International Journal of Medical Informatics最新文献

{"title":"A topic modeling analysis of stigma dimensions, social, and related behavioral circumstances in clinical notes among patients with HIV","authors":"Ziyi Chen ,&nbsp;Yiyang Liu ,&nbsp;Mattia Prosperi ,&nbsp;Krishna Vaddiparti ,&nbsp;Robert L. Cook ,&nbsp;Jiang Bian ,&nbsp;Yi Guo ,&nbsp;Yonghui Wu","doi":"10.1016/j.ijmedinf.2026.106269","DOIUrl":"10.1016/j.ijmedinf.2026.106269","url":null,"abstract":"<div><h3>Objective</h3><div>To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States.</div></div><div><h3>Methods</h3><div>We identified a cohort of PLWHs from the University of Florida (UF) Health Integrated Data Repository and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma-related dimensions and related social and behavioral contexts. Domain experts created a seed list of HIV-related stigma keywords, then applied a snowball strategy to iteratively review notes for additional terms until saturation was reached. To identify more target topics, we tested three keyword-based filtering strategies. The detected topics were evaluated using three widely used metrics and manually reviewed by specialists. Word frequency analysis was used to highlight the prevalent terms associated with each topic. In addition, we conducted topic variation analysis among subgroups to examine differences across age- and sex-specific demographics.</div></div><div><h3>Results</h3><div>We identified 9,140 PLWHs at UF Health and collected 2.9 million clinical notes. Through the iterative keyword approach, we generated a list of 91 keywords associated with HIV-related stigma. Topic modeling on sentences containing at least one keyword uncovered a wide range of topic themes associated with HIV-related stigma, social, and related behaviors circumstances, including “Mental Health Concern and Stigma”, “Social Support and Engagement”, “Limited Healthcare Access and Severe Illness”, “Missed Appointments and HIV Care Monitoring”, “Treatment Refusal and Isolation”, “Intimate Partner Violence and Relationship Concerns”, “Fear of Falling and Physical Health Concerns”, “Substance Abuse”, and “Food Insecurity and Resource Scarcity”. Topic variation analysis across sex and age subgroups revealed no substantial difference between males and females; however, there were differences were observed among different ages. For example, “Fear of Falling and Physical Health Concerns” was notably more prevalent among older adults.</div></div><div><h3>Conclusion</h3><div>Extracting and understanding the HIV-related stigma and associated social and behavioral circumstances from EHR clinical notes enables scalable, time-efficient assessment and overcoming the limitations of traditional questionnaires. Findings from this research provide actionable insights to inform patient care and interventions to improve HIV-care outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106269"},"PeriodicalIF":4.1,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940467","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":"Predicting emergency mortality risk in traumatic brain injury: comparative analysis of machine learning and large language model GPT-5","authors":"Kuan-Chi Tu ,&nbsp;Yung-De Kuo ,&nbsp;Tee-Tau Eric Nyam ,&nbsp;Yu-Shan Ma ,&nbsp;Mei-I Sung ,&nbsp;Chung-Feng Liu ,&nbsp;Ching-Lung Kuo","doi":"10.1016/j.ijmedinf.2026.106268","DOIUrl":"10.1016/j.ijmedinf.2026.106268","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI) has become increasingly important in predicting outcomes of traumatic brain injury (TBI). Traditional machine learning (ML) models, such as support vector machines (SVMs), have shown high accuracy, whereas the potential of large language models (LLMs) in structured clinical prediction remains underexplored.</div></div><div><h3>Purpose</h3><div>This study compared the predictive performance of ML and LLM approaches (ChatGPT-5 with Thinking response mode) using the same TBI dataset and evaluated the impact of prompting strategies and threshold calibration on model reliability and clinical applicability.</div></div><div><h3>Methods</h3><div>A dataset of 5,475 TBI cases with 12 clinical features was used to build an SVM model and four LLM strategies: zero-shot GPT, few-shot GPT, few-shot + chain-of-thought (CoT) GPT, and CoT-only GPT. Performance was evaluated by accuracy, sensitivity, specificity, and ROC-AUC under fixed and balanced thresholds.</div></div><div><h3>Results</h3><div>The SVM model achieved the best overall performance (AUC = 0.920). After threshold adjustment, all LLMs reached comparable AUCs (0.902–0.919). Few-shot GPT most closely matched SVM, CoT + few-shot achieved highest sensitivity, and CoT-only favored specificity.</div></div><div><h3>Conclusion</h3><div>Proper threshold calibration enables LLMs to approximate ML accuracy while offering rapid deployment and interpretability. Prompt engineering combined with adaptive cut-off tuning may enhance clinical usability of LLM-based prediction systems.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106268"},"PeriodicalIF":4.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145918986","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 data-driven, decision tree–based algorithms for identifying Behçet’s disease in claims data","authors":"Ken-ei Sada ,&nbsp;Yoshia Miyawaki ,&nbsp;Ryo Yanai ,&nbsp;Takashi Kida ,&nbsp;Akira Onishi ,&nbsp;Ryusuke Yoshimi ,&nbsp;Kunihiro Ichinose ,&nbsp;Yasuhiro Shimojima","doi":"10.1016/j.ijmedinf.2026.106266","DOIUrl":"10.1016/j.ijmedinf.2026.106266","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and externally validate novel, data-driven algorithms that are based on appropriate variable selection methods for identifying patients with Behçet’s disease in Japan.</div></div><div><h3>Methods</h3><div>This retrospective cross-sectional study included 13,538 patients from six tertiary hospitals (November–December 2023). One year of claims data was linked to chart-confirmed Behçet’s disease diagnoses. Patients were randomly divided into training (n = 8,811) and test (n = 3,775) sets, with external validation (n = 952) from another hospital. Feature selection among Behçet’s disease-coded patients used the Least Absolute Shrinkage and Selection Operator, Boruta, and Recursive Feature Elimination. The diagnostic performance of the rule-based algorithms, which were derived from the decision tree models, was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value, and F1 score.</div></div><div><h3>Results</h3><div>Diagnosis codes alone achieved high sensitivity (1.000) and specificity (0.992) but modest PPV (0.767, test set; 0.850, external validation). Incorporating sulphamethoxazole–trimethoprim and colchicine prescriptions improved the positive predictive value, which was 0.793 in the test set and 0.865 in external validation.</div></div><div><h3>Conclusion</h3><div>Incorporating prescriptions alongside diagnosis codes improved PPV while maintaining high sensitivity and specificity. Building upon a data-driven framework that integrates variable selection methods and decision tree analysis, this study provides a validated and scalable approach for reliable claims-based research on Behçet’s disease.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106266"},"PeriodicalIF":4.1,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936509","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":"Evaluation of electronic health record to HL7® FHIR® mappings in pediatric research studies","authors":"Maryam Y. Garza ,&nbsp;Zhan Wang ,&nbsp;Bhargav Adagarla ,&nbsp;Michael W. Rutherford ,&nbsp;Umit Topaloglu ,&nbsp;Daniel K. Benjamin ,&nbsp;Kanecia O. Zimmerman ,&nbsp;Eric L. Eisenstein ,&nbsp;Karan R. Kumar ,&nbsp;on behalf of the Best Pharmaceuticals for Children Act – Pediatric Trials Network Steering Committee","doi":"10.1016/j.ijmedinf.2026.106265","DOIUrl":"10.1016/j.ijmedinf.2026.106265","url":null,"abstract":"<div><h3>Background</h3><div>eSource technologies that exchange patient data from electronic health records (EHR) to clinical study electronic data capture (EDC) systems can reduce data quality errors and decrease data collection time. However, the availability of site-specific EHR data to support pediatric studies has not been evaluated.</div></div><div><h3>Methods</h3><div>We used a previously developed data element mapping procedure to evaluate the HL7® FHIR® standard’s coverage in multi-center pediatric clinical studies. Four study sites independently mapped three pediatric studies’ case report forms (CRFs) to their site’s EHR and FHIR® server data elements.</div></div><div><h3>Results</h3><div>Site investigators mapped 4152 total and 2070 distinct data elements. Only 33.8 % of total CRF data elements (n = 1402) and 27.4 % of distinct data elements (n = 568) were able to be mapped in FHIR® at the four sites. However, the percent of total data elements mapped varied by pediatric study (55.3 %, 30.8 %, and 26.2 %) and study site (46.4 %, 32.3 %, 27.8 %, and 26.6 %). The percent of total CRF data elements mapped was higher in domains containing standard of care data (e.g., Concomitant Medications, Demographics, Diagnosis/Procedures, Medical History, and Vital Signs) and lower in domains containing protocol-specific data (e.g., Adverse Events, Concomitant Medications, Enrollment/Eligibility/Consent, and study treatment-related Labs, and Vital Signs).</div></div><div><h3>Conclusions</h3><div>There is substantial between-study and between-site variability in the percentage of pediatric study data elements available in FHIR® at study sites. These results suggest that mapping solutions for pediatric studies utilizing eSource technologies will need to be site-specific.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106265"},"PeriodicalIF":4.1,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967966","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":"A machine learning-driven app for predicting the need for post-operative respiratory support in liver transplant recipients","authors":"Ying Wang ,&nbsp;Yanan Zhou ,&nbsp;Yu Gong ,&nbsp;Zhenbin Ding ,&nbsp;Liuxiao Yang ,&nbsp;Ting Wang","doi":"10.1016/j.ijmedinf.2026.106263","DOIUrl":"10.1016/j.ijmedinf.2026.106263","url":null,"abstract":"<div><h3>Backgrounds</h3><div>Liver transplantation (LT) is a life-saving procedure for patients with end-stage liver disease, yet post-operative complications, particularly the need for respiratory support, remain a significant challenge. We aimed to develop and validate a machine learning (ML)-based predictive tool for postoperative respiratory support requirement in liver transplant recipients.</div></div><div><h3>Methods</h3><div>This single-center retrospective study was conducted at Zhongshan Hospital, Fudan University (Shanghai, China) from January 2018 to October 2023. Following data preprocessing, key variables were selected through univariate analysis, recursive feature elimination (RFE), Chi-square test, and correlation analysis. Nine ML models were initially constructed and optimized via grid search with 5-fold cross-validation. The final model was selected based on area under the curve (AUC), accuracy, sensitivity, specificity, and F1-score, followed by comparative analysis with conventional scoring systems. Model interpretability was achieved using shapley additive explanations (SHAP) analysis, providing both global and local explanations. For clinical implementation, we developed an online application platform for real-time prediction.</div></div><div><h3>Results</h3><div>The study included 1121 liver transplant recipients, divided into a discovery cohort (n = 749) and validation cohort (n = 372). Significant differences (P &lt; 0.05) were observed between patients requiring versus not requiring respiratory support across multiple preoperative, intraoperative, and postoperative parameters. After hyperparameter optimization, the random forest (RF), stochastic gradient boosting (SGB), and logistic regression (LR) models were applied to the validation cohort, with RF ultimately being selected as the final predictive tool, achieving an AUC of 0.790 (95 % CI: 0.723–0.857) in the test set and 0.713 (95 % CI: 0.658–0.767) in the validation cohort, significantly outperforming both model for end-stage liver disease (MELD) and acute physiology and chronic health evaluation II (APACHE II) scores. SHAP analysis revealed complex bidirectional relationships between predictors and outcomes, with certain variables showing both protective and risk-enhancing effects depending on clinical context.</div></div><div><h3>Conclusions</h3><div>Based on large-scale clinical data, we developed a robust predictive model that can effectively assess the need for postoperative respiratory support in liver transplant recipients, thereby facilitating clinical decision-making and potentially improving patient outcomes. However, future multi-center validation was warranted to confirm generalizability.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106263"},"PeriodicalIF":4.1,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940479","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":"Advantages and challenges of tracking st-segment elevation myocardial infarction patients with a real-time dashboard: A single-centre experience","authors":"Egidio de Mattia ,&nbsp;Filippo Paoletti ,&nbsp;Daniela Pedicino ,&nbsp;Giovanna Liuzzo ,&nbsp;Carmen Angioletti ,&nbsp;Alessia d’Aiello ,&nbsp;Alessio Perilli ,&nbsp;Andrea Adduci ,&nbsp;Giovanni Arcuri ,&nbsp;Emilio Meneschincheri ,&nbsp;Barbara Ruffo ,&nbsp;Melissa D’Agostino ,&nbsp;Rita De Donno ,&nbsp;Antonio Giulio de Belvis","doi":"10.1016/j.ijmedinf.2026.106261","DOIUrl":"10.1016/j.ijmedinf.2026.106261","url":null,"abstract":"<div><h3>Background</h3><div>Timely primary percutaneous coronary intervention (pPCI) is the most important treatment to improve outcomes in ST-segment elevation myocardial infarction (STEMI), with a strong relationship between treatment delays and morbidity and mortality. The present study aims to define the main steps for setting up a real-time digital monitoring dashboard to improve the clinical performance of STEMI management and to evaluate the impact of its implementation on the proportion of patients receiving primary percutaneous coronary intervention (pPCI) within 90 min.</div></div><div><h3>Methods</h3><div>The set-up of the digital monitoring system required the definition of detailed algorithms for the diagnosis, treatment, and rehab/follow-up phase. For each patient with a diagnosis of STEMI included in the clinical pathway (CP) a multidisciplinary working group identified i) rules for flagging patients alongside the CP, based on specific risk scores; ii) the critical points of the CP to be monitored, such as door-to-balloon time, intensive care unit length of stay, and total hospital length of stay. An interrupted time series analysis and multivariable logistic regression models were performed to assess for changes in the outcome (pPCI within 90 min) after the platform implementation, adjusting for temporal and individual confounders.</div></div><div><h3>Results</h3><div>After the introduction of the dashboard, the proportion of timely pPCI improved from 40 % pre-implementation to 65 % post-implementation. Adjusted models indicated a twofold increase in the odds of meeting the 90-minute benchmark (OR = 2.00; 95 % CI: 0.99–4.12).</div></div><div><h3>Conclusion</h3><div>The real-time monitoring system showed a positive impact on the timely management of STEMI, highlighting the potential for improving healthcare efficiency and patient outcomes.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106261"},"PeriodicalIF":4.1,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979483","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":"A scoping review: how evaluation methods shape our understanding of ChatGPT’s effectiveness in healthcare","authors":"Yuanyuan Liu ,&nbsp;Yu Zhang,&nbsp;Haoran Mao","doi":"10.1016/j.ijmedinf.2025.106248","DOIUrl":"10.1016/j.ijmedinf.2025.106248","url":null,"abstract":"<div><h3>Background</h3><div>The rapid growth in research on ChatGPT’s healthcare applications has led to diverse evaluation methods and substantially heterogeneous findings, undermining evidence reliability and hindering clinical translation.</div></div><div><h3>Objectives</h3><div>This review aims to examine how different evaluation methods shape our understanding of ChatGPT’s effectiveness in healthcare.</div></div><div><h3>Methods</h3><div>Studies published between 2023 and 2024 that assess the use of ChatGPT in medical or healthcare-related contexts were included. Evidence was obtained from peer-reviewed literature analyzing ChatGPT’s applications across clinical, educational, and diagnostic domains. Following the PRISMA guidelines, this systematic review analyzed 131 studies published during 2023–2024 that assess the use of ChatGPT in medical contexts.</div></div><div><h3>Results</h3><div>The results indicate that predominant evaluation approaches—controlled trial studies, expert assessment studies, measurement-based evaluation studies, and prompt generation analysis studies—systematically influence conclusions about ChatGPT’s performance due to their inherent methodological characteristics, such as subjectivity, objectivity, and differences in ecological validity. Further analysis reveals that ChatGPT’s performance is highly context-dependent, shaped by specific application scenarios, model versions, and prompting strategies.</div></div><div><h3>Conclusions</h3><div>To address methodological heterogeneity and the lack of standardization, this study recommends multi-method cross-validation strategies and a risk-stratified, standardized evaluation framework. These steps are essential to enhance the scientific rigor and reliability of ChatGPT’s assessment in healthcare and to provide a solid foundation for its clinical integration.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106248"},"PeriodicalIF":4.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886218","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":"Large language models versus healthcare professionals in providing medical information to patient questions: A systematic review","authors":"Maud M.G. Jacobs ,&nbsp;Jacobien H.F. Oosterhoff ,&nbsp;Rintje Agricola ,&nbsp;Walter van der Weegen","doi":"10.1016/j.ijmedinf.2025.106250","DOIUrl":"10.1016/j.ijmedinf.2025.106250","url":null,"abstract":"<div><h3>Objective</h3><div>The rapid expansion of digital healthcare has heightened the volume of patient communication, thereby increasing the workload for healthcare professionals. Large Language Models (LLMs) hold promises for offering automated responses to patient questions relayed through eHealth platforms, yet concerns persist regarding their effectiveness, accuracy, and limitations in healthcare settings. This study aims to evaluate the current evidence on the performance and perceived suitability of LLMs in healthcare, focusing on their role in supporting clinical decision-making and patient communication.</div></div><div><h3>Materials and methods</h3><div>A systematic search in PubMed and Embase up to June 11, 2025 identified 330 studies, of which 20 met the inclusion criteria for comparing the accuracy and adequacy of medical information provided by LLMs versus healthcare professionals and guidelines. The search strategy combined terms related to LLMs, healthcare professionals, and patient questions. The ROBINS-I tool assessed the risk of bias.</div></div><div><h3>Results</h3><div>A total of nineteen studies focused on medical specialties and one on the primary care setting. Twelve studies favored the responses generated by LLMs, six reported mixed results, and two favored the healthcare professionals’ response. Bias components generally scored moderate to low, indicating a low risk of bias.</div></div><div><h3>Discussion and conclusions</h3><div>The review summarizes current evidence on the accuracy and adequacy of medical information provided by LLMs in response to patient questions, compared to healthcare professionals and clinical guidelines. While LLMs show potential as supportive tools in healthcare, their integration should be approached cautiously due to inconsistent performance and possible risks. Further research is essential before widespread adoption.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106250"},"PeriodicalIF":4.1,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967906","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":"Leveraging large language models to automate the identification of healthcare access barriers for veterans","authors":"Sudarshan Srinivasan ,&nbsp;Caitlin Rizy ,&nbsp;Maria Mahbub ,&nbsp;David Bolme ,&nbsp;Alina Peluso ,&nbsp;Jodie Trafton ,&nbsp;Ioana Danciu","doi":"10.1016/j.ijmedinf.2025.106247","DOIUrl":"10.1016/j.ijmedinf.2025.106247","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency.</div></div><div><h3>Methods</h3><div>We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis.</div></div><div><h3>Results</h3><div>The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1.</div></div><div><h3>Conclusion</h3><div>Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106247"},"PeriodicalIF":4.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936217","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":"Survey on cancer patients’ attitudes towards AI and data protection: A cross-sectional study from an Italian cancer center","authors":"Martina Cavallucci ,&nbsp;Alice Andalò ,&nbsp;Valentina Danesi ,&nbsp;Nicola Gentili ,&nbsp;Ilaria Massa ,&nbsp;Emanuela Scarpi ,&nbsp;Maria Chiara Restuccia ,&nbsp;Roberto Vespignani ,&nbsp;Alice Conficconi ,&nbsp;Michela Palleschi ,&nbsp;Ugo De Giorgi ,&nbsp;Antonino Musolino ,&nbsp;Filippo Merloni","doi":"10.1016/j.ijmedinf.2025.106237","DOIUrl":"10.1016/j.ijmedinf.2025.106237","url":null,"abstract":"<div><div><strong>Background</strong></div><div>Artificial Intelligence (AI) is increasingly integrated into oncology, offering opportunities to improve diagnostics, treatment planning, and operational efficiency. However, patient perspectives on AI, especially regarding data protection and ethical implications, remain underexplored.</div><div><strong>Objective</strong></div><div>The objective of this study is to investigate cancer patients’ attitudes toward the use of Artificial Intelligence (AI) in healthcare, focusing on their awareness of data protection, perceived risks and benefits, and the conditions under which AI is considered acceptable. Additionally, the study aims to examine how demographic and educational factors influence patients’ views within the context of an Italian comprehensive cancer center.</div><div><strong>Methods</strong></div><div>A cross-sectional survey was conducted with 117 cancer patients who completed a 28-item online questionnaire. The survey evaluated levels of AI knowledge, perceptions of data privacy, concerns about AI in medical contexts, and willingness to share health data for research.</div><div><strong>Results</strong></div><div>Most participants demonstrated moderate awareness of AI (70.1%) and its medical applications (85.5%), with higher familiarity observed among younger and more educated individuals. While data protection understanding varied, 76.9% were willing to share personal health data for research aimed at improving cancer care. Concerns included reduced physician autonomy (52.1%) and diminished physician-patient interaction (63.3%). However, 82.9% of respondents found AI acceptable when clinical decisions remained under physician control. AI was most favorably viewed for administrative support and care process optimization.</div><div><strong>Conclusion</strong></div><div>Cancer patients generally view AI in healthcare positively, especially when it maintains physician oversight and safeguards data privacy. To ensure equitable and informed adoption, targeted educational initiatives and transparent communication strategies should address generational, educational, and digital literacy differences.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"209 ","pages":"Article 106237"},"PeriodicalIF":4.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940468","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}