Health Informatics Journal最新文献_第8页

A cross-sectional multilevel study on nurses' experiences with health information systems: A key to understanding documentation hazards and technology-induced errors in different working environments. 一项关于护士使用卫生信息系统经验的横断面多层次研究：了解不同工作环境中文件危害和技术引起的错误的关键。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-01-19 DOI: 10.1177/14604582261415735

Kaija Saranto, Samuli Koponen, Tuulikki Vehko

{"title":"A cross-sectional multilevel study on nurses' experiences with health information systems: A key to understanding documentation hazards and technology-induced errors in different working environments.","authors":"Kaija Saranto, Samuli Koponen, Tuulikki Vehko","doi":"10.1177/14604582261415735","DOIUrl":"10.1177/14604582261415735","url":null,"abstract":"Objective: This study aimed to investigate how nurses' backgrounds, documentation skills, and experiences with documentation practices and information systems usage influence documentation hazards and to determine whether these hazards are linked to technology-induced errors (TIEs). Methods: An online survey was conducted to collect data from 3065 registered nurses working in Finnish hospitals and in acute, primary, and home care services regarding their experiences with electronic health records (EHRs) or client information systems (CIS). The data were analysed using linear and logistic multilevel models to identify patterns and correlations. Results: User interaction with EHR/CIS systems significantly influenced documentation hazards across different work environments. Perceived system-provided documentation support and documentation hazards were identified as contributors to TIEs. Conclusions: Improving system design and documentation support is a desirable goal, but it is not sufficient to mitigate documentation hazards and promote efficient practices. To achieve the best possible results, skilled users are needed to operate these systems.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582261415735"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999889","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}

引用次数: 0

Enhanced threat detection in health care systems with random coupled bootstrapped ensemble classifier. 基于随机耦合自举集成分类器的医疗系统威胁检测。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-03-02 DOI: 10.1177/14604582261429465

Mohammad Alhefdi, Yosuef Alotaibi, Paul Rodrigues, V Pandimurugan

{"title":"Enhanced threat detection in health care systems with random coupled bootstrapped ensemble classifier.","authors":"Mohammad Alhefdi, Yosuef Alotaibi, Paul Rodrigues, V Pandimurugan","doi":"10.1177/14604582261429465","DOIUrl":"10.1177/14604582261429465","url":null,"abstract":"The protection of patient information in modern healthcare demands overcoming major challenges intensified by the integration of Internet of Things (IoT) technologies. The proposed Random Coupled Bootstrapped Ensemble Classifier (RCBEC) offers an advanced intrusion detection framework to enhance cyberattack detection in smart healthcare environments. The model optimizes both accuracy and feature selection to improve computational efficiency and precision. Data preprocessing employs Decimal Score Max Normalization for transformation, duplicate removal, and handling of missing values. Feature extraction through K-Best Kernel Discriminant Analysis (K-BKDA) and optimization via Hunter Canis Algorithm (HCA) ensure effective identification of attack-relevant features. Implemented in a Python-based ECU-IoHT environment, the RCBEC achieves 99.6% accuracy and F1-score, outperforming existing intrusion detection methods. The ensemble classifier combines rapid computational performance with robust threat identification capabilities, enhancing security in IoT-enabled healthcare systems. Comparative analysis demonstrates the system's superior generalization and adaptability across diverse datasets. Overall, the proposed RCBEC model establishes a resilient and intelligent mechanism for detecting and mitigating cybersecurity threats in healthcare networks. This work highlights how machine learning-driven intrusion detection significantly strengthens patient data protection, operational reliability, and trust in next-generation healthcare systems.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582261429465"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328119","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}

引用次数: 0

Factors contributing to decisions for giving treatment and referrals to patients with cognitive complaints in chat-based telemedicine services in Indonesia. 印度尼西亚基于聊天的远程医疗服务中影响对认知疾病患者进行治疗和转诊的因素。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-03-03 DOI: 10.1177/14604582251404785

Pukovisa Prawiroharjo, Irma Widyasari, Iskandar Purba Geraldi, Yetty Ramli, Adre Mayza, Diatri Nari Lastri, Dinda Nisrina

{"title":"Factors contributing to decisions for giving treatment and referrals to patients with cognitive complaints in chat-based telemedicine services in Indonesia.","authors":"Pukovisa Prawiroharjo, Irma Widyasari, Iskandar Purba Geraldi, Yetty Ramli, Adre Mayza, Diatri Nari Lastri, Dinda Nisrina","doi":"10.1177/14604582251404785","DOIUrl":"10.1177/14604582251404785","url":null,"abstract":"Introduction: Chat-based telemedicine has become a popular method for consulting cognitive complaints in Indonesia, especially during the COVID-19 pandemic. This study explores patient characteristics and their correlation with physicians' decisions on treatment and referral. Methods: A retrospective cross-sectional study analyzed secondary data from patient chat databases on Indonesian telemedicine platforms between March 2020 and December 2021. Bivariate and multivariable analysis were conducted. Results: Out of 1,016 patients (0.4% of 2,199,527 users), most were females under 40. Clinicians were more likely to provide pharmacological treatment for patients aged ≥60, females, or those with forgetfulness complaints or a history of cognitive disorders (p < 0.01). Referral likelihood increased when caregivers filed complaints or prior diagnoses existed (p < 0.01). Discussion: Clinicians' decisions in telemedicine were influenced by age, gender, complaint type, and caregiver involvement. Further research is needed to improve telemedicine for cognitive care.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582251404785"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147345041","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}

引用次数: 0

Rule-based natural language processing to extract clinical trial and research study enrollment history from unstructured notes. 基于规则的自然语言处理，从非结构化笔记中提取临床试验和研究入学历史。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-03-30 DOI: 10.1177/14604582261430614

Sergey D Goryachev, Julie Tsu-Yu Wu, Eric Lin, Daphne R Friedman, Robert Zwolinski, Rupali Dhond, Danne C Elbers, Jennifer La, Cenk Yildirim, June K Corrigan, Daniel C R Chen, Mary T Brophy, Nhan V Do, Nathanael R Fillmore

{"title":"Rule-based natural language processing to extract clinical trial and research study enrollment history from unstructured notes.","authors":"Sergey D Goryachev, Julie Tsu-Yu Wu, Eric Lin, Daphne R Friedman, Robert Zwolinski, Rupali Dhond, Danne C Elbers, Jennifer La, Cenk Yildirim, June K Corrigan, Daniel C R Chen, Mary T Brophy, Nhan V Do, Nathanael R Fillmore","doi":"10.1177/14604582261430614","DOIUrl":"10.1177/14604582261430614","url":null,"abstract":"Clinical trials are vital for advancing care. However, a systematic approach to tracking trial participation across different facilities and sponsors has been lacking. We developed natural language processing (NLP) methods to extract study enrollment history, including enrollment status, consent date, and study title from information on clinical trial participation recorded in clinical notes in the electronic health record based on national Veterans Affairs electronic health record data. The method exhibited high test-set precision for enrollment status (0.94), consent date (0.97), and study title (0.87) and acceptably high recall (0.76, 0.70, and 0.84, respectively). From a single center, the classifier correctly identified 111 of 125 trial participants (88.8%) across 12 distinct trials. Our study demonstrates the feasibility of using NLP to capture trial enrollment from a nationwide healthcare system. This algorithm creates a novel data resource for analyzing and tracking trial enrollment at the population level.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582261430614"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147583195","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}

引用次数: 0

Assessing RTS, S malaria vaccine rollout perception in Cameroon: Sentiment analysis from X and facebook using hugging face. 评估RTS， S疟疾疫苗在喀麦隆的推广效果：X和facebook的情感分析

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-01-08 DOI: 10.1177/14604582261416864

Adanze Nge Cynthia, Melvin Njuaka, Nana Koomson, Njinju Zilefac Fogap

{"title":"Assessing RTS, S malaria vaccine rollout perception in Cameroon: Sentiment analysis from X and facebook using hugging face.","authors":"Adanze Nge Cynthia, Melvin Njuaka, Nana Koomson, Njinju Zilefac Fogap","doi":"10.1177/14604582261416864","DOIUrl":"10.1177/14604582261416864","url":null,"abstract":"Background: The introduction of the RTS, S (Mosquirix®) malaria vaccine in Cameroon represents a significant public health milestone. This study analyzed social media sentiment surrounding the vaccine rollout using natural language processing and machine learning. Methods: Data from Twitter (X) and Facebook (Meta) regarding the RTS, S vaccine in Cameroon was analyzed using the Hugging Face Transformer library for sentiment evaluation. The data was pre-processed, cleaned, and visualized with Matplotlib. Results: The sentiment analysis revealed that 42.0% of reactions were negative, 40.0% were positive, and 18.0% were neutral, indicating a nearly even split between skeptical and supportive viewpoints among Cameroonian users regarding the vaccine rollout. Conclusion: The research highlights the necessity for targeted communication strategies to address public concerns and foster vaccine confidence. Sentiment analysis can act as a real-time tool, offering policymakers valuable insights into public reactions and attitudes toward immunization and other health initiatives. These findings reveal significant public skepticism that must be addressed through evidence-based communication strategies focused on vaccine safety, efficacy data from pilot programs, and engagement with community leaders to counter misinformation.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582261416864"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936406","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}

引用次数: 0

Design and usability testing of SmartSHOTS: A mobile app to reduce vaccine barriers for children 0-24 months. SmartSHOTS的设计和可用性测试：一款旨在减少0-24个月儿童接种疫苗障碍的移动应用程序。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-01-29 DOI: 10.1177/14604582261419334

Tami H Wyatt, Sarah Lowe, Jose Tupayachi, Xueping Li, Clea Ann McNeely, Xudong Wang, Penny Dawn Taylor, Aliza Sharmin, Victoria Niederhauser

引用次数: 0

A call to action to close the global digital divide in nursing: Clinical nursing information systems and standardized terminologies in low and middle-income countries. 呼吁采取行动弥合护理领域的全球数字鸿沟：低收入和中等收入国家的临床护理信息系统和标准化术语。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-02-04 DOI: 10.1177/14604582251406986

Animesh Ghimire

{"title":"A call to action to close the global digital divide in nursing: Clinical nursing information systems and standardized terminologies in low and middle-income countries.","authors":"Animesh Ghimire","doi":"10.1177/14604582251406986","DOIUrl":"10.1177/14604582251406986","url":null,"abstract":"Clinical Nursing Information Systems (CNISs) and Standardized Nursing Terminologies (SNTs) significantly enhance the quality of care, promote interoperability, and enable measurable nursing outcomes. However, their adoption remains inconsistent, particularly in low- and middle-income countries (LMICs). This commentary reframes the existing gap as an issue of equity and systems design while providing a feasibility-prioritized roadmap tailored for LMICs. The article supports a sequenced approach that distinguishes between short-term actions and longer-term initiatives. Short-term actions include stabilizing infrastructure, developing open-source CNIS models, initiating terminology localization pilots, implementing essential data privacy safeguards, and providing targeted in-service training. In contrast, longer-term initiatives involve establishing national standards and exchanges, securing sustainable financing, cultivating leadership pipelines and curricula, and promoting cross-border interoperability and evaluation. Furthermore, it delineates various financing mechanisms-including concessional loans, performance-based grants, and collective procurement-while also addressing strategic considerations related to policy and governance frameworks. The commentary concludes with an explicit call to action: policymakers, donors, nursing leaders, educators, and vendors must collaborate to integrate structured nursing data into routine care and national platforms. Bridging this gap will render nursing work more visible, enhance decision support, and foster learning health systems within hospitals and communities worldwide.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582251406986"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146121092","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}

引用次数: 0

Explainable machine learning algorithms for predicting maternal obstetric complications in Ethiopia: Evidence from a retrospective cohort in southern Ethiopia. 预测埃塞俄比亚孕产妇产科并发症的可解释机器学习算法：来自埃塞俄比亚南部回顾性队列的证据。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-02-26 DOI: 10.1177/14604582261428317

Amanuel Yoseph, Yohannes Seifu Berego, Mehretu Belayneh, Francisco Guillen-Grima

{"title":"Explainable machine learning algorithms for predicting maternal obstetric complications in Ethiopia: Evidence from a retrospective cohort in southern Ethiopia.","authors":"Amanuel Yoseph, Yohannes Seifu Berego, Mehretu Belayneh, Francisco Guillen-Grima","doi":"10.1177/14604582261428317","DOIUrl":"10.1177/14604582261428317","url":null,"abstract":"IntroductionEarly identification of high-risk pregnancies is crucial, yet conventional approaches often miss complex clinical and contextual factors. This study measured prevalence and determinants in Southern Ethiopia and applied explainable machine learning as a clinical decision-support tool, linking prediction to actionable digital health insights.MethodsWe conducted a retrospective cohort study of 3,954 mother-infant pairs using routine records from pregnancy to the postpartum. Five supervised machine learning algorithms - Logistic Regression, Random Forest, Support Vector Machine, an Artificial Neural Network and XGBoost were developed and validated. Performance was assessed using AUC, specificity, sensitivity, calibration, and F1-score. SHAP-based analysis enhanced interpretability, revealing the contribution of each predictor at both individual and cohort levels, supporting practical integration into digital maternal health systems.ResultsObstetric complications occurred 16.3% of mothers, with higher incidence in rural settings. XGBoost achieved the highest predictive performance (AUC 0.86). Key predictors identified young maternal age, unplanned pregnancy, low education, previous complications, inadequate antenatal care, anemia, hypertension and long travel distance to health facilities.ConclusionsObstetric complications remain common in Southern Ethiopia. By applying explainable machine learning, this study not only predicts high-risk pregnancies with high accuracy but also provides actionable insights for clinical decision-making and digital health implementation.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582261428317"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147312687","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}

引用次数: 0

Development of an evidence-based foundation for a Belgian nursing reference set in SNOMED CT. 比利时护理参考集SNOMED CT循证基础的发展。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-03-09 DOI: 10.1177/14604582251414237

Jerno Thys, Koen Balcaen, Goele Vanpee, Arabella D'Havé

引用次数: 0

A cloud-edge reference architecture for intertwining health digital domains. 用于交织健康数字域的云边缘参考架构。

IF 2.3 3区医学

Health Informatics Journal Pub Date : 2026-01-01 Epub Date: 2026-02-07 DOI: 10.1177/14604582251383803

Adriano Tramontano, Oscar Tamburis, Giulio Perillo, Guido Iaccarino, Arriel Benis, Mario Magliulo

{"title":"A cloud-edge reference architecture for intertwining health digital domains.","authors":"Adriano Tramontano, Oscar Tamburis, Giulio Perillo, Guido Iaccarino, Arriel Benis, Mario Magliulo","doi":"10.1177/14604582251383803","DOIUrl":"10.1177/14604582251383803","url":null,"abstract":"Objective: In the present work, LinkAll is introduced as a novel architectural model designed to enable real-time monitoring and cross-referential data analysis in remote monitoring systems across human, animal, and environmental health domains. LinkAll leverages Edge-Computing and Internet of Things principles to handle data collection, processing, and presentation from various sources. Methods: Two sibling systems were implemented to demonstrate its capability, one for monitoring urban greenery and the other for elderly home care. These systems were evaluated based on their ability to integrate with existing information systems, collect biophysical parameters, and ensure data cross-referencing. Results: Both systems demonstrate effective pluggability and cross-referenceability performances, meeting the stakeholders' requirements. LinkAll's ability to integrate diverse sensors and devices into existing infrastructures while providing real-time, machine-actionable insights, is also underscored. Conclusion: Pluggability, cross-referenceability, and compliance with FAIR principles make the architectural model introduced a robust solution for integrating human, animal, and environmental health monitoring systems, enhancing decision-making and contributing to One (Digital) Health's strategic goals.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"32 1","pages":"14604582251383803"},"PeriodicalIF":2.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137944","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}

引用次数: 0