BMC Medical Informatics and Decision Making最新文献

{"title":"AMPDECIDE amputation level patient decision aids: a feasibility study.","authors":"Alison W Henderson, Maryam Soltani, Bjoern D Suckow, Alison R Kern, Daniel D Matlock, Joseph M Czerniecki, Daniel C Norvell","doi":"10.1186/s12911-025-03084-7","DOIUrl":"10.1186/s12911-025-03084-7","url":null,"abstract":"<p><strong>Objective: </strong>This was a feasibility study of the AMPDECIDE amputation level selection patient decision aids (one transmetatarsal vs. transtibial, the other transtibial vs. transfemoral) designed to inform a larger efficacy trial. We intended to gather data about usability of the aids, gather efficacy data about an amputation-level specific knowledge scale, identify any patient-barriers to the use of the decision aids, and evaluate the feasibility of our study methods.</p><p><strong>Design: </strong>Feasibility study with an uncontrolled before-after design in two medical centers.</p><p><strong>Methods: </strong>A convenience sample of dysvascular patients (both pre- and post-amputation) seen by either the vascular or orthopaedic surgery services at each facility were recruited. Enrolled patients completed baseline measures (including amputation level knowledge items). They then reviewed the decision aid with a research coordinator, followed by additional measures of control preference, numeracy, literacy and open-ended questions.</p><p><strong>Results: </strong>Eleven patients were enrolled (9-post amputation, 2 pre-amputation). Patients rated the decision aids as easy to navigate. Nearly all patients expressed a desire to see their personalized mobility and reamputation risks should they be made available. Patients demonstrated 17% improved amputation level knowledge after exposure to the decision aids. In addition, 81% of patients indicated wanting to participate in the amputation level decision. The study encountered difficulties identifying and recruiting patients until greater clinician involvement was included.</p><p><strong>Conclusions: </strong>The AMPDECIDE patient decision aids and the study measures appear well suited for a larger efficacy trial. Patients were able to digest the information supplied in the aids and responded well to them. The initial recruitment strategy was insufficient; greater clinician involvement may help in the future.</p><p><strong>Clinical trial number: </strong>Not applicable.</p><p><strong>Trial registration: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"218"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538991","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 dynamic prediction model for predicting the time at which patients with MCI progress to AD based on time-dependent covariates.","authors":"Yanjie Wang, Yu Song, Chengfeng Zhang, Jiaqiao Ren, Pansheng Xue, Yawen Hou, Zheng Chen","doi":"10.1186/s12911-025-03040-5","DOIUrl":"10.1186/s12911-025-03040-5","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that imposes a significant burden on families and society. Timely intervention during the transitional stages from Mild Cognitive Impairment (MCI) to AD can help mitigate this issue. The MCI-to-AD conversion time would be helpful if it could be predicted. Most studies rely on Cox models, which possess certain limitations and do not intuitively forecast the duration until patients with MCI progress to AD. Thus we construct a new dynamic prediction model based on the conditional restricted mean survival time (cRMST) from a time-scale perspective to explore the factors influencing progression to AD in patients with MCI and predict the average time required MCI patients to progress to AD at different time points in the future.</p><p><strong>Methods: </strong>We construct a new two-stage dynamic prediction model (tRMST model) based on the conditional restricted mean survival time (cRMST) in combination with landmark method to apply in the analysis of the ADNI database.</p><p><strong>Results: </strong>The results of the ADNI analysis showed that four variables (Education, MMSE, ADAS-Cog13 and P-tau) have dynamic effects over time. The C-index and the mean prediction error of the cross validation are better than the static RMST model.</p><p><strong>Conclusion: </strong>This study presents a time-scale dynamic prediction model that effectively leverages longitudinal data to identify the dynamic effects of the factors' impact on the outcome over time, thereby assisting physicians in personalizing treatment for patients.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"226"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538989","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":"Artificial intelligence in dentistry: insights and expectations from Swiss dental professionals.","authors":"Jérôme Donati, Michel Beyer, Johannes Brokmeier, Klaus W Neuhaus, Florian M Thieringer, Britt-Isabelle Berg","doi":"10.1186/s12911-025-03066-9","DOIUrl":"10.1186/s12911-025-03066-9","url":null,"abstract":"<p><strong>Background: </strong>The goal of this study was to explore Swiss dentists' opinions on artificial intelligence (AI) and illustrate possible correlations to sex, age or professional background.</p><p><strong>Methods: </strong>An online questionnaire was designed and sent to 1121 Swiss dentists by e-mail. It included questions about current feelings, hopes and worries regarding the future of AI in dentistry and enquired habitual and professional use of AI tools.</p><p><strong>Results: </strong>After initial screening, 114 returned questionnaires were included in the final analysis of gathered data. This study revealed that 21.9% of respondents reported using AI in dentistry at least once a week. No significant differences were found between male and female participants regarding their perceptions of AI safety and utility (p = 0.823); however, a significant negative correlation was found between participants' age and their belief in AI's utility (p = 0.049). The belief that AI might replace jobs in the future correlated with lower perceived AI utility.</p><p><strong>Conclusions: </strong>The findings provide insight into AI's role in Swiss dentistry, highlighting areas for future research. Greater emphasis on digital medicine and AI in dental education is encouraged to advance the field and enhance oral health-related quality of life.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"231"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12219709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538992","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":"Attention-driven hybrid deep learning and SVM model for early Alzheimer's diagnosis using neuroimaging fusion.","authors":"Arjun Kidavunil Paduvilan, Godlin Atlas Lawrence Livingston, Sampath Kumar Kuppuchamy, Rajesh Kumar Dhanaraj, Muthuvel Subramanian, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene","doi":"10.1186/s12911-025-03073-w","DOIUrl":"10.1186/s12911-025-03073-w","url":null,"abstract":"<p><p>Alzheimer's Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is a progressive neurodegenerative disorder that affects millions worldwide and is one of the leading causes of cognitive impairment in older adults. Early diagnosis is critical for enabling effective treatment strategies, slowing disease progression, and improving the quality of life for patients. Existing diagnostic methods often struggle with limited sensitivity, overfitting, and reduced reliability due to inadequate feature extraction, imbalanced datasets, and suboptimal model architectures. This study addresses these gaps by introducing an innovative methodology that combines SVM with Deep Learning (DL) to improve the classification performance of AD. Deep learning models extract high-level imaging features which are then concatenated with SVM kernels in a late-fusion ensemble. This hybrid design leverages deep representations for pattern recognition and SVM's robustness on small sample sets. This study provides a necessary tool for early-stage identification of possible cases, so enhancing the management and treatment options. This is attained by precisely classifying the disease from neuroimaging data. The approach integrates advanced data pre-processing, dynamic feature optimization, and attention-driven learning mechanisms to enhance interpretability and robustness. The research leverages a dataset of MRI and PET imaging, integrating novel fusion techniques to extract key biomarkers indicative of cognitive decline. Unlike prior approaches, this method effectively mitigates the challenges of data sparsity and dimensionality reduction while improving generalization across diverse datasets. Comparative analysis highlights a 15% improvement in accuracy, a 12% reduction in false positives, and a 10% increase in F1-score against state-of-the-art models such as HNC and MFNNC. The proposed method significantly outperforms existing techniques across metrics like accuracy, sensitivity, specificity, and computational efficiency, achieving an overall accuracy of 98.5%.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"219"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210430/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538994","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":"From hospital to home: a comprehensive platform supporting cardiac rehabilitation post-revascularization.","authors":"Samaneh Bakhshayesh, Arash Gholoobi, Khadijeh Moulaei, Farnaz Khoshrounejad, Mohammad Reza Mazaheri Habibi, Zahra Ebnehoseini, Majd Jangi, Saeid Eslami","doi":"10.1186/s12911-025-03079-4","DOIUrl":"10.1186/s12911-025-03079-4","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cardiac rehabilitation (CR) post-revascularization faces significant challenges due to accessibility, cost, and patient adherence issues, particularly in center-based settings. Addressing these challenges, the HomeTele-CR platform, encompassing a web portal and mHealth app, was developed to offer a comprehensive, home-based CR solution for patients unable to attend traditional programs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This two-phase mixed-methods developmental study aimed to design, develop, and evaluate the HomeTele-CR platform, a home-based cardiac rehabilitation (CR) system for post-revascularization patients. Phase 1 involved a needs assessment through a Delphi panel with 13 multidisciplinary healthcare professionals (cardiologists, nutritionists, psychologists, physiotherapists, pharmacists, nurses, and sports medicine). Phase 2 utilized Xiaomi Band 2 sensors, Android Studio, and Node.js to develop a patient-facing app (Android) and clinician portal, integrating real-time monitoring, multimedia education (e.g., exercise animations, CPR tutorials), and secure data transmission. Then, in this phase, think-aloud protocols (n = 18 patients) and heuristic evaluation (n = 5 HCI experts) were employed for usability testing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The HomeTele-CR platform is designed to facilitate home-based CR through a three-component system: a health wristband for users, a smartphone app, and a remote monitoring and mentoring portal for healthcare providers. The Xiaomi band 2 wristband tracks heart rate and steps, connecting to smartphones for real-time data sharing. The app, developed for Android smartphones, supports patient supervision, exercise guidance, mood and relaxation support, symptom tracking, educational content on heart health and medications, and personalized feedback and monitoring. It features multimedia training materials, health measure tracking, and interactive tools for patient engagement and compliance. The web portal allows nurses to access patient data, provide individualized advice, and monitor patient progress through comprehensive dashboards, enhancing the CR process by integrating patient-reported outcomes and sensor data. Usability evaluations highlighted areas for improvement, guiding enhancements to optimize user experience. In an app usability test, 30 issues were identified, 73% related to layout problems, especially the line chart and swipe feature. For the web portal, the most common issues involved \"Privacy,\" \"Help and documentation,\" and error recovery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The HomeTele-CR platform represents a significant advancement in providing accessible, efficient, and effective home-based cardiac rehabilitation. By leveraging technology to overcome traditional barriers to CR, the platform promises to improve patient engagement, adherence, and outcomes in post-revascularization care. Future work will focus on expanding the platform's capabilities and ","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"225"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12219512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539032","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 systematic review of microsimulation models for skin cancer.","authors":"Caroline G Watts, Kirstie G McLoughlin, Stephen Wade, Amelia K Smit, H Peter Soyer, Pablo Fernandez-Peñas, David C Whiteman, Pascale Guitera, Gillian Reyes-Marcelino, Karen Canfell, Anne E Cust, Michael Caruana","doi":"10.1186/s12911-025-03074-9","DOIUrl":"10.1186/s12911-025-03074-9","url":null,"abstract":"<p><strong>Background: </strong>Simulation modelling can assist with health care decision making. To inform the development and improvement of skin cancer focussed microsimulation models, we conducted a systematic review and narrative synthesis of published skin cancer models to assess the structure, parameterisation, and assumptions.</p><p><strong>Methods: </strong>The electronic databases OVIDMedline including Embase and the Cost-Effectiveness Analysis (CEA) Registry were searched up to 7 May 2025. Studies that included microsimulation of individuals who developed or had skin cancer were eligible for inclusion. No restrictions on publication date or language were applied. The outcomes of interest were the purpose of the models, characteristics of the models and applicability for modelling skin cancer screening.</p><p><strong>Results: </strong>Twenty-two models were identified from the systematic review. Nineteen papers modelled melanoma, and two papers modelled keratinocyte or non-melanoma skin cancer, and one paper modelled both melanoma and keratinocyte cancer. The models were developed to assess treatment strategies (n = 10), skin cancer screening programs (n = 7), diagnostic techniques (n = 3), post-diagnosis surveillance (n = 3), preventative interventions (n = 1) and time to treatment (n = 1), with three models reporting dual aims. There was substantial variation in the simulation of the natural history of melanoma between models, with more recent models having separate natural history and clinical modules. The quality was assessed using the Quality Assessment Reporting for Microsimulation Models (QARMM) checklist and the majority of models were assessed to be of moderate quality. Limitations from these models included assuming an average tumour behaviour and constant melanoma development and progression over time. Data availability was also noted as a limitation for some models.</p><p><strong>Conclusions: </strong>Most microsimulation models related to skin cancer have focused on late-stage treatment strategies. Tumour characteristics, apart from stage at diagnosis, were not accounted for in most models.</p><p><strong>Prospero registration number: </strong>CRD42024504250.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"222"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144538990","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":"Risk factors and nomogram model for short-term postoperative complications in patients with hirschsprung disease.","authors":"Aohua Song, Bobin Zhang, Wei Feng, Jinping Hou, Xiaohong Die, Yi Wang, Zhenhua Guo","doi":"10.1186/s12911-025-03053-0","DOIUrl":"10.1186/s12911-025-03053-0","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"214"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12211763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539050","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":"Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables.","authors":"Junming Shi, Alan E Hubbard, Nicholas Fong, Romain Pirracchio","doi":"10.1186/s12911-025-03058-9","DOIUrl":"10.1186/s12911-025-03058-9","url":null,"abstract":"<p><strong>Background: </strong>Systematic disparities in data collection within electronic health records (EHRs), defined as non-random patterns in the measurement and recording of clinical variables across demographic groups, can be reflective of underlying implicit bias and may affect patient outcome. Identifying and mitigating these biases is critical for ensuring equitable healthcare. This study aims to develop an analytical framework for measurement patterns, defined as the combination of measurement frequency (how often variables are collected) and missing data rates (the frequency of missing recordings), evaluate the association between them and demographic factors, and assess their impact on in-hospital mortality prediction.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care III (MIMIC-III) database, which includes data on over 40,000 ICU patients from Beth Israel Deaconess Medical Center (2001-2012). Adult patients with ICU stays longer than 24 h were included. Measurement patterns, including missing data rates and measurement frequencies, were derived from EHR data and analyzed. Targeted Machine Learning (TML) methods were used to assess potential systematic disparities in measurement patterns across demographic factors (age, gender, race/ethnicity) while controlling for confounders such as other demographics and disease severity. The predictive power of measurement patterns on in-hospital mortality was evaluated.</p><p><strong>Results: </strong>Among 23,426 patients, significant demographic systematic disparities were observed in the first 24 h of ICU stays. Elderly patients (≥ 65 years) had more frequent temperature measurements compared to younger patients, while males had slightly fewer missing temperature measurements than females. Racial disparities were notable: White patients had more frequent blood pressure and oxygen saturation (SpO2) measurements compared to Black and Hispanic patients. Measurement patterns were associated with ICU mortality, with models based solely on these patterns achieving an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.74-0.77).</p><p><strong>Conclusions: </strong>This study underscores the significance of measurement patterns in ICU EHR data, which are associated with patient demographics and ICU mortality. Analyzing patterns of missing data and measurement frequencies provides valuable insights into patient monitoring practices and potential systemic disparities in healthcare delivery. Understanding these disparities is critical for improving the fairness of healthcare delivery and developing more accurate predictive models in critical care settings.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"241"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539034","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 the possibilities and limitations of customized large language model to support and improve cervical cancer screening.","authors":"Viola Angyal, Ádám Bertalan, Péter Domján, Elek Dinya","doi":"10.1186/s12911-025-03088-3","DOIUrl":"10.1186/s12911-025-03088-3","url":null,"abstract":"<p><strong>Background: </strong>The rapid advancement of artificial intelligence, driven by Generative Pre-trained Transformers (GPT), has transformed natural language processing. Prompt engineering plays a key role in guiding model outputs effectively. Our primary objective was to explore the possibilities and limitations of a custom GPT, developed via prompt engineering, as a patient education tool, which delivers publicly available information through a user-friendly design that facilitates more effective access to cervical cancer screening knowledge.</p><p><strong>Method: </strong>The system was developed using the OpenAI GPT-4 model and Python programming language, with the interface built on Streamlit for cloud-based accessibility and testing. It initially presented questions to testers for preliminary assessment. For cervical cancer-related information, we referenced medical guidelines. Iterative testing optimized the prompts for quality and relevance; techniques like context provision, question chaining, and prompt-based constraints were used. Human-in-the-loop and two independent medical doctor evaluations were employed. Additionally, system performance metrics were measured.</p><p><strong>Result: </strong>The web application was tested 115 times over a three-week period in 2024, with 87 female (76%) and 28 male (24%) participants. A total of 112 users completed the user experience questionnaire. Statistical analysis showed a significant association between age and perceived personalization (p = 0.047) and between gender and system customization (p = 0.037). Younger participants reported higher engagement, though not significantly. Females valued guidance on screening schedules and early detection, while males highlighted the usefulness of information regarding HPV vaccination and its role in preventing HPV-related cancers. Independent evaluations by medical doctors demonstrated consistent assessments of the system's responses in terms of accuracy, clarity, and usefulness.</p><p><strong>Discussion: </strong>While the system demonstrates potential to enhance public health awareness and promote preventive behaviors, encouraging individuals to seek information on cervical cancer screening and HPV vaccination, its conversational capabilities remain constrained by the inherent limitations of current language model technology.</p><p><strong>Conclusions: </strong>Although custom GPTs can not substitute a healthcare consultations, these tools can streamline workflows, expedite information access, and support personalized care. Further research should focus on conducting well-designed randomized controlled trials to establish definitive conclusions regarding its impact and reliability.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"242"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12220158/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of a patient decision aid for patients with advanced laryngeal carcinoma - a multicenter study.","authors":"Anne N Heirman, Japke F Petersen, Abrahim Al-Mamgani, Simone E J Eerenstein, Bertram J de Kleijn, Frank Hoebers, Bernard M Tijink, Lisette van der Molen, Gyorgy B Halmos, Richard Dirven, Martijn M Stuiver, Michiel W M van den Brekel","doi":"10.1186/s12911-025-03080-x","DOIUrl":"10.1186/s12911-025-03080-x","url":null,"abstract":"<p><strong>Purpose: </strong>Patients with advanced larynx cancer face challenging treatment decisions. To address this, we developed and tested a patient decision aid (PDA), aiming to reduce decisional conflict (DC), and enhance knowledge and perceived shared decision-making (SDM).</p><p><strong>Methods: </strong>In this multicenter study (ClinicalTrials.gov ID: NCT03292341, 2016-2023), a pre/post study design was used. Participants, meeting the inclusion criteria of advanced larynx cancer without distant metastasis, completed questionnaires on knowledge, DC and SDM immediately after counseling (T1) and 6 months post-treatment (T2). The intervention arm utilized the PDA (see https://beslissamen.nl/pda_launch.html?pda=tools/pda_larynx_en/story.html ) before completing T1 questionnaires, while the usual care arm followed standard procedures. Between-group differences in outcomes were estimated using regression models with correction for case mix differences.</p><p><strong>Results: </strong>Total DC score was significantly lower in the intervention arm (n = 46) compared to the usual care arm (n = 45) (adjusted mean difference - 32, 95% CI: -37.4; -26.1, p < 0.001). The intervention group demonstrated significantly higher overall knowledge (mean 69% correct) than the control group (mean 47% correct)(adjusted mean difference 24, 95% CI 15.3; 33.1, p < 0.001). Almost all patients in usual care (44/45, 98%) experienced clinically significant DC (CSDC, DCS > 25), compared to 89% (41/46) in the intervention arm (adjusted OR 0.25, 95%CI 0.01; 1.9) p = 0.238). Perceived SDM was significant higher in the intervention arm (mean 78.16) compared to the usual care arm (mean 70.32); however, both groups exhibited high levels.</p><p><strong>Conclusion: </strong>The PDA for advanced laryngeal cancer effectively reduced decisional conflict, enhanced patients' knowledge and improved perceived SDM.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov ID NCT03292341, 20,151,231.</p><p><strong>Level of evidence: 3: </strong></p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"217"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539051","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}