Medical Decision Making最新文献

{"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":"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":"587-601"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","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":"Co-designing a Structured Expert Elicitation with Clinicians to Enhance Health Care Decision Making in Exercise Oncology.","authors":"Yufan Wang, Alexandra L McCarthy, Haitham Tuffaha","doi":"10.1177/0272989X251332967","DOIUrl":"10.1177/0272989X251332967","url":null,"abstract":"<p><p>BackgroundWhile structured expert elicitation (SEE) is gaining traction in health technology assessment in situations in which data are scarce, its application in practice remains limited. Co-designing a practical and fit-for-purpose SEE with experts could enhance its acceptability and feasibility in clinical research.ObjectivesAn SEE was co-designed with clinicians to elicit expert opinions on 3 uncertain quantities of interest (QoIs) for a decision-analytic model in exercise oncology.MethodsA series of co-design meetings was convened to design 6 elicitation stages. Individual elicitation was conducted using the variable interval method (VIM), via videoconferencing. Linear pooling was adopted to generate group estimates. Semi-structured interviews were conducted after the elicitation exercise to gather the experts' first-hand experience of the elicitation process and to identify areas for improvement. Qualitative data were transcribed and content analyzed.ResultsTwelve experts participated in the co-designed SEE. Three beta distributions were derived and estimated from the experts' responses: the relative risk reduction of cardiovascular events of exercise for women who survived early-stage endometrial cancer (Mean: 0.362, SD: 0.15), the probability that a clinician would refer a patient to the exercise program (Mean: 0.457, SD: 0.218), and the probability that a cancer patient would use such a health service upon referral (Mean: 0.446, SD: 0.203). Most of the experts' first-hand experience of the co-designed SEE was positive. The qualitative feedback highlighted critical aspects of the elicitation process that should be designed and executed with caution when targeting clinicians with no prior experience of SEE.ConclusionsThis is the first expert elicitation conducted in exercise oncology. Engaging diverse stakeholders through co-design meetings and incorporating qualitative feedback proved effective and practical in introducing expert elicitation into clinical research.HighlightsRecent SEE guidelines aim to facilitate the conduct of expert elicitation in model-based economic evaluation, but its application in practice remains limited.Engaging experts in the design of SEE could enhance its acceptability and feasibility in clinical research.This is the first co-designed expert elicitation involving clinicians in the field of exercise oncology.This practical approach to conducting SEE could promote a wider adoption to inform health care policy decisions when the evidence is lacking or uncertain.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"602-613"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019947","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":"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":"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":"510-521"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12166136/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stress-Testing US Colorectal Cancer Screening Guidelines: Decennial Colonoscopy from Age 45 is Robust to Natural History Uncertainty and Colonoscopy Sensitivity Assumptions.","authors":"Pedro Nascimento de Lima, Christopher Maerzluft, Jonathan Ozik, Nicholson Collier, Carolyn M Rutter","doi":"10.1177/0272989X251334373","DOIUrl":"10.1177/0272989X251334373","url":null,"abstract":"<p><p>PurposeThe 2023 American College of Physicians (ACP) guidelines for colorectal cancer (CRC) screening are at odds with the United States Preventive Task Force (USPSTF) guidelines, with the former recommending screening starting at age 50 y and the latter at age 45 y. This article \"stress tests\" CRC colonoscopy screening strategies to investigate their robustness to uncertainties stemming from the natural history of disease and sensitivity of colonoscopy.MethodsThis study uses the CRC-SPIN microsimulation model to project the life-years gained (LYG) under several colonoscopy CRC screening strategies. The model was extended to include birth cohort effects on adenoma risk. We estimated natural history parameters under 2 different assumptions about the youngest age of adenoma initiation. For each, we generated 500 parameter sets to reflect uncertainty in the natural history parameters. We simulated 26 colonoscopy screening strategies and examined 4 different colonoscopy sensitivity assumptions, encompassing the range of sensitivities consistent with prior tandem colonoscopy studies. Across this set of scenarios, we identify efficient screening strategies and report posterior credible intervals for benefits of screening (LYG), burden (number of colonoscopies), and incremental burden-effectiveness ratios.ResultsProjected absolute screening benefits varied widely based on assumptions, but strategies starting at age 45 y were consistently in the efficiency frontier. Strategies in which screening starts at age 50 y with 10-y intervals were never efficient, saving fewer life-years than starting screening at age 45 y and performing colonoscopies every 15 y while requiring more colonoscopies per person.ConclusionsDecennial colonoscopy screening initiation at age 45 y remained a robust recommendation. Colonoscopy screening with a 10-y interval starting at age 50 y did not result in an efficient use of colonoscopies in any of the scenarios evaluated.HighlightsColorectal cancer colonoscopy screening strategies initiated at age 45 y were projected to yield more life-years gained while requiring the least number of colonoscopies across different model assumptions about disease natural history and colonoscopy sensitivity.Colonoscopy screening starting at age 50 y with a 10-y interval consistently underperformed strategies that started at age 45 y.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"557-568"},"PeriodicalIF":3.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12167147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025061","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":"Facilitators and Barriers of the Use of Prognostic Models for Clinical Decision Making in Acute Neurologic Care: A Systematic Review.","authors":"Ellen X Y Hu, Evelien S van Hoorn, Isabel R A Retel Helmrich, Susanne Muehlschlegel, Judith A C Rietjens, Hester F Lingsma","doi":"10.1177/0272989X251343027","DOIUrl":"https://doi.org/10.1177/0272989X251343027","url":null,"abstract":"<p><p>BackgroundPrognostic models are crucial for predicting patient outcomes and aiding clinical decision making. Despite their availability in acute neurologic care, their use in clinical practice is limited, with insufficient reflection on reasons for this scarce implementation.PurposeTo summarize facilitators and barriers among clinicians affecting the use of prognostic models in acute neurologic care.Data SourcesSystematic searches were conducted in Embase, Medline ALL, Web of Science Core Collection, and Cochrane Central Register of Controlled Trials from inception until February 2024.Study SelectionEligible studies included those providing clinicians' perspectives on the use of prognostic models in acute neurologic care.Data ExtractionData were extracted concerning study characteristics, study aim, data collection and analysis, prognostic models, participant characteristics, facilitators, and barriers. Risk of bias was assessed using the Qualsyst tool.Data SynthesisFindings were structured around the Unified Theory of Acceptance and Use of Technology framework. Identified facilitators included improved communication with patients and surrogate decision makers (<i>n</i> = 9), reassurance of clinical judgment (<i>n</i> = 6) perceived improved patient outcomes (<i>n</i> = 4), standardization of care (<i>n</i> = 4), resource optimization (<i>n</i> = 3), and extension of clinical knowledge (<i>n</i> = 3). Barriers included perceived misinterpretation during risk communication (<i>n</i> = 3), mistrust in data (<i>n</i> = 3), perceived reduction of clinicians' autonomy (<i>n</i> = 3), and ethical considerations (<i>n</i> = 2). In total, 15 studies were included, with all but 1 demonstrating good methodological quality. None were excluded due to poor quality ratings.LimitationsThis review identifies limitations, including study heterogeneity, exclusion of gray literature, and the scarcity of evaluations on model implementation.ConclusionsUnderstanding facilitators and barriers may enhance prognostic model development and implementation. Bridging the gap between development and clinical use requires improved collaboration among researchers, clinicians, patients, and surrogate decision makers.HighlightsThis is the first systematic review to summarize published facilitators and barriers affecting the use of prognostic models in acute neurologic care from the clinicians' perspective.Commonly reported barriers and facilitators were consistent with several domains of the Unified Theory of Acceptance and Use of Technology model, including effort expectancy, social influence, and facilitating conditions, with the focus on the performance expectancy domain.Future implementation research including collaboration with researchers from different fields, clinicians, patients, and their surrogate decision makers may be highly valuable for future model development and implementation.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251343027"},"PeriodicalIF":3.1,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530739","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":"STEER: Open Access Resources for Conducting Structured Expert Elicitation for Health Care Decision Making.","authors":"Dina Jankovic, James Horscroft, Dawn Lee, Laura Bojke, Marta Soares","doi":"10.1177/0272989X251343013","DOIUrl":"https://doi.org/10.1177/0272989X251343013","url":null,"abstract":"<p><p>In a landscape of accelerated approvals and a less mature evidence base, constrained health systems make reimbursement decisions based on uncertain evidence about the expected clinical and economic value of a health technology. Uncertain decisions require expert judgments, and there has recently been a drive to improve the accountability and transparency in the way these judgments are collected and reported. Structured expert elicitation (SEE) refers to formal methods to quantify experts' judgments. Protocols for conducting SEE exist; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation. This article describes the development of Structured Expert Elicitation Resources (STEER), a collection of open access resources based on a published protocol for SEE specific to the health care decision-making (HCDM) setting. The resources cover the entire SEE process from design to reporting. The resources include an overview and a practical guide for conducting SEE in this setting, adaptable tools for building bespoke SEE exercises, training materials for experts taking part in SEE, resources used in previous SEE exercises, and examples of published SEE in HCDM. The materials cover practical considerations such as timelines team skills requirements, and administrative requirements such as contracting. The use of off-the-shelf resources can streamline the SEE process in HCDM while maintaining robustness.HighlightsThere is a drive to improve accountability and transparency in the way expert judgments are used in health care decision making; however, the time and resource requirements of SEE and the lack of simple tools for its implementation are potential deterrents to its implementation.Structured Expert Elicitation Resources (STEER) is a collection of open access resources for conducting SEE in health care decision making, based on a published methods protocol for SEE specific to this setting.The use of off-the-shelf resources can streamline the SEE process in health care decision making while maintaining robustness.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251343013"},"PeriodicalIF":3.1,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499006","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":"Impact of Limited Sample Size and Follow-up on Partitioned Survival and Multistate Modeling-Based Health Economic Models: A Simulation Study.","authors":"Jaclyn M Beca, Kelvin K W Chan, David M J Naimark, Petros Pechlivanoglou","doi":"10.1177/0272989X251342596","DOIUrl":"https://doi.org/10.1177/0272989X251342596","url":null,"abstract":"<p><p>BackgroundEconomic models often require extrapolation of clinical time-to-event data for multiple events. Two modeling approaches in oncology that incorporate time dependency include partitioned survival models (PSM) and semi-Markov decision models estimated using multistate modeling (MSM). The objective of this simulation study was to assess the performance of PSM and MSM across datasets with varying sample size and degrees of censoring.MethodsWe generated disease trajectories of progression and death for multiple hypothetical populations with advanced cancers. These populations served as the sampling pool for simulated trial cohorts with multiple sample sizes and various levels of follow-up. We estimated MSM and PSM by fitting survival models to these simulated datasets with different approaches to incorporating general population mortality (GPM) and selected best-fitting models using statistical criteria. Mean survival was compared with \"true\" population values to assess error.ResultsWith near complete follow-up, both PSMs and MSMs accurately estimated mean population survival, while smaller samples and shorter follow-up times were associated with a larger error across approaches and clinical scenarios, especially for more distant clinical endpoints. MSMs were slightly more often not estimable when informed by studies with small sample sizes or short follow-up, due to low numbers at risk for the downstream transition. However, when estimable, the MSM models more commonly produced a smaller error in mean survival than the PSMs did.ConclusionsCaution should be taken with all modeling approaches when the underlying data are very limited, particularly PSMs, due to the large errors produced. When estimable and for selections based on statistical criteria, MSMs performed similar to or better than PSMs in estimating mean survival with limited data.HighlightsCaution should be taken with all modeling approaches when underlying data are very limited.Partitioned survival models (PSMs) can lead to significant errors, particularly with limited follow-up. Incorporating general population mortality (GPM) via internal additive hazards improved estimates of mean survival, but the effects were modest.When estimable, decision models based on multistate modeling (MSM) produced similar or smaller error in mean survival compared with PSM, but small samples or limited deaths after progression produce additional challenges for fitting MSMs; more research is needed to improve estimation of MSMs and similar state transition-based modeling methods with limited data.Future studies are needed to assess the applicability of these findings to comparative analyses estimating incremental survival benefits.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251342596"},"PeriodicalIF":3.1,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499005","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":"Association between Exposure to Statin Choice and Adherence to Statins: An Observational Cohort Study.","authors":"Kathryn A Martinez, Victor M Montori, Fatima Rodriguez, Larisa G Tereshchenko, Jeffrey D Kovach, Christopher Boyer, Heather McKee Hurwitz, Michael B Rothberg","doi":"10.1177/0272989X251346508","DOIUrl":"10.1177/0272989X251346508","url":null,"abstract":"<p><p>BackgroundStatin Choice is a shared decision-making encounter tool embedded in the electronic health record.ObjectiveTo describe the association between the use of Statin Choice, statin prescriptions by clinicians, prescription fills (primary adherence), and statin adherence at 12 mo (secondary adherence).DesignObservational cohort study at the Cleveland Clinic Health System.SubjectsStatin-naïve adults aged 40 to 75 y with a 10-y atherosclerotic cardiovascular disease (ASCVD) risk of ≥5% and a primary care appointment between January 2020 and July 2021.Main MeasuresThe primary exposure was the use of Statin Choice during a clinical encounter. We measured whether the use of Statin Choice was associated with statin prescriptions. We measured statin adherence based on pharmacy fill data at 60 d (primary adherence) and 12 mo (secondary adherence). We used mixed-effects logistic regression to estimate the adjusted odds of statin prescriptions and adherence at the 3 time points by the use of Statin Choice.Key ResultsAmong 17,001 statin-naïve patients, 13% viewed Statin Choice and 7% were prescribed a statin. The median ASCVD risk was 10%. Patients who were shown Statin Choice had 9.04 higher odds of being prescribed a statin compared with patients not shown Statin Choice (95% confidence interval [CI]: 7.86-10.4). Among patients prescribed a statin, the use of Statin Choice was associated with 5.75 higher odds of primary adherence compared with usual care (95% CI: 4.22-7.83). At 12 mo, Statin Choice use was significantly associated with adherence in the unadjusted analysis (OR: 1.58; 95% CI: 1.05-2.08) but was not significant after adjustment for patient factors. Patients shown Statin Choice had an average of 12 mg/dL reduction in low-density lipoprotein cholesterol at 12 mo (95% CI: -16 mg/dL, -10) compared with those not shown Statin Choice.ConclusionIn this observational study, Statin Choice use was strongly associated with statin prescription and fills and weakly associated with adherence to statins for up to 1 y. A randomized trial is needed to confirm causality.HighlightsStatin Choice is an electronic health record-embedded shared decision-making encounter tool available for free in many health care systems.Small randomized controlled trials have found modest associations between the use of Statin Choice and statin adherence using patient-reported data.In our large study using pharmacy fill data, clinician use of Statin Choice during a medical encounter was associated with significantly greater patient adherence with statins up to 1 y later.Exposure to Statin Choice was associated with a significant reduction in low-density lipoprotein cholesterol over 1 y.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251346508"},"PeriodicalIF":3.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477582","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":"Hospital Adoption of Diversity, Equity, and Inclusion (DEI) Disaggregated Data for Organizational Decision Making.","authors":"Tran T Doan, Bradley E Iott","doi":"10.1177/0272989X251346844","DOIUrl":"10.1177/0272989X251346844","url":null,"abstract":"<p><p>IntroductionHospitals are interested in improving the quality of data disaggregation and collection to advance diversity, equity, and inclusion (DEI) goals. We evaluated the extent to which hospitals are adopting DEI disaggregated data to inform organizational decisions and the characteristics associated with this adoption.MethodsWe analyzed data from the 2022 American Hospital Association Annual Survey, which included the final iteration of a new survey item about hospital DEI disaggregated data adoption for decision making. Descriptive statistics, logistic regression, and negative binomial regression were used to evaluate this survey item.ResultsAmong hospitals adopting DEI disaggregated data (<i>n</i> = 2,596, 41.9%), two-thirds used these data to inform decisions about patient outcomes, half about training or professional development, and one-third about supply chain or procurement. Larger, tax-exempt, Veteran Affairs, or metropolitan hospitals are significantly more likely to adopt DEI disaggregated data for decision making.LimitationsOur work is limited by the reporting of 1-y cross-sectional results.ConclusionsMost hospitals adopt DEI disaggregated data to inform decisions about patient outcomes. Future research should explore whether hospital decisions or disaggregated data adoption have advanced DEI and health equity for underserved communities.ImplicationsAnalysis of disaggregated data adoption could reveal how hospitals make decisions and funding allocations to advance DEI goals and health equity.HighlightsThere is a limited understanding of the extent to which hospitals adopt diversity, equity, and inclusion (DEI) disaggregated data to inform organizational decision making, highlighting a knowledge gap at the intersection of data equity and health care management.Among hospitals that adopt DEI disaggregated data, two-thirds use them to inform organizational decisions about patient outcomes, and half about professional development.Larger, tax-exempt, Veteran Affairs, or metropolitan hospitals are more likely to adopt DEI disaggregated data for organizational decision making.Future research is needed to explore whether hospital adoption of DEI disaggregated data has advanced DEI organizational goals and health equity for underserved populations.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251346844"},"PeriodicalIF":3.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477624","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":"How to Report Research on the Communication of Health-Related Numbers: The Research on Communicating Numbers (ReCoN) Guidelines.","authors":"Natalie C Benda, Brian J Zikmund-Fisher, Jessica S Ancker","doi":"10.1177/0272989X251346799","DOIUrl":"10.1177/0272989X251346799","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BackgroundResearch with lay audiences (e.g., patients, the public) can inform the communication of health-related numerical information. However, a recent systematic review (Making Numbers Meaningful) highlighted several common issues in the literature that impair readers' ability to evaluate and replicate these studies.PurposeTo create a set of guidelines for reporting research regarding the research on communicating numbers to lay audiences for health-related purposes.Reporting RecommendationsWe present 6 common reporting issues from research on communicating numbers that pertain to the background motivating the study, experimental design and analysis reporting, description of the outcomes, and reporting of the data presentation formats. To address these issues, we propose a set of 7 reporting guidelines including 1) specifying how study objectives address a gap in evidence on research on communicating numbers, 2) clearly reporting all combinations of data presentation formats (experimental conditions) compared, 3) providing verbatim examples of the data that were presented to the audience, 4) describing whether or not participants had access to the data presentation formats while outcomes were assessed, 5) reporting the wording of all outcome measures, 6) using standardized terms for both outcomes and data presentation formats, and 7) ensuring that broad outcome concepts such as gist, comprehension, or knowledge are concretely defined.ConclusionsFuture studies involving research on communicating health-related numbers should use these guidelines to improve the quality of reporting and ease of evidence synthesis in future efforts.HighlightsOur systematic review allowed us to exhaustively identify and enumerate several common reporting issues from research on communicating numbers that make it challenging to synthesize evidence.Reporting issues involved not including the background motivating the gap the study addresses, insufficiently describing experimental designs and analyses, and failing to report information regarding the outcomes measured.We propose 7 reporting guidelines for future research on communicating numbers to address the issues detected:1. Specification of how study objectives address a gap in evidence on research communicating numbers2. Clearly reporting all combinations of data presentation format elements compared3. Providing verbatim examples of the data presentation formats4. Describing whether participants had access to the data presentation formats while outcomes were assessed5. Reporting the wording of all outcome measures6. Using standardized terms for both outcomes and data presentation formats7. Ensuring that broad outcome concepts such as gist, comprehension, or knowledge are concretely definedImplementation of these guidelines will facilitate knowledge synthesis of research on communicating numbers and support creating evidence-based guidelines of best practices for communicating health-related numbers to lay ","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X251346799"},"PeriodicalIF":3.1,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477625","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}