Nursing Administration Quarterly最新文献

Therapeutic communication is an essential nursing skill to foster nurse-patient relationships. Prelicensure nursing students experience anxiety when communicating with patients, which can impact performance. Generative artificial intelligence is a new method of simulation that offers an opportunity for nursing students to practice therapeutic communication in a safe environment. The purposes of this study were to explore: (1) the effect of generative artificial intelligence simulation on first-year baccalaureate nursing students' therapeutic communication skill; and (2) first-year baccalaureate nursing students' perceptions of using generative artificial intelligence simulation to practice therapeutic communication. Participants in the experimental group engaged in 2 virtual generative artificial intelligence simulation scenarios focused on therapeutic communication through the SimConverse platform. Findings from this study revealed nonstatistically significant higher therapeutic communication skill among students who engaged in generative AI simulation versus those who did not. Qualitative data revealed 3 themes: (1) realism, (2) practice without pressure, and (3) feedback. Given the limited, conflicting empirical findings available related to generative AI simulation and therapeutic communication among prelicensure nursing students, additional research is needed in this area. Moreover, nursing faculty need instruction on artificial intelligence to adequately train generative artificial intelligence models for appropriate use in nursing education.

Background: The objective of this paper was to systematically evaluate and explore what is currently known about the use of virtual reality (VR) as a patient education instrument to convey medical information and how conveying this health information using VR ultimately influences patient outcomes.

Methods: In this literature review, relevant studies were identified. Study characteristics (eg, setting, study design, sample characteristics, VR methods, and outcomes), and findings were extracted revealing diverse applications of VR in patient education, demonstrating efficacy in areas such as preventive care, discharge education, symptom management, and preoperative preparation. Our findings revealed several potential areas for implementation.

Results: The results of the review reveal that VR has positive effects on patient education including improvement in knowledge, self-efficacy, psychological outcomes, and physiological outcomes. These findings provide guidance for improving patient education using VR.

Conclusion: The effectiveness of patient education utilizing VR is critical to optimal health outcomes, and VR can augment this process. Health information given to patients by their provider has profound implications regarding adherence and compliance with their prescribed treatment plan.

The aim of this research is to gather and analyze design ideas for a video-based remote clinical consultation application that uses AI to detect communication elements, such as nonverbal cues, during simulated consultations. The application is intended for use by health professions students and professionals in simulation-based training to develop nonverbal communication and video consultation skills. It provides feedback based on predefined rules applied to communication detected by AI. We conducted semi-structured interviews with 11 health care professionals and educators to explore their perspectives on video consultations, telehealth training in health professions education, and the methods used to teach nonverbal communication in these programs. The interview data were analyzed using inductive thematic analysis. The findings present key themes that can be used to inform design decisions for simulated video consultation situations, automated recognition of nonverbal communication in video consultation simulation, and feedback derived from it. We found that more objective measures of communication skills could enhance health professions education simulations. In addition, automatic feedback should focus on highlighting positive aspects of communication, limit low-level detail, and provide contextual information to support reflection. Finally, incorporating emotionally charged scenarios is valuable for teaching nonverbal communication skills.

Although the significance of big data and data science in predicting health outcomes and identifying causal factors is widely recognized, their application in health disparities research remains limited. Understanding health disparities in the visually impaired population requires examining their health behavior patterns and health literacy levels, which can longitudinally impact their health and well-being. In prior research, one of the authors of the study conducted an online survey with 2718 participants using 5 validated self-reported questionnaires, such as Health Promoting Lifestyle Profile II, Health Literacy Questionnaire, eHealth Literacy Questionnaire, General Self-Efficacy, and Center for Epidemiological Studies Depression. Analysis of the online survey data demonstrated that individuals with blindness exhibited significantly higher levels of health-promoting behaviors, health literacy, and eHealth literacy compared to those with moderate and severe low vision. In this study, the research team developed an R Shiny web application as a follow-up to the online survey to disseminate its findings reproducibly and interactively. The R Shiny web application is expected to facilitate reproducible as well as interactive data analysis and sharing more efficiently than traditional methods, such as appendices or Supplementary Materials, Supplemental Digital Content 2, http://links.lww.com/CIN/A463 in academic journals. Extending the research cycle with open datasets and reproducible data analysis can deepen our understanding of health disparities and foster greater collaboration among researchers with similar interests.

An ST-segment elevation myocardial infarction is a life-threatening condition within the spectrum of acute coronary syndrome, with well-recognized implications for short-term and long-term health outcomes. Retrospective review of our institutional electronic health records identified patterns of inaccurate acute coronary syndrome diagnostic assignment, including the presence of erroneous ST-segment elevation myocardial infarction diagnoses in discharge records. Such erroneous diagnoses in the durable health record may increase risk to patients through inadequate risk assessment and inappropriate follow-up care. Misapplication of a shortcut in our electronic health record system for rapidly ordering emergent cases was identified as a potential cause of the quality gap. To address this issue, a nurse-led quality improvement project was developed by using the define, measure, analyze, improve, and control framework. The project intervention decreased the percentage of patients with erroneous diagnoses from 24% to 7% and marginally improved nursing staff satisfaction. Our project successfully addressed an institutional quality concern. Our goal of reducing erroneous discharge diagnoses with minimal disruption to our existing practice was achieved, positively affecting our patients. Including both nurse leaders and frontline health care workers ensured the success of our project.

The Nursing Value Model (NVM) is a data framework model developed to measure the value of nursing care at the patient level. The NVM was constructed by multiple datasets extracted and assembled from various sources, such as the hospital electronic health records (EHR) and administrative data. Yet, very few studies have examined this model. As such, this study aimed to introduce how to construct NVM using available health care data, and discuss the feasibility of doing so by describing the insights and pitfalls during the development of the dataset. Data from 5 sources were used to build the dataset used to explore the NVM to estimate patient-level nursing cost estimation. Five aspects of data acquisition and synthesis are described: (a) each dataset acquisition, (b) the data wrangling process, (c) dataset construction, (d) data integrability, and (e) the strengths and weaknesses of each dataset. Six datasets from four different data sources were collected and merged, constructing the final dataset used for the NVM. Unique codes for nurses and patients were not always uniform, making the data complex and difficult to merge. To compute nursing value for the future, data systems need to be designed to collect, organize, and synthesize data easily.