Using Model Checking to Detect Simultaneous Masking in Medical Alarms

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.