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Alarm Fatigue clinical trials

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NCT ID: NCT06403397 Not yet recruiting - Patient Safety Clinical Trials

Assessing the Impact of Monitor Maintenance Package Utilization

Start date: June 15, 2024
Phase: N/A
Study type: Interventional

Bedside monitors are frequently used in monitoring vital signs of critically ill patients. Nurses working in healthcare facilities, especially in intensive care units, are required to manage devices with different alarm threshold values, categories, and types of alerts, leading to alarm fatigue. In response to this serious threat to patient safety, the FDA and The Joint Commission worked to develop strategies to address alarm fatigue in 2011. Alarm monitoring, identification of the cause, and silencing are typically performed by nurses. When reviewing alarm control studies in the literature, the CEASE care package developed by Levis et al. in 2019 was encountered. The tool was developed for personalized clinical alarm monitoring for the patient.

NCT ID: NCT04994600 Completed - Alarm Fatigue Clinical Trials

Design and Validation of a German Language Questionnaire for Measuring Alarm Fatigue in Intensive Care Units

alarmZen1
Start date: March 19, 2021
Phase:
Study type: Observational

False-positive and non-actionable alarms can lead to staff desensitization ("alarm fatigue") and thus patient endangerment. With this study the investigators create a basic tool to survey alarm fatigue of intensive care staff: the first German language alarm fatigue questionnaire.

NCT ID: NCT04661735 Recruiting - Alarm Fatigue Clinical Trials

Intensive Care Unit Risk Score

ICURS
Start date: January 1, 2006
Phase:
Study type: Observational

Subject of the planned project is the retrospective analysis of routine data of digital patient files of the Department for Anaesthesiology and Surgical Intensive Care Medicine, to test whether the predictive values of intensive care scoring systems with regard to perioperative mortality and morbidity can be improved by continuous score calculation and by using machine learning and time series analysis methods.