10.1177/0272989X241228612","DOIUrl":"10.1177/0272989X241228612","url":null,"abstract":"<p><strong>Background: </strong>Physician treatment preference may influence how risks are communicated in prostate cancer consultations. We identified persuasive language used when describing cancer prognosis, life expectancy, and side effects in relation to a physician's recommendation for aggressive (surgery/radiation) or nonaggressive (active surveillance/watchful waiting) treatment.</p><p><strong>Methods: </strong>A qualitative analysis was performed on transcribed treatment consultations of 40 men with low- and intermediate-risk prostate cancer across 10 multidisciplinary providers. Quotes pertaining to cancer prognosis, life expectancy, and side effects were randomized. Coders predicted physician treatment recommendations from isolated blinded quotes. Testing characteristics of consensus predictions against the physician's treatment recommendation were reported. Coders then identified persuasive strategies favoring aggressive/nonaggressive treatment for each quote. Frequencies of persuasive strategies favoring aggressive/nonaggressive treatment were reported. Logistic regression quantified associations between persuasive strategies and physician treatment recommendations.</p><p><strong>Results: </strong>A total of 496 quotes about cancer prognosis (<i>n</i> = 127), life expectancy (<i>n</i> = 51), and side effects (<i>n</i> = 318) were identified. The accuracy of predicting treatment recommendation based on individual quotes containing persuasive language (<i>n</i> = 256/496, 52%) was 91%. When favoring aggressive treatment, persuasive language downplayed side effect risks and amplified cancer risk (recurrence, progression, or mortality). Significant predictors (<i>P</i> < 0.05) of aggressive treatment recommendation included favorable side effect interpretation, downplaying side effects, and long time horizon for cancer risk due to longevity. When favoring nonaggressive treatment, persuasive language amplified side effect risks and downplayed cancer risk. Significant predictors of nonaggressive treatment recommendation included unfavorable side effect interpretation, favorable interpretation of cancer risk, and short time horizon for cancer risk due to longevity.</p><p><strong>Conclusions: </strong>Physicians use persuasive language favoring their preferred treatment, regardless of whether their recommendation is appropriate.</p><p><strong>Implications: </strong>Clinicians should quantify risk so patients can judge potential harm without solely relying on persuasive language.</p><p><strong>Highlights: </strong>Physicians use persuasive language favoring their treatment recommendation when communicating risks of prostate cancer treatment, which may influence a patient's treatment choice.Coders predicted physician treatment recommendations based on isolated, randomized quotes about cancer prognosis, life expectancy, and side effects with 91% accuracy.Qualitative analysis revealed that when favoring nonaggressive treatment, physicians used p","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"320-334"},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724726","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":"Causal Estimation of Long-term Intervention Cost-effectiveness Using Genetic Instrumental Variables: An Application to Cancer.","authors":"Padraig Dixon, Richard M Martin, Sean Harrison","doi":"10.1177/0272989X241232607","DOIUrl":"10.1177/0272989X241232607","url":null,"abstract":"<p><strong>Background: </strong>This article demonstrates a means of assessing long-term intervention cost-effectiveness in the absence of data from randomized controlled trials and without recourse to Markov simulation or similar types of cohort simulation.</p><p><strong>Methods: </strong>Using a Mendelian randomization study design, we developed causal estimates of the genetically predicted effect of bladder, breast, colorectal, lung, multiple myeloma, ovarian, prostate, and thyroid cancers on health care costs and quality-adjusted life-years (QALYs) using outcome data drawn from the UK Biobank cohort. We then used these estimates in a simulation model to estimate the cost-effectiveness of a hypothetical population-wide preventative intervention based on a repurposed class of antidiabetic drugs known as sodium-glucose cotransporter-2 (SGLT2) inhibitors very recently shown to reduce the odds of incident prostate cancer.</p><p><strong>Results: </strong>Genetic liability to prostate cancer and breast cancer had material causal impacts on either or both health care costs and QALYs. Mendelian randomization results for the less common cancers were associated with considerable uncertainty. SGLT2 inhibition was unlikely to be a cost-effective preventative intervention for prostate cancer, although this conclusion depended on the price at which these drugs would be offered for a novel anticancer indication.</p><p><strong>Implications: </strong>Our new causal estimates of cancer exposures on health economic outcomes may be used as inputs into decision-analytic models of cancer interventions such as screening programs or simulations of longer-term outcomes associated with therapies investigated in randomized controlled trials with short follow-ups. Our method allowed us to rapidly and efficiently estimate the cost-effectiveness of a hypothetical population-scale anticancer intervention to inform and complement other means of assessing long-term intervention value.</p><p><strong>Highlights: </strong>The article demonstrates a novel method of assessing long-term intervention cost-effectiveness without relying on randomized controlled trials or cohort simulations.Mendelian randomization was used to estimate the causal effects of certain cancers on health care costs and quality-adjusted life-years (QALYs) using data from the UK Biobank cohort.Given causal data on the association of different cancer exposures on costs and QALYs, it was possible to simulate the cost-effectiveness of an anticancer intervention.Genetic liability to prostate cancer and breast cancer significantly affected health care costs and QALYs, but the hypothetical intervention using SGLT2 inhibitors for prostate cancer may not be cost-effective, depending on the drug's price for the new anticancer indication. The methods we propose and implement can be used to efficiently estimate intervention cost-effectiveness and to inform decision making in all manner of preventative and therapeutic con","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"283-295"},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998105","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 Generalizable Decision-Making Framework for Selecting Onsite versus Send-out Clinical Laboratory Testing.","authors":"Lee F Schroeder, Paul Rebman, Parastu Kasaie, Ernest Kenu, Jon Zelner, David W Dowdy","doi":"10.1177/0272989X241232666","DOIUrl":"10.1177/0272989X241232666","url":null,"abstract":"<p><strong>Background: </strong>Laboratory networks provide services through onsite testing or through specimen transport to higher-tier laboratories. This decision is based on the interplay of testing characteristics, treatment characteristics, and epidemiological characteristics.</p><p><strong>Objectives: </strong>Our objective was to develop a generalizable model using the threshold approach to medical decision making to inform test placement decisions.</p><p><strong>Methods: </strong>We developed a decision model to compare the incremental utility of onsite versus send-out testing for clinical purposes. We then performed Monte Carlo simulations to identify the settings under which each strategy would be preferred. Tuberculosis was modeled as an exemplar.</p><p><strong>Results: </strong>The most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing. When the sensitivity decrements of onsite testing were minimal, onsite testing tended to be preferred when send-out delays reduced clinical utility by >20%. By contrast, when onsite testing incurred large reductions in sensitivity, onsite testing tended to be preferred when utility lost due to delays was >50%. The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs.</p><p><strong>Conclusions: </strong>Decision makers can select onsite versus send-out testing in an evidence-based fashion using estimates of the percentage of clinical utility lost due to send-out delays and the relative accuracy of onsite versus send-out testing. This model is designed to be generalizable to a wide variety of use cases.</p><p><strong>Highlights: </strong>The design of laboratory networks, including the decision to place diagnostic instruments at the point-of-care or at higher tiers as accessed through specimen transport, can be informed using the threshold approach to medical decision making.The most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing.The threshold approach to medical decision making can be used to compare point-of-care testing accuracy decrements with the lost utility of treatment due to send-out testing delays.The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"307-319"},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10987262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140050846","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":"Comparing Survival Extrapolation within All-Cause and Relative Survival Frameworks by Standard Parametric Models and Flexible Parametric Spline Models Using the Swedish Cancer Registry.","authors":"Enoch Yi-Tung Chen, Yuliya Leontyeva, Chia-Ni Lin, Jung-Der Wang, Mark S Clements, Paul W Dickman","doi":"10.1177/0272989X241227230","DOIUrl":"10.1177/0272989X241227230","url":null,"abstract":"<p><strong>Background: </strong>In health technology assessment, restricted mean survival time and life expectancy are commonly evaluated. Parametric models are typically used for extrapolation. Spline models using a relative survival framework have been shown to estimate life expectancy of cancer patients more reliably; however, more research is needed to assess spline models using an all-cause survival framework and standard parametric models using a relative survival framework.</p><p><strong>Aim: </strong>To assess survival extrapolation using standard parametric models and spline models within relative survival and all-cause survival frameworks.</p><p><strong>Methods: </strong>From the Swedish Cancer Registry, we identified patients diagnosed with 5 types of cancer (colon, breast, melanoma, prostate, and chronic myeloid leukemia) between 1981 and 1990 with follow-up until 2020. Patients were categorized into 15 cancer cohorts by cancer and age group (18-59, 60-69, and 70-99 y). We right-censored the follow-up at 2, 3, 5, and 10 y and fitted the parametric models within an all-cause and a relative survival framework to extrapolate to 10 y and lifetime in comparison with the observed Kaplan-Meier survival estimates. All cohorts were modeled with 6 standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, and generalized gamma) and 3 spline models (on hazard, odds, and normal scales).</p><p><strong>Results: </strong>For predicting 10-y survival, spline models generally performed better than standard parametric models. However, using an all-cause or a relative survival framework did not show any distinct difference. For lifetime survival, extrapolating from a relative survival framework agreed better with the observed survival, particularly using spline models.</p><p><strong>Conclusions: </strong>For extrapolation to 10 y, we recommend spline models. For extrapolation to lifetime, we suggest extrapolating in a relative survival framework, especially using spline models.</p><p><strong>Highlights: </strong>For survival extrapolation to 10 y, spline models generally performed better than standard parametric models did. However, using an all-cause or a relative survival framework showed no distinct difference under the same parametric model.Survival extrapolation to lifetime within a relative survival framework agreed well with the observed data, especially using spline models.Extrapolating parametric models within an all-cause survival framework may overestimate survival proportions at lifetime; models for the relative survival approach may underestimate instead.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"269-282"},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139681815","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 Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies.","authors":"Shuai Chen, Heejung Bang, Jeffrey S Hoch","doi":"10.1177/0272989X241230071","DOIUrl":"10.1177/0272989X241230071","url":null,"abstract":"<p><strong>Highlights: </strong>We illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set.We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"239-251"},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10987289/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724702","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":"CAR T-cell Therapy for Diffuse Large B-cell Lymphoma in Canada: A Cost-Utility Analysis.","authors":"Lisa Masucci, Feng Tian, Stephen Tully, Zeny Feng, Tom McFarlane, Kelvin K W Chan, William W L Wong","doi":"10.1177/0272989X241234070","DOIUrl":"10.1177/0272989X241234070","url":null,"abstract":"<p><strong>Background: </strong>Chimeric antigen receptor (CAR) T-cell therapy is a novel cell therapy for treating non-Hodgkin lymphoma. The development of CAR T-cell therapy has transformed oncology treatment by offering a potential cure. However, due to the high cost of these therapies, and the large number of eligible patients, decision makers are faced with difficult funding decisions. Our objective was to assess the cost-effectiveness of tisagenlecleucel for adults with relapsed/refractory diffuse large B-cell lymphoma in Canada using updated survival data from the recent JULIET trial.</p><p><strong>Methods: </strong>We developed an individual-simulated discrete event simulation model to assess the costs and quality-adjusted life-years (QALY) of tisagenlecleucel compared with salvage chemotherapy. Survival estimates were obtained from a published clinical trial and retrospective analysis. If patients remained progression free for 5 y, they were assumed to be in long-term remission. Costing and utility data were obtained from reports and published sources. A Canadian health care payer perspective was used, and outcomes were modeled over a lifetime horizon. Costs and outcomes were discounted at 1.5% annually, with costs reported in 2021 Canadian dollars. A probabilistic analysis was used, and model parameters were varied in 1-way sensitivity analyses and scenario analyses.</p><p><strong>Results: </strong>After we incorporated the latest clinical evidence, tisagenlecleucel led to an additional cost of $503,417 and additional effectiveness of 2.48 QALYs, with an incremental cost-effectiveness ratio of $202,991 compared with salvage chemotherapy. At a willingness-to-pay threshold of $100,000/QALY, tisagenlecleucel had a 0% likelihood of being cost-effective.</p><p><strong>Conclusions: </strong>At the current drug price, tisagenlecleucel was not found to be a cost-effective option. These results heavily depend on assumptions regarding long-term survival and the price of CAR T. Real-world evidence is needed to reduce uncertainty.</p><p><strong>Highlights: </strong>For patients with diffuse large B-cell lymphoma who failed 2 or more lines of systemic therapy, CAR T was not found to be a cost-effective treatment option at a willingness-to-pay threshold of $100,000.These results heavily depend on the expected long-term survival. The uncertainty in the model may be improved using real-world evidence reported in the future.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"296-306"},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140133110","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":"Perceptions of Clinical Experience and Scientific Evidence in Medical Decision Making: A Survey of a Stratified Random Sample of Swedish Health Care Professionals.","authors":"Barry Dewitt, Johannes Persson, Annika Wallin","doi":"10.1177/0272989X241234318","DOIUrl":"10.1177/0272989X241234318","url":null,"abstract":"<p><strong>Background: </strong>Evidence-based medicine recognizes that clinical expertise gained through experience is essential to good medical practice. However, it is not known what beliefs clinicians hold about how personal clinical experience and scientific knowledge contribute to their clinical decision making and how those beliefs vary between professions, which themselves vary along relevant characteristics, such as their evidence base.</p><p><strong>Design: </strong>We investigate how years in the profession influence health care professionals' beliefs about science and their clinical experience through surveys administered to random samples of Swedish physicians, nurses, occupational therapists, dentists, and dental hygienists. The sampling frame was each profession's most recent occupational registry.</p><p><strong>Results: </strong>Participants (<i>N</i> = 1,627, 46% response rate) viewed science as more important for decision making, more certain, and more systematic than experience. Differences among the professions were greatest for systematicity, where physicians saw the largest gap between the 2 types of knowledge across all levels of professional experience. The effect of years in the profession varied; it had little effect on assessments of importance across all professions but otherwise tended to decrease the difference between assessments of science and experience. Physicians placed the greatest emphasis on science over clinical experience among the 5 professions surveyed.</p><p><strong>Conclusions: </strong>Health care professions appear to share some attitudes toward professional knowledge, despite the variation in the age of the professions and the scientific knowledge base available to practitioners. Training and policy making about clinical decision making might improve by accounting for the ways in which knowledge is understood across the professions.</p><p><strong>Highlights: </strong>Study participants, representing 5 health care professions-medicine, nursing, occupational therapy, dentistry, and dental hygiene-viewed science as more important for decision making, more certain, and more systematic than their personal clinical experience.Of all the professions represented in the study, physicians saw the greatest differences between the 2 types of knowledge.The effect of years of professional experience varied but tended to be small, attenuating the differences seen between science and clinical experience.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"335-345"},"PeriodicalIF":4.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140141029","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":"Preferences for Genetic Testing to Predict the Risk of Developing Hereditary Cancer: A Systematic Review of Discrete Choice Experiments.","authors":"N Morrish, T Snowsill, S Dodman, A Medina-Lara","doi":"10.1177/0272989X241227425","DOIUrl":"10.1177/0272989X241227425","url":null,"abstract":"<p><strong>Background: </strong>Understanding service user preferences is key to effective health care decision making and efficient resource allocation. It is of particular importance in the management of high-risk patients in whom predictive genetic testing can alter health outcomes.</p><p><strong>Purpose: </strong>This review aims to identify the relative importance and willingness to pay for attributes of genetic testing in hereditary cancer syndromes.</p><p><strong>Data sources: </strong>Searches were conducted in Medline, Embase, PsycINFO, HMIC, Web of Science, and EconLit using discrete choice experiment (DCE) terms combined with terms related to hereditary cancer syndromes, malignancy synonyms, and genetic testing.</p><p><strong>Study selection: </strong>Following independent screening by 3 reviewers, 7 studies fulfilled the inclusion criteria, being a DCE investigating patient or public preferences related to predictive genetic testing for hereditary cancer syndromes.</p><p><strong>Data extraction: </strong>Extracted data included study and respondent characteristics, DCE attributes and levels, methods of data analysis and interpretation, and key study findings.</p><p><strong>Data synthesis: </strong>Studies covered colorectal, breast, and ovarian cancer syndromes. Results were summarized in a narrative synthesis and the quality assessed using the Lancsar and Louviere framework.</p><p><strong>Limitations: </strong>This review focuses only on DCE design and testing for hereditary cancer syndromes rather than other complex diseases. Challenges also arose from heterogeneity in attributes and levels.</p><p><strong>Conclusions: </strong>Test effectiveness and detection rates were consistently important to respondents and thus should be prioritized by policy makers. Accuracy, cost, and wait time, while also important, showed variation between studies, although overall reduction in cost may improve uptake. Patients and the public would be willing to pay for improved detection and clinician over insurance provider involvement. Future studies should seek to contextualize findings by considering the impact of sociodemographic characteristics, health system coverage, and insurance policies on preferences.</p><p><strong>Highlights: </strong>Test effectiveness and detection rates are consistently important to respondents in genetic testing for hereditary cancer syndromes.Reducing the cost of genetic testing for hereditary cancer syndromes may improve uptake.Individuals are most willing to pay for a test that improves detection rates, identifies multiple cancers, and for which results are shared with a doctor rather than with an insurance provider.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"252-268"},"PeriodicalIF":3.1,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10988993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139698785","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":"45th Annual North American Meeting of the Society for Medical Decision Making, Philadelphia, Pennsylvania, United States, 22-25 October 2023","authors":"","doi":"10.1177/0272989x241236400","DOIUrl":"https://doi.org/10.1177/0272989x241236400","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"157 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034383","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":"Patient Characteristics and the Extent to Which Clinicians Involve Patients in Decision Making: Secondary Analyses of Pooled Data","authors":"Sascha M. Keij, Megan E. Branda, Victor M. Montori, Juan P. Brito, Marleen Kunneman, Arwen H. Pieterse","doi":"10.1177/0272989x241231721","DOIUrl":"https://doi.org/10.1177/0272989x241231721","url":null,"abstract":"BackgroundThe occurrence of shared decision making (SDM) in daily practice remains limited. Various patient characteristics have been suggested to potentially influence the extent to which clinicians involve patients in SDM.ObjectiveTo assess associations between patient characteristics and the extent to which clinicians involve patients in SDM.MethodsWe conducted a secondary analysis of data pooled from 10 studies comparing the care of adult patients with (intervention) or without (control) a within-encounter SDM conversation tool. We included studies with audio(-visual) recordings of clinical encounters in which decisions about starting or reconsidering treatment were discussed.Main MeasuresIn the original studies, the Observing Patient Involvement in Decision Making 12-items (OPTION<jats:sup>12 item</jats:sup>) scale was used to code the extent to which clinicians involved patients in SDM in clinical encounters. We conducted multivariable analyses with patient characteristics (age, gender, race, education, marital status, number of daily medications, general health status, health literacy) as independent variables and OPTION<jats:sup>12</jats:sup> as a dependent variable.ResultsWe included data from 1,614 patients. The between-arm difference in OPTION<jats:sup>12</jats:sup> scores was 7.7 of 100 points ( P &lt; 0.001). We found no association between any patient characteristics and the OPTION<jats:sup>12</jats:sup> score except for education level ( p = 0.030), an association that was very small (2.8 points between the least and most educated), contributed mostly by, and only significant in, control arms (6.5 points). Subanalyses of a stroke prevention trial showed a positive association between age and OPTION<jats:sup>12</jats:sup> score ( P = 0.033).ConclusionsMost characteristics showed no association with the extent to which clinicians involved patients in SDM. Without an SDM conversation tool, clinicians devoted more efforts to involve patients with higher education, a difference not observed when the tool was used.HighlightsMost sociodemographic patient characteristics show no association with the extent to which clinicians involve patients in shared decision making. Clinicians devoted less effort to involve patients with lower education, a difference that was not observed when a shared decision-making conversation tool was used. SDM conversation tools can be useful for clinicians to better involve patients and ensure patients get involved equally regardless of educational background.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"42 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034586","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}