Data for: Assessing Causes of Alarm Fatigue in Long-Term Acute Care and Its Impact on Identifying Clinical Changes in Patient Conditions
Description of this data
Physiologic alarms are an important modality in the care of critically ill patients. Yet the many electronic devices used in patient care and the combination of alarms can cause sensory overload in caregivers. This sensory overload can lead to monitor fatigue, and caregivers may miss critical alarms, which can be fatal for patients. Many factors not related to a change in patients’ condition can be directly linked to desensitization and alarm fatigue, leading to a failure to recognize or attend to true instability in spite of the alarm. Research demonstrates that the majority of alarms are non-actionable, and staff can develop alarm fatigue trying to determine which alarms are valid and which are not (Hravnak et al., 2018). We postulate that more experience detecting false alarms among professionals in a long-term acute care unit will lead to improved clinical changes and better survival rates among patients. Our proportional hazards model relates missing clinical changes in patients’ condition as time passes, after reduced attention to false alarms, to professional experience. In our proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. Therefore, reduced attention to false alarms by experienced professionals decreases the hazard rate for missing a clinical change. We use survival analyses, the hazard function, the receiver-operating characteristic curve, and the Hosmer-Lemeshow test to support our conclusions. Our results show that monitoring equipment is instrumental in alerting staff in a long-term care unit to serious changes in patients’ condition and in preventing false positives and false negatives.
Experiment data files
Cite this dataset
Rodger, James; Baker, Kathy (2020), “Data for: Assessing Causes of Alarm Fatigue in Long-Term Acute Care and Its Impact on Identifying Clinical Changes in Patient Conditions”, Mendeley Data, v2 http://dx.doi.org/10.17632/dr5hvdydbv.2