NCT Number: NCT02916212
Quality Improvement - Monitoring Alarm Optimization Study
This project aims to reduce the frequency of duplicate, false and clinically insignificant alarms in hospital units, and subsequent alarm fatigue resulting from excessive alarm frequency. The investigators will implement evidence-based guidelines for alarm optimization according to patient-population specific parameters, and evaluate alarm frequency and staff perception of alarm fatigue at baseline and 60 days after implementation of this quality improvement initiative.
Looking for future studies?
Notify MeKey information
Sex eligibility
All sexes
Study type
Interventional
Phase
Not applicable
Who can participate
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- Nursing staff at selected units at Duke University Hospital
Exclusion criteria
- NA
Treatment and study plan
Primary outcomes
-
Change from baseline of frequency of total alarms by hospital unit at 60 days
Time frame: At baseline and 60 days following implementation
Secondary outcomes
-
Change from baseline of number of alarms per bed
Time frame: At baseline and 60 days following implementation
-
Change from baseline of number of alarms per day per bed
Time frame: At baseline and 60 days following implementation
-
Change from baseline of perceived alarm fatigue, assessed using a questionnaire
Time frame: At baseline and 60 days following implementation
Sponsors and collaborators
Lead sponsor
Duke University
Other
Registry information
Acronym: QI-MAOS
Important dates
- Study start
- 2015
- Primary completion
- 2016
- Study completion
- 2016
- First posted
- Sep 27, 2016
- Registry last updated
- Jan 30, 2019
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Related clinical trials
Published trials that share one or more normalized conditions with this study.
Novel Self-charging, Medical-Grade Smart Insoles With AI/ML Edge Computing to Monitor Biometrics.
NCT07273422
No Applicable Condition; Study of Physiologic Monitor Alarms
Williamsport, Pennsylvania, United States
View Trial Details