A systematic review of candidate genes and their relevant pathways for metastasis among adults diagnosed with breast cancer.

Background: Presently incurable, metastatic breast cancer is estimated to occur in as many as 30% of those diagnosed with early-stage breast cancer. Timely and accurate identification of those at risk for developing metastasis using validated biomarkers has the potential to have profound impact on overall survival rates. Our primary goal was to conduct a systematic review and synthesize the existing body of scientific knowledge on the candidate genes and their respective single nucleotide polymorphisms associated with metastasis-related outcomes among patients diagnosed with breast cancer.

PICOT questions and search strategies formulation: A novel approach using artificial intelligence automation.

Aim: The aim of this study was to evaluate and compare artificial intelligence (AI)-based large language models (LLMs) (ChatGPT-3.5, Bing, and Bard) with human-based formulations in generating relevant clinical queries, using comprehensive methodological evaluations.

Methods: To interact with the major LLMs ChatGPT-3.

Anxiety and depression among individuals with long COVID: Associations with social vulnerabilities.

Background: We examined the association between symptoms of anxiety and depression among individuals with long COVID and five social vulnerabilities (expenses, employment, food insufficiency, housing, and insurance).

Methods: Data from the Census Bureau's Household Pulse Survey (HPS) detailing COVID incidence, duration, and symptoms between June 1st and November 14th, 2022 contained versions of the Generalized Anxiety Disorder (GAD-2) and the Patient Health Questionnaire (PHQ-2) questionnaires. Associations between anxiety, depression, and the five social vulnerabilities among respondents from different racial and ethnic groups experiencing long COVID were evaluated using generalized binomial logistic regression.

Symptom Descriptors in Individuals Living With Undiagnosed Lower Extremity Peripheral Artery Disease.

Objective: Most individuals with lower extremity peripheral artery disease (PAD) experience symptoms other than claudication and live with undiagnosed PAD yet no tools exist to detect atypical PAD symptoms. The purpose of this study was to identify discriminating PAD symptom descriptors from a community-based sample of patients with no current diagnosis of PAD.

Methods: Symptoms descriptors were obtained in a sample of 22 participants with persistent lower extremity symptoms pre/post exercise.

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

Purpose: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.

Design: This study was a retrospective, observational study.

Methods: We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model.

Mobile Applications in Patients with Chronic Kidney Disease: A Systematic Review.

Justification: Worldwide, 850 million people suffer from chronic kidney disease (CKD), and in Mexico it is the tenth cause of mortality with 13,167 deaths per year. CKD patients undergoing hemodialysis present challenges in following the prescribed treatment and managing care; Therefore, different health strategies have been proposed to address those challenges, including mobile health applications.

Objective: Analyze the scientific evidence available worldwide on mobile health applications for patients with CKD on hemodialysis that have been validated, evaluated, implemented or in the process of development.

Future Needs in Nursing Informatics - Preliminary Findings from the Global Nursing Informatics Survey.

This study investigates the evolving landscape of nursing informatics by conducting a follow-up survey initiated by the International Medical Informatics Association (IMIA) Students and Emerging Professionals (SEP) Nursing Informatics (NI) group in 2015 and 2019. The participants were asked to describe what they thought should be done in their institutions and countries to advance nursing informatics in the next 5-10 years. For this paper, responses in English acquired by December 2023 were analysed using inductive content analysis.

The Relationship Between Patient Perceived Pain and the Numerical Rating System: A Prospective Validation Using Electronic Health Record Data.

We developed a method of using the Clinically Aligned Pain Assessment (CAPA) measures to reconstruct the Numeric Rating System (NRS). We used an observational retrospective cohort study design with prospective validation using de-identified adult patient data derived from a major health system. Data between 2011-2017 were used for development and 2018-2020 for validation.

An Information Model on Pain Management: Cultural Validation and Refinement.

This study aimed to validate and refine an information model on pain management in a Brazilian hospital, considering the institutional culture, using an expert consensus approach. The first stage took place through a computerized questionnaire and Content Validity Index calculation. Pain management attributes were considered validated with 75% consensus among 19 experts.

Technological Challenges and Solutions in Emergency Remote Teaching for Nursing: An International Cross-Sectional Survey.

Objectives: With the sudden global shift to online learning modalities, this study aimed to understand the unique challenges and experiences of emergency remote teaching (ERT) in nursing education.

Methods: We conducted a comprehensive online international cross-sectional survey to capture the current state and firsthand experiences of ERT in the nursing discipline. Our analytical methods included a combination of traditional statistical analysis, advanced natural language processing techniques, latent Dirichlet allocation using Python, and a thorough qualitative assessment of feedback from open-ended questions.

The ChatGPT Effect: Nursing Education and Generative Artificial Intelligence.

This article examines the potential of generative artificial intelligence (AI), such as ChatGPT (Chat Generative Pre-trained Transformer), in nursing education and the associated challenges and recommendations for their use. Generative AI offers potential benefits such as aiding students with assignments, providing realistic patient scenarios for practice, and enabling personalized, interactive learning experiences. However, integrating generative AI in nursing education also presents challenges, including academic integrity issues, the potential for plagiarism and copyright infringements, ethical implications, and the risk of producing misinformation.

Towards Automated Evaluation of Patient Centered Care-Assessing the Potential of Electronic Health Records.

Providing patient centered care is a crucial element of high quality care. It can be defined as a responsive way of caring for and empowering patients, embodying compassion, empathy, and responsiveness to the patient's needs. The aim of this study was to assess the potential of using EHRs as information source in the development of tools for assessing PCC.

The ChatGPT effect and transforming nursing education with generative AI: Discussion paper.

Aim: The aim of this study is to present the possibilities of nurse education in the use of the Chat Generative Pre-training Transformer (ChatGPT) tool to support the documentation process.

Background: The success of the nursing process is based on the accuracy of nursing diagnoses, which also determine nursing interventions and nursing outcomes. Educating nurses in the use of artificial intelligence in the nursing process can significantly reduce the time nurses spend on documentation.

Representation of Environmental Concepts Associated with Health Impacts in Computer Standardized Clinical Terminologies.

Objective: To evaluate the representation of environmental concepts associated with health impacts in standardized clinical terminologies.

Methods: This study used a descriptive approach with methods informed by a procedural framework for standardized clinical terminology mapping. The United Nations Global Indicator Framework for the Sustainable Development Goals and Targets was used as the source document for concept extraction.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature.

Objectives: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

Methods: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021.

Psychosocial problems and high amount of opioid administration are associated with opioid dependence and abuse after first exposure for chronic pain patients.

Controversy surrounding the use of opioids for the treatment and the unique characteristics of chronic pain heighten the risks for abuse and dependence; however, it's unclear if higher doses of opioids and first exposure are associated with dependence and abuse. This study aimed to identify patients who developed dependence or opioid abuse after exposed to opioids for the first time and what were the risks factors associated with the outcome. A retrospective observational cohort study analyzed 2,411 patients between 2011 and 2017 who had a diagnosis of chronic pain and received opioids for the first time.

A Predictive Model for Developing Long Term Opioid Use After Neurosurgery and Orthopedic Surgery.

This study aimed to identify patient characteristics that predict long-term opioid use after an orthopedic or neurosurgery procedure. Long-term opioid use was defined as opioid use for 90 or more days following the surgical procedure. A retrospective analysis was conducted of orthopedic and neurosurgery patients 18 years and older from 01/01/2011 through 12/31/2017 (n = 12,301).

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.

Background: The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence.

Background: Research on technologies based on artificial intelligence in healthcare has increased during the last decade, with applications showing great potential in assisting and improving care. However, introducing these technologies into nursing can raise concerns related to data bias in the context of training algorithms and potential implications for certain populations. Little evidence exists in the extant literature regarding the efficacious application of many artificial intelligence -based health technologies used in healthcare.