Using Model Checking to Detect Masking in IEC 60601-1-8-compliant Alarm Configurations

Bolton, Matthew L.; Hasanain, Bassam; Boyde, Andrew D.; Edworthy, Judy

doi:10.1177/1541931213601146

Cited by 4 publications

(17 citation statements)

References 18 publications

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“…However, because increasing the search depth exponentially increased computational time [36], this was not always possible in practice. This version of the method successfully improved upon the older version of the method [34,35] by both being more usable (due to the spreadsheet-based modeling) and more scalable (the computational efficiency of using the optimized lookup tables) [11,36]. In showing this, we analyzed the alarm system evaluated in the early versions of the method [34,35] as well as the alarms from a real telemetry monitoring system, a GE CARESCAPE TM Monitor B850 [31].…”

Section: The Previous Version Of Our Methodsmentioning

confidence: 99%

“…Researchers have used model checking to successfully find and correct human factors issues in automated systems [6,10,20,51,63] and medical systems [3, 4, 7-9, 54, 60]. However, outside of our previous efforts on alarm masking modeling and detection [11,[34][35][36], no work has used model checking to find safety problems associated with human sensation and perception. Below we describe how our previous efforts worked.…”

Section: Model Checkingmentioning

confidence: 99%

“…Below we describe how this new approach was realized. After this, we use the new approach to evaluate the alarm configurations reported in previous results [11,[34][35][36] to compare prediction performance and time. We also characterize how the method scales with a series of synthetic test cases.…”

Section: Objectivesmentioning

confidence: 99%

“…To address this problem, we developed a computational method [11,[34][35][36] that can detect masking in configurations of tonal medical alarms. The method uses a novel combination of psychoacoustics and model checking.…”

Section: Introductionmentioning

confidence: 99%

“…When these technologies are used together in our method, an analyst can model the sounding behavior of multiple alarms and use model checking to prove whether the represented alarms can mask each other. This method has been used to analyze real medical alarm configurations [11,36]. However, these analyses could take days to analyze even one alarm.…”

Section: Introductionmentioning

confidence: 99%

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A computationally efficient formal method for discovering simultaneous masking in medical alarms

et al. 2018

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Numerous patient injuries and deaths have been caused by medical practitioners failing to respond to medical alarms. Simultaneous masking, where concurrently sounding medical alarms result in one or more being unhearable, is partially responsible for this problem. In previous work, we introduced a computational formal method capable of proving (formally verifying) if masking could occur in a modeled configuration of medical alarms. However, the scalability of the method limited the applicability and completeness of its analyses. In the work presented here, we show how we re-implemented the method to address these shortcomings. We evaluated the detection capabilities and scalability of the new version of the method with a series of realistic and synthetic case studies. Our results show that the new version of the method replicates and improves detection capabilities compared to the legacy method and does so with significant reductions in verification times. We discuss the patient safety implications of our results and explore directions for future research.

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Section: The Previous Version Of Our Methodsmentioning

confidence: 99%

Section: Model Checkingmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A computationally efficient formal method for discovering simultaneous masking in medical alarms

et al. 2018

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A formal approach to discovering simultaneous additive masking between auditory medical alarms

et al. 2017

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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.

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An Experimental Validation of Masking in IEC 60601-1-8:2006-Compliant Alarm Sounds

Bolton

Zheng

et al. 2019

Hum Factors

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Objective: This research investigated whether the psychoacoustics of simultaneous masking, which are integral to a model-checking-based method, previously developed for detecting perceivability problems in alarm configurations, could predict when IEC 60601-1-8-compliant medical alarm sounds are audible. Background: The tonal nature of sounds prescribed by IEC 60601-1-8 makes them potentially susceptible to simultaneous masking: where concurrent sounds render one or more inaudible due to human sensory limitations. No work has experimentally assessed whether the psychoacoustics of simultaneous masking accurately predict IEC 60601-1-8 alarm perceivability. Method: In two signal detection experiments, 28 nursing students judged whether alarm sounds were present in collections of concurrently sounding standard-compliant tones. The first experiment used alarm sounds with single-frequency (primary harmonic) tones. The second experiment’s sounds included the additional, standard-required frequencies (often called subharmonics). T tests compared miss, false alarm, sensitivity, and bias measures between masking and nonmasking conditions and between the two experiments. Results: Miss rates were significantly higher and sensitivity was significantly lower for the masking condition than for the nonmasking one. There were no significant differences between the measures of the two experiments. Conclusion: These results validate the predictions of the psychoacoustics of simultaneous masking for medical alarms and the masking detection capabilities of our method that relies on them. The results also show that masking of an alarm’s primary harmonic is sufficient to make an alarm sound indistinguishable. Application: Findings have profound implications for medical alarm design, the international standard, and masking detection methods.

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Using Model Checking to Detect Masking in IEC 60601-1-8-compliant Alarm Configurations

Cited by 4 publications

References 18 publications

A computationally efficient formal method for discovering simultaneous masking in medical alarms

A computationally efficient formal method for discovering simultaneous masking in medical alarms

A formal approach to discovering simultaneous additive masking between auditory medical alarms

An Experimental Validation of Masking in IEC 60601-1-8:2006-Compliant Alarm Sounds

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