BMC Medical Informatics and Decision Making最新文献

{"title":"Development and evaluation of an Instagram version (prototype) of decision-making aid for women on the methods of pharmacological and non-pharmacological pain relief during childbirth.","authors":"Eri Shishido, Anna Hayashi, Risa Takahashi, Shigeko Horiuchi","doi":"10.1186/s12911-025-03162-w","DOIUrl":"https://doi.org/10.1186/s12911-025-03162-w","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"317"},"PeriodicalIF":3.8,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12382101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144943985","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 challenges to ICD-11 implementation: a qualitative study using the consolidated framework for implementation science.","authors":"Erwyn Chin Wei Ooi, Zaleha Md Isa, Mohd Rizal Abdul Manaf, Ahmad Soufi Ahmad Fuad, Hammad Fahli Sidek, Azman Ahmad, Mimi Nurakmal Mustapa, Mohamad Fadli Kharie, Shahidah Adilah Shith, Nuraidah Mohd Marzuki","doi":"10.1186/s12911-025-03157-7","DOIUrl":"https://doi.org/10.1186/s12911-025-03157-7","url":null,"abstract":"<p><strong>Background: </strong>The eleventh version of ICD (ICD-11) is the latest version of ICD adopted by the 72nd World Health Assembly in 2019. Worldwide, countries are piloting the ICD-11 and conducting relevant feasibility studies. ICD-11 implementation was not a straightforward initiative, requiring the involvement of various stakeholders. The challenges and facilitators beyond the pilot implementation settings were less well understood. There is a need to understand the perspective of implementers who have experienced ICD-11 implementation involving heterogeneous systems.</p><p><strong>Methods: </strong>We used primary data gathered between April and May 2024 via semi-structured interviews with the implementers (n = 15) who were members of Malaysia's national-level ICD-11 implementation committee. We collected and analyzed the qualitative data using the Consolidated Framework for Implementation Research (CFIR) 2022 to understand how the key informants implemented ICD-11. Permission was obtained to record the interviews, which were transcribed and coded using NVivo 12. We used conventional qualitative content analysis to identify key facilitators and challenges to ICD-11 implementation.</p><p><strong>Results: </strong>By applying CFIR 2022, we determined the relevant factors influencing the implementation of ICD-11 in Malaysia. Defining the facilitators and challenges provided direction on areas of focus and improvement in the ICD-11 implementation context. The facilitators included the lead organizations' reputation, fulfilment of existing use case, extensive content with terminology service, trialability, lower cost, collaboration with external agencies, improvement of existing laws, clear roles within the organization, effective communication, emerging needs, suitability with existing workflow, easy access to knowledge, motivated team, availability of existing frameworks, and engaging team. The challenges were ICD-11's complexity, customization, support by top management, vendor's steep learning curve, inadequate documentation, outdated infrastructure, data duplication and validity, workforce, impact on work processes, funding, and technical expertise.</p><p><strong>Conclusions: </strong>This study identified key facilitators and challenges in nationwide ICD-11 adoption, providing critical insights for implementation across heterogeneous systems. Successful adoption requires addressing coding, technical and policy aspects. Future research should evaluate user perspectives and the adaptability of implementation strategies in diverse 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":"314"},"PeriodicalIF":3.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12372348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144944036","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":"Can LLMs effectively assist medical coding? Evaluating GPT performance on DRG and targeted clinical tasks.","authors":"Yeli Feng","doi":"10.1186/s12911-025-03151-z","DOIUrl":"10.1186/s12911-025-03151-z","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"312"},"PeriodicalIF":3.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144882242","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 machine learning approach to predict self-efficacy in breast cancer survivors.","authors":"İsmail Toygar, Su Özgür, Gülcan Bağçivan, Ezgi Karaçam, Hilal Benzer, Ferda Akyüz Özdemir, Halise Taşkın Duman, Özlem Ovayolu","doi":"10.1186/s12911-025-03155-9","DOIUrl":"10.1186/s12911-025-03155-9","url":null,"abstract":"<p><strong>Purpose: </strong>To determine predictors of self-efficacy in breast cancer survivors and identify vulnerable groups.</p><p><strong>Methods: </strong>This descriptive study was conducted between November 2023 and April 2024 at three hospitals in Türkiye and involved 430 breast cancer survivors. Data were collected through face-to-face surveys using a patient identification form and the Breast Cancer Survivor Self-Efficacy Scale. This study identified patient characteristics that indicate a tendency towards higher self-efficacy using four machine learning models; Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), XGBoost (XGB).</p><p><strong>Results: </strong>The mean age of participants was 50.7 ± 11.5 years. Majority of the participants (n = 425) were female. AUC values were used as ranker for the machine learning models. The ranks of the models were as follows; logistic regression model (0.715), RF (0.710), SVM (0.704), and XGBoost (0.694). Education level ranked first in the LR (0.3874), RF (0.3290), and SVM (0.1250) models, and was the second most important variable in the XGB (0.2327) model. Conversely, the cancer stage stood out in the LR (0.2466) and RF (0.1935) models, ranking third and fourth, respectively, while it ranked third in SVM (0.0683) and fourth in XGB (0.1872). Additionally, comorbidity ranked third in importance in the LR (0.2213) and RF (0.1681) models, but second in SVM (0.0705) and seventh in XGB (0.1393).</p><p><strong>Conclusion: </strong>The study demonstrated that the self-efficacy of breast cancer survivors was associated with their sociodemographic and medical characteristics. These characteristics may assist healthcare professionals in enhancing the care provided to breast cancer survivors. It is of the utmost importance to consider the aforementioned patient group as being vulnerable with regard to breast cancer survivor self-efficacy. There is a clear need for a focus on this vulnerable cohort.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"313"},"PeriodicalIF":3.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144882241","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 factor analysis and predictive nomogram development for in-hospital mortality in patients with ST-segment elevation myocardial infarction.","authors":"Tahereh Roostami, Maryam Farhadian, Amirhossein Yazdi, Hossein Mahjub","doi":"10.1186/s12911-025-03154-w","DOIUrl":"10.1186/s12911-025-03154-w","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"311"},"PeriodicalIF":3.8,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144871575","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 prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better?","authors":"Hsiang-Wen Lin, Tien-Chao Lin, Chien-Ning Hsu, Tzu-Pei Yeh, Yu-Chieh Chen, Liang-Chih Liu, Chen-Yuan Lin","doi":"10.1186/s12911-025-03091-8","DOIUrl":"10.1186/s12911-025-03091-8","url":null,"abstract":"<p><strong>Background: </strong>Cancer patients receiving targeted therapies need to prevent QTc prolongation and life-threatening cardiovascular (CV) events to maintain a balanced benefit-risk ratio. This study aimed to develop an optimal prediction model for QTc prolongation risk and estimate its risk probability in cancer patients treated with oral tyrosine kinase inhibitors (TKIs).</p><p><strong>Methods: </strong>This retrospective cohort study analyzed electronic medical records (EMR) of cancer patients newly treated with commonly used oral TKIs at a medical center between January 2016 and December 2020. QTc prolongation was defined as ≥ 450 ms in males and ≥ 470 ms in females using Bazett's formula. The study followed four key steps: (1) Managing missing data, (2) Identifying important variables, (3) Training and testing the best prediction models, (4). Estimating risk probability and determining cut-off points. Both univariate logistic regression (LR) and supervised machine learning (ML) approaches were used for variable selection. The backward LR method and seven ML algorithms were applied to train and test the prediction models. The best model was identified based on model performance, fitting criteria, area under the receiver operating characteristic curve (AUROC), risk probability cut-off points, and clinical relevance.</p><p><strong>Results: </strong>The statistical 12-parameter model demonstrated excellent performance (AUROC = 0.89, sensitivity = 0.91, specificity = 0.75) and strong discrimination ability for risk probability prediction (AUROC = 0.78, cut-off = 0.46), outperforming other ML models. In the final best model: the baseline risk probability of QTc prolongation was 0.13, even in the absence of other contributing factors. Baseline QTc prolongation and a history of cardiovascular disease (excluding arrhythmia, cardiomyopathy, etc.) contributed the most to incremental risk probability (0.471 and 0.282, respectively), after controlling for other factors. The remaining 10 factors each contributed to an increased probability of QTc prolongation for more than 0.14 probability.</p><p><strong>Conclusions: </strong>A logistic regression model utilizing 12 easily accessible variables from EMRs outperformed ML models in predicting the risk probability of QTc prolongation in cancer patients newly treated with five oral TKIs. These findings serve as a valuable clinical reference for integrating digital monitoring into cardiovascular care for cancer survivors undergoing targeted therapy with TKIs. They also underscore the importance of screening baseline ECG before initiating TKIs to assess the risk of QTc prolongation, facilitating early intervention and prevention in the future.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"310"},"PeriodicalIF":3.8,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858869","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":"Process mining in healthcare: a tertiary study.","authors":"Adauto Santos, Gislaine Camila Lapasini Leal, Renato Balancieri","doi":"10.1186/s12911-025-02967-z","DOIUrl":"10.1186/s12911-025-02967-z","url":null,"abstract":"<p><p>Business processes in healthcare are complex and multidisciplinary, involving various professional profiles and different healthcare structures, and each medical treatment may require distinct clinical pathways. Process mining can assist in discovering trajectories, verifying compliance, and enabling an understanding of the involvement of different organizational aspects. The main goal of this study is to provide a comprehensive overview of the application of process mining in healthcare. For this, a tertiary review was conducted, gathering 18 secondary reviews that addressed different aspects, such as the objectives of process mining in healthcare, types of activities and perspectives, available resources, primary medical specialties, types of medical processes, and limitations and challenges. The study reveals that process discovery is the most common activity, while the control flow was the most used perspective. The Heuristics Miner and Fuzzy Miner algorithms were the most relevant, and oncology was the medical specialty in which process mining was most used. Process mining has proven to be an effective tool for analyzing healthcare workflows, improving understanding of clinical guidelines and protocols, and supporting decision-making. However, it is necessary to deal with noisy or missing data and establish visualization mechanisms that ensure clarity in data presentation.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"306"},"PeriodicalIF":3.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144854647","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":"Exploration of clinical pathway practice for optimization of DRG costing results based on resource consumption.","authors":"Shu Qian Wu, Xiao Cui Wang, Andrew D Boyd, Dan Feng, Min Zhong, Dandan Nie","doi":"10.1186/s12911-025-03152-y","DOIUrl":"10.1186/s12911-025-03152-y","url":null,"abstract":"<p><strong>Background: </strong>China's Diagnosis-Related Groups (DRGs) payment reform focuses on clinical pathway standardization and cost accounting to optimize resource use. Mengchao Hepatobiliary Hospital (MC Hospital), a tertiary care institution and DRG pilot site, implemented a \"double helix model\" integrating cost accounting with clinical pathway optimization.</p><p><strong>Methods: </strong>A retrospective analysis of data extracted from the hospital cost system was conducted in 2022 at a tertiary hospital in China. The study integrated Hospital Information System (HIS), Laboratory Information Management System (LIMS), and Hospital Resource Planning (HRP) systems into a centralized cost data hub. The equivalent coefficient approach was applied to calculate medical service costs based on labor inputs, procedural complexity, and risk levels. Costs of DRGs, including services, pharmaceuticals, and consumables, were aggregated through item-wise summation. A double helix model was developed to iteratively optimize clinical pathways by linking cost variance analysis with pathway adjustments.</p><p><strong>Results: </strong>The intervention achieved a 44.56% cost reduction (¥4,000 per case) and reduced average hospitalization duration from 17.8 to 12.8 days, and infection rates dropped by 4.12%. Efficiency: High-performing departments (e.g., 9.45-day stays) showed lower cost variance. Traditional Chinese Medicine (TCM) Integration: Usage increased 3.7% without compromising treatment costs.</p><p><strong>Conclusions: </strong>The double helix model effectively aligns cost accounting with clinical pathways, reducing expenses while maintaining health quality. While effective, its adoption requires alignment with institutional capabilities and regional resource realities. It requires advanced health information technology (HIT), and is less effective for homogeneous treatments.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"305"},"PeriodicalIF":3.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12351855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144844509","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":"Building an ultrasound appointment system: focusing on instrument diversity and the scope of physician expertise.","authors":"Qingqing Wu, Yan Ouyang, Li Li, Yiwen Liu, Jierong Liu, Bo Li, Xihong Li","doi":"10.1186/s12911-025-03150-0","DOIUrl":"10.1186/s12911-025-03150-0","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"308"},"PeriodicalIF":3.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144854645","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":"Multimodal artificial intelligence for subepithelial lesion classification and characterization: a multicenter comparative study (with video).","authors":"Jiao Li, Xiaojuan Jing, Qin Zhang, Xiaoxiang Wang, Li Wang, Jing Shan, Zhengkui Zhou, Lin Fan, Xun Gong, Xiaobin Sun, Song He","doi":"10.1186/s12911-025-03147-9","DOIUrl":"10.1186/s12911-025-03147-9","url":null,"abstract":"<p><strong>Background: </strong>Subepithelial lesions (SELs) present significant diagnostic challenges in gastrointestinal endoscopy, particularly in differentiating malignant types, such as gastrointestinal stromal tumors (GISTs) and neuroendocrine tumors, from benign types like leiomyomas. Misdiagnosis can lead to unnecessary interventions or delayed treatment. To address this challenge, we developed ECMAI-WME, a parallel fusion deep learning model integrating white light endoscopy (WLE) and microprobe endoscopic ultrasonography (EUS), to improve SEL classification and lesion characterization.</p><p><strong>Methods: </strong>A total of 523 SELs from four hospitals were used to develop serial and parallel fusion AI models. The Parallel Model, demonstrating superior performance, was designated as ECMAI-WME. The model was tested on an external validation cohort (n = 88) and a multicenter test cohort (n = 274). Diagnostic performance, lesion characterization, and clinical decision-making support were comprehensively evaluated and compared with endoscopists' performance.</p><p><strong>Results: </strong>The ECMAI-WME model significantly outperformed endoscopists in diagnostic accuracy (96.35% vs. 63.87-86.13%, p < 0.001) and treatment decision-making accuracy (96.35% vs. 78.47-86.13%, p < 0.001). It achieved 98.72% accuracy in internal validation, 94.32% in external validation, and 96.35% in multicenter testing. For distinguishing gastric GISTs from leiomyomas, the model reached 91.49% sensitivity, 100% specificity, and 96.38% accuracy. Lesion characteristics were identified with a mean accuracy of 94.81% (range: 90.51-99.27%). The model maintained robust performance despite class imbalance, confirmed by five complementary analyses. Subgroup analyses showed consistent accuracy across lesion size, location, or type (p > 0.05), demonstrating strong generalizability.</p><p><strong>Conclusions: </strong>The ECMAI-WME model demonstrates excellent diagnostic performance and robustness in the multiclass SEL classification and characterization, supporting its potential for real-time deployment to enhance diagnostic consistency and guide clinical decision-making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"307"},"PeriodicalIF":3.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144854646","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}