The ALARM Monitoring System – Intelligent Decision Making under Uncertainty

Beinlich, Ingo A.; Gaba, Divya

doi:10.1097/00000542-198909001-00337

Cited by 8 publications

(1 citation statement)

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“…Integrating signals can overcome the problems of noisy or missing signals that arise when each signal is assessed separately and without considering the overall context. Beinlich and Gaba (1989) implemented a logical alarm reduction mechanism (ALARM), a diagnostic system for patient monitoring that calculates probabilities for a differential diagnosis based on the available data and a belief network for probabilistic reasoning. Rather than producing alarms based on individual data points, this integrated alarm system generated text messages indicating higher-level problems.…”

Section: Data Fusion and Smart Alarmsmentioning

confidence: 99%

Human factors error and patient monitoring

Walsh

Beatty²

2002

Physiol. Meas.

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A wide range of studies have shown that human factors errors are the major cause of critical incidents that threaten patient safety in the medical environments where patient monitoring takes place, contributing to approximately 87% of all such incidents. Studies have also shown that good cognitively ergonomic design of monitoring equipment for use in these environments should reduce the human factors errors associated with the information they provide. The purpose of this review is to consider the current state of knowledge concerning human factors engineering in its application to patient monitoring. It considers the prevalence of human factors error, principles of good human factors design, the effect of specific design features and the problem of the measurement of the effectiveness of designs in reducing human factors error. The conclusion of the review is that whilst the focus of human factors studies has, in recent years, moved from instrument design to organizational issues, patient monitor designers still have an important contribution to make to improving the safety of the monitored patient. Further, whilst better psychological understanding of the causes of human factors errors will in future guide better human factors engineering, in this area there are still many practical avenues of research that need exploring from the current base of understanding.

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Section: Data Fusion and Smart Alarmsmentioning

confidence: 99%

Human factors error and patient monitoring

Walsh

Beatty²

2002

Physiol. Meas.

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Artificial Intelligence and Expert Systems

Gaba¹

1995

Control and Automation in Anaesthesia

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Integration of monitoring for intelligent alarms in anesthesia: Neural networks—Can they help?

Mylrea

Orr

Westenskow

1993

J Clin Monitor Comput

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Although there has been a decrease in the number of anesthesia-related critical incidents, there are still opportunities for further improvement. We discuss the potential of integrated monitoring and artificial neural networks as a means of vigilantly watching for patterns in multiple variables to detect incidents and reduce false alarms. We estimate that half the anesthesia-related events could be detected with integrated monitoring using only 5 variables. A review of research using artificial intelligence/expert systems indicates limited potential for success using these tools alone for integrated monitoring in the operating room. We present artificial neural networks as an approach that is more suited to the type of multivariable monitoring and pattern recognition required. Along with rule-based artificial intelligence, these now have the potential to help develop innovative monitoring in the operating room.

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The ALARM Monitoring System – Intelligent Decision Making under Uncertainty

Cited by 8 publications

References 0 publications

Human factors error and patient monitoring

Human factors error and patient monitoring

Artificial Intelligence and Expert Systems

Integration of monitoring for intelligent alarms in anesthesia: Neural networks—Can they help?

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