Case 2—1992 Unintentional delivery of vasoactive drugs with an electromechanical infusion device

Cook, Richard I.; Woods, David D.; Howie, Michael B.; Horrow, Jan C.; Gaba, David M.

doi:10.1016/1053-0770(92)90207-n

Cited by 44 publications

(6 citation statements)

References 5 publications

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“…It involves the risk that unexpected changes in system behaviorsuch as uncommanded mode transitions (Sarter & Woods, 1994 -go undetected and lead to automation surprises (Sarter, Woods, & Billings, 1997) and a loss of mode awareness (i.e., a lack of knowledge about the current and future states and behavior of the automation), especially in the context of highly dynamic and nonroutine operations (e.g., Sarter & Woods, 1994Vakil, Hansman, Midkiff, & Vaneck, 1995;Wiener, 1989). Automation surprises and mode errors have contributed to numerous incidents and accidents in domains such as civil aviation and medicine (e.g., Cook, Woods, Howie, Horrow, & Gaba, 1992;Sparaco, 1994).…”

Section: Introductionmentioning

confidence: 99%

Peripheral Visual Feedback: A Powerful Means of Supporting Effective Attention Allocation in Event-Driven, Data-Rich Environments

Nikolic

Sarter

2001

Hum Factors

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Breakdowns in human-automation coordination in data-rich, event-driven domains such as aviation can be explained in part by a mismatch between the high degree of autonomy yet low observability of modern technology. To some extent, the latter is the result of an increasing reliance in feedback design on foveal vision--an approach that fails to support pilots in tracking system-induced changes and events in parallel with performing concurrent flight-related tasks. One possible solution to the problem is the distribution of tasks and information across sensory modalities and processing channels. A simulator study is presented that compared the effectiveness of current foveal feedback and two implementations of peripheral visual feedback for keeping pilots informed about uncommanded changes in the status of an automated cockpit system. Both peripheral visual displays resulted in higher detection rates and faster response times, without interfering with the performance of concurrent visual tasks any more than does currently available automation feedback. Potential applications include improved display designs that support effective attention allocation in a variety of complex dynamic environments, such as aviation, process control, and medicine.

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Section: Introductionmentioning

confidence: 99%

Peripheral Visual Feedback: A Powerful Means of Supporting Effective Attention Allocation in Event-Driven, Data-Rich Environments

Nikolic

Sarter

2001

Hum Factors

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“…User interfaces of medical equipment demand a high level of reliability in order to create prerequisites for safe and effective equipment operation, installation and maintenance [8]. Poorly designed human-machine interfaces in medical equipment increase the risk of human error [1,9], as well as incidents and accidents in medical care. Medication errors are estimated to be the major source in those errors that compromise patient safety [10][11][12][13][14][15].…”

Section: Usability Of Medical Devicesmentioning

confidence: 99%

With how many users should you test a medical infusion pump? Sampling strategies for usability tests on high-risk systems

Schmettow

Vos

Schraagen³

2013

Journal of Biomedical Informatics

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Usability testing is recognized as an effective means to improve the usability of medical devices and prevent harm for patients and users. Effectiveness of problem discovery in usability testing strongly depends on size and representativeness of the sample. We introduce the late control strategy, which is to continuously monitor effectiveness of a study towards a preset target. A statistical model, the LNB(zt) model, is presented, supporting the late control strategy. We report on a case study, where a prototype medical infusion pump underwent a usability test with 34 users. On the data obtained in this study, the LNB(zt) model is evaluated and compared against earlier prediction models. The LNB(zt) model fits the data much better than previously suggested approaches and improves prediction. We measure the effectiveness of problem identification, and observe that it is lower than is suggested by much of the literature. Larger sample sizes seem to be in order. In addition, the testing process showed high levels of uncertainty and volatility at small to moderate sample sizes, partly due to users' individual differences. In reaction, we propose the idiosyncrasy score as a means to obtain representative samples. Statistical programs are provided to assist practitioners and researchers in applying the late control strategy.

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“…Systems with multiple modes are particularly problematic in terms of observability and directability, thus increasing the probability of automation-related errors. In medicine, for example, hidden modes of operation combined with inconsistent mappings between signals and actions and misleading displays were found to undermine anesthesiologists' performance (Cook, Woods, Howie, Horrow, & Gaba, 1992). Mode awareness and mode management have also caused problems on flight decks, as the proliferation and layering of system modes increases complexity, hinders the development of accurate mental models, and increases the chances of programming mistakes (Javaux, 1998;Sarter, 2008).…”

Section: Likelihood Of Automation-related Errorsmentioning

confidence: 99%

Automation, Task, and Context Features

Mosier

Fischer

Morrow

et al. 2013

Journal of Cognitive Engineering and Decision Making

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Human-automation interaction (HAI) takes place in virtually every high-technology domain under a variety of operational conditions. Because operators make HAI decisions such as which mode to use, and when to engage, disengage, monitor, or cross-check automation, it is important to understand their perceptions of how system and situational characteristics affect their interaction with automation. The objective of this study was to examine how systematic variations of automation interface, task and context features influence professional pilots' judgments of HAI situations. Pilots received descriptions of crews interacting with flight deck automation in specific situations and were asked to rate cognitive demands and predict behaviors. Results reflect a complex interplay among automation features, task, and context. Automation features influenced judgments of workload, task management, and potential for automation-related errors; however, the impact of automation on situation awareness seems to be moderated by task features. Unanticipated tasks had broader effects on pilots' judgments than operational stressors. Results suggest that although changes to automated systems may be small in technical terms, their cognitive and behavioral impact on operators may be significant. Performance effects of automation changes in aviation as well as other domains need to be addressed with reference to task characteristics and situational demands.

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Case 2—1992 Unintentional delivery of vasoactive drugs with an electromechanical infusion device

Cited by 44 publications

References 5 publications

Peripheral Visual Feedback: A Powerful Means of Supporting Effective Attention Allocation in Event-Driven, Data-Rich Environments

Peripheral Visual Feedback: A Powerful Means of Supporting Effective Attention Allocation in Event-Driven, Data-Rich Environments

With how many users should you test a medical infusion pump? Sampling strategies for usability tests on high-risk systems

Automation, Task, and Context Features

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