Improving Alarm Performance in the Medical Intensive Care Unit Using Delays and Clinical Context
ABSTRACT In an intensive care unit, alarms are used to call attention to a patient, to alert a change in the patient's physiology, or to warn of a failure in a medical device; however, up to 94% of the alarms are false. Our purpose in this study was to identify a means of reducing the number of false alarms.
An observer recorded time-stamped information of alarms and the presence of health care team members in the patient room; each alarm response was classified as effective (action taken within 5 min), ineffective (no response to the alarm), and ignored (alarm consciously ignored or actively silenced).
During the 200-h study period, 1271 separate entries by an individual to the room being observed were recorded, 1214 alarms occurred and 2344 tasks were performed. On average, alarms occurred 6.07 times per hour and were active for 3.28 min per hour; 23% were effective, 36% were ineffective, and 41% were ignored. The median alarm duration was 17 s. A 14-s delay before alarm presentation would remove 50% of the ignored and ineffective alarms, and a 19-s delay would remove 67%. Suctioning, washing, repositioning, and oral care caused 152 ignored or ineffective ventilator alarms.
Introducing a 19-s alarm delay and automatically detecting suctioning, repositioning, oral care, and washing could reduce the number of ineffective and ignored alarms from 934 to 274. More reliable alarms could elicit more timely response, reduce workload, reduce noise pollution, and potentially improve patient safety.
SourceAvailable from: Catherine Marie Burns[Show abstract] [Hide abstract]
ABSTRACT: Detection of patient deterioration and triage (prioritization of care) are two critical tasks in the high risk environment and interdisciplinary care model of patients in an intensive care unit. To make decisions and plan treatments, clinicians need to observe, integrate, communicate, and understand a wide range of information from various devices located at the bedsides of multiple patients. However, several technological and physical limitations prevent them from optimally performing these tasks, which negatively impact the capabilities of healthcare teams. The Monitoring Messenger concept was developed to overcome some of these challenges by integrating information on a mobile device and supporting team decision-making and information exchange. Results from the initial phases of this project: requirements definition using Cognitive Work Analysis and rapid device prototyping are presented in this paper.2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013); 10/2013
[Show abstract] [Hide abstract]
ABSTRACT: Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p < 0.001, Wilcoxon signed rank test). Using trend templates might therefore aid in detection of short seizures by EEG monitoring at the NICU.Physiological Measurement 02/2015; 36(3):369-384. DOI:10.1088/0967-3334/36/3/369 · 1.62 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Purpose Physiologic monitors are plagued with alarms that create a cacophony of sounds and visual alerts causing “alarm fatigue” which creates an unsafe patient environment because a life-threatening event may be missed in this milieu of sensory overload. Using a state-of-the-art technology acquisition infrastructure, all monitor data including 7 ECG leads, all pressure, SpO2, and respiration waveforms as well as user settings and alarms were stored on 461 adults treated in intensive care units. Using a well-defined alarm annotation protocol, nurse scientists with 95% inter-rater reliability annotated 12,671 arrhythmia alarms. Results A total of 2,558,760 unique alarms occurred in the 31-day study period: arrhythmia, 1,154,201; parameter, 612,927; technical, 791,632. There were 381,560 audible alarms for an audible alarm burden of 187/bed/day. 88.8% of the 12,671 annotated arrhythmia alarms were false positives. Conditions causing excessive alarms included inappropriate alarm settings, persistent atrial fibrillation, and non-actionable events such as PVC's and brief spikes in ST segments. Low amplitude QRS complexes in some, but not all available ECG leads caused undercounting and false arrhythmia alarms. Wide QRS complexes due to bundle branch block or ventricular pacemaker rhythm caused false alarms. 93% of the 168 true ventricular tachycardia alarms were not sustained long enough to warrant treatment. Discussion The excessive number of physiologic monitor alarms is a complex interplay of inappropriate user settings, patient conditions, and algorithm deficiencies. Device solutions should focus on use of all available ECG leads to identify non-artifact leads and leads with adequate QRS amplitude. Devices should provide prompts to aide in more appropriate tailoring of alarm settings to individual patients. Atrial fibrillation alarms should be limited to new onset and termination of the arrhythmia and delays for ST-segment and other parameter alarms should be configurable. Because computer devices are more reliable than humans, an opportunity exists to improve physiologic monitoring and reduce alarm fatigue.PLoS ONE 10/2014; 9(10):e110274. DOI:10.1371/journal.pone.0110274 · 3.53 Impact Factor