1) Background: Culture shock is a common experience by internationally educated nurses (IENs) working in foreign countries, characterized by disorientation and discomfort due to distinct norms, values, and rituals. 2) Aim: this study explores culture shock in IENs and explores mitigation techniques to enhance understanding of challenges faced in foreign cultural contexts. 3) Method: Using Concept Analysis by Walker and Avant (2019). 4) Results: A total of 20 articles were reviewed. Four major attributes were identified: psychological and emotional impact, communication barriers, acculturation and quality of life, and organizational challenges. 5) Conclusion: This paper explores the challenges faced by nurses from foreign countries due to cultural adjustment and proposes solutions to minimize its effects. It is beneficial for nurses, healthcare organizations, and policymakers, aiming to improve patient care and health outcomes. 6) Implication for Practice: Addressing culture shock can promote a smooth transition, enhance nurses' experience, and improve their cultural competence. Providing tailored orientation and mentorship programs can help IENs feel supported and empowered, leading to increased job satisfaction, retention rates, and better patient outcomes.

This commentary letter was conducted to evaluate Wang et al.'s (2025) research study on the relationship between perceived staffing and quality of care among hospitals. The current study's findings show that the relationship between professional self-efficacy and job performance is mediated by work engagement. Life contentment influences work engagement, which is associated with enhanced job performance. However, involvement acts as a mediator between job performance and burnout, which could affect the quality of patient care. Staffing satisfaction and quality patient care are closely related, and it is essential for healthcare institutions to prioritize appropriate workforce levels and address the nursing shortage. However, there are still unanswered questions in this sector, such as researching nursing-specific care procedures, addressing data challenges, and understanding the connections between nursing practice and patient care outcomes. Future research should address the "black box" of nursing practice and address variations in the quality of patient care provided by nurses.

The commentary paper reviewed the above research study conducted by Wang et al. (2025), and the investigators examined the association between nurse staffing, job satisfaction, and work engagement, and how these variables impact the quality of care provision provided among the Chinese hospitals. Despite knowing that low staffing within the healthcare facilities is a global issue, Wang and colleagues believed that low staffing is negatively and significantly associated with nurse’s welfare and patient care outcome. This issue causes an increase in burnout and decreased retention of healthcare providers within the clinical setting. It is important to consider and focus on improving and fostering job satisfaction and work engagement among nurses to provide better quality care even within a low staffing environment. According to Wang and colleagues, low staffing outcomes could be mitigated by encouraging workplaces to create healthy and supportive environments for the engaged and satisfied nurses. These would result in better out among patients and increase job fulfilment and welfare among nurses.

Background: Disasters such as typhoons, floods, and earthquakes frequently impact the Philippines, placing nurses at the forefront of response and care. Given these recurring threats, ensuring that nurses possess adequate awareness, knowledge, and skills is crucial to mitigate risks, enhance preparedness, and promote effective disaster management. Aim/Objectives: This study sought to (1) assess the current levels of awareness, knowledge, skills, and involvement of private hospital nurses in Rizal Province across four phases of disaster management—mitigation and prevention, preparedness, response, and rehabilitation and recovery; and (2) propose a contingency and training plan based on identified gaps. Methods: A descriptive correlational design was employed. A total of 350 nurses from Level 1, 2, and 3 hospitals participated by completing a validated questionnaire. Data were analyzed using descriptive statistics, analysis of variance, and correlation tests to identify differences and relationships among variables. Results: Overall, the nurses reported very high levels of awareness and skills, coupled with a high level of knowledge and significant involvement in disaster-related activities. Nurses in larger (Level 3) hospitals exhibited higher practical readiness and engagement, while those in Level 1 and 2 facilities had comparatively lower scores. Positive correlations emerged between higher levels of awareness, knowledge, and skills and increased engagement in disaster initiatives. Conclusion: Building on these findings, a targeted contingency and training plan was designed using Pucel’s Performance-based Instructional Design, emphasizing hands-on simulations, structured policy briefings, and collaborative efforts with local disaster risk reduction offices. Addressing these specific gaps can bolster hospital preparedness, strengthen community resilience, and ensure more effective disaster response and patient care.

Drug shortages represent a significant and growing challenge within the pharmaceutical supply chain, with profound implications for patient care, public health, and healthcare costs. This manuscript provides a comprehensive examination of the causes and impacts of drug shortages, highlighting the multifaceted nature of this issue. Key factors contributing to shortages include manufacturing complications, limited availability of active pharmaceutical ingredients (APIs), market dynamics that discourage the production of less profitable medications, and regulatory challenges that slow down the approval process for new manufacturing capacities. The consequences of these shortages are far-reaching. Patients often face treatment delays, which can lead to adverse health outcomes, increased hospitalization rates, and even mortality. Healthcare providers experience heightened operational costs as they seek alternative therapies and manage complications resulting from inadequate treatment. Furthermore, the frequent occurrence of drug shortages erodes public trust in both the healthcare system and the pharmaceutical industry, leading to decreased patient adherence to prescribed therapies. To mitigate the impacts of drug shortages, this manuscript proposes several strategic solutions, including enhanced communication among stakeholders, diversification of supply sources, increased regulatory flexibility, and collaborative approaches between public and private sectors. Additionally, raising awareness among healthcare providers and patients regarding the causes and potential alternatives can empower stakeholders to navigate shortages effectively. Ultimately, addressing drug shortages necessitates a proactive and coordinated effort from all participants in the pharmaceutical supply chain. By implementing these strategies, stakeholders can enhance the resilience of the supply chain, ensuring that essential medications remain accessible and that patient care is not compromised. The findings of this manuscript underscore the urgent need for ongoing vigilance and collaborative action to tackle the challenges posed by drug shortages, safeguarding public health and improving healthcare outcomes globally.

Background: Over the past decade, there has been a rapid advancement in technology and virtual reality applications, leading to the emergence of the metaverse - a virtual universe where users interact with each other and their surroundings through immersive experiences. In the nursing profession, the metaverse presents unique opportunities to enhance patient care, education, and collaboration. Aim: To analyze and identify the attributes of metaverse in nursing, exploring its dimensions, benefits, challenges, and implications. By examining relevant literature, this study will contribute to a better understanding of the metaverse in nursing. Method/Design: Concept analysis by Walker and Avant (2019). Results: Metaverse in nursing involves three defining attributes: use of advanced technologies, better access to education and healthcare, and collaboration and community building. Antecedents of metaverse in nursing require technological advancements, increased digital literacy, demand for innovative education, globalization of healthcare education Conclusion: Metaverse in Nursing suggests that it is a promising technology that has the potential to enhance nursing practice and improve patient outcomes, but further research is needed to fully explore the impact of its integration.

Artificial intelligence (AI) based triage and diagnostic systems are increasingly being used in healthcare. Although these online tools can improve patient care, their reliability and accuracy remain variable. We hypothesized that an artificial intelligence (AI) powered triage and diagnostic system (MayaMD) would compare favorably with human doctors with respect to triage and diagnostic accuracy. We performed a prospective validation study of the accuracy and safety of an AI powered triage and diagnostic system. Identical cases were evaluated by an AI system and individual Indian healthcare practitioners (HCPs) to draw comparison for accuracy and safety. The same cases were validated with the help of consensus received from an expert panel of 3 doctors. These cases in the form of clinical vignettes were provided by an expert medical team. Overall, the study showed that the MayaMD AI based platform for virtual triage was able to recommend the most appropriate triage ensuring patient safety. In fact, the accuracy of triage recommendation by MayaMD was significantly better than that provided by individual HCPs (74% vs. 91.67%, p=0.04) with consensus being used as standard.

Background and aim: Developing clinical skills and its assessment is one of the most important components in nursing education which prepares the student for the reality of practice. Objective structured clinical examination (OSCE) is extensively used and widely accepted by nurse educators across the globe to assess the competency skills of nursing students. The present study aimed at identifying the attitude and perceptions of faculty, and exploring their feedback and experience in conducting OSCE as an assessment tool. Methods: A triangulation research approach was used with convenience sampling. Data collection was carried out using questionnaires and semi-structured interviews. Participants were ten faculty members who were involved in conducting OSCE for students. Results and conclusion: Most of the faculty felt that OSCE reflected the skills of delivery of safe patient care, and the structure reflected mastery of knowledge and skills, which are related to course objectives. OSCE was regarded by the faculty as a consistent, reliable, valid, and objective measure to assess students’ performance and to improve students’ confidence in clinical skills. Concerns were raised about a high level of stress in students, the time required for the proper performance of tasks, OSCE scenarios lacking real-life situations in assessment, and the need for repeated practice and intensive mock training sessions. The applicability of OSCE in terms of limitations in human and material resources with a large number of students would necessitate rethinking in developing other assessment strategies to improve the overall process.

The growing complexity and variability in healthcare delivery and costs within Medicare Advantage (MA) and Medicare Supplement (Medigap) plans present significant challenges for improving health outcomes and managing expenditures. Neural networks, a subset of artificial intelligence (AI), have shown considerable promise in optimizing healthcare processes, particularly in predictive modeling, personalized treatment recommendations, and risk stratification. This paper explores the application of neural networks in enhancing health outcomes within the context of Medicare Advantage and Supplement plans. We review how deep learning models can be leveraged to predict patient risk, optimize resource allocation, and identify at-risk populations for preventive interventions. Additionally, we discuss the potential for neural networks to improve claims processing, reduce fraud, and streamline administrative burdens. By integrating various data sources, including medical records, claims data, and demographic information, neural networks enable more accurate and efficient decision-making processes. Ultimately, this approach can lead to better patient care, reduced healthcare costs, and improved satisfaction for beneficiaries of these programs. The paper concludes by highlighting the current limitations, ethical considerations, and future directions for AI adoption in the Medicare Advantage and Supplement sectors.

Neural networks are bringing a transformation in wearable healthcare technology analytics. These networks are able to analyze a vast amount of data to help in making decisions concerning patient care. Advancements in deep learning have brought neural networks to the forefront, making data analytics a straightforward process. This study will help in unveiling the use of ICT and AI in medical healthcare technology, crawling through some industry giants. Wearable Healthcare Technologies are becoming more popular every day. These technologies facilitate collecting, monitoring, and sharing every vital aspect of the human body necessary for diagnosing and treating an ailment. At the advent of global digitization, health data storage and systematic analysis are taking shape to ensure better diagnostics, preventive, and predictive healthcare. Healthcare analytics powered by neural networks can significantly improve health outcomes, maximizing individuals' potential and quality of life. The breadth and possibilities of connected devices are getting wider. From personal activity monitoring to quantifying every bit of health statistics, connected devices are making an impact in measurement, management, and manipulation. In healthcare, early diagnosis could be a lifesaver. Data analytics can help in a big way to make moves and predictions to save lives. We are in another phase of the digitization era, "Neural Network and Wearable Healthcare Technology Analytics." A neural network could be conceived as an adaptive system made up of a large number of neurons connected in multiple layers. A neural network processes data in a similar way as the human brain does. Using a collection of algorithms, for many neural networks, objects are composed of 'input' and 'output' layers along with the layers of the neural network.

This work details how the integration of cloud computing and advanced data engineering can innovate and reshape patient care and digital infrastructure. In the healthcare sector, cloud services offer the necessary support to generate digitally-oriented services and service kits. These services can contain high levels of availability, low levels of latency, and on-demand scaling capabilities, while following the strictest data protection laws and regulations. On the other hand, these services can be combined with data engineering techniques to construct an ecosystem that enhances and adds an optimized data layer on any cloud environment. This ecosystem includes technologies to acquire, process, and manage healthcare data while respecting all regulatory obligations and institutions and can be part of a comprehensive digitalization strategy. The objective is to augment the healthcare services that the industry offers by leveraging healthcare data and AI technologies. Designed services, processes, and technologies can be described either as industry-agnostic services or healthcare-specific services that process and manage electronic healthcare records (EHR). Industry-agnostic services offer a set of tools and methodologies to conduct optimized data experiments. The goal is to exploit any variety, velocity, volume, and veracity of medical data. Healthcare-specific services offer a set of tools and methodologies to connect to any common EHR vendor in a privacy-preserving manner. Participating companies are thus able to hold, share, and make use of healthcare data in real-time. The proposed architecture can be transformative for the healthcare industry, opening up and facilitating experimentation on new and scalable service models. The transition to a more digital health approach would help overcome the limits encountered in traditional settings. Limitations in the availability of healthcare facilities and healthcare professionals have underpinned the increasing share of telemedicine in the care process. However, the record-keeping of the patients that undergo care outside of traditional healthcare facilities is often missing and can severely influence the continuity of treatment. Identifying new methods to implement disease prevention and early intervention processes is crucial to avoid more extensive treatment and to support those on multiple line therapies. For chronic patients, having a service available that monitors the state of health and intervenes when parameters go off the wanted range is crucial. However, the same patients are the most under the influence of the decision of care providers; a second opinion might be given remotely which the patient can access at any time on-demand. To address these different kinds of services, an ecosystem composed of a dictionary's worth data layer is outlined, able to live and operate seamlessly in any cloud environment. This future work's envisioned outcome is the rapid evolution and re-definition of the European healthcare landscape.