Computers, informatics, nursing : CIN最新文献

{"title":"Antecedents of Technological Readiness Among Nurses Explaining Attitudes Toward a Virtual Care Assistant in Healthcare Based on an Online Survey: Erratum.","authors":"Wolfgang Aschauer, Florian Nemetz, Etter Christopher, Krutter Simon, Osterbrink Juergen, Kutschar Patrick","doi":"10.1097/CIN.0000000000001559","DOIUrl":"10.1097/CIN.0000000000001559","url":null,"abstract":"","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147849106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Machine-Learning Model for Predicting Postoperative Complication Occurrence After Radical Gastrectomy Using Electronic Medical Records.","authors":"Suyeon Lim, Ja Yun Choi","doi":"10.1097/CIN.0000000000001486","DOIUrl":"10.1097/CIN.0000000000001486","url":null,"abstract":"<p><p>This study aims to develop a machine-learning model using electronic medical records to predict postoperative complications after radical gastrectomy for gastric cancer. Data from 4892 patients who underwent the procedure between January 2012 and March 2022 were analyzed. Complications were defined as grade 1 or higher according to the Clavien-Dindo classification. Thirty-two predictors identified from previous research were matched to electronic medical records. After preprocessing, 5 machine-learning models-logistic regression, random forest, extreme gradient boosting, CatBoost, and multilayer perceptron-were developed. Model performance was assessed using the F1 score, accuracy, and area under the precision-recall curve. Key predictors were identified using permutation-based feature importance. The random forest model performed best, with an F1 score of 0.86, an accuracy of 0.95, and an area under the precision-recall curve of 0.90. Important predictors included late fever on postoperative days 4-7, postoperative pain management, operating time, age, and perioperative blood transfusion. The model demonstrated strong performance in predicting postoperative complications by incorporating insights of nurses throughout factor selection and model development. This approach may assist nursing decision-making by identifying high-risk patients for early, personalized interventions. Further validation through prospective and external studies is required.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation, Governance, and Trustworthy AI in Nursing: A Report From AINurse-26 and the Launch of the NAIL Digest: AI-Driven Nursing Informatics.","authors":"Lisiane Pruinelli, Maxim Topaz, Laura-Maria Peltonen, Charlene Ronquillo, Martin Michalowski","doi":"10.1097/CIN.0000000000001620","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001620","url":null,"abstract":"","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":"44 9","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148915053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Patient Safety Through EMR-smart Pump Interoperability: A Mixed-methods Study in an Australian Health Service.","authors":"Viviane Khalil, Madonna Dixon, Amelia Rattle, Julie Ngo","doi":"10.1097/CIN.0000000000001514","DOIUrl":"10.1097/CIN.0000000000001514","url":null,"abstract":"<p><p>Smart infusion pumps are widely used to reduce medication errors. When integrated with electronic medical records (EMR), they support a closed-loop medication management system. This study evaluates the impact of EMR-smart pump interoperability on patient safety and user acceptability in a multicenter Australian health service. A comparative study was conducted using baseline data from a tertiary hospital without smart pump integration (December 2021 to July 2022) and post-implementation data from the newly established digital cardiac hospital (March 2023 to December 2023). Both sites utilized a coronary care-specific medication library to ensure consistency. The evaluation focused on key indicators such as medication library compliance, the frequency of pump reprogramming and high-risk medication alerts, the number of keystrokes required for programming, and the rate of alert overrides. An anonymous survey captured nursing staff perceptions related to satisfaction, workflow efficiency, and confidence in using the pump systems. Following the implementation EMR-smart pump interoperability, medication library compliance improved significantly, rising from 93% to 97%, P <.0001. Alerts related to pump reprogramming and high-risk medications also increased, suggesting enhanced safety monitoring. At the same time, programming became more efficient, with the average number of keystrokes required dropping from 13±3.4 to 3±1.2, P <.0001. However, despite these gains, survey responses revealed a decline in clinician's perceived ease of use (from 90% to 45%, P <.0001) and confidence in programming (93% to 65%, P <.0001). While the rate of cancelled infusions alerts (20% vs. 14%, P <.0001) and all other nonspecific pump alerts (23% vs. 16%, P <.0001) reduced. The integration of EMR-smart pump interoperability enhances medication safety by increasing the frequency of clinical alerts and reducing manual data entry. However, this advancement may introduce usability challenges that impact staff confidence and workflow. These findings highlight the importance of balancing technological integration with user experience to optimize patient safety outcomes.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147477885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noninvasive Wearable Sensors for Monitoring Bladder Dysfunction: A Systematic Review of Nurse and Urologist Involvement.","authors":"Gennaro Laus, Filippo Azzarone","doi":"10.1097/CIN.0000000000001566","DOIUrl":"10.1097/CIN.0000000000001566","url":null,"abstract":"<p><strong>Background: </strong>Bladder dysfunction requires noninvasive and accurate tools for continuous monitoring in clinical and home settings. Wearable sensors represent a promising technology, with potential applications in bladder assessment and nursing practice, but available evidence is still limited.</p><p><strong>Methods: </strong>A systematic review of studies published from 2015 to 2025 on noninvasive wearable sensors for monitoring bladder dysfunction was conducted. Clinical, pilot, and technology studies reporting data on accuracy, usability, adherence, quality of life, and healthcare implications were included. The synthesis was narrative.</p><p><strong>Results: </strong>Seven studies met the inclusion criteria. The devices demonstrated good accuracy and acceptance, with concerns related to positioning, motion artifacts, and the lack of standardized quality of life measures. The central role of nursing staff in the use, training, and management of notifications was highlighted.</p><p><strong>Discussion: </strong>Wearable sensors show potential in the management of bladder dysfunction, but clinical evidence is preliminary. Pragmatic studies are needed to define their clinical, organizational, and economic impact.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148020454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discharge Readiness and Self-Efficacy After Coronary Artery Bypass Surgery: The Power of Different Discharge Education Materials.","authors":"Dilara Şahan, Nurdan Gezer","doi":"10.1097/CIN.0000000000001459","DOIUrl":"10.1097/CIN.0000000000001459","url":null,"abstract":"<p><p>The aim of this study is to examine the effect of different discharge education methods on readiness for discharge and self-efficacy in individuals undergoing coronary artery bypass graft surgery. This quasi-experimental study included 72 CABG patients in a university hospital in the Aegean Region, Turkey, from October 2019 to March 2022. The Standard Care Group (n=24) received no intervention, while the Education Booklet Group (n=24) received discharge education via a booklet, and the Mobile Application Group (n=24) via a mobile app. There was no statistically significant difference between the Education Booklet Group and the Mobile Application Group in terms of readiness for discharge and self-efficacy scores, but the scores of the Education Booklet Group and the Mobile Application Group were statistically higher than those of the Standard Care Group. While readiness for discharge and self-efficacy showed no significant difference between the 2 intervention groups, the mobile app was found to be at least as effective, usable, and highly recommended as the education traditionally given with the education booklet.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pilot Study for Integrating Automatic Speech Recognition Into Nursing Documentation.","authors":"Keum-Sook Jeun, Ye-Il Kim, Myeung-Ja Lee, Eun-Young Hong, Harin Jun, Min-Jeoung Kang","doi":"10.1097/CIN.0000000000001490","DOIUrl":"10.1097/CIN.0000000000001490","url":null,"abstract":"<p><p>The purpose of this study is to develop and integrate Automatic Speech Recognition (ASR) technology into electronic health record (EHR) for nursing documentation. This study was conducted at a hospital in Seoul, South Korea, and followed the constructive ehealth evaluation method (CeHEM) framework, encompassing 6 phases: research and planning, design, development, implementation, evaluation, and redesign. A multidisciplinary ASR Project task force team, composed of experts from diverse domains including nursing, ASR technology, and EHR systems, led the project. Version 1 of the Nursing ASR system underwent 2 pilot deployments. It was initially integrated into nursing notes and later expanded to the Kardex, which entails considerable nursing documentation. Following the pilots, feedback was gathered from clinical nurses through interviews and an online survey to identify ASR system issues and areas for improvement. Based on these findings, the task force team proposed targeted solutions that informed the development of Version 2. The updated system was redesigned to support a broader range of nursing tasks, offer more intuitive voice commands, and provide a mobile application version to enhance accessibility. This study highlights the importance of nurse-centered, iterative development and demonstrates how interdisciplinary collaboration can translate user feedback into system improvements. The findings provide practical insights for health care organizations adopting ASR technologies.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146204628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pilot Study for Integrating Automatic Speech Recognition Into Nursing Documentation.","authors":"","doi":"10.1097/CIN.0000000000001606","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001606","url":null,"abstract":"","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":"44 9","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148915063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Technology Enhancing Emergency Department Triage: An Integrative Review.","authors":"Matthew Brady, Jaime Thiel, Charlotte Seckman","doi":"10.1097/CIN.0000000000001612","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001612","url":null,"abstract":"<p><p>Accurate triage is vital in emergency departments, where misidentification delays care, increases morbidity and mortality, and strains resources, particularly amidst overcrowding. Despite advances, gaps persist in understanding the role of artificial intelligence tools across diverse settings and their integration into clinical workflows. The aim of this integrative review was to evaluate artificial intelligence's potential to enhance triage efficiency and accuracy, guided by the Technology Acceptance Model (TAM) and Systems Thinking Approach. CINAHL, PubMed, Ovid Medline, EBSCOhost, and IEEE Xplore were searched, identifying 283 articles, refined to 20 through exclusions for duplicates, abstracts, and relevance. Retrospective cohort designs predominated, complemented by cross-sectional, prospective, and observational studies spanning global contexts. The key message was that artificial intelligence tools significantly outperformed traditional triage methods like the Emergency Severity Index (ESI). Findings revealed artificial intelligence predicted critical outcomes (eg, mortality, intensive care unit admissions), reduced mistriage rates, and leveraged diverse data (structured and unstructured), though challenges like limited generalizability and data quality remain. These insights advance health care informatics by highlighting the need for enhanced electronic health record systems and training, offering a pathway to optimize ED resource allocation and patient outcomes. Future research should focus on multicenter validations and real-time implementations to fully realize the transformative potential of artificial intelligence in emergency care.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Informatics Techniques in Nursing Course and Readiness for Medical Artificial Intelligence: The Mediating Role of Informatics Competence.","authors":"Gürkan Özden, Seher Çevik Aktura, Ahmet Ceviz, Rana Ceviz","doi":"10.1097/CIN.0000000000001611","DOIUrl":"https://doi.org/10.1097/CIN.0000000000001611","url":null,"abstract":"<p><p>This descriptive and correlational study examined the relationship between completion of an Informatics Techniques in Nursing course and nursing students' readiness to use medical artificial intelligence, as well as the mediating role of informatics competence in this relationship. The study was conducted with 321 nursing students at a university in eastern Turkey. Data were collected online using a Personal Information Form, the Nursing Informatics Competency Self-Assessment Scale, and the Medical Artificial Intelligence Readiness Scale. Completion of the course was not directly associated with readiness to use medical artificial intelligence (standardized coefficient=0.412, P>.05). However, informatics competence was found to fully mediate this relationship (indirect effect: standardized coefficient=3.988, 95% CI [2.900-5.133]). Course completion was associated with higher informatics competence (standardized coefficient=0.400, P<.001), and higher informatics competence was associated with greater self-reported readiness to use medical artificial intelligence (standardized coefficient=0.994, P<.001). Students' informatics competence and readiness levels were moderate. Because the measures were self-reported, the findings reflect perceived competence and readiness rather than objective performance.</p>","PeriodicalId":520598,"journal":{"name":"Computers, informatics, nursing : CIN","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148805146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}