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Due to the COVID-19 pandemic, telehealth resurfaced as a convenient efficient healthcare delivery method. Researchers indicate that Artificial Intelligence (AI) could further facilitate delivering quality care in telehealth. It is essential to find supporting evidence to use AI-assisted telehealth interventions in nursing.This scoping review focuses on finding users’ satisfaction and perception of AI-assisted telehealth intervention, performances of AI algorithms, and the types of AI technology used.A structured search was performed in six databases, PubMed, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest, following the guidance of the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument.Eight of the 41 studies published between 2017 and 2022 were included in the final review. Six studies were conducted in the United States, one in Japan, and one in South Korea. Four studies collected data from participants (n = 3014). Two studies used image data (n = 1986), and two used sensor data from smart homes to detect patients’ health events for nurses (n = 35). The quality of studies implied moderate to high-quality study (mean = 10.1, range = 7.7–13.7). Two studies reported high user satisfaction, three assessed user perception of AI in telehealth, and only one showed high AI acceptability. Two studies revealed the high performance of AI algorithms. Five studies used machine learning algorithms.AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing. [ABSTRACT FROM AUTHOR]

The risk of pressure ulcers is widely assessed using the Braden Scale for Predicting Pressure Ulcer Risk, which describes patient characteristics for various severity levels. However, many of these characteristics are described in vague terms that nurses may interpret inconsistently, potentially threatening scale reliability. To examine the consistency of nurses’ interpretations of five vaguely described patient characteristics on the Braden Scale we surveyed a convenience sample of 102 nurses and compared their interpretations with those of two nurse experts. The results show large variations in nurses’ interpretations. Although the highest frequency of nurses’ responses to the majority of descriptions was consistent with the experts’ interpretation, the large variation in responses may seriously threaten consistent and accurate assessment of pressure-ulcer risk with the Braden Scale. Our findings suggest that training programs provide operational definitions of these vague patient descriptions, so the Braden Scale can be used consistently in patient care. [ABSTRACT FROM AUTHOR]

The article presents a study describing nursing workload, patient acuity, and length of stay (LOS) and their relationship through a case study of Spaulding Rehabilitation Hospital (SRH) in Boston, Massachusetts. Changes in patient profile were investigated during two periods, between July 2002 and September 2006, and between October 2006 and September 2008. During the study, a 10% or more increase was observed in the categories of close observation, cardiovascular/neurologic management, information/instructional needs, educational needs/lifestyle change, and coping support. It also shows that patients admitted to SRH need intense nursing care.

Abstract: Objective: A rule-based prototype decision support tool; Braden-scale based Automated Risk-assessment Tool (BART) was developed to test whether pressure ulcer risk scores can be determined automatically based on the documented patient data. Methods: The data items required for assessing pressure ulcer risk were identified by analyzing the parameter definitions of the Braden scale and by consulting the nurses specialized in pressure ulcer prevention and care. Documentation coverage and formats of the required data was evaluated. The decision rules were developed based on the inputs from the expert nurses, and were implemented as a web-based prototype tool, BART. The agreement rates between nurses and BART on assigning scores to the six Braden-scale parameters were calculated with 39 convenience samples of patient data. Results: Although several items required for the automated decision were not found from the documentation, the majority of the required data items were documented with feasible formats (i.e., coded lists or free text with nominal or numeric values) for algorithmic processing. When evaluated with 39 test cases, BART and the nurses showed varying levels of agreement (from “slight” to “substantial”) on assigning scores for the six parameters of the Braden scale. They showed “fair” level of agreement with an “at risk” decision. Conclusion: BART has limitations that need to be addressed through future enhancements. However, it demonstrates potential for reuse of documented patient data to automatically populate pressure ulcer risk using the Braden scale. [ABSTRACT FROM AUTHOR]

Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies–Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as 'witnessed e-cigarette use in their household' and 'perception of their tobacco use', could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Clinical practice guidelines (CPGs) are common tools for clinicians in daily practice. In order to use CPGs effectively at the point of care, representing CPGs into computer-interpretable format is essential. Since computer-interpretable guidelines (CIGs) have been reported to increase clinicians’ usage of guidelines and improve patient''s outcomes, it is critical to assess health care knowledge translated from CPGs into CIGs. The overall goal of this study was to illustrate the steps involved in encoding a guideline in guideline interchange format-3 (GLIF3) through a case study of a depression screening and management CPG for a nursing decision support system (DSS). Methods: This study consisted of five steps: (1) Selection of a CPG; (2) extraction and categorization in GLIF3 of concepts related to depression screening and management tasks; (3) converting GLIF3 steps to a flowchart; (4) creation of scenarios representing possible execution paths; (5) guideline execution engine (GLEE) execution of scenarios. Discussion: This study contributes to the body of knowledge regarding creation of CIGs and the use of GLEE as an evaluation tool for the encoded CIG in GLIF format for a depression CPG. CPG representation using GLIF3 and its evaluation by GLEE are useful methods to prepare nursing CPGs for implementation in a DSS. [Copyright &y& Elsevier]

Aim: To determine the relationship between knowledge, attitude and the practice of hand hygiene by nursing and midwifery students in Cambodia. Background: Hand hygiene is the most cost‐effective means to prevent hospital‐acquired infections. Techniques of hand hygiene are simple; however, many researchers have found hand hygiene knowledge, attitude and practice to be poor in many healthcare settings worldwide, especially in developing countries. Cambodia is a developing country in Southeast Asia and data regarding hand hygiene are limited. Methods: A cross‐sectional descriptive study was conducted to assess the level of knowledge, attitude and practice of hand hygiene. Students in nursing and midwifery programmes were the target population (n = 300). Survey data were collected from January to May 2017. Descriptive statistics, t‐tests and correlation coefficients were calculated to assess relationships between student knowledge, attitude and hand hygiene practice. Result: The level of knowledge, attitude and practice of hand hygiene in nursing and midwifery students was moderate. A majority of students had received hand hygiene training. There was no significant difference between nursing and midwifery students in knowledge, attitude and practice of hand hygiene. However, a Pearson correlation of attitude and practice had a weak positive relationship. Conclusion: Both nursing and midwifery students demonstrated moderate levels of knowledge, attitudes and practice of hand hygiene. Implication for nursing and health policy: This study identified a need for hand hygiene training. Developing training programmes to improve attitudes about hand hygiene is strongly recommended. Effective training may contribute to change behaviours of hand hygiene (attitude), improve practice and ultimately reduce hospital‐acquired infections. [ABSTRACT FROM AUTHOR]

As a first step of pursuing the vision of “Big Data science in nursing,” we described the characteristics of nursing research data reported in 194 published nursing studies. We also explored how completely the Version 1 metadata specification of biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) represents these metadata. The metadata items of the nursing studies were all related to one or more of the bioCADDIE metadata entities. However, values of many metadata items of the nursing studies were not sufficiently represented through the bioCADDIE metadata. This was partly due to the differences in the scope of the content that the bioCADDIE metadata are designed to represent. The 194 nursing studies reported a total of 1,181 unique data items, the majority of which take non-numeric values. This indicates the importance of data standardization to enable the integrative analyses of these data to support Big Data science in nursing. [ABSTRACT FROM AUTHOR]