Healthcare Informatics Research最新文献

{"title":"How New Chatbots Can Support Personalized Medicine.","authors":"Leonardo J Ramírez López, Ana María Campos Mora","doi":"10.4258/hir.2025.31.3.245","DOIUrl":"10.4258/hir.2025.31.3.245","url":null,"abstract":"<p><strong>Objectives: </strong>This study proposes the integration of chatbots into personalized medicine by demonstrating how these tools can support the personalized medicine model. Chatbots can deliver tailored health recommendations, facilitate patient-doctor communication, and provide decision support in clinical settings. The goal is to establish a reference framework aligned with national and international standards for personalized healthcare solutions.</p><p><strong>Methods: </strong>The chatbot model was developed by reviewing 30 scientific and academic articles focused on artificial intelligence and natural language processing in healthcare. The study analyzed the capabilities of existing healthcare chatbots, particularly their capacity to support personalized medicine through accurate data collection and processing of individual health information.</p><p><strong>Results: </strong>Key parameters identified for effective chatbot deployment in personalized medicine include user engagement, data accuracy, adaptability, and regulatory compliance. The study established a compliance benchmark of 25% based on current industry standards and application performance. The results indicate that the proposed chatbot model significantly increased the precision and efficacy of personalized medical recommendations, surpassing baseline requirements set by standardization organizations.</p><p><strong>Conclusions: </strong>This model provides healthcare professionals and patients with a robust framework for utilizing chatbots in personalized medicine, focusing on improved patient outcomes and engagement. The research identifies a gap in the application of artificial intelligence-driven tools in personalized healthcare and suggests strategic directions for future innovations. Implementing this model aims to bridge this gap, offering a standardized approach to developing chatbots that support personalized medicine.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 3","pages":"245-252"},"PeriodicalIF":2.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in Parkinson's Disease Prediction Using Machine Learning: A Neurological Perspective.","authors":"Aravalli Sainath Chaithanya, Nadipudi Kiran Kumar, Gugulothu Venkatesh Prasad, Bejawada Keerthana","doi":"10.4258/hir.2025.31.3.274","DOIUrl":"10.4258/hir.2025.31.3.274","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to predict the severity of Parkinson's disease (PD) by leveraging a comprehensive dataset integrating cerebrospinal fluid protein and peptide data sourced from UniProt, normalized protein expression metrics, clinical assessments, and gait data. The dataset comprised 248 PD patients monitored longitudinally, with periodic evaluations including 227 proteins, 971 peptides, gait parameters, and Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores at baseline 0, 6, 12, and 24 months.</p><p><strong>Methods: </strong>A multifaceted machine learning framework was employed, consisting of random forest, TensorFlow decision forests, and a custom-developed phaseshift ensembling model. Additionally, regression techniques such as linear regression, random forest regressor, decision tree regressor, and K-nearest neighbors were utilized to support the predictions. These models aimed to forecast PD severity as reflected by UPDRS scores.</p><p><strong>Results: </strong>The custom phase-shift ensembling model demonstrated superior predictive performance, achieving an average symmetric mean absolute percentage error (sMAPE) of 55 across all UPDRS sections. Notably, the random forest regressor excelled in predicting motor function severity (UPDRS-III), attaining an sMAPE of 77.32, indicating its ability to model complex disease progression dynamics effectively.</p><p><strong>Conclusions: </strong>Integrating biological markers, clinical scores, and gait dynamics facilitates accurate modeling of PD progression. The ensemble-based approach, particularly phase-shift ensembling, improves prediction robustness and interpretability, offering a powerful strategy for the early prediction of PD severity. This study highlights the value of multi-source data fusion and advanced machine learning techniques in supporting early diagnosis and informed treatment planning for neurodegenerative diseases.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 3","pages":"274-283"},"PeriodicalIF":2.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of Nursing Informatics Courses in Latin America and the Caribbean.","authors":"Daniel F Condor-Camara, Cinthia Pio-Del-Aguila, Guido Bendezu-Quispe, Alexandra González-Aguña, José María Santamaría-García","doi":"10.4258/hir.2025.31.3.253","DOIUrl":"10.4258/hir.2025.31.3.253","url":null,"abstract":"<p><strong>Objectives: </strong>Understanding and utilizing technology in nursing practice are crucial for adapting to digital environments and enhancing patient care. In this context, integrating nursing informatics courses into university curricula is essential. These courses facilitate a deeper understanding of patient needs and concerns and foster key competencies in technology management. This study aims to identify and characterize the current status of nursing informatics subjects within the undergraduate nursing curricula of Spanish-speaking universities in Latin America and the Caribbean, thereby emphasizing their importance in nursing education and informatics.</p><p><strong>Methods: </strong>A descriptive cross-sectional study was conducted from January to March 2022, involving a systematic search of nursing informatics subjects in the curricula of accredited Spanish-speaking universities offering undergraduate nursing degrees in the Latin American and Caribbean region.</p><p><strong>Results: </strong>Twenty-three out of 400 universities in seven Latin American and Caribbean countries (Argentina, Ecuador, Colombia, Chile, Mexico, Peru, and the Dominican Republic) were identified as offering nursing informatics courses. The syllabi typically include health information systems, database utilization, standardized terminology, health informatics regulations, applications, and nursing informatics fundamentals.</p><p><strong>Conclusions: </strong>Despite the growing importance of nursing informatics, the availability of related courses in university curricula remains limited. These courses are generally offered midway through the degree programs, are not integrated into a sequential curriculum structure, and are predominantly provided by public institutions. However, the course content aligns with international recommendations, highlighting their potential to enhance nursing education and informatics practices.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 3","pages":"253-262"},"PeriodicalIF":2.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling.","authors":"Songsoo Kim, Donghyun Kim, Jaewoong Kim, Jalim Koo, Jinsik Yoon, Dukyong Yoon","doi":"10.4258/hir.2025.31.3.295","DOIUrl":"10.4258/hir.2025.31.3.295","url":null,"abstract":"<p><strong>Objectives: </strong>This study assessed the effectiveness of in-context learning using Generative Pre-trained Transformer-4 (GPT-4) for labeling radiology reports.</p><p><strong>Methods: </strong>In this retrospective study, radiology reports were obtained from the Medical Information Mart for Intensive Care III database. Two structured prompts-the \"basic prompt\" and the \"in-context prompt\"- were compared. An optimization experiment was conducted to assess consistency and the occurrence of output format errors. The primary labeling experiments were performed on 200 unseen head computed tomography (CT) reports for multilabel classification of predefined labels (Experiment 1) and on 400 unseen abdominal CT reports for multi-label classification of actionable findings (Experiment 2).</p><p><strong>Results: </strong>The inter-reader accuracies in Experiments 1 and 2 were 0.93 and 0.84, respectively. For multi-label classification of head CT reports (Experiment 1), the in-context prompt led to notable increases in F1-scores for the \"foreign body\" and \"mass\" labels (gains of 0.66 and 0.22, respectively). However, improvements for other labels were modest. In multi-label classification of abdominal CT reports (Experiment 2), in-context prompts produced substantial improvements in F1-scores across all labels compared to basic prompts. Providing context equipped the model with domain-specific knowledge and helped align its existing knowledge, thereby improving performance.</p><p><strong>Conclusions: </strong>Incontext learning with GPT-4 consistently improved performance in labeling radiology reports. This approach is particularly effective for subjective labeling tasks and allows the model to align its criteria with those of human annotators for objective labeling. This practical strategy offers a simple, adaptable, and researcher-oriented method that can be applied to diverse labeling tasks.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 3","pages":"295-309"},"PeriodicalIF":2.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digitalizing Emergency Referral System and its Evaluation in Northern Thailand.","authors":"Pudtan Phanthunane, Kritsada Wattanasaovaluk, Atipan Suwatmakin, Anon Suksak, Udomsak Tangchaisuriya, Utsana Tonmukayakul, Nopparat Ratchadaporn, Woravut Kowatcharakul, Direk Patmasiriwat","doi":"10.4258/hir.2025.31.3.235","DOIUrl":"10.4258/hir.2025.31.3.235","url":null,"abstract":"<p><strong>Objectives: </strong>Thailand recently implemented an electronic emergency referral system, known as \"HIS.SANSAI,\" to improve the speed, efficiency, and quality of patient care. This study evaluated the impact of HIS.SANSAI on user experiences and health outcomes.</p><p><strong>Methods: </strong>A multimethod approach was employed, combining data analysis from a cross-sectional survey that quantified users' preferences and perceptions with an examination of de-identified emergency referral records from 2019 to 2021. Multiple regression analysis assessed whether HIS.SANSAI significantly reduced the duration of medical services, while logistic regression evaluated changes in health outcomes before and after its implementation.</p><p><strong>Results: </strong>The survey results revealed high proficiency in system capabilities. Referring hospitals (sending hospitals) rated the system highest, with a score of 8.00 for \"Reducing coordinated time.\" Referral hospitals (receiving hospitals) expressed moderate satisfaction, scoring highest (7.03) for \"Reducing mistakes in patient information transfer\" and lowest (4.27) for \"Ease of use when recording.\" The effects of HIS.SANSAI were partially supported. Positive outcomes included shorter service times and a 13.16% reduction in severity at emergency room discharge for ischemic stroke patients. However, negative consequences were observed, such as notable treatment delays for acute appendicitis patients.</p><p><strong>Conclusions: </strong>HIS.SANSAI demonstrated robust system capabilities and reduced errors in patient information transfer. Its impact on health outcomes was mixed, with both positive and negative effects. Further evaluation and enhancements are necessary to optimize the system's overall effectiveness.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 3","pages":"235-244"},"PeriodicalIF":2.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models for Pre-mediation Counseling in Medical Disputes: A Comparative Evaluation against Human Experts.","authors":"Min Seo Kim, Jung Su Lee, Hyuna Bae","doi":"10.4258/hir.2025.31.2.200","DOIUrl":"10.4258/hir.2025.31.2.200","url":null,"abstract":"<p><strong>Objectives: </strong>Assessing medical disputes requires both medical and legal expertise, presenting challenges for patients seeking clarity regarding potential malpractice claims. This study aimed to develop and evaluate a chatbot based on a chain-of-thought pipeline using a large language model (LLM) for providing medical dispute counseling and compare its performance with responses from human experts.</p><p><strong>Methods: </strong>Retrospective counseling cases (n = 279) were collected from the Korea Medical Dispute Mediation and Arbitration Agency's website, from which 50 cases were randomly selected as a validation dataset. The Claude 3.5 Sonnet model processed each counseling request through a five-step chain-of-thought pipeline. Thirty-eight experts evaluated the chatbot's responses against the original human expert responses, rating them across four dimensions on a 5-point Likert scale. Statistical analyses were conducted using Wilcoxon signed-rank tests.</p><p><strong>Results: </strong>The chatbot significantly outperformed human experts in quality of information (p < 0.001), understanding and reasoning (p < 0.001), and overall satisfaction (p < 0.001). It also demonstrated a stronger tendency to produce opinion-driven content (p < 0.001). Despite generally high scores, evaluators noted specific instances where the chatbot encountered difficulties.</p><p><strong>Conclusions: </strong>A chain-of-thought-based LLM chatbot shows promise for enhancing the quality of medical dispute counseling, outperforming human experts across key evaluation metrics. Future research should address inaccuracies resulting from legal and contextual variability, investigate patient acceptance, and further refine the chatbot's performance in domain-specific applications.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"200-208"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Agent Approach for Sepsis Management.","authors":"Victor Iapascurta, Ion Fiodorov, Adrian Belii, Viorel Bostan","doi":"10.4258/hir.2025.31.2.209","DOIUrl":"10.4258/hir.2025.31.2.209","url":null,"abstract":"<p><strong>Objectives: </strong>The high incidence of sepsis necessitates the development of practical decision-making tools for intensivists, especially during the early, critical phases of management. This study evaluates a multi-agent system intended to assist clinicians with antibiotic therapy and adherence to current sepsis management guidelines before diagnostic results become available.</p><p><strong>Methods: </strong>A multi-agent system incorporating three specialized agents was developed: a sepsis management agent, an antibiotic recommendation agent, and a sepsis guidelines compliance agent. A sepsis case from the MIMIC IV database, organized as a clinical vignette, was used to integrate and test these agents for generating management recommendations. The system leverages retrieval-augmented generation to improve decision-making through the integration of current literature and guidelines.</p><p><strong>Results: </strong>The application produced management recommendations for a sepsis case associated with pneumonia, including early initiation of broad-spectrum antibiotics and close monitoring for clinical deterioration. Two expert intensivists evaluated these recommendations as \"acceptable\" and reported moderate interrater agreement (Cohen's kappa = 0.622, p = 0.003) across various aspects of recommendation usefulness.</p><p><strong>Conclusions: </strong>The multi-agent system shows promise in enhancing decision-making for sepsis management by optimizing antibiotic therapy and ensuring guideline compliance. However, reliance on a single case study limits the generalizability of the findings, highlighting the need for broader validation in diverse clinical settings to improve patient outcomes.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"209-214"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Symptom and Sentiment Analysis of Older People with Cancer and Caregivers: A Text Mining Approach Using Korean Social Media Data.","authors":"Kyunghwa Lee, Soomin Hong","doi":"10.4258/hir.2025.31.2.175","DOIUrl":"10.4258/hir.2025.31.2.175","url":null,"abstract":"<p><strong>Objectives: </strong>This study examined the symptoms and emotions expressed by older adults with cancer and their caregivers in South Korean online cancer communities. It aimed to identify narrative patterns and provide insights to inform personalized care strategies.</p><p><strong>Methods: </strong>We analyzed 6,908 user-generated posts collected from major online cancer communities in South Korea. Keyword frequency analysis, term frequency-inverse document frequency, 2-gram analysis, and latent Dirichlet allocation-based topic modeling were applied to explore language patterns. Sentiment analysis identified 12 emotional categories, and Pearson correlation coefficients were calculated to examine associations between symptoms and emotional expressions. All data were cleaned and standardized prior to analysis.</p><p><strong>Results: </strong>Many users expressed anxiety (20.63%) and depression (19.59%), frequently associated with chemotherapy and sleep disturbances. Among reported symptoms, sleep problems carried the highest negative sentiment (79.81%), underscoring their profound impact on well-being. Topic modeling consistently revealed seven recurring themes, including treatment decision-making, symptom management, and concerns about family, demonstrating the layered and personalized experiences of older cancer patients and their caregivers.</p><p><strong>Conclusions: </strong>This study explored treatment-related and symptom-related difficulties faced by older adults with cancer. Many reported significant emotional strain, especially anxiety, depression, and sleep disturbances. These findings highlight the necessity for supportive strategies addressing both psychological and physical aspects of care. Future research could investigate the utility of large language models in analyzing these narratives, provided the data is ethically managed and appropriate for such use.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"175-188"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144092920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consensus on the Potential of Large Language Models in Healthcare: Insights from a Delphi Survey in Korea.","authors":"Ah-Ram Sul, Seihee Kim","doi":"10.4258/hir.2025.31.2.146","DOIUrl":"10.4258/hir.2025.31.2.146","url":null,"abstract":"<p><strong>Objectives: </strong>Given the rapidly growing expectations for large language models (LLMs) in healthcare, this study systematically collected perspectives from Korean experts on the potential benefits and risks of LLMs, aiming to promote their safe and effective utilization.</p><p><strong>Methods: </strong>A web-based mini-Delphi survey was conducted from August 27 to October 14, 2024, with 20 selected panelists. The expert questionnaire comprised 84 judgment items across five domains: potential applications, benefits, risks, reliability requirements, and safe usage. These items were developed through a literature review and expert consultation. Participants rated their agreement or perceived importance on a 5-point scale. Items meeting predefined thresholds (content validity ratio ≥0.49, degree of convergence ≤0.50, and degree of consensus ≥0.75) were prioritized.</p><p><strong>Results: </strong>Seventeen participants (85%) responded to the first round, and 16 participants (80%) completed the second round. Consensus was achieved on several potential applications, benefits, and reliability requirements for the use of LLMs in healthcare. However, significant heterogeneity was found regarding perceptions of associated risks and criteria for safe usage of LLMs. Of the 84 total items, 52 met the criteria for statistical validity, confirming the diversity of expert opinions.</p><p><strong>Conclusions: </strong>Experts reached a consensus on certain aspects of LLM utilization in healthcare. Nonetheless, notable differences remained concerning risks and requirements for safe implementation, highlighting the need for further investigation. This study provides foundational insights to guide future research and inform policy development for the responsible introduction of LLMs into the healthcare field.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"146-155"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI-Based Nursing Diagnosis and Documentation Recommendation Using Virtual Patient Electronic Nursing Record Data.","authors":"Hongshin Ju, Minsul Park, Hyeonsil Jeong, Youngjin Lee, Hyeoneui Kim, Mihyeon Seong, Dongkyun Lee","doi":"10.4258/hir.2025.31.2.156","DOIUrl":"10.4258/hir.2025.31.2.156","url":null,"abstract":"<p><strong>Objectives: </strong>Nursing documentation consumes approximately 30% of nurses' professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system's impact on overall documentation efficiency and quality.</p><p><strong>Methods: </strong>Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI's ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency.</p><p><strong>Results: </strong>Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency.</p><p><strong>Conclusions: </strong>Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI's potential to improve nursing documentation efficiency and accuracy in future clinical settings.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"31 2","pages":"156-165"},"PeriodicalIF":2.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}