JMIR Publications

ABSTRACT

Alarm fatigue, a multifactorial desensitization of staff to alarms, can harm both patients and healthcare staff in intensive care units (ICU), especially due to false and non-actionable alarms. Increasing amounts of alarm and routinely collected ICU patient data are paving the way for training machine learning (ML) models that may be able to reduce the number of non-actionable alarms and thus alarm fatigue. Today, however, there exists no publicly available dataset or process that routinely collects information on alarm actionability, whether an alarm triggered a medical intervention or not—a key feature for developing meaningful ML models for alarm management. Case-based manual annotation is too slow and resource intensive for large amounts of data. In a multidisciplinary, consensus-based approach, we defined pre-processing steps and a rule-based annotation method to classify alarms as either actionable or non-actionable based on data from the patient data management system. Our annotation method will serve in the future to semi-automatically, retrospectively, and time-savingly label large amounts of alarms using patient data to generate annotated datasets for ML models in alarm fatigue research. We present our experience in developing the annotation method and provide resources that we generated, such as rule sets and data mappings. These could be used by others to prepare data for future ML projects, even beyond the topic of alarms.