Understanding Nursing Informatics: A Survey of Nurses' Perception.

This paper aims to explore nurses' understanding of nursing informatics (NI). The structured and open-ended questionnaires were used to explore their knowledge of NI. Between 17 and 18 October 2023, the survey was conducted via a web portal and targeted 124 nurses attending in a nursing management training programme at the West China Hospital, Sichuan University. A total of 57.3% (71/124) of the nurses completed the survey. Of these, 29.6% (21/71) were unaware of NI and 70.4% (50/71) were aware of NI. However, only one of the nurses gave an accurate definition of NI. The results of this study suggest that there is a limited understanding of NI among senior nurses in mainland China, indicating a need to improve NI education for nurses.

Nursing Undergraduates' Perceptions of Nursing Informatics Courses.

The aim of this paper is to explore how nursing undergraduates perceive a nursing informatics (NI) course. Between 7 and 14 September 2023, a survey was conducted with 51 nursing undergraduates who enrolled in 2021. A structured and open-ended questionnaire was used to explore their perceptions of the course. A total of 90.2% (46/51) of the students completed the survey. Of the respondents, 69.6% (32/46) were unaware of NI, while 30.4% (14/46) were aware of it. Furthermore, 93.5% (43/46) of the respondents supported the introduction of a nursing informatics course. The results of this study will guide the development of NI curricula.

Evaluation of a Nursing Informatics Course by Nurses: A Continuing Education.

This study aimed to evaluate the effectiveness of a nursing informatics continuing education course and nurses' perceptions of it. This study investigates the evaluation and satisfaction of 103 nurses who attended the course on 10-11 June 2023. The survey was divided into two parts: the first part focused on the evaluation of teaching and the second part focused on the evaluation of the course. The first part contained 7 structured questions and 1 open-ended question, while the second part contained 11 structured questions. The results show a high level of satisfaction, with the teaching receiving a score of 9.9 out of 10. 95% of the participants were "very satisfied" and 5% were "satisfied".

Perspectives of Multidisciplinary Faculty on the Design of Nursing Informatics Curriculum.

This study aimed to understand the experiences and challenges of interdisciplinary collaboration in nursing informatics curriculum design. The study was conducted from 9-20 August 2023 with 14 multidisciplinary faculty members. Surveys and interviews focused on perceptions of interdisciplinary communication, importance of collaboration, challenges encountered, and training needs to identify barriers to effective curriculum development. Participation was voluntary and anonymous. The results revealed significant challenges, including differences in terminology and thought processes between disciplines, role ambiguity, and cultural/methodological differences. The results of the study highlight the importance of collaboration, which is critical to the development of an effective and cohesive nursing informatics curriculum.

Perception of ChatGPT by Nursing Undergraduates.

The study aimed to investigate the awareness and use of ChatGPT among undergraduate nursing students. A structured questionnaire was used to assess the awareness and use of ChatGPT. The questionnaire was undergraduate nursing students enrolled in 2021. The response rate to the survey was 90.2% (46/51). Of the respondents, 45 students were aware of ChatGPT, and only one student was not aware of ChatGPT. In terms of usage, 23 students responded. Among them, 16 used ChatGPT to enhance their learning experience, six for homework, five for chatting, four for essay writing, and one for other purposes. This study provides valuable insights for the better use of ChatGPT in nursing education.

Nurses' Perspectives on ChatGPT: A Survey Study.

The aim of this study was to assess nurses' awareness and use of ChatGPT. The study was conducted in October 2023 with an online questionnaire for 124 nurses in the nursing education programme at West China Hospital. The questionnaire included participants' demographic information, awareness of ChatGPT, and actual experience of using it. A total of 57.3% (71/124) of the nurses completed the survey. Of these, 56.3% (40/71) were aware of ChatGPT and 43.7% (31/71) were not aware of ChatGPT. In terms of use, of the 20 who used ChatGPT, 13 used it for studying, 10 for essay writing, five for research and two for chatting. This study highlights the potential of ChatGPT to improve nurses' professional competence and effectiveness. Further research will focus on how ChatGPT can be used more effectively to support nurses' professional development and growth.

Influence of short-term hypoxia exposure on dynamic visual acuity.

Background: To quantify the changes in dynamic visual acuity (DVA) and explain the hidden reasons after acute exposure to hypobaric hypoxia status. Methods: The study group comprised 18 healthy male and 15 healthy female participants aged 20-24 years old. DVA was measured with the self-developed software of Meidixin (Tianjin) Co., Ltd. Measurements were taken at eight altitudes. Data analysis was performed using the Kolmogorov-Smirnov test, paired sample T-test, and two-way repeated measures analysis of variance (ANOVA) for repeated measurements. Results: At constant altitude, DVA showed an overall decreasing trend with increasing angular velocity and a fluctuating decrease at the vast majority of altitudes. At constant angular velocities, DVA gradually increased with altitude, with the most pronounced increase in DVA at altitude 5, and thereafter a gradual decrease in DVA as altitude increased. Finally, as altitude decreased, DVA increased again and reached a higher level at the end of the experiment, which was superior to the DVA in the initial state. Conclusion: Under a hypobaric hypoxic environment at high altitude, DVA was affected by the angular velocity and the degree of hypoxia, manifesting as an increase or decrease in DVA, which affects the pilot's observation of the display and control interfaces during the driving process, acquisition of information, and decision-making ability, which in turn may potentially jeopardize the safety of the flight.

Using large language model to guide patients to create efficient and comprehensive clinical care message.

OBJECTIVE: This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations. METHODS: We collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios. We used 3 LLMs to generate follow-up questions: (1) Comprehensive LLM Artificial Intelligence Responder (CLAIR): a locally fine-tuned LLM, (2) GPT4 with a simple prompt, and (3) GPT4 with a complex prompt. Five physicians rated them with the actual follow-ups written by healthcare providers on clarity, completeness, conciseness, and utility. RESULTS: For five scenarios, our CLAIR model had the best performance. The GPT4 model received higher scores for utility and completeness but lower scores for clarity and conciseness. CLAIR generated follow-up questions with similar clarity and conciseness as the actual follow-ups written by healthcare providers, with higher utility than healthcare providers and GPT4, and lower completeness than GPT4, but better than healthcare providers. CONCLUSION: LLMs can generate follow-up patient messages designed to clarify a medical question that compares favorably to those generated by healthcare providers.

Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis.

BACKGROUND: Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. OBJECTIVE: This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. METHODS: We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. RESULTS: The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57). CONCLUSIONS: The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.

The enhancement of astaxanthin production in Phaffia rhodozyma through a synergistic melatonin treatment and zinc finger transcription factor gene overexpression.

Astaxanthin has multiple physiological functions and is applied widely. The yeast Phaffia rhodozyma is an ideal source of microbial astaxanthin. However, the stress conditions beneficial for astaxanthin synthesis often inhibit cell growth, leading to low productivity of astaxanthin in this yeast. In this study, 1 mg/L melatonin (MT) could increase the biomass, astaxanthin content, and yield in P. rhodozyma by 21.9, 93.9, and 139.1%, reaching 6.9 g/L, 0.3 mg/g DCW, and 2.2 mg/L, respectively. An RNA-seq-based transcriptomic analysis showed that MT could disturb the transcriptomic profile of P. rhodozyma cell. Furthermore, differentially expressed gene (DEG) analysis show that the genes induced or inhibited significantly by MT were mainly involved in astaxanthin synthesis, metabolite metabolism, substrate transportation, anti-stress, signal transduction, and transcription factor. A mechanism of MT regulating astaxanthin synthesis was proposed in this study. The mechanism is that MT entering the cell interacts with components of various signaling pathways or directly regulates their transcription levels. The altered signals are then transmitted to the transcription factors, which can regulate the expressions of a series of downstream genes as the DEGs. A zinc finger transcription factor gene (ZFTF), one of the most upregulated DEGs, induced by MT was selected to be overexpressed in P. rhodozyma. It was found that the biomass and astaxanthin synthesis of the transformant were further increased compared with those in MT-treatment condition. Combining MT-treatment and ZFTF overexpression in P. rhodozyma, the biomass, astaxanthin content, and yield were 8.6 g/L, 0.6 mg/g DCW, and 4.8 mg/L and increased by 52.1, 233.3, and 399.7% than those in the WT strain under MT-free condition. In this study, the synthesis and regulation theory of astaxanthin is deepened, and an efficient dual strategy for industrial production of microbial astaxanthin is proposed.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.

OBJECTIVES: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts. MATERIALS AND METHODS: We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness. RESULTS: Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001). CONCLUSION: End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline.

Leveraging large language models for generating responses to patient messages-a subjective analysis.

OBJECTIVE: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal. MATERIALS AND METHODS: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism. By combining with this dataset, we further fine-tuned our model (CLAIR-Long). To evaluate fine-tuned models, we used 10 representative patient portal questions in primary care to generate responses. We asked primary care physicians to review generated responses from our models and ChatGPT and rated them for empathy, responsiveness, accuracy, and usefulness. RESULTS: The dataset consisted of 499 794 pairs of patient messages and corresponding responses from the patient portal, with 5000 patient messages and ChatGPT-updated responses from an online platform. Four primary care physicians participated in the survey. CLAIR-Short exhibited the ability to generate concise responses similar to provider's responses. CLAIR-Long responses provided increased patient educational content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, receiving positive evaluations for responsiveness, empathy, and accuracy, while receiving a neutral rating for usefulness. CONCLUSION: This subjective analysis suggests that leveraging large language models to generate responses to patient messages demonstrates significant potential in facilitating communication between patients and healthcare providers.

Leveraging explainable artificial intelligence to optimize clinical decision support.

OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches. METHODS: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. RESULTS: The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues. CONCLUSION: We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.

Telemedicine and COVID-19 Pandemic: Experiences of Western China Hospital.

COVID-19 has brought unprecedented challenges to the healthcare system. In response to COVID-19, hospitals can replace some routine medical services with telemedicine. At the beginning of the pandemic, West China Hospital developed a new model of telemedicine platform against COVID-19. The telemedicine platform played a critical role in fighting the pandemic in Sichuan Province and significantly improved healthcare outcomes.

Development of Ward Visitor Management System in the COVID-19 Epidemic.

The purpose of this study was to design, develop, and deploy a visitor management system (VMS) to effectively manage visitors during COVID-19. The VMS was designed using the User-Centered Design (UCD) methodology. The iterative process of UCD includes 3 interviews and 5 usability tests and cognitive walkthrough cycles. This system comprised six parts: the WEB server provides visit scheduling service; the database server stores visit data and provides visit data services; the mobile application server provides security checks and scanning services; the electronic medical record (EMR) server provides ward data service; the Internet application gateway provides health code data service and exchanges health code data with the Sichuan Tianfu Health Code platform, and the service bus enables the centralized exchange and integration of visit data. The visit management system optimizes the workflow of ward visitors, improves staff productivity, and reduces the risk of infection transmission.

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study.

BACKGROUND: Artificial intelligence models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone. OBJECTIVE: In this study, we explored the performance of ChatGPT for primary screening of mild cognitive impairment (MCI) and standardized the design steps and components of the prompts. METHODS: We gathered a total of 174 participants from the DementiaBank screening and classified 70% of them into the training set and 30% of them into the test set. Only text dialogues were kept. Sentences were cleaned using a macro code, followed by a manual check. The prompt consisted of 5 main parts, including character setting, scoring system setting, indicator setting, output setting, and explanatory information setting. Three dimensions of variables from published studies were included: vocabulary (ie, word frequency and word ratio, phrase frequency and phrase ratio, and lexical complexity), syntax and grammar (ie, syntactic complexity and grammatical components), and semantics (ie, semantic density and semantic coherence). We used R 4.3.0. for the analysis of variables and diagnostic indicators. RESULTS: Three additional indicators related to the severity of MCI were incorporated into the final prompt for the model. These indicators were effective in discriminating between MCI and cognitively normal participants: tip-of-the-tongue phenomenon (P<.001), difficulty with complex ideas (P<.001), and memory issues (P<.001). The final GPT-4 model achieved a sensitivity of 0.8636, a specificity of 0.9487, and an area under the curve of 0.9062 on the training set; on the test set, the sensitivity, specificity, and area under the curve reached 0.7727, 0.8333, and 0.8030, respectively. CONCLUSIONS: ChatGPT was effective in the primary screening of participants with possible MCI. Improved standardization of prompts by clinicians would also improve the performance of the model. It is important to note that ChatGPT is not a substitute for a clinician making a diagnosis.