Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set.

Background: Too many unnecessary alarms in the intensive care unit are one of the main reasons for alarm fatigue: Medical staff is overburdened and fails to respond appropriately. This endangers both patients and staff. Currently, there are no algorithms that can determine which alarms are clinically relevant and which are not.

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

Background: In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue.

Objective: We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany.

Developing a Change Readiness Survey for Health Technology Implementation.

Addressing the challenges of health technology implementation, this study aims to develop a survey that assesses staff readiness for change in clinical settings. The survey items were refined from 67 to 38 through a narrative literature review, expert focus groups, and cognitive interviews. The survey suggests an approach that prioritizes the user's needs in identifying barriers and facilitators to the adoption of health technology in order to ensure successful implementation by proactively addressing potential obstacles.

Factors Influencing the Strategic Governance of EHR Interoperability: A Rapid Literature Review.

While the importance of Electronic Health Records (EHR) interoperability is widely recognised in the healthcare digitalisation context, its optimal governance structure remains controversial, requiring further research. Through the rapid literature review of 32 articles retrieved from PubMed and EBSCO, 47 distinct factors under ten categories were established. The three most cited factors in the reviewed 32 articles were "Robust inter-institutional connections, trust, and the technologies to ensure security", "Legal adaptations to the evolving digitalisation needs", and "Standardisation of terminologies and codes, and harmonised data structure".

Evaluation of End-User Participation in Artificial Intelligence Nursing Projects.

Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Active participation of end-users in the development and implementation of AI can enhance acceptance. This study proposes a scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers.