
Studies identified false and non-actionnable alarms as a factor for alarm fatigue in intensive care units. To annotate patient alarms, and analyse the alarm situation in intensive care units, we conceptualized and performed data mappings related to airway management and medication interventions. The mappings were based on information retrieved from the patient data management system (PDMS) and clinical expertise. For the airway management mappings, we used additional resources such as ISO 19223:2019 or ventilator instruction manuals. The mappings do not include patient data. As the mappings are generic, they could be used in other contexts than alarm annotation. 1. Airway Management Mappings: Tables including PDMS entries for airway devices (ADs), ventilation devices (VDs), and ventilation modes (VMs) Mapping of AD entries (from the PDMS) to defined categories Mapping of VDs, VMs, and ADs to defined respiratory support therapies, including information on invasiveness Table specifying suitable ventilation parameters in the context of each respiratory support therapy 2. Medication Mappings: General tables providing information on physiological alarm conditions (PACs), interventions, routes, and techniques of administration of interest Mapping of routes of administration to techniques of administration including PDMS entries Mapping of active ingredients, related PDMS information, and routes and techniques of administration to defined PACs and interventions
data mapping, alarm management, patient monitoring, ventilation data, alarm informativeness, digital health, dataset annotation, transdisciplinary research, alarm system, Machine Learning, Intensive Care Units, machine learning, alarm system quality, technological innovation, Clinical Alarms, Patient-Centered Care, medication data, patient safety, Digital Health, data science, alarm annotation, alarm fatigue
data mapping, alarm management, patient monitoring, ventilation data, alarm informativeness, digital health, dataset annotation, transdisciplinary research, alarm system, Machine Learning, Intensive Care Units, machine learning, alarm system quality, technological innovation, Clinical Alarms, Patient-Centered Care, medication data, patient safety, Digital Health, data science, alarm annotation, alarm fatigue
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