BMC Medical Informatics and Decision Making最新文献

{"title":"Anomaly-based threat detection in smart health using machine learning.","authors":"Muntaha Tabassum, Saba Mahmood, Amal Bukhari, Bader Alshemaimri, Ali Daud, Fatima Khalique","doi":"10.1186/s12911-024-02760-4","DOIUrl":"10.1186/s12911-024-02760-4","url":null,"abstract":"<p><strong>Background: </strong>Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate the data. This article focuses around the anomalies under different contexts.</p><p><strong>Methodology: </strong>This research has proposed methodology to secure Electronic Health Records (EHRs) within a complex environment. We have employed a systematic approach encompassing data preprocessing, labeling, modeling, and evaluation. Anomalies are not labelled thus a mechanism is required that predicts them with greater accuracy and less false positive results. This research utilized unsupervised machine learning algorithms that includes Isolation Forest and Local Outlier Factor clustering algorithms. By calculating anomaly scores and validating clustering through metrics like the Silhouette Score and Dunn Score, we enhanced the capacity to secure sensitive healthcare data evolving digital threats. Three variations of Isolation Forest (IForest)models (SVM, Decision Tree, and Random Forest) and three variations of Local Outlier Factor (LOF) models (SVM, Decision Tree, and Random Forest) are evaluated based on accuracy, sensitivity, specificity, and F1 Score.</p><p><strong>Results: </strong>Isolation Forest SVM achieves the highest accuracy of 99.21%, high sensitivity (99.75%) and specificity (99.32%), and a commendable F1 Score of 98.72%. The Isolation Forest Decision Tree also performs well with an accuracy of 98.92% and an F1 Score of 99.35%. However, the Isolation Forest Random Forest exhibits lower specificity (72.84%) than the other models.</p><p><strong>Conclusion: </strong>The experimental results reveal that Isolation Forest SVM emerges as the top performer showcasing the effectiveness of these models in anomaly detection tasks. The proposed methodology utilizing isolation forest and SVM produced better results by detecting anomalies with less false positives in this specific EHR of a hospital in North England. Furthermore the proposal is also able to identify new contextual anomalies that were not identified in the baseline methodology.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"347"},"PeriodicalIF":3.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675271","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":"DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction.","authors":"Haoyu Tian, Xiong He, Kuo Yang, Xinyu Dai, Yiming Liu, Fengjin Zhang, Zixin Shu, Qiguang Zheng, Shihua Wang, Jianan Xia, Tiancai Wen, Baoyan Liu, Jian Yu, Xuezhong Zhou","doi":"10.1186/s12911-024-02756-0","DOIUrl":"10.1186/s12911-024-02756-0","url":null,"abstract":"<p><strong>Background: </strong>Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease progression, the capacity and completeness of clinical data required for training deep learning models remains a significant challenge. This study aims to explore a new method that reduces data dependency and achieves predictive performance comparable to existing research.</p><p><strong>Methods: </strong>This study proposed DAPNet, a deep learning-based disease progression prediction model that solely utilizes the comorbidity duration (without relying on multi-modal data or comprehensive medical records) and disease associations from biomedical knowledge graphs to deliver high-performance prediction. DAPNet is the first to apply multi-view graph contrastive learning to disease progression prediction tasks. Compared with other studies on comorbidities, DAPNet innovatively integrates molecular-level disease association information, combines disease co-occurrence and ICD10, and fully explores the associations between diseases; RESULTS: This study validated DAPNet using a de-identified clinical dataset derived from medical claims, which includes 2,714 patients and 10,856 visits. Meanwhile, a kidney dataset (606 patients) based on MIMIC-IV has also been constructed to fully validate its performance. The results showed that DAPNet achieved state-of-the-art performance on the severe pneumonia dataset (F1=0.84, with an improvement of 8.7%), and outperformed the six baseline models on the kidney disease dataset (F1=0.80, with an improvement of 21.3%). Through case analysis, we elucidated the clinical and molecular associations identified by the DAPNet model, which facilitated a better understanding and explanation of potential disease association, thereby providing interpretability for the model.</p><p><strong>Conclusions: </strong>The proposed DAPNet, for the first time, utilizes comorbidity duration and disease associations network, enabling more accurate disease progression prediction based on a multi-view graph contrastive learning, which provides valuable insights for early diagnosis and treatment of patients. Based on disease association networks, our research has enhanced the interpretability of disease progression predictions.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"345"},"PeriodicalIF":3.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675282","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":"Paramedic perceptions of decision-making when managing mental health-related presentations: a qualitative study.","authors":"Kate Emond, George Mnatzaganian, Michael Savic, Dan I Lubman, Melanie Bish","doi":"10.1186/s12911-024-02768-w","DOIUrl":"10.1186/s12911-024-02768-w","url":null,"abstract":"<p><strong>Background: </strong>Mental health presentations account for a considerable proportion of paramedic workload; however, the decision-making involved in managing these cases is poorly understood. This study aimed to explore how paramedics perceive their clinical decision-making when managing mental health presentations.</p><p><strong>Methods: </strong>A qualitative descriptive study design was employed. Overall, 73 paramedics participated in semi structured interviews, and data were analyzed from transcribed interviews in NVivo.</p><p><strong>Results: </strong>Four themes emerged that reflected participants' perceptions: the assessment process, experience, the use of documents and standard procedures, and consultation with other healthcare providers. There were conflicting perceptions about the clinical decision-making process, with perception of role having a potential impact. The dual process theory of clinical decision-making, which includes both analytical and intuitive approaches, was evident in the decision-making process.</p><p><strong>Conclusion: </strong>Incorporating dual process theory into education and training, which highlights the strengths and weaknesses of analytical and intuitive decision-making, may reduce clinical errors made by cognitive bias. To further support clinical decision-making, additional education and training are warranted to promote critical thinking and clarify the scope of practice and roles when attending to mental health-related presentations.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"348"},"PeriodicalIF":3.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675310","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 prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network.","authors":"Wenlong Zou, Haipeng Zhao, Ming Ren, Chaoxiong Cui, Guobin Yuan, Boyi Yuan, Zeyu Ji, Chao Wu, Bin Cai, Tingting Yang, Jinjun Zou, Guangzhi Liu","doi":"10.1186/s12911-024-02762-2","DOIUrl":"10.1186/s12911-024-02762-2","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to identify the risk factors of acute ischemic stroke (AIS) occurring during hospitalization in patients following off-pump coronary artery bypass grafting (OPCABG) and utilize Bayesian network (BN) methods to establish predictive models for this disease.</p><p><strong>Methods: </strong>Data were collected from the electronic health records of adult patients who underwent OPCABG at Beijing Anzhen Hospital from January 2018 to December 2022. Patients were allocated to the training and test sets in an 8:2 ratio according to the principle of randomness. Subsequently, a BN model was established using the training dataset and validated against the testing dataset. The BN model was developed using a tabu search algorithm. Finally, receiver operating characteristic (ROC) and calibration curves were plotted to assess the extent of disparity in predictive performance between the BN and logistic models.</p><p><strong>Results: </strong>A total of 10,184 patients (mean (SD) age, 62.45 (8.7) years; 2524 (24.7%) females) were enrolled, including 151 (1.5%) with AIS and 10,033 (98.5%) without AIS. Female sex, history of ischemic stroke, severe carotid artery stenosis, high glycated albumin (GA) levels, high D-dimer levels, high erythrocyte distribution width (RDW), and high blood urea nitrogen (BUN) levels were strongly associated with AIS. Type 2 diabetes mellitus (T2DM) was indirectly linked to AIS through GA and BUN. The BN models exhibited superior performance to logistic regression in both the training and testing sets, achieving accuracies of 72.64% and 71.48%, area under the curve (AUC) of 0.899 (95% confidence interval (CI), 0.876-0.921) and 0.852 (95% CI, 0.769-0.935), sensitivities of 91.87% and 89.29%, and specificities of 72.35% and 71.24% (using the optimal cut-off), respectively.</p><p><strong>Conclusion: </strong>Female gender, IS history, carotid stenosis (> 70%), RDW-CV, GA, D-dimer, BUN, and T2DM are potential predictors of IS in our Chinese cohort. The BN model demonstrated greater efficiency than the logistic regression model. Hence, employing BN models could be conducive to the early diagnosis and prevention of AIS after OPCABG.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"349"},"PeriodicalIF":3.3,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142674788","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 novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach.","authors":"Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, Yan Li","doi":"10.1186/s12911-024-02753-3","DOIUrl":"10.1186/s12911-024-02753-3","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer is characterized by high morbidity and mortality due to the lack of practical early diagnostic and prognostic tools. The present study uses machine learning algorithms to construct a clinical predictive model for non-small cell lung cancer (NSCLC) patients.</p><p><strong>Methods: </strong>Laboratory indices of the NSCLC patients at their initial visit were collected for quality control and exploratory analysis. By comparing the levels of the above indices between the survival and death groups, the statistically significant indices were selected for subsequent machine learning modeling. Ten machine learning algorithms were then employed to develop the predictive models with survival and recurrence as outcomes, respectively. Moreover, regression models were constructed using the random survival forest algorithm by incorporating the survival time dimension. Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application.</p><p><strong>Results: </strong>682 patients were enrolled according to the inclusion and exclusion criteria. The preliminary comparison results revealed that except for fast blood glucose, CD₃⁺T cell proportion, NK cell proportion, and CA72-4, there were significant statistical differences in other tumor markers, inflammation, metabolism, and immune-related indices between the survival and death groups (p < 0.01). Subsequently, indices with statistical differences were incorporated into machine learning modeling and evaluation. The results showed that among the ten prognostic models constructed using survival status as the outcome, the neural network model obtained the best predictive performance, with accuracy, sensitivity, specificity, AUC, and precision values of 0.993, 0.987, 1.000, 0.994, and 1.000, respectively. The corresponding SHAP16 algorithm revealed that the top five variables in terms of importance were interleukin6 (IL-6), soluble interleukin2 receptor (sIL-2R), cholesterol, CEA, and Cy211, respectively. The random survival forest model also confirmed the critical role of CEA, sIL-2R, and IL-6 in predicting the prognosis of NSCLC patients. A decision tree model with seven cut-off points based on the above three indices was eventually built for clinical application.</p><p><strong>Conclusion: </strong>The neural network model exhibited ideal predictive performance in the survival status of NSCLC patients, and the decision tree model constructed based on selected important variables was conducive to rapid bedside prognosis assessment and decision-making.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"344"},"PeriodicalIF":3.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11575007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667366","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":"Classification of lumbar spine disorders using large language models and MRI segmentation.","authors":"Rongpeng Dong, Xueliang Cheng, Mingyang Kang, Yang Qu","doi":"10.1186/s12911-024-02740-8","DOIUrl":"10.1186/s12911-024-02740-8","url":null,"abstract":"<p><strong>Background: </strong>MRI is critical for diagnosing lumbar spine disorders but its complexity challenges diagnostic accuracy. This study proposes a BERT-based large language model (LLM) to enhance precision in classifying lumbar spine disorders through the integration of MRI data, textual reports, and numerical measurements.</p><p><strong>Methods: </strong>The segmentation quality of MRI data is evaluated using dice coefficients (cut-off: 0.92) and intersection over union (IoU) metrics (cut-off: 0.88) to ensure precise anatomical feature extraction. The CNN extracts key lumbar spine features, such as lumbar lordotic angle (LLA) and disc heights, which are tokenized as direct scalar values representing positional relationships. A data source of 28,065 patients with various disorders, including degenerative disc disease, spinal stenosis, and spondylolisthesis, is used to establish diagnostic standards. These standards are refined through post-CNN processing of MRI texture features. The BERT-based spinal LLM model integrates these CNN-extracted MRI features and numerical values through early fusion layers.</p><p><strong>Results: </strong>Segmentation analysis illustrate various lumbar spine disorders and their anatomical changes. The model achieved high performance, with all key metrics nearing 0.9, demonstrating its effectiveness in classifying conditions like spondylolisthesis, herniated disc, and spinal stenosis. External validation further confirmed the model's generalizability across different populations. External validation on 514 expert-validated MRI cases further confirms the model's clinical relevance and generalizability. The BERT-based model classifies 61 combinations of lumbar spine disorders.</p><p><strong>Conclusions: </strong>The BERT-based spinal LLM significantly improves the precision of lumbar spine disorder classification, supporting accurate diagnosis and treatment planning.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"343"},"PeriodicalIF":3.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667367","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":"Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning.","authors":"Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu","doi":"10.1186/s12911-024-02751-5","DOIUrl":"10.1186/s12911-024-02751-5","url":null,"abstract":"<p><strong>Objective: </strong>To construct a highly accurate and interpretable feeding intolerance (FI) risk prediction model for preterm newborns based on machine learning (ML) to assist medical staff in clinical diagnosis.</p><p><strong>Methods: </strong>In this study, a sample of 350 hospitalized preterm newborns were retrospectively analysed. First, dual feature selection was conducted to identify important feature variables for model construction. Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. Finally, we apply the SHapley Additive exPlanation (SHAP) interpretable framework to analyse the decision-making principles of the optimal model and expound upon the important factors affecting FI in preterm newborns and their modes of action.</p><p><strong>Results: </strong>The accuracy of XGBoost was 87.62%, and the area under the curve (AUC) was 92.2%. After the application of tenfold cross-validation, the accuracy was 83.43%, and the AUC was 89.45%, which was significantly better than those of the other models. Analysis of the XGBoost model with the SHAP interpretable framework showed that a history of resuscitation, use of probiotics, milk opening time, interval between two stools and gestational age were the main factors affecting the occurrence of FI in preterm newborns, yielding importance scores of 0.632, 0.407, 0.313, 0.313, and 0.258, respectively. A history of resuscitation, first milk opening time ≥ 24 h and interval between stools ≥ 3 days were risk factors for FI, while the use of probiotics and gestational age ≥ 34 weeks were protective factors against FI in preterm newborns.</p><p><strong>Conclusions: </strong>In practice, we should improve perinatal care and obstetrics with the aim of reducing the occurrence of hypoxia and preterm delivery. When feeding, early milk opening, the use of probiotics, the stimulation of defecation and other measures should be implemented with the aim of reducing the occurrence of FI.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"342"},"PeriodicalIF":3.3,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572196/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667368","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":"Evaluating the usability of Iran's national comprehensive health information system: a think-aloud study to uncover usability problems in the recording of childcare data.","authors":"Razieh Farrahi, Ehsan Nabovati, Reyhane Bigham, Fateme Rangraz Jeddi","doi":"10.1186/s12911-024-02746-2","DOIUrl":"10.1186/s12911-024-02746-2","url":null,"abstract":"<p><strong>Introduction: </strong>Health information systems play a crucial role in the delivery of efficient and effective healthcare. Poor usability is one of the reasons for their lack of acceptance and low usage by users. The aim of this study was to identify the usability problems of a national comprehensive health information system using the concurrent think-aloud method in the recording of childcare data.</p><p><strong>Methods: </strong>A descriptive cross-sectional study was conducted in the health centers of Kashan University of Medical Sciences, Iran, in 2020. Ten healthcare providers as system's users were purposively selected to evaluate the system. To identify problems, a concurrent think-aloud evaluation was conducted. Two administrators of the system designed scenarios for ten childcare data recording tasks. By analysing the recorded files, usability problems were identified. The severity of the problems was then determined with the help of the users and problems were assigned to usability attributes based on their impact on the user.</p><p><strong>Results: </strong>A total of 68 unique problems were identified in the system, of which 47.1% were rated as catastrophic problems. The participants assigned 47 problems (69%) to the user satisfaction attribute and 45 problems (66%) to the efficiency attribute; they also did not assign any problems to the effectiveness attribute.</p><p><strong>Conclusion: </strong>The problems identified in the national comprehensive health information system using the think-aloud method were rated as major and catastrophic, which indicates poor usability of this system. Therefore, resolving the system problems will help increase user satisfaction and system efficiency, allowing more time to be spent on patient care and parent's education as well as improving overall quality of care.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"341"},"PeriodicalIF":3.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643435","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":"Measuring healthcare professionals' perceptions of their ability to adopt shared decision making: Translation and psychometric evaluation of the Danish version of the IcanSDM questionnaire.","authors":"Jeanette Finderup, Hilary L Bekker, Nadia Thielke Albèr, Susanne Boel, Louise Engelbrecht Buur, Helle Sørensen von Essen, Anne Wilhøft Kristensen, Kristian Damgaard Lyng, Tina Wang Vedelø, Gitte Susanne Rasmussen, Pernille Christiansen Skovlund, Stine Rauff Søndergaard, Anik Giguère","doi":"10.1186/s12911-024-02747-1","DOIUrl":"10.1186/s12911-024-02747-1","url":null,"abstract":"<p><strong>Background: </strong>Shared  decision making in healthcare is a fundamental right for patients. Healthcare professionals' perception of their own abilities to enable shared decision making is crucial for implementing shared decision making within service. IcanSDM (I can shared decision making) is a brief measure to investigate healthcare professionals' perception of shared decision making approaches to their practices. It was developed in Canada with French and English versions, and recently translated into German. This study aims to adapt the IcanSDM measure for Danish-speaking healthcare professionals, and evaluate its psychometric properties.</p><p><strong>Methods: </strong>Cultural adaptation and translation based on Beaton et al.'s approach was applied. A forward translation by ten people and a backward translation by two people were performed. To assess comprehensibility, cognitive interviews were conducted with 24 healthcare professionals. Eighty healthcare professionals who were trained in shared decision making for either one hour (n = 65) or one day (n = 15) participated in the psychometric evaluation. The evaluation concerned acceptance, item characteristics, skewness, item difficulties, corrected item-total correlations, inter-item correlations, factorial structure, internal consistency, and responsiveness.</p><p><strong>Results: </strong>The forward and backward translation revealed few discrepancies, and participants understood the items well. The psychometric evaluation showed a high completion rate and acceptable item difficulties and discrimination values. Both the factor analysis and the internal consistency showed a 2-factor structure: 1) healthcare professionals' capacity to implement shared decision making; and 2) healthcare professionals' capacity to practise shared decision making. The IcanSDM_Danish obtained a Cronbach's alpha coefficient of 0.74. The evaluation of responsiveness showed improvement, but was not statistically significant.</p><p><strong>Conclusion: </strong>The IcanSDM_Danish has good cross-cultural validity and internal consistency, and a 2-factor structure. The IcanSDM_Danish is capable of providing reliable and valid measurement when evaluating constructed knowledge about shared decision making, and may be able to support the implementation of shared decision making training and evaluation of its impact.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"340"},"PeriodicalIF":3.3,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643438","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 qualitative study to inform the development of a decision support tool for the diagnosis of pulmonary tuberculosis in Tigray, Ethiopia.","authors":"Gebremedhin Berhe Gebregergs, Gebretsadik Berhe, Kibrom Gebreslasie Gebrehiwot, Afework Mulugeta","doi":"10.1186/s12911-024-02765-z","DOIUrl":"10.1186/s12911-024-02765-z","url":null,"abstract":"<p><strong>Background: </strong>Tuberculosis (TB) is Ethiopia's leading infectious killer disease. The war in the Tigray region of Ethiopia has resulted in the disruption of TB care services. Prediction models are recommended to aid the diagnosis of TB in resource-limited settings. However, the development of such decision-support tools without the participation of end users may not be successful. To inform the tool development, we described barriers to diagnosing TB and identified applicable and desirable parameters for the proposed tool.</p><p><strong>Methods: </strong>We conducted a qualitative study between February and June 2023 in two cities in Tigray, Northern Ethiopia. We conducted 12 in-depth interviews and four focus group discussions with healthcare workers (HCWs). Interviews were translated, coded, and analyzed to identify predefined and emergent themes during the thematic analysis.</p><p><strong>Results: </strong>Healthcare workers used symptoms, risk factors, signs, and investigations to diagnose TB. However, failure to ask about antibiotic use, the absence and non-affordability of investigations, and patient load were barriers affecting the diagnosis of TB. Most of the classic TB symptoms and their duration were sorted as very important, simple, reliable, generalizable, and desirable indices. In addition, a trial of antibiotics, being chronically sick-looking, having HIV, having a contact history with a TB patient, and an erythrocyte sedimentation rate fulfilled the above criteria.</p><p><strong>Conclusions: </strong>In the TB diagnostic process, HCWs account for a variety of data, but they prefer the classic symptoms of TB to heighten their clinical suspicion. Antibiotic trials and some risk factors were also considered reasonable. However, when HCWs have a heavy workload and a shortage of investigations, they experience a suboptimal TB diagnostic process. Hence, appropriate context consideration and care providers' preferences for parameters will inform tool development.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"24 1","pages":"338"},"PeriodicalIF":3.3,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142615267","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}