JMIR Nursing

For clinical nurses, manually entering information into hospital information systems (HISs) remains time-consuming and prone to omissions. Although speech recognition can reduce the need for manual entry, its use in clinical settings has historically been limited by code-switching, medical terminology, and noisy ward environments. Recent advances in customized automatic speech recognition (ASR) and large language models (LLMs) now make speech-based, structured documentation aligned with nursing frameworks such as DART (data, action, response, and teaching) increasingly feasible.

Nursing Development Units (NDUs) are structured clinical environments designed to enhance professional development, collaboration, and organizational learning. While NDUs have been widely studied for their impact on nursing practice, their role in supporting digital transformation in health care has been less explicitly examined.

Integrating artificial intelligence (AI) systems into nursing care often encounters obstacles stemming from unmet requirements and insufficient engagement with well-documented sociotechnical pitfalls. Readiness models offer a systematic way to evaluate project preparedness and to build the capabilities needed for successful artificial intelligence in nursing care (AINC) research, development, and implementation. As of yet, an evidence-based AI readiness assessment prioritizing AINC projects and accounting for their diversity in care settings is missing.

Patient safety is a fundamental pillar of health care quality. Simulation-based training provides a controlled environment for nursing students to develop safety competencies and error-recognition skills before clinical practice.

The growing aging population and staff shortages are placing pressure on Dutch nursing homes (NHs). These challenges have led to an increased interest in digital health technologies. Among these are wearable devices that allow for remote continuous monitoring of vital signs. An example is the Healthdot (smartQare), a wearable electronic device that continuously monitors heart rate, respiratory rate, and physical activity. In the context of acute respiratory infections (ARIs) in NHs, where initial symptoms can go unnoticed, continuous monitoring may aid in early recognition, timely intervention, and reduce staff workloads. However, little is known about how health care professionals perceive the use of continuous vital signs monitoring devices, such as the Healthdot, for this cause in NHs.

Ineffective clinical handover has the potential to compromise patient safety and quality of care. Standardizing the handover process is a widely adopted improvement strategy intended to reduce failures of information transfer. By enabling real-time access to patient information, electronic medical records (EMRs) could address communication issues inherent to nursing handover.

The global nursing workforce crisis demands a shift from reactive staffing to strategic workforce optimization through data-driven decision-support systems. This viewpoint paper reflects on the development and attempted implementation of the balanced nursing teams system, a decision-support tool integrating approximately 250 data points—of which roughly 150 are extracted from existing organizational systems (human resources, scheduling, electronic health records, quality registries) through flexible import mechanisms, and the remainder collected through a built-in 360-degree staff survey with automated analysis—across 10 domains to evaluate nursing team balance between capacity, performance, and outcomes. Following crowdfunding by 18 Belgian health care organizations, balanced nursing teams were implemented across 8 diverse settings (home health care, general hospitals, academic centers) between 2019 and 2023. Using the Human-Organization-Technology fit framework, we analyze why evidence-informed, organization-endorsed digital innovations struggle to achieve adoption. Our analysis reveals 3 interdependent barrier categories: technological fragmentation (vendor lock-in, legacy systems, prohibitive integration costs), organizational siloing (Chief Nursing Officers [CNOs] lacking budgetary authority, nursing framed as peripheral to strategic priorities), and managerial hesitance (fear of punitive data use, cognitive overload from staffing crises). These barriers were worsened by the substantial data-integration burden that the system’s breadth imposed on organizations with limited digital maturity. Critically, only one site (ie, a nurse-led home health care organization where leadership held both strategic authority and resource control) achieved sustained implementation. This contrast demonstrates that workforce optimization through data depends not on software maturity alone, but on achieving simultaneous fit across human, organizational, and technological domains. We argue that the persistent marginalization of nursing leadership within hospital governance structures represents the fundamental barrier to digital transformation in nursing workforce management. The urgency paradox is striking: while nursing represents health care organizations’ highest operational cost and most direct patient interface, workforce optimization tools are consistently deprioritized in favor of regulatory compliance systems and billing infrastructure. Bridging this gap requires systemic investment in nursing leadership authority, data interoperability standards, and recognition that data-driven workforce decisions are strategic imperatives rather than operational luxuries.

Understanding current nursing workflows is essential to informing future workforce redesign strategies, including virtual nursing roles. However, granular insights into current nursing workflows over a 24-hour period and across different staff grades are lacking.

The integration of digital health technologies into nursing education in Saudi Arabia requires reliable tools to assess nursing informatics competency and digital technology self-efficacy among students.

The role of nursing informaticians is well-established in countries like the United States, Canada, and Australia, supported by competency frameworks and educational programs that enable nurses to lead technological integration in health care. However, in Spain, this role is not formally recognized, and specialized university training is scarce, creating a significant gap in digital health leadership among nurses.

The integration of robotic systems into nursing practice is increasingly discussed as a potential strategy to alleviate workload and support care processes in response to demographic changes and staffing shortages. However, the acceptance of nursing staff as primary end users remains a critical determinant for successful implementation. Despite technological advances, the practical requirements and perspectives of nursing staff have not been adequately considered in research and development efforts to date.

Nursing informatics is essential for digital health transformation; however, the technology acceptance of undergraduate nursing students in Saudi Arabia remains underexplored.