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Resources for a "Do-it-Yourself Analysis" of the Patient Monitoring Alarm Data from Intensive Care Units

Authors: Poncette, Akira-Sebastian; Wunderlich, Maximilian Markus; Spies, Claudia; Heeren, Patrick; Vorderwülbecke, Gerald; Salgado, Eduardo; Kastrup, Marc; +2 Authors

Resources for a "Do-it-Yourself Analysis" of the Patient Monitoring Alarm Data from Intensive Care Units

Abstract

The clinical audit logs were manually collected from the patient monitoring system of a 21-bed intensive care unit (ICU) from a large German hospital via USB stick from the central patient monitoring device three times during 2019 (in winter, summer and autumn). The data consists of the time, bed number, alarm type (i.e., parameter, device, alarm criticality) and alarm handling (e.g., threshold adjustments, use of the pause function). In total, data span 93 calendar days. No actual patient identifying data elements were collected. For further deidentification, dates were shifted into the future by a pseudo-random offset for all patients; the bed number was replaced by a pseudonym. Day and night rhythm, weekends, the season and the bed characteristic (double room, single room) were not affected by this process. We provide the fully annotated R scripts that we used to conduct the alarm data analysis to enable even beginners in R to do likewise. Further explanations can be found in the results section of the accompanying publication and in the scripts. The R-Markdown file can be used to create comprehensive alarm reports.

Keywords

patient monitoring, alarm management, digital health, alarm system, clincal alarms, intensive care unit, medical devices, alarm system quality, technological innovation, ICU, patient safety, data science, alarm fatigue

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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