Anaesthesia monitor alarms: a theory-driven approach

Abstract

The development of physiologic monitors has contributed to the decline in morbidity and mortality in patients undergoing anaesthesia. Diverse factors (physiologic, technical, historical and medico-legal) create challenges for monitor alarm designers. Indeed, a growing body of literature suggests that alarms function sub-optimally in supporting the human operator. Despite existing technology that could allow more appropriate design, most anaesthesia alarms still operate on simple, pre-set thresholds. Arguing that more alarms do not necessarily make for safer alarms is difficult in a litigious medico-legal environment and a competitive marketplace. The resultant commitment to the status quo exposes the risks that a lack of an evidence-based theoretical framework for anaesthesia alarm design presents. In this review, two specific theoretical foundations with relevance to anaesthesia alarms are summarised. The potential significance that signal detection theory and cognitive systems engineering could have in improving anaesthesia alarm design is outlined and future research directions are suggested.

Practitioner Summary: The development of physiologic monitors has increased safety for patients undergoing anaesthesia. Evidence suggests that the full potential of the alarms embedded within those monitors is not being realised. In this review article, the authors propose a theoretical framework that could lead to the development of more ergonomic anaesthesia alarms.

Keywords:

• alarms and warnings
• advanced human–machine interfaces
• anaesthesia alarms
• anaesthesia equipment
• equipment design
• monitoring
• patient safety
• socio-technical systems

Notes

1. Monitored physiological variables include heart rate (HR) and rhythm, ST segment analysis, blood pressure, oxygen saturation, end-tidal carbon dioxide tension, temperature and bispectral index.

2. Much has changed since the early days of anaesthesiology, including the fact that in many jurisdictions, nurse anaesthetists monitor the patient, under the supervision of an anaesthesiologist who may or may not be present. Indeed, other healthcare providers, including the surgeon, may be in the position of responding to anaesthesia monitor alarms. The authors recognise this diversity, but for simplicity use ‘anaesthesiologist’ as a collective term.

3. Whittingham (2004, pp. 171–175) described a fatal rail crash where the conductor repeatedly ‘acknowledged’ an alarm that was indicating the increasing proximity of a train ahead, without taking the corrective action of slowing the train. The authors concluded that his action of depressing the acknowledge button had become a conditioned response, a phenomenon that was not uncommon amongst conductors. They also found that the alarm system was ‘inadequate for its intended purpose’. The conductor survived, but served 18 months in jail for manslaughter.

4. Tachycardia is the term for an elevated HR (in adults, >100 beats per minute).

5. It is not easy to make improvements in d’, which is often fixed by both hardware limitations and our own limited understanding of the ways in which a true event differs from normal with respect to the patterns it will present to the monitor. This is an area in which more research clearly needs to be done if clinically appropriate alarms are to be developed.

6. The criterion determination is influenced by the a-priori probability of a signal and the risk/benefit ratio of each of the decision outcomes elaborated in Figure 1.

7. Pollack and Maddans (1964) examined the performance of a two-stage detection system, finding that optimal system performance occurred when each stage had equal discriminability and equal criteria. Interestingly, they found that system performance was 1.2d’, just slightly less than the theorised maximal performance (cited in Sorkin and Woods 1985).

8. The machine itself is a cognitive system, as is the operator. Working together, they are a JCS.

9. The six challenges outlined by Klein et al. (2005) for joint human–machine activity are:(1) Achievement of basic compact.(2) Mutual predictability.(3) Goal negotiation.(4) Phase co-ordination.(5) Attention management.(6) Control of co-ordination costs.