Medical Decision Making最新文献

{"title":"Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.","authors":"Carlos Pineda-Antunez, Claudia Seguin, Luuk A van Duuren, Amy B Knudsen, Barak Davidi, Pedro Nascimento de Lima, Carolyn Rutter, Karen M Kuntz, Iris Lansdorp-Vogelaar, Nicholson Collier, Jonathan Ozik, Fernando Alarid-Escudero","doi":"10.1177/0272989X241255618","DOIUrl":"10.1177/0272989X241255618","url":null,"abstract":"<p><strong>Purpose: </strong>To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.</p><p><strong>Methods: </strong>We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.</p><p><strong>Results: </strong>The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.</p><p><strong>Conclusions: </strong>Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.</p><p><strong>Highlights: </strong>We use artificial neural networks (ANNs) to build emulators that surrogate complex individual-based models to reduce the computational burden in the Bayesian calibration process.ANNs showed good performance in emulating the CISNET-CRC microsimulation models, despite having many input parameters and outputs.Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis.This work aims to support health decision scientists who want to quantify the uncertainty of calibrated parameters of computationally intensive simulation models under a Bayesian framework.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"543-553"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11281870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301977","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":"<i>Medical Decision Making</i> and <i>MDM Policy & Practice Reviewers</i>, 2023.","authors":"","doi":"10.1177/0272989X241258539","DOIUrl":"10.1177/0272989X241258539","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"601-603"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158718","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":"The Health Impact of Waiting for Elective Procedures in the NHS in England: A Modeling Framework Applied to Coronary Artery Bypass Graft and Total Hip Replacement.","authors":"Naomi Kate Gibbs, Susan Griffin, Nils Gutacker, Adrián Villaseñor, Simon Walker","doi":"10.1177/0272989X241256639","DOIUrl":"10.1177/0272989X241256639","url":null,"abstract":"<p><strong>Introduction: </strong>The aim of this study is to demonstrate a practical framework that can be applied to estimate the health impact of changes in waiting times across a range of elective procedures in the National Health Service (NHS) in England. We apply this framework by modeling 2 procedures: coronary artery bypass graft (CABG) and total hip replacement (THR).</p><p><strong>Methods: </strong>We built a Markov model capturing health pre- and postprocedure, including the possibility of exiting preprocedure to acute NHS care or self-funded private care. We estimate the change in quality-adjusted life-years (QALYs) over a lifetime horizon for 10 subgroups defined by sex and Index of Multiple Deprivation quintile groups and for 7 alternative scenarios. We include 18 wk as a baseline waiting time consistent with current NHS policy. The model was populated with data from routinely collected data sets where possible (Hospital Episode Statistics, Patient-Reported Outcome Measures, and Office for National Statistics Mortality records), supplemented by the academic literature.</p><p><strong>Results: </strong>Compared with 18 wk, increasing the wait time to 36 wk resulted in a mean discounted QALY loss in the range of 0.034 to 0.043 for CABG and 0.193 to 0.291 for THR. The QALY impact of longer NHS waits was greater for those living in more deprived areas, partly as fewer patients switch to private care.</p><p><strong>Discussion/conclusion: </strong>The proposed framework was applied to 2 different procedures and patient populations. If applied to an expanded group of procedures, it could provide decision makers with information to inform prioritization of waiting lists. There are a number of limitations in routine data on waiting for elective procedures, primarily the lack of information on people still waiting.</p><p><strong>Highlights: </strong>We present a modeling framework that allows for an estimation of the health impact (measured in quality-adjusted life-years) of waiting for elective procedures in the NHS in England.We apply our model to waiting for coronary artery bypass graft (CABG) and total hip replacement (THR). Increasing the wait for THR results in a larger health loss than an equivalent increase in wait for CABG.This model could potentially be used to estimate the impact across an expanded group of procedures to inform prioritization of activities to reduce waiting times.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"572-585"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297182","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":"Thinking Fast, Slow, and Forever: Daniel Kahneman Obituary.","authors":"Donald A Redelmeier","doi":"10.1177/0272989X241256121","DOIUrl":"https://doi.org/10.1177/0272989X241256121","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X241256121"},"PeriodicalIF":3.6,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181071","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":"Danish Women Make Decisions about Participation in Breast Cancer Screening prior to Invitation Information: An Online Survey Using Experimental Methods","authors":"Eeva-Liisa Røssell, Hilary Louise Bekker, Mara A. Schonberg, Ivar Sønbø Kristiansen, Signe Borgquist, Henrik Støvring","doi":"10.1177/0272989x241248142","DOIUrl":"https://doi.org/10.1177/0272989x241248142","url":null,"abstract":"IntroductionAt mammography screening invitation, the Danish Health Authority recommends women aged 50 to 69 y make an informed decision about whether to be screened. Previous studies have shown that women have very positive attitudes about screening participation. Therefore, we hypothesized that Danish women may already have decided to participate in breast cancer screening prior to receiving their screening invitation at age 50 y.MethodsWe invited a random sample of 2,952 Danish women aged 44 to 49 y (prescreening age) to complete an online questionnaire about barriers to informed screening decision making using the official digital mailbox system in Denmark. We asked participants about their screening intentions using 3 different questions to which women were randomized: screening presented 1) as an opportunity, 2) as a choice, and 3) as an opportunity plus a question about women’s stage of decision making. All women completed questions about background characteristics, intended participation in the screening program, use and impact of screening information, and preferences for the decision-making process. Data were linked to sociodemographic register data.ResultsA total of 790 (26.8%) women participated in the study. Herein, 97% (95% confidence interval: 96%–98%) reported that they wanted to participate in breast cancer screening when invited at age 50 y. When presented with the choice compared with the opportunity framing, more women rejected screening. When asked about their stage of decision making, most (87%) had already made a decision about screening participation and were unlikely to change their mind.ConclusionIn our study, almost all women of prescreening age wanted to participate in breast cancer screening, suggesting that providing information at the time of screening invitation may be too late to support informed decision making.HighlightsAlmost all women of prescreening age (44–49 y) in our study wanted to participate in the Danish national mammography screening program starting at age 50 y. Early decision making represents a barrier for informed decision making as women in this study had intentions to participate in breast cancer screening prior to receiving an official screening invitation, and therefore, providing information at the time of screening invitation may be too late to support informed decision making. Very few women rejected screening participation; however, more women rejected screening when the information was framed as an active choice between having or declining breast cancer screening (continue with usual care) compared with presenting only the option of screening with no description of the alternative. Two-thirds of women reading the screening information in this study had unchanged attitudes toward screening after reading the presented information.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"28 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140839499","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":"A Comparison of Additional Benefit Assessment Methods for Time-to-Event Endpoints Using Hazard Ratio Point Estimates or Confidence Interval Limits by Means of a Simulation Study.","authors":"Christopher A Büsch, Marietta Kirchner, Rouven Behnisch, Meinhard Kieser","doi":"10.1177/0272989X241239928","DOIUrl":"10.1177/0272989X241239928","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;For time-to-event endpoints, three additional benefit assessment methods have been developed aiming at an unbiased knowledge about the magnitude of clinical benefit of newly approved treatments. The American Society of Clinical Oncology (ASCO) defines a continuous score using the hazard ratio point estimate (HR-PE). The European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) developed methods with an ordinal outcome using lower and upper limits of the 95% HR confidence interval (HR-CI), respectively. We describe all three frameworks for additional benefit assessment aiming at a fair comparison across different stakeholders. Furthermore, we determine which ASCO score is consistent with which ESMO/IQWiG category.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In a comprehensive simulation study with different failure time distributions and treatment effects, we compare all methods using Spearman's correlation and descriptive measures. For determination of ASCO values consistent with categories of ESMO/IQWiG, maximizing weighted Cohen's Kappa approach was used.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Our research depicts a high positive relationship between ASCO/IQWiG and a low positive relationship between ASCO/ESMO. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 corresponds to ESMO categories. Using ASCO values of 21 and 38 as cutoffs represents IQWiG categories.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Limitations: &lt;/strong&gt;We investigated the statistical aspects of the methods and hence implemented slightly reduced versions of all methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;IQWiG and ASCO are more conservative than ESMO, which often awards the maximal category independent of the true effect and is at risk of overcompensating with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and underpowered/overpowered studies influence all methods in some degree. Nevertheless, ESMO is the most liberal one.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Highlights: &lt;/strong&gt;For the additional benefit assessment, the American Society of Clinical Oncology (ASCO) uses the hazard ratio point estimate (HR-PE) for their continuous score. In contrast, the European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) use the lower and upper 95% HR confidence interval (HR-CI) to specific thresholds, respectively. ESMO generously assigns maximal scores, while IQWiG is more conservative.This research provides the first comparison between IQWiG and ASCO and describes all three frameworks for additional benefit assessment aiming for a fair comparison across different stakeholders. Furthermore, thresholds for ASCO consistent with ESMO and IQWiG categories are determined, enabling a comparison of the methods in practice in a fair manner.IQWiG and ASCO are the more conservative methods, while ESMO awards high percentages of ","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"365-379"},"PeriodicalIF":3.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899374","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":"Nurses' Anxiety Mediates the Relationship between Clinical Tolerance to Uncertainty and Antibiotic Initiation Decisions in Residential Aged-Care Facilities.","authors":"Saniya Singh, Chris Degeling, Peta Drury, Amy Montgomery, Peter Caputi, Frank P Deane","doi":"10.1177/0272989X241239871","DOIUrl":"10.1177/0272989X241239871","url":null,"abstract":"<p><strong>Highlights: </strong>The impact of non-clinical factors (e.g., resident and family preferences) on prescribing is well-established. There is a gap in the literature regarding the mechanisms through which these preferences are experienced as pressure by prescribers within the unique context of residential aged-care facilities (RACFs).A significant relationship was found between nurses' anxiety, clinical tolerance of uncertainty, and the perceived need for antibiotics and assessment.As such, there is a need to expand stewardship beyond education alone to include interventions that help nurses manage uncertainty and anxiety and include other stakeholders (e.g., family members) when making clinical decisions in the RACF setting.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"415-425"},"PeriodicalIF":3.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140295126","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":"Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models.","authors":"Arantzazu Arrospide, Oliver Ibarrondo, Rubén Blasco-Aguado, Igor Larrañaga, Fernando Alarid-Escudero, Javier Mar","doi":"10.1177/0272989X241232967","DOIUrl":"10.1177/0272989X241232967","url":null,"abstract":"<p><strong>Purpose: </strong>To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data.</p><p><strong>Methods: </strong>Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the <i>R</i>² statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure.</p><p><strong>Results: </strong>The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit.</p><p><strong>Conclusions: </strong>Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data.</p><p><strong>Highlights: </strong>We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual dataWe used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty.Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"359-364"},"PeriodicalIF":3.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139974239","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":"Communicating the Imperfect Diagnostic Accuracy of COVID-19 Rapid Antigen Self-Tests: An Online Randomized Experiment","authors":"Huijun Li, Megha Kalra, Lin Zhu, Deonna M. Ackermann, Melody Taba, Carissa Bonner, Katy J.L. Bell","doi":"10.1177/0272989x241242131","DOIUrl":"https://doi.org/10.1177/0272989x241242131","url":null,"abstract":"ObjectiveTo investigate the potential impacts of optimizing coronavirus disease 2019 (COVID-19) rapid antigen test (RAT) self-testing diagnostic accuracy information.DesignOnline randomized experiment using hypothetical scenarios: in scenarios 1 to 3 (RAT result positive), the posttest probability was considered to be very high (likely true positives), and in scenarios 4 and 5 (RAT result negative), the posttest probability was considered to be moderately high (likely false negatives).SettingDecember 12 to 22, 2022, during the mixed-variant Omicron wave in Australia.ParticipantsAustralian adults. Intervention: diagnostic accuracy of a COVID-19 self-RAT presented in a health literacy-sensitive way; usual care: diagnostic accuracy information provided by the manufacturer; control: no diagnostic accuracy information.Main Outcome MeasureIntention to self-isolate.ResultsA total of 226 participants were randomized (control n = 75, usual care n = 76, intervention n = 75). More participants in the intervention group correctly interpreted the meaning of the diagnostic accuracy information ( P = 0.08 for understanding sensitivity, P &lt; 0.001 for understanding specificity). The proportion who would self-isolate was similar across scenarios 1 to 3 (likely true positives). The proportion was higher in the intervention group than in the control for scenarios 4 and 5 (likely false negatives). These differences were not statistically significant. The largest potential effect was seen in scenario 5 (dinner party with confirmed cases, the person has symptoms, negative self-RAT result), with 63% of the intervention group and 49% of the control group indicating they would self-isolate (absolute difference 13.3%, 95% confidence interval: −2% to 30%, P = 0.10).ConclusionHealth literacy sensitive formatting supported participant understanding and recall of diagnostic accuracy information. This may increase community intentions to self-isolate when there is a likely false-negative self-RAT result. Trial registration: Australia New Zealand Clinical Trial Registry (ACTRN12622001517763)HighlightsCommunity-based diagnostic accuracy studies of COVID-19 self-RATs indicate substantially lower sensitivity (and higher risk of false-negative results) than the manufacturer-supplied information on most government public Web sites. This online randomized study found that a health literacy–sensitive presentation of the imperfect diagnostic accuracy COVID-19 self-RATs supported participant understanding and recall of diagnostic accuracy information. Health literacy–sensitive presentation may increase community intentions to self-isolate after a negative test result where the posttest probability is still moderately high (i.e., likely false-negative result). To prevent the onward spread of infection, efforts to improve communication about the high risk of false-negative results from COVID-19 self-RATs are urgently needed.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"238 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800984","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":"Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting","authors":"Matthew D. Blanchard, Stefan M. Herzog, Juliane E. Kämmer, Nikolas Zöller, Olga Kostopoulou, Ralf H. J. M. Kurvers","doi":"10.1177/0272989x241241001","DOIUrl":"https://doi.org/10.1177/0272989x241241001","url":null,"abstract":"BackgroundGeneral practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS).MethodsWe simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP’s diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3–9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group’s final diagnosis. Diagnostic accuracy was used as the performance measure.ResultsAggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance.DiscussionCombining independent diagnoses may substantially improve a GP’s diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice.HighlightsWe examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy. Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority). Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size. Combining independent diagnoses may substantially improve GP’s diagnostic accuracy and subsequent patient outcomes.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":"102 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140594450","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}