Health Informatics Journal最新文献

{"title":"Reflective openEHR-to-FHIR: Java class-based generator for transforming openEHR compositions to fast healthcase interoperability resources (FHIR) bundles.","authors":"Nektarios Ladas, Stefan Franz, Alina Rehberg, Andreas Büchner, Michael Marschollek, Matthias Gietzelt","doi":"10.1177/14604582261448775","DOIUrl":"https://doi.org/10.1177/14604582261448775","url":null,"abstract":"<p><p>BackgroundAt our hospital's university, the data integration center (DIC) has the primary task of importing data from various systems, including unstructured medical reports and structured laboratory data into the openEHR data repository. As we participate in the German Medical Informatics Initiative (MII), we have an agreement to share common consent data for research purposes. The requirement for this is that the data must be available in the FHIR format.ObjectivesAs a result, we have to transform our openEHR into FHIR data. Both formats are different, and simply programming a module to transform each openEHR template into a FHIR resource can be time-consuming and neither supportable nor updatable.MethodsWe have designed a method and developed an open-source software artifact that transforms an openEHR composition into a single FHIR resource or a bundle of resources, through the dynamic declaration of openEHR fields to API methods. The mappings can be updated at runtime, while the software artifact allows developers and end users to test and validate the mappings.ResultsWe currently use our software, which is able to do robust transformations and has, until now, successfully stored and processed over 30 million FHIR bundles.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261448775"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690195","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 order management performance within ERP-supported healthcare supply chains: Evidence from the UAE.","authors":"Dina Al Nuaimi, Md Hafizur Rahman, Amina Tariq","doi":"10.1177/14604582261478483","DOIUrl":"https://doi.org/10.1177/14604582261478483","url":null,"abstract":"<p><p>ObjectivesThis study aimed to evaluate order management (OM) efficiency and fulfilment performance within ERP-supported healthcare supply chains during the COVID-19 pandemic in the United Arab Emirates (UAE). The study was motivated by the increasing need for resilient digital supply chain systems capable of supporting uninterrupted access to critical medical supplies during public health emergencies.MethodsA quantitative retrospective longitudinal design was employed using secondary ERP data extracted from 14 Emirates Health Services (EHS) healthcare facilities between 2020 and 2022. A total of 87 essential medical and pharmaceutical items across four categories, including COVID-19 medications, supportive medications, personal protective equipment (PPE)/disposables, and vaccines, were analyzed. Order fulfilment rate was used as the primary performance indicator and compared against the healthcare industry benchmark of 95%. Wilcoxon Signed-Rank and Friedman tests were conducted to assess differences in fulfilment performance across study years and healthcare facility types.ResultsERP-supported healthcare supply chains demonstrated standardized order processing and operational coordination during the early stages of the pandemic; however, observed performance patterns were likely influenced by additional factors including supply disruptions, supplier constraints, and changing demand conditions. Hospitals and primary healthcare centres demonstrated relatively high and stable fulfilment performance in 2020 and 2021. However, statistically significant variability and declines in fulfilment rates were observed in 2022 across several supply categories, particularly within public health centres. Overall, fulfilment rates remained below the 95% benchmark throughout the study period.ConclusionThe findings indicate that healthcare supply chains operating within ERP-supported environments maintained operational continuity and supply chain coordination during the COVID-19 pandemic. However, the observed performance patterns suggest that ERP systems alone may not be sufficient to ensure optimal healthcare supply chain resilience during large-scale disruptions. Integrating ERP systems with advanced analytics, supplier diversification strategies, and real-time decision-support tools may further strengthen healthcare supply chain performance and emergency preparedness.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261478483"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893173","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":"Developing supporter-focused text messages to enhance cessation support for individuals with mental health conditions who smoke: A formative study.","authors":"Catherine S Nagawa, Sydney Newell Chesebro, Katie Qualls Fay, Madeline Hardage, Junru Zhao, Frances Wen, Michelle R vanDellen","doi":"10.1177/14604582261488344","DOIUrl":"https://doi.org/10.1177/14604582261488344","url":null,"abstract":"<p><p>IntroductionThis formative study aimed to develop and evaluate supporter-focused text messages intended to enhance helpful support behaviors and reduce counterproductive support behaviors in the context of smoking cessation.MethodsWe developed 65 messages informed by responsiveness theory and established research on cessation. One hundred and ninety six adults (eligibility: ≥18 years, knowing someone who smokes) rated the messages on three dimensions of helpfulness (i.e., clarity, usefulness, relevance). Using mixed-effects regression models, we examined whether helpfulness ratings differed by sociodemographic factors, relationship factors, and tobacco use perceptions.ResultsMean ratings ranged from 4.04 to 4.31/5. Compared with participants who reported knowing a spouse/partner who smoked, those who knew a friend (β̂= 0.34, SE = 0.16, 95% CI: 0.04 - 0.64), family member (β̂= 0.38, SE = 0.15, 95% CI: 0.07 - 0.67), or coworker/roommate (β̂= 0.56, SE = 0.22, 95% CI:0.14 - 0.98) provided relatively higher ratings. Greater perceived relationship closeness to the individual who smokes was positively associated with higher ratings (β̂= 0.08, SE = 0.03, 95% CI:0.03- 0.14).ConclusionThe high ratings suggest that potential support providers found the messages helpful and tailoring them to relationship characteristics might provide a small additional benefit.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261488344"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901775","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":"Understanding Women's use of period-tracking applications: The role of personal, cognitive, and environmental factors in sexual and reproductive health tracking.","authors":"Elizabeth Babin Pask, Qiwei Luna Wu, Grace Ellen Brannon","doi":"10.1177/14604582261488920","DOIUrl":"https://doi.org/10.1177/14604582261488920","url":null,"abstract":"<p><p>Though period-tracking apps are valuable tools to monitor and record sexual and reproductive health information, the factors underlying different purposes of period app use are largely unexamined. Based on social cognitive theory, this study explored women's purposes for using period-tracking apps and the personal and environmental factors that influence frequency of use related to (a) menstruation, (b) pregnancy, (c) sexual activity, and (d) information management. Our cross-sectional survey of 249 women living in the United States of America who reported using a period-tracking app in the last 12 months showed that that older age, likelihood/risk of unintended pregnancy, pregnancy anxiety, and living in a state with an abortion ban predicted greater frequency of app use for pregnancy-related tracking purposes, while education level, likelihood of unintended pregnancy, and sexual/reproductive health history predicted tracking purposes related to sexual activity. Our findings highlight women's motives underlying their period app use and the implications of app use for women's health self-monitoring.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261488920"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148914448","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":"Characteristics of adverse events following immunization reporting systems: A scoping review.","authors":"Hassan Asadi, Haleh Ayatollahi, Seyed Mohsen Zahraei, Sana Eybpoosh","doi":"10.1177/14604582261461067","DOIUrl":"10.1177/14604582261461067","url":null,"abstract":"<p><p>ObjectiveThis study aimed to identify characteristics of adverse events following immunization (AEFI) reporting systems and vaccine adverse events reporting systems (VAERS) including technical platforms, user groups, data elements, functional and non-functional requirements.MethodsIn this scoping review, various databases were searched from 1^st January 2015 to 31^st December 2024, and all types of studies that explained AEFI reporting system/VAERS characteristics were considered. The findings were reported descriptively.ResultsDifferent technical platforms including web-based, mobile-based, or hybrid platforms were used by multiple user groups. Data elements included personal, clinical, vaccination, and adverse events data. The functional requirements included recording vaccination and adverse events data as well as generating reports. Non-functional requirements were related to system security, data privacy, etc.ConclusionThis review presented a set of characteristics that has been considered for different AEFI reporting systems and VAERS. The results can be used for designing more comprehensive AEFI reporting systems in different countries. These features along with new digital technologies and analytical tools including artificial intelligence offer more potential to enhance efficiency and effectiveness. Future research should focus on AI-driven methodologies, including natural language processing, machine learning techniques, and predictive analytics, while addressing ethical, regulatory, and practical challenges.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261461067"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148439172","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":"Understanding user mood and motivation in a mobile health behavior change support system: Insights from a mixed-methods approach.","authors":"Sharon Nabwire, Renata Savian Colvero de Oliveira, Heta Merikallio, Markku J Savolainen, Janne Hukkanen, Harri Oinas-Kukkonen","doi":"10.1177/14604582261480535","DOIUrl":"https://doi.org/10.1177/14604582261480535","url":null,"abstract":"<p><p>BackgroundHealth behavior change support systems (HBCSS) have emerged as effective tools for promoting well-being. Mood and motivation are key psychological aspects of these systems, influencing user engagement. This study aimed to understand mood and motivation within a weight-management HBCSS by analyzing user sentiments reflected in self-monitoring diary entries, examining their relationship with weight-loss outcomes, and identifying factors that influence motivation.MethodsA sequential explanatory mixed-methods approach was employed, incorporating sentiment analysis and ordinal logistic regression, followed by thematic analysis.ResultsHigher valence scores were associated with higher weight-loss categories. Temporal patterns indicated a decline in positive sentiment and an increase in neutral sentiment over the course of intervention use. Thematic analysis revealed four main themes, each comprising subthemes reflecting motivational facilitators and challenges: sustained self-motivation; self-awareness and progress evaluation; self-regulation and control; and context and environment.ConclusionThese findings deepen our understanding of how mood and motivation influence behavior change in weight management, offering insights for developing systems that support behavior change.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261480535"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802093","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":"AI-based epidemic early warning using social media and search engine signals: A scoping review.","authors":"Amir Bayat, Ayissha Pavakopethan, Nadine Kashmar, Iman Yousuf, Antoine Saab, Elie Salem Sokhn, Yahya El-Lahib, Christo El Morr","doi":"10.1177/14604582261479106","DOIUrl":"10.1177/14604582261479106","url":null,"abstract":"<p><p>BackgroundEarly detection of infectious disease outbreaks is essential for timely public health response. Artificial intelligence (AI) combined with digital traces from social media and search engines may strengthen epidemic early warning systems (EWS).ObjectiveTo map how AI-based EWS use social media and search engine signals for outbreak detection/forecasting, and to characterize whether studies integrate clinical and climate/environmental data.MethodsFollowing PRISMA-ScR, we reviewed 38 studies (past five years) across six databases. We extracted study characteristics, data sources, target diseases, prediction tasks, validation practices, and performance metrics. For cross-tabulated evidence maps, each study was coded once using its primary data-source category and primary modeling approach to avoid double-counting.ResultsDeep learning and ensemble approaches were the most common methods. COVID-19 and influenza-like illness dominated the literature. Few studies combined social media and search engine signals. Clinical and climate/environmental data were rarely integrated. No study reported prospective, real-time evaluation. Geographic coverage was skewed toward high-income settings.ConclusionAI-based epidemic early warning systems using digital signals show promise for outbreak detection and forecasting. However, the evidence remains largely retrospective, fragmented, and geographically uneven. Future research should prioritize multi-stream data integration, equity-aware design, and prospective validation to support real-world global applicability.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261479106"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148725774","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":"Solving patient scheduling problems in hospitals using a dedicated GenAI system.","authors":"Tin-Chih Toly Chen, Min-Chi Chiu, Hsin-Chieh Wu","doi":"10.1177/14604582261466227","DOIUrl":"10.1177/14604582261466227","url":null,"abstract":"<p><p>ObjectivesPatient scheduling is a vital yet complex task that strongly influences patient satisfaction and optimizes healthcare efficiency. Recent studies have emphasized the importance of devising innovative approaches and developing new scheduling frameworks. Therefore, this study establishes a dedicated generative artificial intelligence (GenAI) system for patient scheduling.MethodsThe proposed system first imports scheduling data to formulate the default patient scheduling problem. Subsequently, users enter their scheduling requirements using natural language via the system interface, which are parsed using a deep neural network to establish the corresponding extended three-field notations. A customized genetic algorithm is automatically generated to solve the customized patient scheduling problem.ResultsThe dedicated GenAI system was applied to a real-world case obtained from the literature, involving 12 anesthesiologists, surgeons, and anesthesia resuscitation doctors; 12 operating rooms; and 15 patients undergoing three types of surgeries, each consisting of three operations. The experimental results reveal that the difference in the optimal fitness achieved using this system and branch-and-bound was less than 1% on average, demonstrating that the proposed methodology is effective. In addition, the most complex customized patient scheduling problem could be automatically modeled and solved in 20 s. Furthermore, the scheduling performance achieved using this system was significantly higher (<i>α</i> = 0.05) than those achieved using two current practices. Moreover, customized patient scheduling problems are often substantially more complex than problems addressed using traditional methods reported in previous studies.ConclusionsApplying this dedicated GenAI system improved the effectiveness of patient scheduling. This is expected to considerably enhance patient satisfaction and overall healthcare efficiency.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261466227"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148392609","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":"Assessment of Dengue fever-related videos on TikTok (Douyin) and YouTube: A cross-sectional study on quality, content, and reliability.","authors":"Meiying Wang, Hongxia Gu, Yuyue Zhang","doi":"10.1177/14604582261485143","DOIUrl":"https://doi.org/10.1177/14604582261485143","url":null,"abstract":"<p><p>ObjectiveThis study aimed to evaluate the quality and reliability of dengue fever-related videos on social media platforms.MethodsA cross-sectional study was conducted on January 10, 2026. Top 120 videos from each platform (YouTube and Douyin) were screened. Video characteristics were extracted. Quality and reliability were assessed using the Global Quality Scale (GQS), modified DISCERN, JAMA Benchmark criteria, and the Content Completeness Score (CCS).Results170 videos (70 from YouTube, 100 from Douyin) were included. YouTube videos were longer (118.50 vs. 78.50, Z = -3.79, p < 0.001), had higher CCS (8.00 vs. 6.00, Z = -3.50, p < 0.001) and mDISCERN scores (3.00 vs. 2.50, Z = -3.74, p < 0.001). YouTube videos were dominated by institutions (91.43%), followed by 8.57% healthcare professionals (HCPs), whereas nearly one third of contributors on Douyin were HCPs (31.00%). Associations between engagement and quality indicators were generally weak and varied across metrics, while engagement indicators on Douyin were strongly intercorrelated.ConclusionsYouTube provided more comprehensive and reliable information, whereas Douyin content was shorter and less comprehensive. Both platforms showed inconsistent alignment between popularity and informational quality, with this disconnect appearing more pronounced on Douyin.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261485143"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851897","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":"Cost-effectiveness of Artificial Intelligence applications in healthcare: A systematic review.","authors":"Ali Imani, Elham Monaghesh","doi":"10.1177/14604582261460314","DOIUrl":"10.1177/14604582261460314","url":null,"abstract":"<p><p>ObjectiveWith increasing emphasis on value-based healthcare and rising costs, it is essential to assess the economic impact of artificial intelligence (AI). This study systematically reviews the evidence on the cost-effectiveness of AI applications in healthcare.MethodsA systematic search of PubMed, Scopus and Web of Science was conducted up to 21 August 2025. Studies that had full text and were peer-reviewed articles in English and reported formal economic evaluations, including cost-effectiveness, cost-utility or cost-benefit analysis of AI-based healthcare interventions, were included in this study. Key economic indicators such as incremental cost-effectiveness ratios (ICERs), quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) were extracted. Methodological quality was assessed using the Health Economics Consensus Criteria (CHEC-list). Data were qualitatively combined.ResultsA total of 26 studies met the inclusion criteria. Applications of AI in screening, diagnosis, treatment decision support and rehabilitation were assessed. Deep learning approaches were the most common approaches studied. Nine studies were classified as cost-effective, six as cost-neutral or somewhat cost-effective and eleven as not cost-effective. Overall methodological quality ranged from low to high, with considerable heterogeneity in model structure, perspective and time horizon.ConclusionsAI in healthcare, particularly in screening and early diagnosis, shows promising cost-effectiveness. However, the strength of current evidence is moderate and highly context-dependent, and the results are sensitive to methodological assumptions, implementation costs, and healthcare system characteristics.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 3","pages":"14604582261460314"},"PeriodicalIF":2.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802044","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}