Reported causes of alarm fatigue, and relationship to alarm response

Alarm fatigue is an umbrella term meant to encompass all phenomena that are responsible for clinicians’ increased response time and decreased response rate to alarms. It is reported as the cause when clinicians improperly ignore, override, silence, or mute clinical alarms that signify critical patient events.6 This decreased response rate to alarms has been associated with patients’ deaths,1 ,7 as well as permanent loss of function, and prolonged hospitalisation.8 However, of the many phenomena that are thought of as part of alarm fatigue, only some have been shown to predict the alarm system's ability to redirect attention to emerging hazards and, therefore, improve patient outcomes.

Where to start? Informativeness

Although there are many potential contributors to a specific alarm response problem, it is likely that informativeness plays a prominent role. Across all healthcare settings, informativeness consistently ranges from 1% to 20%.6 Across multiple studies of electronic health records, alerts’ informativeness ranged from 1% to 12%.34–36 In separate studies of haemodynamical and ventilator and pulse oximetry alarms, informativeness was 20%37 and 23%,38 respectively. In a national survey performed in 2005–2006, 78% respondents stated that alarms were often overridden due to distrust.39 Using findings from Bliss,22 these rates predict the high proportion of alarms that are overridden or ignored.23 ,36 ,40 These levels of informativeness are well beneath the tentative theoretical threshold of 71% that Wickens and Dixon38 suggested that alarms need in order to be more helpful than harmful to the overall system.

Collecting data to measure informativeness requires more data, and is, therefore, more labour intensive than some of the other measures. Besides knowing the event that the alarm is signifying, context of the alarm and of the patient immediately preceding and following the alarm must also be known. The waveform representation of the sensor data that is used to generate the alarm can be used to determine the presence of artefact and, therefore, false alarms.41 Additionally, video footage or direct observation of patients can be used to determine if the event signified by the alarm is occurring.42 Clinical experts are used to determining whether the alarm was clinically relevant (ie, not unnecessary) based on the patient's recent activity and history.42–45 Informativeness is most often measured as positive predictive value (PPV), which measures the proportion of true positives out of all positive responses,6 ,17 but have also been measured by negative predictive value, sensitivity and specificity,45 and could be measured by any measure of discrimination power. Most studies calculate PPV based only on the traditional definition of false alarms, but there is recent awareness that requiring that true positives be neither false nor unnecessary may be a better predictor of alarm response in some settings.18 ,46

Although improving informativeness will likely improve alarm response, it is possible that other contributors in the setting are the more substantial contributors. It is prudent to consider the likelihood of the other evidence-based factors as well, many of which are noted above. Being aware of these other factors and the current evidence-based guidance for each, where applicable, may reveal potential contributors that otherwise would have been obscured. Finally, it is also prudent to assess response time and response rate directly, to ensure that the current alarm response requires intervention, and that the intervention results in response times and rates within acceptable safety boundaries. Alarm response can sometimes be measured without direct observation if it can be discerned from the technology, for example, when it requires clinicians to explicitly override guidance and give a reason.36 Most often, though, the machine data that is available will not contain sufficient context to make a determination. In these cases, direct observation of the clinician or unit is necessary, recording whether each alarm was attended to or not, and if corrective action was taken. A good example is given by Seagull and Sanderson.23

In our institution, the alarm management team uses a triangulation of three measures to determine the relative priority of multiple competing needs for intervention. Response times to the most important alarm signals, as were determined per TJC NPSG 6,2 are collected electronically via a Secondary Alarm Notification System, which also serves as an alarm aggregator. From these data, units are selected for direct observation, both to measure informativeness and alarm response with the necessary context. Analysis of these data and consideration of potential other contributors all drive the ultimate design of interventions. Based on this analysis, the intervention may be to change default alarm thresholds, allow more clinician discretion in setting thresholds for a given patient, procure sensors with better discrimination power, or procure software that can use data from multiple parameters, trends over time, and signal quality to reduce false alarms. Direct observation is then conducted to determine if informativeness and alarm response have improved.

Alternatives when traditional alarms are insufficient

In addition to improving these evidence-based measures on existing alarm systems, the literature offers guidance on alternatives for information representation and sensory modalities. Often, these are most valuable when the discrimination power of available sensors and software, or the auditory characteristics of the environment, make traditional audible alarms ineffective or harmful to overall system performance.47 In these situations, alternative visual, auditory, tactile and olfactory displays are recommended. Using alternative sensory modalities can make use of less saturated sensory channels, thereby reducing overall mental workload,48 and can leverage the unique benefits of each modality. Using alternative representations, especially those containing continuous non-interrupting data, can increase informativeness and reduce the overall mental workload required for directing attention and interpretation.49–52 The continuous beep of an echocardiogram that signifies heart rate is a good example of this. This always-on signal allows the listener to detect change, not by commanding attention on onset, but by presenting a continuous baseline that new events are subconsciously compared with. Additionally, visual analogue representations can reveal trends and other data relationships that traditional threshold alarms often obscure.49 ,51 Although these alternate modalities and representations are not optimal in all situations,49 their benefits should be considered as part of an optimal alarm system design process.

With the increased attention and investment that clinical alarm management is currently receiving, it is more critical now than ever to adopt a pragmatic and rigorous approach to diagnose and treat the alarm-related issues that we face in our organisations. That approach should systematically examine and rule out all the known contributors that are likely responsible for the observed symptoms before considering those that are less likely. It is our intent to increase awareness and use of these generalised models, although we understand that they may need to be refined further for specific healthcare settings. In this way, we can more effectively, efficiently and reliably redirect clinician attention, ultimately improving alarm response.