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.

User Experience Design for Intensive Care Alarm Management.

Alarm fatigue is a pressing issue in intensive care units. Based on user experience design, including clinical shadowings and feedback loops, we developed a prototype for a redesigned patient monitor: The prototype moves away from today's threshold-based alarm systems. It combines a sleek design with machine learning driven clinical insights to mitigate alarm fatigue.

Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire using Confirmatory Factor Analysis.

Background: The Charité Alarm Fatigue Questionnaire (CAFQa) is a 9-item questionnaire that aims to standardize how alarm fatigue in nurses and physicians is measured. We previously hypothesized that it has 2 correlated scales, one on the psychosomatic effects of alarm fatigue and the other on staff's coping strategies in working with alarms.

Objective: We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity.

Alarm Management in Intensive Care: Qualitative Triangulation Study.

Background: The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential.

Objective: This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions.

Interdisciplinary collaboration in critical care alarm research: A bibliometric analysis.

Background: Alarm fatigue in nurses is a major patient safety concern in the intensive care unit. This is caused by exposure to high rates of false and non-actionable alarms. Despite decades of research, the problem persists, leading to stress, burnout, and patient harm resulting from true missed events.

A brief questionnaire for measuring alarm fatigue in nurses and physicians in intensive care units.

When exposed to hundreds of medical device alarms per day, intensive care unit (ICU) staff can develop "alarm fatigue" (i.e., desensitisation to alarms).

Staff perspectives on the influence of patient characteristics on alarm management in the intensive care unit: a cross-sectional survey study.

Background: High rates of clinical alarms in the intensive care unit can result in alarm fatigue among staff. Individualization of alarm thresholds is regarded as one measure to reduce non-actionable alarms. The aim of this study was to investigate staff's perceptions of alarm threshold individualization according to patient characteristics and disease status.

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study.

Background: Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.

The medical profession transformed by artificial intelligence: Qualitative study.

Background: Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear.

Objective: We analyzed physicians' and AI experts' stances towards AI-induced changes.

The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study.

Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs.

Pleural effusions are associated with adverse outcomes after cardiac surgery: a propensity-matched analysis.

Background: Pleural effusions commonly occur in patients recovering from cardiac surgery; however, the impact on outcomes is not well characterized. The purpose of this study is to characterize the clinical outcomes of cardiac surgery patients with pleural effusion.

Methods: All patients undergoing cardiac surgery between 2006 and 2019 at a tertiary care university hospital were included in this observational, cross-sectional analysis using propensity matching.

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review.

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs.

Implementation of a digital health module for undergraduate medical students: A comparative study on knowledge and attitudes.

Background: Digital competencies are more and more required in everyday work, and training future healthcare professionals in digital health is highly important.

Objective: Aim of this study was to assess medical students' gain of knowledge by participation in a teaching module "Digital Health", and to evaluate their attitudes towards digital health and its role in medical education.

Methods: Students of the module were asked to complete a questionnaire and a multiple-choice-test before and after completing the classes.

Artificial Intelligence in Undergraduate Medical Education.

AI will take on an increasingly important role in medicine. Therefore, AI competencies should be taught in medical school. We investigated the inventory of AI-related courses at German medical schools.

Artificial Intelligence Competencies in Postgraduate Medical Training in Germany.

Routine medical care is to be transformed by the introduction of artificial intelligence (AI), requiring medical professionals to acquire a novel set of skills. We assessed the density of AI learning objectives and the availability of courses containing AI content in postgraduate medical education in Germany. The results reveal general paucity in AI learning objectives and content across (sub-)specialty training and continuing medical education (CME) in Germany.

MIMIC-IV as a Clinical Data Schema.

Routinely collected electronic health records (EHR) in clinical information systems (CIS) are often heterogeneous, have inconsistent data formats and lack of documentation. We use the well-known open-source database schema of MIMIC-IV to address this issue aiming to support collaborative secondary analysis. Over 154 million data records from a German ICU have already been mapped and inserted into the schema successfully.

Utilizing Intensive Care Alarms for Machine Learning.

Alarms help to detect medical conditions in intensive care units and improve patient safety. However, up to 99% of alarms are non-actionable, i.e.

Creation of an Evidence-Based Implementation Framework for Digital Health Technology in the Intensive Care Unit: Qualitative Study.

Background: Digital health technologies such as continuous remote monitoring and artificial intelligence-driven clinical decision support systems could improve clinical outcomes in intensive care medicine. However, comprehensive evidence and guidelines for the successful implementation of digital health technologies into specific clinical settings such as the intensive care unit (ICU) are scarce. We evaluated the implementation of a remote patient monitoring platform and derived a framework proposal for the implementation of digital health technology in an ICU.

A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation.

Background: Continuous monitoring of vital signs is critical for ensuring patient safety in intensive care units (ICUs) and is becoming increasingly relevant in general wards. The effectiveness of health information technologies such as patient-monitoring systems is highly determined by usability, the lack of which can ultimately compromise patient safety. Usability problems can be identified and prevented by involving users (ie, clinicians).

Telemedicine in Intensive Care Units: Scoping Review.

Background: The role of telemedicine in intensive care has been increasing steadily. Tele-intensive care unit (ICU) interventions are varied and can be used in different levels of treatment, often with direct implications for the intensive care processes. Although a substantial body of primary and secondary literature has been published on the topic, there is a need for broadening the understanding of the organizational factors influencing the effectiveness of telemedical interventions in the ICU.

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions.

Background: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.

Objective: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system.