Background: Clinical skills training is an essential component of nursing education. However, sometimes education does not sufficiently prepare nurses for the real world. Virtual reality (VR) is an innovative method to complement existing learning strategies, yet few studies investigate its effectiveness. This study compared educational outcomes achieved by three groups learning with either of two different VR simulation variants, with varying technological features, or a video training on the endotracheal suctioning skill. Methods: The investigated outcomes were knowledge and skill acquisition, learner satisfaction, and technology acceptance. 131 undergraduate nursing students were randomised into three groups, based on the interventions they received. Knowledge was assessed through a pre-post-test design, skill through a post-intervention objective structured clinical examination on a manikin, learning satisfaction and technology acceptance through standardised questionnaires, and qualitative feedback through focus groups. Results: All interventions led to a significant knowledge acquisition, with no significant difference between the groups. The video intervention group performed significantly better than the VR groups in skill demonstration. One of the two VR intervention groups had a significantly higher learner satisfaction than the video group. Technology acceptance was high for both VR groups, with the simpler VR simulation resulting in higher technology acceptance than the one with more experimental features. Students described the VR experience as realistic, interactive, and immersive, and saw the opportunity to practise skills in a safe environment, learn from mistakes, and increase knowledge and confidence. Conclusions: For the development of VR trainings, we recommend keeping them simple and targeting a specific educational outcome since trying to optimise for multiple outcomes is resource intensive and hard to achieve. Psychomotor skills were easier for participants to learn by watching a video on the procedure rather than practically learning it with the VR hardware, which is a more abstract representation of reality. We therefore recommend using VR as a complementing resource to skills labs, rather than replacing existing learning strategies. Perhaps VR is not ideal for practising practical psychomotor skills at the moment, but it can increase knowledge, satisfaction, motivation, confidence and prepare for further practical training. Trial registration: Not applicable.

Background: Digital nursing technologies (DNTs) are a promising solution to address challenges in health care systems, such as demographic shifts, nursing shortages, or difficulties in retaining nurses. Despite their potential benefits, the integration of DNTs into care settings remains complex due to multiple factors influencing their implementation and adoption. Objective: We aimed to examine factors that influence the implementation and adoption of DNTs used in nursing care settings from the perspective of nurses. Methods: We used an umbrella review methodology to synthesize the evidence on DNTs and the complexities of their implementation. We searched for systematic reviews that focused on DNTs in formal care settings across 4 databases (PubMed, CINAHL, Cochrane Library, and Business Source Premier) and examined reference lists of the included reviews published in English until January 2025. Two researchers independently performed data extraction and quality assessment. Data analysis was structured by embedding the results in the NASSS (nonadoption, abandonment, scale-up, spread, and sustainability) framework, a model for explaining the adoption and abandonment of health and care technologies, as well as challenges to their scaling, diffusion, and sustainability. Reporting of this study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Results: A total of 4803 reviews were identified, of which 65 (1.36%) met the inclusion criteria. We identified 52 influencing factors across 6 NASSS domains, with particular emphasis on adopter-related barriers and facilitators. Key barriers included insufficient training, increased workload, and low technological confidence, which impacted efficiency and the quality of care. In addition, concerns regarding professional role, autonomy, and privacy influenced nurses’ acceptance of DNTs. Facilitators included leadership support, a positive corporate culture, and targeted training initiatives. Conclusions: We synthesized key facilitators and barriers to implementation and adoption of DNTs in nursing care. Leadership support, adequate training, and alignment with care needs drive successful implementation, while resource constraints and workflow disruptions pose challenges. Addressing both technological requirements and nursing needs is critical. Future research should focus on long-term studies and practical tools to support stakeholders in effectively integrating DNTs into nursing practice. Trial Registration: OSF Registries 10.17605/OSF.IO/BG8CY; https://osf.io/bg8cy

In homecare, networks are formed by professional, semiprofessional, and informal actors, who collaborate to care for people in need. Modern information and communication technology (ICT) might play an important role to enhance cooperation in homecare networks. Through infrastructuring work, the authors seek to build a comprehensive understanding of the types of collaboration complexity in homecare networks to determine if, when and which technologies are most suitable. This paper examines how homecare networks can be classified according to collaboration complexity. A four-stage research design was followed to develop a taxonomy for homecare collaboration. The taxonomy was applied to 21 care networks, and five types of homecare networks were identified. The taxonomy considers network, tasks, and communication particularities across 13 dimensions, each of which includes three characteristics. Three clusters were identified as more likely than the others to benefit from increased technology use. The taxonomy and archetypes highlight which homecare network types could benefit from increased technology use. Additionally, the taxonomy allows for an iterative re-evaluation of networks to initiate measures for improvement.

Background: In the last few years, there has been an increasing interest in the development of artificial intelligence (AI)–based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods. Objective: The aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians. Methods: This scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study. Results: This scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024. Conclusions: This review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners. International Registered Report Identifier (IRRID): DERR1-10.2196/58185

During emergency response to mass casualty disasters, one of the main logistic impediments faced by the on-site organization chief is to track the patients at the disaster site. We had proposed a new system based on a location aware wireless sensor network (WSN) to overcome these impediments and assist the responders in providing efficient emergency response. In this paper we have implemented a new ranging algorithm called ranging using environment and mobility adaptive RSSI (REMA) filter which will provide the distance estimates and yield the real time localization of patients at the disaster site. We have conducted ranging experiments both in indoor and outdoor environments and have built an offline database which is given as input for the REMA filter simulation. The output of REMA filter is given as input to the new position estimation algorithm currently being developed by us and the position estimation error is calculated using simulation. The REMA filter simulation results show the suitability of the algorithm for ranging during tracking of patients at the disaster site.

Scientists promote the fostering of caring communities as a means of sustainably coping with demographic changes. They consider community-based technologies to have a high potential for supporting the establishment of caring communities. However, implementing community-based technologies is a complex endeavor, making sustainable adoption difficult. We have developed a maturity model aimed at standardizing the monitoring and evaluation of neighborhood projects. Based on a maturity model for integrated care, we conducted a Delphi study, to develop a maturity model for information and communication technology (ICT)-supported neighborhood development. In eight case studies, the model was validated and subsequently adapted to the specific needs and requirements of neighborhood projects. These studies emphasized the importance of at least 12 decisive dimensions and showed that the maturity model could be applied at different stages of a project. The current form of the maturity model can be used to help orient, as well as monitor and evaluate, neighborhood development projects. Future work will be necessary to further develop accompanying materials and services and to foster the exchange of best practices and experience between projects.