The failure of humans to respond to auditory medical alarms has resulted in numerous patient injuries and deaths and is thus a major safety concern. A relatively understudied source of response failures has to do with simultaneous masking, a condition where concurrent sounds interact in ways that make one or more of them imperceptible due to physical limitations of human perception. This paper presents a method, which builds on a previous implementation, that uses a novel combination of psychophysical modeling and formal verification with model checking to detect masking in a modeled configuration of medical alarms. Specifically, the new method discussed here improves the original method by adding the ability to detect additive masking while concurrently improving method usability and scalability. This paper describes how these additions to our method were realized. It then demonstrates the scalability and detection improvements via three different case studies. Results and future research are discussed.

The ability of people to hear and respond to auditory medical alarms is critical to the health and safety of patients. Unfortunately, concurrently sounding alarms can perceptually interact in ways that mask one or more of them: making them impossible to hear. Because masking may only occur in extremely specific and/or rare situations, experimental evaluation techniques are insufficient for detecting masking in all of the potential alarm configurations used in medicine. Thus, a real need exists for computational methods capable of determining if masking exists in medical alarm configurations before they are deployed. In this work, we present such a method. Using a combination of formal modeling, psychoacoustic modeling, temporal logic specification, and model checking, our method is able to prove whether a modeled of a configuration of alarms can interact in ways that produce masking. This paper provides the motivation for this method, presents its details, describes its implementation, demonstrates its power with an case study, and outlines future work.

The perceptibiliy of auditory medical alarms is critical to patient health and safety. Unfortunately concurrently sounding alarms can interact in ways that can mask one or more of them: render them imperceptible. Masking may only occur in extremely specific and/or rare situations. Thus, experimentation is insufficient for detecting it in all of the potential alarm configurations used in medicine. Therefore, there is a real need for computational methods capable of determining if masking exists in medical alarm configurations. In this work, we present such a method. Using a combination of formal modeling, psychoacoustic modeling, temporal logic specification, and model checking, our method is able to prove whether a configuration of alarms can interact in a way that produces masking. This paper motivates and presents this method, describes its implementation, demonstrates its power with an application, and outlines future developments.

The failure of humans to respond to auditory medical alarms has resulted in numerous patient injuries and deaths. The widely used IEC 60601-1-8 international medical alarm standard was created to improve alarm discernibility and identification. Unfortunately, the melodic tonal patterns of IEC 60601-1-8’s alarms are particularly susceptible to simultaneous masking, a condition where concurrent sounds interact in ways that make one or more of them imperceptible. This paper presents a method, which builds on a previous implementation, that uses a novel combination of psychophysical modeling and model checking to detect masking in a modeled configuration of IEC 60601-1-8 alarms. We describe our updated method and demonstrate its power by using it to find masking in the alarms of an actual IEC 60601-1-8-compliant telemetry monitoring system. Results and future research are discussed.

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