Background: Digital education using immersive virtual reality (VR) technology is available in nursing. Evidence of its effectiveness is essential. Purpose: This review analyzed the effectiveness of and barriers to using immersive VR in nursing education. Methods: A structured search was performed in PubMed, Cumulative Index to Nursing and Allied Health Literature, EMBASE, PsycINFO, Web of Science, and ProQuest Nursing & Allied Health Database. The Medical Education Research Study Quality Instrument was used to assess the quality of studies. Results: The final review was composed of 9 studies of moderate to high quality published between 2018 and 2020. The review indicated that immersive VR increased learning, cognitive, and psychomotor performance. Most participants of the studies preferred using immersive VR in education and with a variety of experiential scenarios. Barriers were associated with technological hardware and software (eg, simulation sickness, lack of visual comfort). Conclusion: The review supports the viability of immersive VR technology in nursing education.

Background: 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. Objectives: 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. Methods: 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. Results: 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. Conclusions: AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing.

Nursing has embraced online education to increase its workforce while providing flexible advanced education to nurse professionals. Faculty use virtual simulation and other adaptive learning technologies to enhance learning efficiency and student outcomes in online courses. The purpose of this study was to assess the impact of simulated Electronic Health Records (EHRs) on informatics competency in a graduate online informatics course. A two-group independent measures study design was adopted to assess students' perception of a simulated EHR while comparing differences in informatics competencies between an intervention group and a control group. A simulated EHR assignment was provided to students in the intervention group, and a paper assignment was provided to those in the control group. The informatics competency of the students was measured using the Self-Assessment of Informatics Competency Scale for Health Professionals (SICS). Students who were enrolled in a family nurse practitioner program in fall of 2019 participated in this study (n = 39). The students expressed positive perceptions of a simulated EHR experience. The SICS results indicated that students in the intervention (simulated EHR) group showed higher informatics competency than those in the control group. The positive results of this study support incorporating simulated EHR exercises in online courses. Higher informatics competency in the intervention group implies that the use of simulated EHR facilitated learning of complicated informatics concepts.

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.