Health Informatics Journal最新文献

{"title":"Assessing the clinical reasoning of large language models on complex rheumatology cases: A multidimensional evaluation of four artificial intelligence.","authors":"Yannick Laurent Tchenadoyo Bayala, Fulgence Kaboré, Charles Sougué, Aboubakar Ouedraogo, Yamyellé Enselme Zongo, Wendlassida Joelle Stéphanie Zabsonré/Tiendrebeogo, Dieu-Donné Ouedraogo","doi":"10.1177/14604582261448687","DOIUrl":"https://doi.org/10.1177/14604582261448687","url":null,"abstract":"<p><p>BackgroundLarge language models (LLMs) have demonstrated promising capabilities in medical diagnostic reasoning, yet their performance in specialized clinical domains such as rheumatology remains incompletely characterized. While diagnostic accuracy has been evaluated, critical dimensions including calibration, reasoning quality, and temporal stability have not been systematically assessed across contemporary models.ObjectivesThis study aimed to comprehensively evaluate and compare the diagnostic accuracy, certainty expression, reasoning quality, and hallucination rates of four state-of-the-art LLMs ChatGPT-4, Claude 3.5, DeepSeek-V3, and Gemini 1.5 Pro in complex rheumatologic case scenarios.DesignA cross-sectional, analytical, and comparative study was conducted following STARD and TRIPOD guidelines, adapted for LLM evaluation. Nine complex rheumatologic cases from published case reports were evaluated at three time points (Days 1, 5, and 10) between July 1 and September 18,2025.MethodsStandardized clinical vignettes were submitted to each LLM under controlled experimental conditions. Two blinded senior rheumatologists independently assessed diagnostic accuracy, reasoning quality across five analytical dimensions using Likert scales, and hallucination frequency. Certainty expression and temporal stability were quantified using intraclass correlation coefficients. Correlation analyses examined relationships between reasoning quality and confidence expression.ResultsAll models achieved near-perfect diagnostic accuracy, with ChatGPT, Claude and Gemini correctly identifying the primary diagnosis in 100% of cases and DeepSeek in 88.9%. However, Spearman correlation analysis revealed uniformly weak and non-significant associations between reasoning quality and expressed certainty across all models (ρ range: -0.156 to 0.215, all p>0.05), indicating fundamental miscalibration. ChatGPT demonstrated the highest reasoning score (3.89±0.23) and lowest hallucination rate (7.4%), while Gemini showed the highest hallucination frequency (18.5%). Temporal stability was excellent for ChatGPT (ICC=0.84) and good for DeepSeek (ICC=0.79).ConclusionDespite exceptional diagnostic accuracy, current LLMs exhibit critical limitations in confidence calibration and variable hallucination rates, representing significant barriers to safe clinical deployment in rheumatology.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261448687"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a population based patient cancer data warehouse from multiple electronic health record systems.","authors":"Karla Siegler, Jason Smith, Emily Walker, Jingyu Bu, Paula Robson, Lorraine Shack","doi":"10.1177/14604582261440410","DOIUrl":"https://doi.org/10.1177/14604582261440410","url":null,"abstract":"<p><p>ObjectiveCancer is a significant cause of death globally. Effective management requires use of real-world data to inform treatment and planning. This demands improvements in data management to support healthcare delivery, research, and patient outcomes. Electronic health records (EHR) offer a potential solution yet integrating and interpreting the data poses challenges. To address this we developed Data Environment for Cancer Inquiries and Decisions or DECIDe. DECIDe is a cancer data warehouse consolidating patient data from multiple systems in use at Alberta Health Services (AHS).MethodsDECIDe's development began with stakeholder engagement and requirements gathering followed by design. The warehouse includes landing, staging, analysis, and reporting schemas that facilitate integration and transformation. Primary EHR, cancer registry, and legacy EHR data were extracted, transformed, and loaded into DECIDe.ResultsDECIDe integrated 2,100+ tables into 36 analysis tables with data from 1,055,186 unique patients. Integration exceeded 93% across all data sources demonstrating robust consolidation. DECIDe's structured format enables efficient storage, retrieval, and analysis, laying the foundation for comprehensive reporting and analytics.ConclusionDECIDe addresses challenges in managing healthcare data, supporting analytics into cancer trends and treatment outcomes. Unique to Canadian healthcare DECIDe provides a comprehensive view of cancer care, enhancing decision-making and research opportunities.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261440410"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An informatics-based data-led prioritization strategy to facilitate objective and equitable care for an ethnically diverse urban cohort of people with type 1 diabetes: A proof-of-concept study.","authors":"Panagiotis Pavlou, Khuram Chaudhry, Ollie French, Sarah Keane, Thomas Johnston, Anna Brackenridge, Stephen Thomas, Yuk-Fun Liu, Daghni Rajasingam, Dulmini Kariyawasam, Janaka Karalliedde","doi":"10.1177/14604582261436276","DOIUrl":"https://doi.org/10.1177/14604582261436276","url":null,"abstract":"<p><p>BackgroundEthnicity and socioeconomic factors contribute to higher morbidity and mortality in people with type 1 diabetes (pwT1D), partly due to reduced access to specialised care and technology. Objectively prioritising high-risk individuals in resource-limited settings is challenging. Data-led prioritisation (DLP) uses health informatics to stratify pwT1D based on new-onset risk factors since their last review. This may help overcome implicit bias, structural racism, and care barriers. However, data on its use in pwT1D are limited.MethodsIn this proof-of-concept study, DLP was implemented from July to September 2023 in a university hospital serving an ethnically diverse population. Clinical and demographic data were collected from 697 adults with T1D (50.5% female, 23.5% non-White, 37% from poor socioeconomic backgrounds). DLP identified 76 individuals (10.9%) as highest risk.ResultsNon-White patients were more likely to be in the highest-risk group (30/164, 18.3%) than White patients (35/372, 9.4%), <i>p</i>=0.004. Those from the most deprived socioeconomic backgrounds were also more likely to be high-risk (40/256, 15.7%) vs others (36/433, 8.3%), <i>p</i>=0.008.ConclusionDLP may enable objective risk stratification in pwT1D and could help reduce bias linked to ethnicity and deprivation. Further large-scale research is warranted to demonstrate the use of such systems in diabetes care.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261436276"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the quality and reliability of endometrial cancer related videos on Chinese short-video platforms: A cross-sectional study.","authors":"Jinghong Liang, Mengyao Huang, Shiyuan Qi, Qingjian Ye, Xiaomao Li","doi":"10.1177/14604582261445744","DOIUrl":"https://doi.org/10.1177/14604582261445744","url":null,"abstract":"<p><p>ObjectiveTo conduct a multidimensional evaluation of endometrial cancer (EC)-related videos on major Chinese short-video platforms.MethodsA cross-sectional study conducted on May 8, 2025 analyzed 226 eligible EC-related videos from TikTok, Rednote, and Bilibili. Video quality was assessed using Global Quality Scale (GQS) and Video Information and Quality Index (VIQI), reliability using modified DISCERN (mDISCERN), and understandability and actionability using Patient Education Materials Assessment Tool (PEMAT).ResultsAmong 226 videos, TikTok emphasized symptoms/risk factors and scored highest in engagement, reliability (mDISCERN=2.0, <i>P=</i>0.002), and understandability (88%). Bilibili led in VIQI (median=17.0) but had the lowest understandability (67%). Professional videos outperformed patient-generated content (all <i>P<</i>0.05). Video length correlated positively with quality but negatively with engagement and understandability (all <i>P<</i>0.01).ConclusionsEC-related videos vary widely in quality and lack consistent actionability. Strategic content design and platform-level verification are needed to improve reliability and public health impact.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261445744"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of medical device calibration knowledge, perceived importance, and implementation support among healthcare professionals in Ghana.","authors":"Isaac Acquah, Benjamin Appiah Yeboah, Mawusi Gbemavor-Assonhe, Kojo Sam Micah","doi":"10.1177/14604582261448684","DOIUrl":"https://doi.org/10.1177/14604582261448684","url":null,"abstract":"<p><p>ObjectivesMedical device calibration underpins accuracy, precision, and reliability, which are critical to optimal device performance and quality healthcare delivery. Despite its importance, little empirical evidence exists on calibration knowledge among healthcare professionals in sub-Saharan Africa. This study assessed such knowledge in Ghana by examining familiarity with calibration principles, procedures, device-specific practices, institutional support, and factors influencing competence.MethodsA cross-sectional survey of 425 healthcare workers (technicians/clinical engineers, nurses, midwives, and doctors) employed a structured questionnaire. Statistical analyses (<i>t</i>-tests, ANOVA, and correlation analysis) were conducted to identify differences and associations in calibration knowledge and practices.ResultsAnalyses revealed significant knowledge gaps. Technicians/clinical engineers outperformed other groups, while early-career professionals (1-3 years' experience) scored higher than mid-career counterparts (7-10 years), highlighting shortcomings in continuous professional development. Formal training was also associated with higher scores.ConclusionThese findings highlight the urgent need for structured, role-specific training and ongoing professional education to close calibration knowledge gaps, safeguard patient safety, and optimise device performance. The results provide evidence for policy reforms and workforce development strategies to strengthen medical device management and healthcare quality.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261448684"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive behavioral intervention on digital addiction behavior among nursing students in Saudi Arabia.","authors":"Vanitha Innocent Rani, Khalda Ahmed Mohammed Ahmed, Hanem Ahmed Abdelkhalek Ahmed, Shylaja Jeyapaul, Nawal Yahya H Asiri, Chithra Radhamany Arackal Thekkathil, Manal Abdu B Albishi, Hanan Awad Moawad Elmashad, Iman Awad Siddig Mohammed","doi":"10.1177/14604582261444118","DOIUrl":"https://doi.org/10.1177/14604582261444118","url":null,"abstract":"<p><p>AimTo assess the effectiveness of a brief cognitive behavioral intervention (CBI) on digital dependence among nursing students in Saudi Arabia and to examine demographic and usage predictors of post-intervention outcomes.MethodsA pretest-posttest quasi-experimental study was conducted with 163 students (aged 18-23 years) at K' University. Participants completed the Digital Addiction Scale (DAS) before and after a three-session group CBI. Paired t-tests and correlations explored inter-domain relationships, and linear regressions examined predictors of post-intervention scores.ResultsMean DAS scores improved significantly for overuse (mean difference 0.40, p < .001), non-restraint (0.22, p = .010), and dependence (0.39, p < .001). Emotional state increased but not significantly (p = .135) and inhibiting the flow of life was unchanged (p = .742). Post-intervention overuse was predicted by daily hours of device use (β = 0.94 for 3-4 h; β = 1.04 for ≥7 h; all p < .05), while other demographic factors were non-significant.ConclusionA brief CBI improved behavioral aspects of digital dependence but had limited effect on emotional dimensions. Integrating culturally adapted CBIs and digital-wellness modules into nursing curricula could reduce digital distraction and enhance self-regulation. Further controlled studies are needed to validate and expand upon these results.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261444118"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable heart disease risk prediction via FCA-constrained logistic regression.","authors":"Arman Salehi, Ashkan Heydarian, Hamid Reza Goudarzi, Zahra Farzin Rad","doi":"10.1177/14604582261444612","DOIUrl":"https://doi.org/10.1177/14604582261444612","url":null,"abstract":"<p><p>ObjectiveTo develop an interpretable and clinically coherent heart disease risk prediction model by integrating Formal Concept Analysis (FCA) with a novel closure-constrained logistic regression that enforces coefficient coherence within FCA-derived concepts.MethodsWe used the Heart Disease Health Indicators dataset (BRFSS 2015; N≈380,000). Predictors were discretized into binary attributes, and closed itemsets were extracted via FCA. A closure penalty, which minimizes within-concept coefficient variance, was added to the logistic regression objective. Hyperparameters (closure strength λ, FCA minimum support) were selected using five-fold cross-validation on the training set. Baselines included L2-regularized logistic regression, Random Forest, and Gradient Boosting. Performance was evaluated on a held-out test set using AUC, accuracy, precision, recall, F1, PR-AUC, and Brier Score.ResultsOn the held-out test set, the FCA-constrained model achieved Accuracy = 0.906, AUC = 0.810, Precision = 0.709, Recall = 0.544, F1 = 0.556, PR-AUC = 0.265, and Brier Score = 0.078. Compared to baselines, the FCA model produced well-calibrated probabilities and the highest precision and F1-score, while providing concept-level explanations grounded in clinically coherent closed itemsets.ConclusionEmbedding FCA structure directly into model training yields an interpretable linear model with competitive discrimination and improved precision at clinically relevant thresholds.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261444612"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influencing factors and acceptance levels of robotic and smart home health technologies among older adults: A systematic review.","authors":"Reyhaneh Jaberi, Sharareh Rostam Niakan Kalhori, Fateme Bahador, Saeedeh Heydarian, Zohreh Javanmard","doi":"10.1177/14604582261436632","DOIUrl":"https://doi.org/10.1177/14604582261436632","url":null,"abstract":"<p><p>BackgroundSmart home technologies and assistive robots play a role in enhancing the well-being of older adults. This study aims to evaluate the factors and acceptance level of these technologies among seniors and to explore the factors affecting their acceptance.MethodsThree databases were systematically searched using keywords to identify relevant articles. The retrieved studies were screened based on eligibility criteria. Key features of the studies and the acceptance status of the aforementioned technologies among seniors were documented in a data extraction form.ResultsTwenty-seven studies met the eligibility criteria. Robots (74.1%), sensors (18.5%), wireless technologies (3.7%), and smart home voice assistants (3.7%) were utilized by seniors. Remote patient monitoring (33.3%) was the most prevalent application of these technologies. Approximately 89% of the studies reported positive attitudes toward these technologies. \"Technology convenience\" (22.2%) emerged as the most significant reason for smart technology acceptance. Conversely, \"concerns about privacy and security\" (14.8%) and \"lack of need for technology\" (14.8%) were the most frequently cited reasons for non-acceptance.ConclusionsTo enhance the adoption of reviewed technologies, it is crucial to implement strategies that raise awareness, ensure data security, and address the actual needs of this demographic in both the design and implementation phases.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261436632"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting mortality risk of COVID-19 among chronic kidney disease patients using machine learning algorithms.","authors":"Raoof Nopour","doi":"10.1177/14604582261425071","DOIUrl":"https://doi.org/10.1177/14604582261425071","url":null,"abstract":"<p><p><b>Objective:</b> COVID-19 disease has a high prevalence and mortality rate among chronic kidney disease patients. Previous studies have shown that ML-based prediction models are effective for developing preventive strategies. Despite several efforts to establish prediction models for COVID-19 mortality risk among different populations, few studies have focused on COVID-19 mortality among patients with chronic kidney disease. The current study aimed to develop an effective, efficient preventive strategy to improve prognosis and survival among these patients by constructing a machine-learning-based prediction model. <b>Methods:</b> The current retrospective study used single-center data from 556 hospitalized patients with CKD. All patients in the cohort had respiratory failure following COVID-19. We leveraged ensemble and non-ensemble algorithms to construct a prediction model and scored the chosen features for better interpretability. <b>Results:</b> The empirical results of this study showed that XG-Boost achieved an AU-ROC of 0.921 with a 95% CI of [0.906-0.941] and an AU-ROC of 0.851 with a 95% CI of [0.835-0.877] in training and validation modes, respectively, yielding more favourable predictive performance than the models. <b>Conclusion:</b> XG-Boost demonstrated predictive merit that can be leveraged as an auxiliary tool to aid clinicians in making more informed decisions in clinical settings.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261425071"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nurses' adaptation to digital transformation in healthcare: A grounded theory of progressive digital adaptation.","authors":"Ebtsam Aly Abou Hashish, Noura Mohamed Abdel Razek","doi":"10.1177/14604582261440411","DOIUrl":"https://doi.org/10.1177/14604582261440411","url":null,"abstract":"<p><p>BackgroundDigital transformation in healthcare requires nurses to use new technologies while maintaining care quality. Adaptation becomes challenging in resource-limited settings where training, support, and infrastructure vary and where nurses often face competing demands.ObjectiveThis study aimed to develop a grounded theory explaining how nurses progressively adapt to digital transformation, the conditions that facilitate or hinder their movement, and the strategies they use to manage challenges.MethodsA grounded theory design guided the study. Thirty nurses in an Egyptian private hospital participated in in-depth semi-structured interviews conducted over four months. Data collection and analysis followed constant comparison and continued until theoretical saturation.ResultsThe study produced the Progressive Digital Adaptation theory. Two trajectories were identified. The sustaining trajectory showed confident and continuous integration, while the struggling trajectory reflected resistance or stalled movement. Five themes shaped adaptation. These were recurring resistance to technology, iterative learning and skill acquisition, integration into clinical routines, contextual factors influencing progression, and mechanisms supporting sustained transformation.ConclusionsEffective digital adaptation depends on consistent organizational commitment. The theory clarifies how nurses progress or regress between stages and identifies practical targets for interventions that strengthen digital transformation in varied healthcare settings.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 2","pages":"14604582261440411"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}