BMC Medical Informatics and Decision Making最新文献

{"title":"AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis.","authors":"Shixin Yuan, Zihuan Yang, Junjie Li, Changde Wu, Songqiao Liu","doi":"10.1186/s12911-025-03048-x","DOIUrl":"10.1186/s12911-025-03048-x","url":null,"abstract":"<p><strong>Background: </strong>Clinical deterioration is often preceded by subtle physiological changes that, if unheeded, can lead to adverse patient outcomes. The precision of traditional scoring systems in detecting these precursors has limitations, prompting the exploration of AI-based predictive models as a means to enhance predictive accuracy and, consequently, patient outcomes.</p><p><strong>Methods: </strong>A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. Databases including PubMed, and Web of Science were searched for relevant studies as of April 8, 2024. Studies were selected based on predefined criteria, specifically targeting AI-based models designed to predict in-hospital clinical deterioration.</p><p><strong>Results: </strong>A total of five studies met the inclusion criteria, all of which underwent prospective clinical validation. These studies demonstrated that AI-based models significantly reduced in-hospital and 30-day mortality rates. Although a downward trend in ICU transfers was observed, the results were not statistically significant. Additionally, the use of AI models shortened overall hospital stays but resulted in a significant increase in ICU length of stay.</p><p><strong>Conclusion: </strong>The findings suggest that AI-based early warning models positively impact patient outcomes in real-world clinical settings. Despite the potential benefits, the effectiveness and real-world applicability of these models require further research. Challenges such as clinician adherence to AI warnings remain to be addressed.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"203"},"PeriodicalIF":3.3,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12131336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207799","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":"Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading.","authors":"Jiabin Yu, Qi Liu, Chenjie Xu, Qinli Zhou, Jiajun Xu, Lingying Zhu, Chen Chen, Yahan Zhou, Binggang Xiao, Lin Zheng, Xiaofeng Zhou, Fengming Zhang, Yuhang Ye, Hongmei Mi, Dongping Zhang, Li Yang, Zhiwei Wu, Jiayi Wang, Ming Chen, Zhirui Zhou, Haoyang Wang, Vicky Y Wang, Enyu Wang, Dong Xu","doi":"10.1186/s12911-025-03029-0","DOIUrl":"10.1186/s12911-025-03029-0","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.</p><p><strong>Materials and methods: </strong>A retrospective analysis was conducted using data from 1,251 patients in the BraTS2021 dataset as the main cohort and 181 clinical cases collected from a medical center between April 2013 and June 2018 (51 years ± 17; 104 males) as the external test set. We propose a PatchGAN-based modality imputation network with an Aggregated Residual Transformer (ART) module combining Transformer self-attention and CNN feature extraction via residual links, paired with a U-Net variant for segmentation. Generative accuracy used PSNR and SSIM for modality conversions, while segmentation performance was measured with DSC and HD95 across necrotic core (NCR), edema (ED), and enhancing tumor (ET) regions. Senior radiologists conducted a comprehensive Likert-based assessment, with diagnostic accuracy evaluated by AUC. Statistical analysis was performed using the Wilcoxon signed-rank test and the DeLong test.</p><p><strong>Results: </strong>The best source-target modality pairs for imputation were T1 to T1ce and T1ce to T2 (p < 0.001). In subregion segmentation, the overall DSC was 0.878 and HD95 was 19.491, with the ET region showing the highest segmentation accuracy (DSC: 0.877, HD95: 12.149). Clinical validation revealed an improvement in grading accuracy by the senior radiologist, with the AUC increasing from 0.718 to 0.913 (P < 0.001) when using the combined imputation and segmentation models.</p><p><strong>Conclusion: </strong>The proposed deep learning framework improves high-grade glioma grading by modality imputation and segmentation, aiding the senior radiologist and offering potential to advance clinical decision-making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"200"},"PeriodicalIF":3.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186562","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":"Monitoring patient pathways at a secondary healthcare services through process mining via Fuzzy Miner.","authors":"Güzin Özdağoğlu, Muhammet Damar, Fatih Safa Erenay, Hale Turhan Damar, Osman Himmetoğlu, Andrew David Pinto","doi":"10.1186/s12911-025-03016-5","DOIUrl":"10.1186/s12911-025-03016-5","url":null,"abstract":"<p><strong>Background: </strong>This study explored workflow pathways followed by patients seeking secondary healthcare services at a local hospital in a rural part of Turkey using process mining to improve hospital resource management.</p><p><strong>Methods: </strong>The study used process mining to discover process flows as patient pathways implied by hospital records for in-patient, out-patient, biochemical laboratory, and radiology services. Utilizing its flexibility, visualizations and robustness, authors implemented fuzzy-miner algorithm. First, we processed the relevant data from patient records. Then, this data was transformed into event and activity logs. Subsequently, all data components were collected into a data warehouse, and the process mining algorithm was applied. The process mining specified resource usage levels and workload, service waiting times, associated bottlenecks in hospital services, and related statistics/measures.</p><p><strong>Results: </strong>The results from the proposed process mining analysis offer insights and decision support to improve hospital resource management. For example, the resulting statistics indicate the high waiting times (e.g., median of waiting times around 2 h within the selected time period) in the General Surgery and Cardiology services, whose resources were highly utilized (2,699 and 6,162 times). Overloads at laboratories and radiological imaging seem to be contributing to these long waiting times, and capacities for the associated services may need to be improved. Waiting times and resource workloads are higher on specific dates related to local commercial and social activities.</p><p><strong>Conclusions: </strong>Process mining successfully identified the real work flows, bottlenecks, and long waiting times at services within the considered local hospital and provided insights to the hospital management for improving their practices. Moreover, the analyses revealed unique challenges in providing care at a local hospital located far from the city center, emphasizing the potential of process mining to improve healthcare delivery tailored to the specific hospital environment.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"199"},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12117757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144157039","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":"Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.","authors":"Shuxing Wei, Hongmeng Dong, Weidong Yao, Ying Chen, Xiya Wang, Wenqing Ji, Yongsheng Zhang, Shubin Guo","doi":"10.1186/s12911-025-03033-4","DOIUrl":"10.1186/s12911-025-03033-4","url":null,"abstract":"<p><strong>Background: </strong>Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU).</p><p><strong>Method: </strong>Our study utilized data from three distinct sources: the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV databases, and Beijing Chaoyang Hospital. We systematically developed and evaluated 11 distinct machine learning (ML) models, employing a comprehensive set of evaluation metrics to assess model performance, including the area under the curve (AUC). To enhance interpretability and identify key predictive features, we implemented Shapley Additive Explanations (SHAP) analysis for the top-performing model. Furthermore, we developed a streamlined version of the model through strategic feature reduction, followed by rigorous hyperparameter optimization (HPO) to maximize predictive performance. To facilitate clinical implementation, we designed and deployed an intuitive web-based calculator, enabling convenient access and practical application of our optimized predictive model.</p><p><strong>Result: </strong>The study analyzed 1802 AP patients, with 266 (14.8%) experiencing in-hospital mortality. A set of 27 features was utilized to construct various models, and among them, CatBoost demonstrated the highest performance in both the validation and test sets. To create a more concise model, we selected the top 13 features. After HPO, the AUC in the test set reached 0.835 (95% CI: 0.793-0.872), the AUC in the external validation from Beijing Chaoyang hospital was 0.782 (95% CI: 0.699-0.860).</p><p><strong>Conclusion: </strong>ML models have shown promising reliability in predicting in-hospital mortality among patients with AP in the ICU. Among these models, the CatBoost model exhibits superior predictive performance, providing valuable assistance to clinical practitioners in identifying high-risk patients and facilitating early interventions to enhance prognosis. The development of a compact model and a web-based calculator further enhances the convenience of using these models in clinical practice.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"198"},"PeriodicalIF":3.3,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12117972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144157036","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":"Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach.","authors":"Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu","doi":"10.1186/s12911-025-03039-y","DOIUrl":"10.1186/s12911-025-03039-y","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Cervical cancer, which includes squamous cell carcinoma and adenocarcinoma, is a leading cause of cancer-related deaths globally, particularly in low- and middle-income countries (LMICs). It is preventable through early screening, but incidence and mortality rates are significantly higher in LMICs, with 94% of deaths occurring in these regions. Poor implementation of screening programs, in addition to multiple health system barriers, leads to a high burden from cervical cancer in these countries. Projections show increasing cases and deaths due to the disease by 2030. Using machine learning instead of the usual statistical tests will incorporate the complex and non-linear relationship of factors in predicting the outcome variable.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method: &lt;/strong&gt;The secondary data for ten Sub-Saharan African countries were utilized from the Demographic and Health Survey, DHS, to evaluate cervical cancer screening uptake among women aged 25-49 years. During cleaning missing values and outliers were removed. Class balancing by Synthetic minority oversampling techniques (SMOT) was done and tuning hyperparameters via grid search was used in the models before splitting into training and validation sets containing 89% and 20%, respectively. The following machine learning classification algorithms were used in the study: Logistic Regression, Decision Tree Classifier, Random Forest, K-Nearest Neighbor, Gradient Boosting, AdaBoost, and Extra Trees. These algorithms were employed to predict cervical cancer screening uptake. The performance of the models was evaluated using accuracy, precision, recall, and F1 score.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result: &lt;/strong&gt;In this study, a cervical cancer screening uptake was predicted among 75,360 weighted samples of women from an African country, aged 25-49 with the final data for model formulation of 53,461, where the Extra Trees Classifier obtained an accuracy of 94.13%, a precision of 95.76%, recall of 94.12%, F1-score of 93.80%. Then followed Random Forest: accuracy = 93.87, precision = 99.18%. Health visits, proximity to health care, using contraceptives, residing in urban settings, and exposure to media were its most crucial predictors. The ensemble methods, such as Extra Trees and Random Forest, showed the best generalization, indicating that this work well on complex datasets and can help devise targeted intervention strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This study demonstrates that the ensemble machine learning models, such as Extra Trees Classifier and Random Forest, are promising in predicting cervical cancer screening uptake among African women with accuracies of 94.13% and 93.87%, respectively. Key predictors include healthcare access, sociocultural factors, media exposure, residence in urban areas, and contraceptive use. The findings emphasize the need for a reduction in care barriers and the use of family planning visits and mass media in promoting screening. These results wi","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"197"},"PeriodicalIF":3.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149583","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 deep learning model integrating domain-specific features for enhanced glaucoma diagnosis.","authors":"Jie Xu, Erkang Jing, Yidong Chai","doi":"10.1186/s12911-025-02925-9","DOIUrl":"10.1186/s12911-025-02925-9","url":null,"abstract":"<p><p>Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remain undiagnosed, especially in less developed regions. Glaucoma diagnosis is a costly task and some models have been proposed to automate diagnosis based on images of the retina, specifically the area known as the optic cup and the associated disc where retinal blood vessels and nerves enter and leave the eye. However, diagnosis is complicated because both normal and glaucoma-affected eyes can vary greatly in appearance. Some normal cases, like glaucoma, exhibit a larger cup-to-disc ratio, one of the main diagnostic criteria, making it challenging to distinguish between them. We propose a deep learning model with domain features (DLMDF) to combine unstructured and structured features to distinguish between glaucoma and physiologic large cups. The structured features were based upon the known cup-to-disc ratios of the four quadrants of the optic discs in normal, physiologic large cups, and glaucomatous optic cups. We segmented each cup and disc using a fully convolutional neural network and then calculated the cup size, disc size, and cup-to-disc ratio of each quadrant. The unstructured features were learned from a deep convolutional neural network. The average precision (AP) for disc segmentation was 98.52%, and for cup segmentation it was also 98.57%. Thus, the relatively high AP values enabled us to calculate the 15 reliable features from each segmented disc and cup. In classification tasks, the DLMDF outperformed other models, achieving superior accuracy, precision, and recall. These results validate the effectiveness of combining deep learning-derived features with domain-specific structured features, underscoring the potential of this approach to advance glaucoma diagnosis.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"195"},"PeriodicalIF":3.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132136","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":"Enhancing responses from large language models with role-playing prompts: a comparative study on answering frequently asked questions about total knee arthroplasty.","authors":"Yi-Chen Chen, Sheng-Hsun Lee, Huan Sheu, Sheng-Hsuan Lin, Chih-Chien Hu, Shih-Chen Fu, Cheng-Pang Yang, Yu-Chih Lin","doi":"10.1186/s12911-025-03024-5","DOIUrl":"10.1186/s12911-025-03024-5","url":null,"abstract":"<p><strong>Background: </strong>The application of artificial intelligence (AI) in medical education and patient interaction is rapidly growing. Large language models (LLMs) such as GPT-3.5, GPT-4, Google Gemini, and Claude 3 Opus have shown potential in providing relevant medical information. This study aims to evaluate and compare the performance of these LLMs in answering frequently asked questions (FAQs) about Total Knee Arthroplasty (TKA), with a specific focus on the impact of role-playing prompts.</p><p><strong>Methods: </strong>Four leading LLMs-GPT-3.5, GPT-4, Google Gemini, and Claude 3 Opus-were evaluated using ten standardized patient inquiries related to TKA. Each model produced two distinct responses per question: one generated under zero-shot prompting (question-only), and one under role-playing prompting (instructed to simulate an experienced orthopaedic surgeon). Four orthopaedic surgeons evaluated responses for accuracy and comprehensiveness on a 5-point Likert scale, along with a binary measure for acceptability. Statistical analyses (Wilcoxon rank sum and Chi-squared tests; P < 0.05) were conducted to compare model performance.</p><p><strong>Results: </strong>ChatGPT-4 with role-playing prompts achieved the highest scores for accuracy (3.73), comprehensiveness (4.05), and acceptability (77.5%), followed closely by ChatGPT-3.5 with role-playing prompts (3.70, 3.85, 72.5%, respectively). Google Gemini and Claude 3 Opus demonstrated lower performance across all metrics. In between-model comparisons based on zero-shot prompting, ChatGPT-4 achieved significantly higher scores of both accuracy and comprehensiveness relative to Google Gemini (P = 0.031 and P = 0.009, respectively) and Claude 3 Opus (P = 0.019 and P = 0.002), and demonstrated higher acceptability than Claude 3 Opus (P = 0.006). Within-model comparisons showed role-playing significantly improved all metrics for ChatGPT-3.5 (P < 0.05) and acceptability for ChatGPT-4 (P = 0.033). No significant prompting effects were observed for Gemini or Claude.</p><p><strong>Conclusions: </strong>This study demonstrates that role-playing prompts significantly enhance the performance of LLMs, particularly for ChatGPT-3.5 and ChatGPT-4, in answering FAQs related to TKA. ChatGPT-4, with role-playing prompts, showed superior performance in terms of accuracy, comprehensiveness, and acceptability. Despite occasional inaccuracies, LLMs hold promise for improving patient education and clinical decision-making in orthopaedic practice.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"196"},"PeriodicalIF":3.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12102839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132061","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":"Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.","authors":"Tamrat Endebu, Girma Taye, Wakgari Deressa","doi":"10.1186/s12911-025-03030-7","DOIUrl":"10.1186/s12911-025-03030-7","url":null,"abstract":"<p><strong>Background: </strong>Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying patients at greater risk of LTFU and facilitating personalized and proactive interventions. This study aimed to develop a prediction model to assess the future risk of LTFU in HIV care in Ethiopia.</p><p><strong>Methods: </strong>The study used a retrospective design in which machine learning (ML) methods were applied to the electronic medical records (EMRs) data of adult HIV-positive individuals who were newly enrolled in antiretroviral therapy between July 2019 and April 2024. The data were collected across eight randomly selected high-volume healthcare facilities. Six supervised ML classifiers-J48 decision tree, random forest, K-nearest neighbors, support vector machine, logistic regression, and naïve Bayes-were utilized for training via Weka 3.8.6 software. The performance of each algorithm was evaluated through a 10-fold cross-validation approach. Algorithm performance was compared via the corrected resampled t test (p < 0.05), and decision curve analysis (DCA) was used to assess the model's clinical utility.</p><p><strong>Results: </strong>A total of 3,720 individuals' EMR data were analyzed, with 2,575 (69.2%) classified as not LTFU and 1,145 (30.8%) classified as LTFU. On the basis of the ML feature selection process, six strong predictors of LTFU were identified: differentiated service delivery model, adherence, tuberculosis preventive therapy, follow-up period, nutritional status, and address information. The random forest algorithm showed superior performance, with an accuracy of 84.2%, a sensitivity of 82.4%, a specificity of 85.7%, a precision of 83.7%, an F1 score of 83.1%, and an area under the curve of 89.5%. The model demonstrated greater clinical utility, offering greater net benefit than both the 'intervention for all' approach and the 'intervention for none' approach, particularly at threshold probabilities of 10% and above.</p><p><strong>Conclusions: </strong>This study developed a machine learning-based predictive model for assessing the future risk of LTFU in HIV care within low-resource settings. Notably, the model built via the random forest algorithm exhibited high accuracy and strong discriminative performance, highlighting its positive net benefit for clinical applications. Furthermore, ongoing external validation across diverse populations is important to ensure the model's reliability and generalizability.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"192"},"PeriodicalIF":3.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101441","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":"Psychometric properties of the Danish SDM-Q-9 questionnaire for shared decision-making in patients with pelvic floor disorders and low back pain: item response theory modelling.","authors":"Mette Hulbaek, Sofie Ronja Petersen, Charlotte Ibsen","doi":"10.1186/s12911-025-03023-6","DOIUrl":"10.1186/s12911-025-03023-6","url":null,"abstract":"<p><strong>Background: </strong>Worldwide, involving patients in healthcare has become a focus point. Shared decision-making (SDM) is one element of patient involvement and, in many countries, including Denmark, requires culturally adapted and validated questionnaires to measure diverse patient populations' perceptions of this concept. SDM-Q-9, a widely used nine-item generic questionnaire, assesses patients' perception of nine elements during decision-making in consultations. The primary aim of this study is to assess the psychometric performance of the Danish version of the SDM-Q-9 through item response theory (IRT). Additionally, to assess the questionnaire's generic applicability among patients with pelvic floor disorders or low back pain.</p><p><strong>Methods: </strong>After treatment decisions, Danish patients with pelvic floor disorders or low back pain rated the level of SDM by completing the SDM-Q-9 questionnaire. Iitem response theory (the Graded Response Model by Samejima) was applied to assess each item's psychometric performance and the questionnaire's generic applicability (among others discriminative ability, precision and item differential functioning).</p><p><strong>Results: </strong>The study invited 825 patients for participation and comprised 758 patients for analysis;73% were women, with a mean age of 52 years and a mean SDM score of 3.87. Discrimination parameters (a-scores) for the model ranged from 2.39 (item 1) to 4.48 (item 8). Analysis of the item-information function curves reflected that item 8 demonstrated the highest maximum, indicating higher precision, while items 1, 2 and 9 showed the lowest maxima. Chi²-test statistics showed no significant differential item functioning at the 0.01-significance level for any item between the two patient groups. A ceiling effect was observed as most patients selected the highest score, while a low information load was identified in the SDM's upper load for each item and the overall instrument.</p><p><strong>Conclusions: </strong>The Danish SDM-Q-9 demonstrates strong overall performance, with the ability to differentiate between the distinct levels of the underlying construct of SDM. However, the high ceiling effect is a critical limitation. While the SDM-Q-9 could serve as a generic questionnaire across samples with varying demographic composition, further exploration of these findings is warranted, particularly across patient samples encompassing more diverse decisions, e.g. patients with life-threatening diseases.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"194"},"PeriodicalIF":3.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090475/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101364","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 Beacon Wiki: Mapping oncological information across the European Union.","authors":"Veronica Coppini, Giulia Ferraris, Maria Vittoria Ferrari, Dario Monzani, Margherita Dahò, Elisa Fragale, Roberto Grasso, Ricardo Pietrobon, Aline Machiavelli, Lucas Teixeira, Victor Galvão, Gabriella Pravettoni","doi":"10.1186/s12911-025-03015-6","DOIUrl":"10.1186/s12911-025-03015-6","url":null,"abstract":"<p><strong>Background: </strong>Accessing comprehensive oncological data is essential for efficient and quality healthcare delivery and research. However, obstacles, such as data fragmentation and privacy concerns which may hold back progress in this area, exist. The Cancer Care Beacon project addresses these barriers consolidating oncological information across the 27 member states of the European Union (EU) with the goal of creating a Beacon wiki free data online repository.</p><p><strong>Methods: </strong>The Cancer Care Beacon project involves thorough data collection from various sources, including hospital websites, PubMed, ClinicalTrials.gov, and national health institutions. The main focus of metadata retrieval is placed on descriptive details about data sources, thus warranting compliance with privacy regulations and ethical standards. In addition, manual examination and semi-automated methods are included in the process, enabling a registry of administrative databases, cancer registries, and other relevant databases.</p><p><strong>Results: </strong>Project findings demonstrate the success in the realisation of a comprehensive repository of oncological data sources across the EU assisting informed decision-making regarding the selection and utilisation of resources. Still, challenges such as limited accessibility and low engagement from database providers persist.</p><p><strong>Conclusion: </strong>The Beacon Wiki represents a significant step in addressing disparities in oncological data access and advancing cancer care and research in Europe. By providing comprehensive metadata on cancer-related data sources, Beacon Wiki empowers stakeholders and promotes collaboration in cancer care and research. Continuous efforts are needed to enhance data accessibility and engagement from database providers, ultimately improving data-driven decision-making and patient outcomes in the EU.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"193"},"PeriodicalIF":3.3,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12090651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101369","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}