Abstract
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 paper, 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 a case study, and outlines future work.
Highlights
M EDICAL alarms are used by automation to notify human observers that monitored patient health measures have passed a threshold, indicating a potentially unsafe condition that requires immediate attention
Bassam Hasanain is with the Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL
In the method we have developed (Fig. 1), an analyst must: (a) examine the documentation associated with a configuration of medical alarms and model their behavior using our formal modeling architecture (Fig. 2); (b) specify the absence of masking using specification property patterns we provide; and (c) use model checking to formally verify that the specification properties hold for the model
Summary
M EDICAL alarms (which are usually auditory) are used by automation to notify human observers that monitored patient health measures have passed a threshold, indicating a potentially unsafe condition that requires immediate attention. This article significantly extends the description of the utilized modeling and analysis approach It builds on the original methodology with additional detection capabilities, presents new modeling and verification results, and features a deeper discussion. When used together in our method [1], these techniques can allow health care providers to determine if masking exists in a modeled configuration of alarms computationally. With such a detection capability, health care providers should be able to deploy systems that will avoid masking: enabling medical personnel to respond to alarms appropriately and potentially save patient lives. We first discuss the literature relevant to understanding our method This includes material on masking in medical alarms, psychoacoustic models of masking, and model checking. We conclude by discussing our results and future avenues of research
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