Healthcare Informatics Research最新文献

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Objectives: The integration of large language models (LLMs), particularly those based on the generative pre-trained transformer (GPT) architecture, has begun to revolutionize various fields, including dentistry. Despite these promising applications, the use of GPT in dentistry presents several challenges. Ongoing research and the development of robust ethical frameworks are essential to mitigate these issues and enhance the responsible deployment of GPT technologies in clinical settings. Hence, this systematic review aims to explore the trends, applications, strengths, and challenges associated with the use of GPT in dentistry.

Methods: Articles were selected if they contained detailed information on the application of GPT in dentistry. The search strategy used in systematic reviews follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search of databases and other sources yielded a total of 704 studies. After removing duplicates and conducting a full-text screening, 16 articles were included in the review. The methodological quality of the research was evaluated using the Critical Appraisal Skills Programme (CASP) checklist.

Results: Out of a total of 91 articles published on GPT in dentistry, 20 were editorials and 11 were narrative reviews; these were excluded, leaving 60 original research articles for further analysis. The articles were assessed based on the type of results they provided. Ultimately, 16 articles that reported positive findings with robust methodology were included in this review.

Conclusions: The results highlight mixed responses; therefore, further research on integration into clinical workflows must be conducted with extensive methodological rigor.

Objectives: This study presents a comprehensive review of the clinical applications, technical challenges, and ethical considerations associated with using large language models (LLMs) in medicine.

Methods: A literature survey of peer-reviewed articles, technical reports, and expert commentary from relevant medical and artificial intelligence journals was conducted. Key clinical application areas, technical limitations (e.g., accuracy, validation, transparency), and ethical issues (e.g., bias, safety, accountability, privacy) were identified and analyzed.

Results: LLMs have potential in clinical documentation assistance, decision support, patient communication, and workflow optimization. The level of supporting evidence varies; documentation support applications are relatively mature, whereas autonomous diagnostics continue to face notable limitations regarding accuracy and validation. Key technical challenges include model hallucination, lack of robust clinical validation, integration issues, and limited transparency. Ethical concerns involve algorithmic bias risking health inequities, threats to patient safety from inaccuracies, unclear accountability, data privacy, and impacts on clinician-patient interactions.

Conclusions: LLMs possess transformative potential for clinical medicine, particularly by augmenting clinician capabilities. However, substantial technical and ethical hurdles necessitate rigorous research, validation, clearly defined guidelines, and human oversight. Existing evidence supports an assistive rather than autonomous role, mandating careful, evidence-based integration that prioritizes patient safety and equity.