Improving Alarm Performance in the Medical Intensive Care Unit Using Delays and Clinical Context

Department of Anesthesiology, University of Utah, Salt Lake City, UT 84132, USA.
Anesthesia and analgesia (Impact Factor: 3.47). 06/2009; 108(5):1546-52. DOI: 10.1213/ane.0b013e31819bdfbb
Source: PubMed


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.

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    • "Among this large volume of alarms, as many as 80 – 99% have been identified as not clinically actionable [1]. This combination of high alarm rates and few actionable alarms has been found across a wide range of care settings, including the Intensive Care Unit (ICU) [2], Progressive Care Unit (PCU) [3] and Medical/Surgical floors [4]. "
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    ABSTRACT: In many critical care units, default patient monitor alarm settings are not fine-tuned to the vital signs of the patient population. As a consequence there are many alarms. A large fraction of the alarms are not clinically actionable, thus contributing to alarm fatigue. Recent attention to this phenomenon has resulted in attempts in many institutions to decrease the overall alarm load of clinicians by altering the trigger thresholds for monitored parameters. Typically, new alarm settings are defined based on clinical knowledge and patient population norms and tried empirically on new patients without quantitative knowledge about the potential impact of these new settings. We introduce alarm regeneration as a method to estimate the alarm rate of new alarm settings using recorded patient monitor data. This method enables evaluation of several alarm setting scenarios prior to using these settings in the clinical setting. An expression for the alarm rate variance is derived for the calculation of statistical confidence intervals on the results.
    Full-text · Article · Aug 2014
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    • "Alarms were mandated to increase patient safety but with little consideration of the detrimental effects on patients and staff [5] or of the impact of human factors [6]. Studies suggest that as many 94% of alarms sound when there is no threat to the patient [7] [8] [9]; leading to alarm fatigue and thus diminish patient safety [10]. There are few studies that consider the impact of noise on the critically ill patient and the staff that care for them, but the noise levels commonly generated in an ICU have been related to sleep disruption and to delayed patient recovery [11] [12] [13]. "
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    ABSTRACT: Intensive Care Units (ICUs) can be immensely noisy places, where high noise levels may have deleterious effects on patients, visitors and staff alike. Many studies have identified sound levels exceeding World Health Organisation’s recommendations, although very few measured for more than 24 h or concurrently in multiple locations, as normally done in outdoor studies. In order to assess the feasibility of installing a continuous monitoring system in the indoor environment of an 18 bedded general intensive care, a MEMS-based microphone was used to monitor the noise levels for 7 days. Results showed minimal variation between night and day, but changes in sound level could be correlated with regularly occurring activities. The impact of microphone-holding structure on the measurements and the possibility of inferring patient and visitor’ exposure from a fixed measurement position are also discussed. Laboratory analysis, confirmed by in situ testing, identified ideal microphone positioning, and led to a correction of −1 dB for the sound pressure level measured at the microphone to obtain the level experienced by the patient.
    Full-text · Article · May 2014 · Applied Acoustics
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    • "The objective was to quickly develop a prototype , allowing design feedback and early user evaluations, without overburdening the users with extensive observations and interviews. Previous adult ICU observation experience of the first author [8], [9] allowed quick and effective collection of unit-and setting-specific data for the generation of a work domain analysis, task analysis, scenarios, and finally the prototype. The following steps were performed: "
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    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.
    Full-text · Conference Paper · Oct 2013
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