Auditory Alarms: From Alerting to Informing

The current alarm and warning systems in medical domains are integrated into patient monitoring devices and are centered around the concept of alerting operators to potential problems without specifying the specific nature of the problem. In contrast, informative auditory alarms and warning systems have been proposed for next-generation monitoring equipment. These “auditory displays” would present meaningful diagnostic information to care providers while not interrupting their other activities, and not demanding visual attention. Our work has documented the problems in the critical care setting with alarms. We report here the results of observations made during experiments conducted using eye-tracking techniques to determine patterns of information during real and simulated anesthesia cases. We classify monitoring behaviors into four categories of information seeking, and discuss the implications of these behaviors for design of informative auditory displays.

Reducing and Mitigating Human Error in Medicine

Although precise definitions and models of human error in medicine remain elusive, there is little doubt that adverse events, sometimes involving human error, threaten patient safety and can be addressed by human factors approaches to error. In this chapter, we combine an information-processing framework that identifies perceptual, cognitive, and behavioral requirements of operators involved in health care activities with a system-based perspective that helps define when these needs are met by the health care context. We focus on errors and adverse events related to four broad areas of medical activities: medical device use, medication use, team collaboration, and diagnostic/decision support. For each area, we review evidence for specific error types, operator and system factors that contribute to these errors, and possible mitigating strategies related to design and training interventions that enable health care systems to better meet operators' perceptual, cognitive, and behavioral needs. This review reveals progress in identifying sources of human error and developing mitigating strategies in the areas of medical device and medication use, in part because of tools from human factors engineering that identify user needs and how to design environments to support them. Much less is known about how error emerges from work practices in complex settings, such as collaboration among team members. There is a need for theoretical frameworks to analyze error in the context of routine work practices. Such frameworks will bridge cognitive analyses of individual operators and tasks and more comprehensive theories of organizations, to guide interventions that target medical error at multiple levels.

The influence of distraction and driving context on driver response to imperfect collision warning systems

Automotive collision warning systems (CWS) can enhance hazard identification and management. However, false alarms (FAs), which occur as a random activation of the system not corresponding to a threat and not interpretable by the driver, and unnecessary alarms (UAs), which occur in situations judged hazardous by the algorithm but not by the driver, may limit CWS effectiveness. A driving simulator was used to investigate the influence of CWS (accurate, UA, FA, none) and distraction on driver performance during non-critical and critical events. FAs and UAs differentially influenced trust and compliance. FAs diminished trust and compliance, whereas the context associated with UAs fostered trust and compliance during subsequent events. This study suggests current warning descriptions based on signal detection theory need to be expanded to represent how different types of alarms affect drivers.

Alarm mistrust in automobiles: how collision alarm reliability affects driving

As roadways become more congested, there is greater potential for automobile accidents and incidents. To improve roadway safety, automobile manufacturers are now designing and incorporating collision avoidance warning systems; yet, there has been little investigation of how the reliability of alarm signals might impact driver performance. We measured driving and alarm reaction performances following alarms of various reliability levels. In Experiment One, 70 participants operated a driving simulator while being presented console emitted collision alarms that were 50%, 75%, or 100% reliable. In Experiment Two, the same participants were presented spatially generated collision alarms of the same reliability levels. The results were similar in both experiments: alarm and automobile swerving reactions were significantly better when alarms were more reliable; however, drivers still failed to avoid collisions following reliable alarms. These results emphasize that alarm designers should maximize alarm reliability while minimizing alarm invasiveness.