Task Switching, Multitasking, and Errors: A Psychologic Perspective on the Impact of Interruptions

Diagnostic reasoning and cognitive error in emergency medicine: Implications for teaching and learning

BACKGROUND

Accurate diagnosis in emergency medicine (EM) is high stakes and challenging. Research into physicians' clinical reasoning has been ongoing since the late 1970s. The dual-process theory has established itself as a valid model, including in EM. It is based on the distinction between two information-processing systems. System 1 rapidly generates one or more diagnostic hypotheses almost instantaneously, driven by experiential knowledge, while System 2 proceeds more slowly and analytically, applying formal rules to arrive at a final diagnosis.

METHODS

We reviewed the literature on dual-process theory in the fields of cognitive science, medical education and emergency medicine.

RESULTS AND CONCLUSION

The literature reflects two prominent interpretations regarding the relationship between the fast and slow phases and these interpretations carry very different implications for the training of clinical learners. One interpretation, prominent in the EM community, presents it as a "check-and-balance" framework in which most diagnostic error is caused by cognitive biases originating within System 1. As a result, EM residents are frequently advised to deploy analytical (System 2) strategies to correct such biases. However, such teaching approaches are not supported by research into the nature of diagnostic reasoning. An alternative interpretation assumes a harmonious relationship between Systems 1 and 2 in which both fast and slow processes are driven by underlying knowledge that conditions performance and the occurrence of errors. Educational strategies corresponding to this alternative have not been explored in the EM literature. In this paper, we offer proposals for improving the teaching and learning of diagnostic reasoning by EM residents.

Enhancing human-AI collaboration: The case of colonoscopy

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Improving electrotactile communication with a multi-pad electrode under cognitive load

BACKGROUND

Electrotactile systems are compact interfaces that can be used to convey information through the skin by producing a range of haptic sensations. In many applications, however, the user needs to perceive and interpret haptic stimulation while being engaged in parallel activities. Developing methods that ensure reliable recognition of electrotactile messages despite additional cognitive load is, therefore, an important step for the practical application of electrotactile displays.

METHODS

This study investigated if a simple strategy of repeating electrotactile messages can improve message identification during multitasking. Ten participants identified 36 spatiotemporal electrotactile messages delivered through a 3 × 2 pad-matrix electrode placed on the torso while performing a concomitant cognitive task in three conditions: the messages were presented once (No-REP), and each message was repeated three (REP3) and five (REP5) times. The main outcome measure was the success rate (SR) of message identification.

RESULTS

During multitasking, in the No-REP condition, the SR (median (IQR)) dropped to 56.25% (22.62%), demonstrating that the cognitive task decreased performance. However, the SR significantly improved with message repetitions, reaching 72.92% (21.87%) and 81.25% (18.66%) in REP3 and REP5 conditions respectively, without a statistically significant difference between REP3 and REP5.

CONCLUSIONS

Multitasking affected the efficacy of haptic communication, but message repetition was shown to be an effective strategy for improving performance. Additionally, only three repetitions were enough, as an additional increase in the duration of message transmission (5 repetitions) did not lead to further improvement. This study is an important step toward delivering electrotactile communication that can cope with the demands of real-world applications.