International Journal of Medical Informatics最新文献

{"title":"Comparison of machine learning and logistic regression models for predicting emergence delirium in elderly patients: A prospective study","authors":"Yufan Lu ,&nbsp;Ying Li ,&nbsp;Shengqiang Chi ,&nbsp;Yan Feng ,&nbsp;Gaowei Li ,&nbsp;Xuezheng Lin ,&nbsp;Jie Jin ,&nbsp;Ying Wang","doi":"10.1016/j.ijmedinf.2025.105888","DOIUrl":"10.1016/j.ijmedinf.2025.105888","url":null,"abstract":"<div><h3>Objective</h3><div>To compare the performance of machine learning and logistic regression algorithms in predicting emergence delirium (ED) in elderly patients.</div></div><div><h3>Methods</h3><div>A prospective study was carried out in a Chinese teaching tertiary hospital and collected the details of 1045 patients who underwent noncardiac surgery with general anesthesia. Characteristic variables related to ED were selected by least absolute shrinkage and selection operator (LASSO). Finally, seven machine learning models (gradient boosting machine, extreme gradient boosting, light gradient boosting machine, support vector machine, decision tree, neural network, and random forest) and logistic regression were used in the training set, and the predictive performance of the models was validated in the test set.</div></div><div><h3>Results</h3><div>ED was identified in 316 (30.2%) patients. The logistic regression model performed better than the machine learning models (area under the curve [AUC] of 0.790, 95% confidence interval [CI] 0.736–0.843). Besides, the calibration curve indicated good consistency between predicted and actual ED probabilities, and decision curve analysis demonstrated that the logistic regression model could bring clinical benefits.</div></div><div><h3>Conclusion</h3><div>The optimal application of logistic regression can provide rapid and efficient risk prediction of ED for medical workers so that reasonable prevention and treatment measures can be taken.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105888"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using large language models as decision support tools in emergency ophthalmology","authors":"Ante Kreso ,&nbsp;Zvonimir Boban ,&nbsp;Sime Kabic ,&nbsp;Filip Rada ,&nbsp;Darko Batistic ,&nbsp;Ivana Barun ,&nbsp;Ljubo Znaor ,&nbsp;Marko Kumric ,&nbsp;Josko Bozic ,&nbsp;Josip Vrdoljak","doi":"10.1016/j.ijmedinf.2025.105886","DOIUrl":"10.1016/j.ijmedinf.2025.105886","url":null,"abstract":"<div><h3>Background</h3><div>Large language models (LLMs) have shown promise in various medical applications, but their potential as decision support tools in emergency ophthalmology remains unevaluated using real-world cases.</div></div><div><h3>Objectives</h3><div>We assessed the performance of state-of-the-art LLMs (GPT-4, GPT-4o, and Llama-3-70b) as decision support tools in emergency ophthalmology compared to human experts.</div></div><div><h3>Methods</h3><div>In this prospective comparative study, LLM-generated diagnoses and treatment plans were evaluated against those determined by certified ophthalmologists using 73 anonymized emergency cases from the University Hospital of Split. Two independent expert ophthalmologists graded both LLM and human-generated reports using a 4-point Likert scale.</div></div><div><h3>Results</h3><div>Human experts achieved a mean score of 3.72 (SD = 0.50), while GPT-4 scored 3.52 (SD = 0.64) and Llama-3-70b scored 3.48 (SD = 0.48). GPT-4o had lower performance with 3.20 (SD = 0.81). Significant differences were found between human and LLM reports (P &lt; 0.001), specifically between human scores and GPT-4o. GPT-4 and Llama-3-70b showed performance comparable to ophthalmologists, with no statistically significant differences.</div></div><div><h3>Conclusion</h3><div>Large language models demonstrated accuracy as decision support tools in emergency ophthalmology, with performance comparable to human experts, suggesting potential for integration into clinical practice.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105886"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records","authors":"Wanlin Jin ,&nbsp;Lulu Xu ,&nbsp;Chun Yue ,&nbsp;Li Hu ,&nbsp;Yuzhou Wang ,&nbsp;Yaqian Fu ,&nbsp;Yuanwei Guo ,&nbsp;Fan Bai ,&nbsp;Yanyi Yang ,&nbsp;Xianmei Zhao ,&nbsp;Yingquan Luo ,&nbsp;Xiyu Wu ,&nbsp;Zhifeng Sheng","doi":"10.1016/j.ijmedinf.2025.105889","DOIUrl":"10.1016/j.ijmedinf.2025.105889","url":null,"abstract":"<div><h3>Background</h3><div>Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and healthcare costs. Approximately 90% of hip fractures in the elderly are associated with osteoporosis, making it particularly important to screen the population for hip osteoporosis and intervene early. Dual-energy X-ray absorptiometry (DXA) has limited accessibility, so predictive models for hip osteoporosis that do not use bone mineral density (BMD) data are essential. We aimed to develop and validate prediction models for female hip osteoporosis using electronic health records without BMD data.</div></div><div><h3>Methods</h3><div>This retrospective study used anonymized medical electronic records, from September 2013 to November 2023, from the Health Management Center of the Second Xiangya Hospital. A total of 8039 women were included in the derivation dataset. The set was then randomized into a 75% training dataset and a 25% testing dataset. Four algorithms for feature selection were used to identify predictors of osteoporosis. The identified predictors were then used to train and optimize eight machine learning models. The models were tuned using 5-fold cross-validation to assess model performance in the testing dataset and the independent validation dataset from the National Health and Nutrition Examination Surveys (NHANES). The SHapley Additive explanation (SHAP) method was used to rank feature importance and explain the final model.</div></div><div><h3>Results</h3><div>A combination of the Boruta, LASSO, varSelRF, and RFE methods identified systolic blood pressure, red blood cell count, glycohemoglobin, alanine aminotransferase, aspartate aminotransferase, uric acid, age, and body mass index as the most important predictors of osteoporosis in women. The XGBoost model outperformed the other models, with an Area Under the Curve (AUC) of 0.805 (95%CI: 0.779–0.831), and a moderate sensitivity of 0.706. The externally validated XGBoost model had an AUC of 0.811 (95% CI: 0.793–0.828), with a moderate sensitivity of 0.775.</div></div><div><h3>Conclusions</h3><div>The XGBoost model demonstrates high identification performance even without questionnaire data, out-performing both the traditional the logistic regression model and the OSTA model. It can be integrated into routine clinical workflows to identify females at high risk for osteoporosis.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105889"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a nomogram to predict the risk of in-hospital MACE for emergence NSTE-ACS: A retrospective multicenter study based on the Chinese population","authors":"Qianhui Zhou ,&nbsp;Rui He ,&nbsp;Hong Li ,&nbsp;Manping Gu","doi":"10.1016/j.ijmedinf.2025.105884","DOIUrl":"10.1016/j.ijmedinf.2025.105884","url":null,"abstract":"<div><h3>Purpose</h3><div>Our study aims to develop and validate an effective in-hospital major adverse cardiovascular events(MACE) prediction model for patients with emergency Non-ST elevation acute coronary syndrome(NSTE-ACS).</div></div><div><h3>Methods</h3><div>We retrospectively collected NSTE-ACS patients in three tertiary hospitals in Chongqing. In-hospital MACE was the predicted outcome. Patients from one hospital were divided into training set and internal validation set according to the ratio of 7:3. Besides, 662 patients from two other tertiary hospitals were for external validation. Patient information including demographics, laboratory tests results and disease course records were for comprehensive analysis. Finally, LASSO were used to identify the predictors and develop the model. This model was subsequently visualized as a nomogram, followed by both internal and external validations.The receiver operating characteristic curve, calibration curve and clinical decision curve analysis were used to assess the model’s discrimination, calibration and clinical applicability, respectively.</div></div><div><h3>Results</h3><div>A total of 3,308 patients were included, 375 of whom developed in-hospital MACE. The LR model demonstrated that length of stay, neutrophils, myoglobin, NYHA, CCI, NT-proBNP, LVEF and respiratory failure were risk factors for in-hospital MACE in emergence NSTE-ACS patients. In the training set, the AUC was 0.860 (95%CI:0.831–0.889). In external validation,the AUC was 0.855(95%CI:0.808–0.902), and both the calibration curve and DCA in validation set also revealed stable predictive accuracy and clinical validity.Additionally,<!--> <!-->it is available to calculate the MACE risk online via the web page (<span><span>https://cocozhou99.shinyapps.io/DynNomapp/</span><svg><path></path></svg></span>).</div></div><div><h3>Conclusion</h3><div>The prediction model we constructed has good predictive performance and can help healthcare professionals accurately assess the risk of in-hospital MACE in emergence NSTE-ACS patients.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105884"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma","authors":"Gian Maria Zaccaria,&nbsp;Nicola Altini,&nbsp;Valentina Mongelli,&nbsp;Francescomaria Marino,&nbsp;Vitoantonio Bevilacqua","doi":"10.1016/j.ijmedinf.2025.105883","DOIUrl":"10.1016/j.ijmedinf.2025.105883","url":null,"abstract":"<div><h3>Background</h3><div>In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited.</div></div><div><h3>Materials and Methods</h3><div>We analyzed CpG sites from two publicly available datasets, the TCGA-PAAD used as discovery set and the CPTAC-PDA as external test set. Single mutations and co-mutation of <em>KRAS</em> and <em>TP53</em> genes were identified as targets, and differentially methylated CpG sites (DMC) were detected accordingly. We trained and validated Random Forest (RF) models to predict each target. Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision-Recall curve (AUPRC) were used as performance metrics. Then, we performed consensus clustering from the DMCs to identify novel patients’ profiles. Finally, we trained and validated a combination of eXtreme Gradient Boosting (XGB) and tree models to select an epigenomic prognostic determinant.</div></div><div><h3>Results</h3><div>From 598 DMCs extracted, an RF model predicted <em>KRAS</em> and <em>TP53</em> co-mutation on the external test set with AUROC of 0.77 and AUPRC of 0.87. The consensus clustering allowed us to identify 4 clusters (C1, C2, C3, and C4) of patients. The C4 cluster captured a subgroup of patients with favorable Overall Survival (OS) with respect to others. The XGB model perfectly predicted C4 vs other clusters on the discovery set. In both cohorts, patients were stratified into two risk groups according to methylation levels of cg16854533, individuated as the most important CpG site.</div></div><div><h3>Conclusion</h3><div>We analyzed methylation data to develop a classifier for the <em>TP53</em> and <em>KRAS</em> mutational status. Four prognostic clusters were pointed out and a prognostic model using a CpG site was validated in an independent cohort. Our results evidence that the proposed use of methylation data facilitates risk stratification for PDAC.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105883"},"PeriodicalIF":3.7,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unified modeling language for patient-centered telerehabilitation: A comprehensive framework integrating medical and biopsychosocial pathways","authors":"Thomas Roy ,&nbsp;Aurélie Bertaux ,&nbsp;Ouassila Labbani Narsis ,&nbsp;Jean-Pierre Didier ,&nbsp;Davy Laroche","doi":"10.1016/j.ijmedinf.2025.105882","DOIUrl":"10.1016/j.ijmedinf.2025.105882","url":null,"abstract":"<div><div><strong>Background:</strong> Effective telerehabilitation requires robust, standardized models to ensure comprehensive and continuous patient monitoring. However, existing rehabilitation models often lack integration, failing to cover the entire care continuum and its interdisciplinary aspects. This gap limits their applicability in real-world settings.</div><div><strong>Objective:</strong> This study introduces a semi-formal, Unified Modeling Language (UML)-based framework that provides a holistic, patient-centered representation of the rehabilitation pathway. The model is designed to bridge gaps in care coordination, aligning with recent scientific advances and healthcare policies emphasizing patient empowerment and interdisciplinary collaboration.</div><div><strong>Methods:</strong> Using a professional didactics approach, we conducted a literature review, field observations, and expert consultations (questionnaires, interviews) to map rehabilitation pathways across diverse conditions and settings. The model was iteratively refined based on expert feedback to ensure its accuracy and usability.</div><div><strong>Results:</strong> Our findings reveal significant fragmentation in rehabilitation pathways, driven by diverse clinical practices and discontinuities in care. To address this, the proposed UML-based model integrates medical, functional, psychosocial, and organizational data, ensuring a cohesive, capability-driven approach. The structured design enhances communication between stakeholders and improves interoperability across healthcare systems.</div><div><strong>Conclusion:</strong> The proposed model provides a scalable foundation for digital telerehabilitation solutions, adaptable to various healthcare environments. By facilitating data integration and standardization, it supports better patient monitoring, decision-making, and personalized rehabilitation strategies. Future research will focus on refining the model to incorporate specialized rehabilitation fields and enhance interoperability with existing medical information systems.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105882"},"PeriodicalIF":3.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143695998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing logistic regression and machine learning for obesity risk prediction: A systematic review and meta-analysis","authors":"Nancy Fosua Boakye ,&nbsp;Ciarán Courtney O'Toole ,&nbsp;Amirhossein Jalali ,&nbsp;Ailish Hannigan","doi":"10.1016/j.ijmedinf.2025.105887","DOIUrl":"10.1016/j.ijmedinf.2025.105887","url":null,"abstract":"<div><h3>Background</h3><div>Logistic regression (LR) has traditionally been the standard method used for predicting binary health outcomes; however, machine learning (ML) methods are increasingly popular.</div></div><div><h3>Objective</h3><div>This study aimed to compare the performance of ML and LR for obesity risk prediction, identify how LR and ML were being compared, and identify the commonly used ML methods.</div></div><div><h3>Methods</h3><div>We conducted comprehensive searches in PubMed, Scopus, Embase, IEEE Xplore, and Web of Science databases on 24th November 2023, with no restrictions on publication dates. Meta-analyses were performed to quantify the overall predictive performance of the methods using the area under the curve (AUC) for LR, AUC for the best performing ML, as well as the difference in the AUC between the two approaches as the effect measures.</div></div><div><h3>Results</h3><div>We included 28 studies out of 913 abstracts screened. Accuracy and sensitivity were the most commonly used performance measures. More than half of the studies used AUC, with no calibration assessment conducted in any of the studies. Decision trees followed by boosting algorithms were the most commonly used ML methods. Seventy-five percent of the studies were at high risk of bias. There were 14 included studies in the meta-analysis. The pooled AUC for LR was 0.75 (95% CI 0.70 to 0.80) and the pooled AUC for ML was 0.76 (95% CI 0.70 to 0.82). The pooled difference in logit(AUC) between ML and LR was 0.13 (95% CI -0.11 to 0.37).</div></div><div><h3>Conclusion</h3><div>We conclude that there is no significant difference in the performance of ML and LR for obesity risk prediction. However, there is a need for improved quality of reporting of studies, the use of more performance measures particularly calibration, and to validate models in different populations.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105887"},"PeriodicalIF":3.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical decision support systems in mental health: A scoping review of health professionals’ experiences","authors":"Fangziyun Tong,&nbsp;Reeva Lederman,&nbsp;Simon D’Alfonso","doi":"10.1016/j.ijmedinf.2025.105881","DOIUrl":"10.1016/j.ijmedinf.2025.105881","url":null,"abstract":"<div><h3>Background</h3><div>Clinical decision support systems (CDSSs) have the potential to assist health professionals in making informed and cost-effective clinical decisions while reducing medical errors. However, compared to physical health, CDSSs have been less investigated within the mental health context. In particular, despite mental health professionals being the primary users of mental health CDSSs, few studies have explored their experiences and/or views on these systems. Furthermore, we are not aware of any reviews specifically focusing on this topic. To address this gap, we conducted a scoping review to map the state of the art in studies examining CDSSs from the perspectives of mental health professionals.</div></div><div><h3>Method</h3><div>In this review, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, we systematically searched the relevant literature in two databases, PubMed and PsycINFO.</div></div><div><h3>Findings</h3><div>We identified 23 articles describing 20 CDSSs Through the synthesis of qualitative findings, four key barriers and three facilitators to the adoption of CDSSs were identified. Although we did not synthesize quantitative findings due to the heterogeneity of the results and methodologies, we emphasize the issue of a lack of valid quantitative methods for evaluating CDSSs from the perspectives of mental health professionals.</div></div><div><h3>Significance</h3><div>To the best of our knowledge, this is the first review examining mental health professionals’ experiences and views on CDSSs. We identified facilitators and barriers to adopting CDSSs and highlighted the need for standardizing research methods to evaluate CDSSs in the mental health space.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105881"},"PeriodicalIF":3.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying episodes of care in hospital admissions data for measures of disease burden: A tutorial and protocol for individual-level data analysis","authors":"Jedidiah I. Morton ,&nbsp;Adam Livori ,&nbsp;Lee Nedkoff ,&nbsp;Dianna J Magliano ,&nbsp;Derrick Lopez ,&nbsp;Ingrid Stacey ,&nbsp;Zanfina Ademi","doi":"10.1016/j.ijmedinf.2025.105847","DOIUrl":"10.1016/j.ijmedinf.2025.105847","url":null,"abstract":"<div><h3>Background</h3><div>We are not aware of any comprehensive, publicly available, standardised protocol or syntax for the processing of hospital admissions data for individual-level analysis. Failure to appropriately process and analyse data in a standardised manner could lead to misestimation of event rates, inconsistency between studies, and incorrect findings informing clinical practice and health policy.</div></div><div><h3>Aim</h3><div>To develop an open source, standardised protocol for processing of admitted episodes data that can be regularly updated.</div></div><div><h3>Methods</h3><div>We identified common data structures that require processing to define single episodes of care (i.e., events) and developed Stata code to address these. We then presented a full worked example using UK admission data. The code is stored on a public online platform that allows living updates. Results: Common data structures requiring processing include duplicated records, shorter records within a longer period of care, and mis-coded transfers. Using the UK admission data sample, data processing resulted in 33,170 records with myocardial infarction as the primary diagnosis being refined to 18,289 episodes of care (i.e., events). The ratio of records to episodes of care varied for different primary diagnoses: for example, for lung cancer, there were 29,274 records and 26,389 events; for pneumonia, 21,029 records and 12,334 events; and for head injury, 21,957 records and 17,736 events.</div></div><div><h3>Conclusion</h3><div>Appropriate data processing is vital to derive accurate results from hospital admissions data. We have presented open source, live syntax for this purpose.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105847"},"PeriodicalIF":3.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adopting electronic prescribing by pharmacists","authors":"Mohammadhiwa Abdekhoda ,&nbsp;Pegah Morovati","doi":"10.1016/j.ijmedinf.2025.105877","DOIUrl":"10.1016/j.ijmedinf.2025.105877","url":null,"abstract":"<div><h3>Introduction</h3><div>In the era of electronic health records, incorporating electronic prescribing is imperative to improve healthcare quality and safeguard patient well-being. Therefore, it becomes essential to recognize the influential elements that drive the adoption of e-prescribing. Despite the numerous advantages of electronic prescribing, there is a lack of acceptance and resistance from health care providers and users, creating barriers to its implementation. This research aimed to identify the key factors that influence pharmacists in adopting electronic prescribing (e-prescribing).</div></div><div><h3>Method</h3><div>In this descriptive-analytical study, a total of 196 pharmacists were chosen through random selection. The study evaluated the pharmacists’ inclinations towards embracing e-prescribing in health services by employing the conceptual path model that combines the Unified Theory of Acceptance and Use of Technology (UTAUT) with The Task-Technology Fit (TTF).</div></div><div><h3>Results</h3><div>The findings demonstrated that the integration of UTAUT and TTF effectively explained pharmacists’ intention to adopt e-prescribing. Specifically, task characteristics, performance expectancy, and facilitating conditions exhibited a direct and substantial impact on the adoption of e-prescribing. However, the influence of task-technology fit, social influences, and effort expectancy on prescribing adoption was not reported.</div></div><div><h3>Conclusion</h3><div>In this research, a conceptual path model was introduced to comprehend the factors that influence pharmacists’ acceptance of e-prescribing. By conducting this study, essential elements that determine the adoption of e-prescribing in health services were identified. The study’s results provide valuable knowledge for practitioners, policymakers, and the ministry of health, assisting them in effectively designing and implementing e-prescribing initiatives.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"199 ","pages":"Article 105877"},"PeriodicalIF":3.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}