Interpreting electrocardiograms (ECGs) is an important but complex and error-prone task. While diagnostic support algorithms exist, how support is displayed and how clinicians interact with ECG diagnostic and clinical decision support systems in general remain underexplored. In this preregistered experiment, we studied how providing clinicians with different versions of diagnostic support affects ECG interpretation. All four support types improved diagnosis accuracy compared to a no-support control condition, but the most effective was support offering visual ECG trace markings. User experience, in the form of psychological need satisfaction of competence and security, was highest when clinicians first viewed the ECG independently and then received support in a second stage. The latter two-stage support also resulted in the shortest diagnosis times. We conclude with design and research implications for creating clinician-algorithmic support interactions that improved user experience, efficacy, and effectiveness in the present study, and may ultimately contribute to patient safety.

Recent advancements in artificial intelligence have sparked discussions on how clinical decision-making can be supported. New clinical decision support systems (CDSSs) have been developed and evaluated through workshops and interviews. However, limited research exists on how CDSSs affect decision-making as it unfolds, particularly in settings such as acute care, where decisions are made collaboratively under time pressure and uncertainty. Using a mixed-method study, we explored the impact of a CDSS on decision-making in anesthetic teams during simulated operating room crises. Fourteen anesthetic teams participated in high-fidelity simulations, half using a CDSS prototype for comparative analysis. Qualitative findings from conversation analysis and quantitative results on decision-making efficiency and workload revealed that the CDSS changed team structure, communication, and diagnostic processes. It homogenized decision-making, empowered nursing staff, and introduced friction between analytical and intuitive thinking. We discuss whether these changes are beneficial or detrimental and offer insights to guide future CDSS design.

Research has explored head-worn displays (HWD) in various professional contexts. However, evaluations have been limited by short-term use, a focus on the person using the HWD, and on performance variables. In a field study, we evaluated a monocular, opaque HWD for multi-patient monitoring, which supervising anesthesiologists wore for 8-10 days each. We investigated the effect of prolonged HWD use on the experience of the supervising anesthesiologists and their co-workers using interviews and repeated observations. A reflexive thematic analysis showed (1) interaction and mindset changes over time, (2) information on the HWD is more than numbers, (3) the HWD affects co-workers' collaboration with supervisors, and (4) distraction depends on the point of view. Using activity theory, we discuss the fact that HWD use develops and changes over time and that even a single-user HWD influences the collaboration with co-workers. We conclude with implications for HWD design, implementation, and evaluation.

Technology use in acute care has so far been studied with a predominant focus on safety and efficiency rather than the experience of acute care staff. In the present paper, we report a qualitative study including 130 hours of participant observation on acute care teams and retrospective interviews with nine anesthesiologists on their experiences with technology. Our approach is theoretically guided by four modern HCI perspectives, namely psychological need satisfaction, activity theory, embodied interaction, and media equation theory. We analyzed the interview and observation data using a reflexive thematic analysis and identified four themes. Technology as necessary (evil) describes the pervasiveness of technology and how acute care teams depend on technology. While technology as second patient covers the additional load for anesthesiologists on a functional and emotional level, technology as problem solver and safety net highlights relieving aspects of technology use. Technology as artificial limb focuses on so-called transparent tools that extend the body and capabilities of users. We then discuss the identified themes concerning the previously presented theories. Based on our findings and theoretical perspectives, we report general insights for research and design in acute care, such as the benefits of mixed methods or the importance of the gut feelings of experts. Finally, we propose possible directions for future work, such as developing a UX questionnaire adapted to the context of safety-critical domains, such as acute care.

Supervising anesthesiologists oversee junior anesthesiologists in several operating rooms to instruct and provide advice, and hence ensure high patient safety. Supervisors benefit from good situation awareness but accessing information about multiple patients can be challenging. In a user-centered design process, we have developed an interface concept for a head-worn display (HWD) to support multi-patient monitoring. The design includes data about the patients’ physiology, vital status, vital trends, and medications. We conducted an exploratory evaluation of a prototype in a high-fidelity medical simulation with six supervising anesthesiologists. Our results show that the supervisors’ situation awareness of all simulated operations improved because participants noticed and responded to multiple events over the course of the scenario. However, the supervisors reported increased mental workload due to the constant availability of patient data. We discuss insights about the HWD display, the design process, and the experience and views of staff on using the HWD.

In health care, there are still many devices with poorly designed user interfaces that can lead to user errors. Especially in acute care, an error can lead to critical conditions in patients. Previous research has shown that the use of augmented reality can help to better monitor the condition of patients and better detect unforeseen events. The system created in this work is intended to aid in the detection of changes in patient and equipment-data in order to increase detection of critical conditions or errors.

Despite many advances, clinical decision support tools (DSTs) often suffer from implementation and acceptance problems in the actual clinical context. We suggest that considering psychological needs-based and embodied user experience theories in the design of DSTs could help to overcome these problems. To examine this idea, we iteratively developed a DST called Cassandra supporting anesthetic teams in crisis management, specifically focusing on psychological needs and fluent interaction with the social and physical environment. We preliminarily evaluated Cassandra in a medical simulation, requiring anesthetic teams to handle a crisis. Although not all features of Cassandra had the intended effect, the results indicated that interacting with Cassandra supported the fulfillment of the identified needs for autonomy, competence, and relatedness and was seamlessly integrated into existing diagnostic processes. Considering user experience theories for the design of DSTs seems a promising way to overcome implementation and acceptance problems and eventually improve patient safety.

In visually rich working environments, it is important for operators to distribute their visual attention in an optimal fashion in order to operate safely. Computational models can provide a systematic method of investigating the attention distribution of humans. In this article, we reanalyze eye tracking data from anesthesiologists when inducing general anesthesia to test whether the so-called expectancy value version of the salience, effort, expectancy, value (SEEV) model can accommodate the visual attention distribution of anesthesiologists, and to investigate the effect of case (real versus simulated cases) and experience (junior versus senior) on the expectancy value model fit. The overall model fit is good (predicted-observed percentage dwell time correlation of 0.810, R2= 0.656). We observe that the model fit is better in simulated cases than real ones. In addition, the model fit is good for junior anesthesiologists independent of the case, but that there is an even better model fit in simulated cases than in real ones for senior anesthesiologists (case × experience interaction). Overall, the expectancy value model can be validated. However, at least within the context of anesthesiology, the full SEEV model may be needed to capture the large and distractive visual work environment of anesthesiologists. From a practical point of view, previous research suggested that anesthesiologists pay more attention to monitoring in simulated cases. However, the SEEV analysis suggests that anesthesiologists do not pay extra attention to monitoring equipment in simulated cases, but may not be able to pay enough attention to monitoring equipment in real cases.

Cognitive aids - artefacts that support a user in the completion of a task at the time - have raised great interest to support healthcare staff during medical emergencies. However, the mechanisms of how cognitive aids support or affect staff remain understudied. We describe the iterative development of a tablet-based cognitive aid application to support in-hospital resuscitation team leaders. We report a summative evaluation of two different versions of the application. Finally, we outline the limitations of current explanations of how cognitive aids work and suggest an approach based on embodied cognition. We discuss how cognitive aids alter the task of the team leader (distributed cognition), the importance of the present team situation (socially situated), and the result of the interaction between mind and environment (sensorimotor coupling). Understanding and considering the implications of introducing cognitive aids may help to increase acceptance and effectiveness of cognitive aids and eventually improve patient safety.