High Workload Environment Research Articles

BarlowTwins-CXR: enhancing chest X-ray abnormality localization in heterogeneous data with cross-domain self-supervised learning.

Chest X-ray imaging based abnormality localization, essential in diagnosing various diseases, faces significant clinical challenges due to complex interpretations and the growing workload of radiologists. While recent advances in deep learning offer promising solutions, there is still a critical issue of domain inconsistency in cross-domain transfer learning, which hampers the efficiency and accuracy of diagnostic processes. This study aims to address the domain inconsistency problem and improve autonomic abnormality localization performance of heterogeneous chest X-ray image analysis, particularly in detecting abnormalities, by developing a self-supervised learning strategy called "BarlwoTwins-CXR". We utilized two publicly available datasets: the NIH Chest X-ray Dataset and the VinDr-CXR. The BarlowTwins-CXR approach was conducted in a two-stage training process. Initially, self-supervised pre-training was performed using an adjusted Barlow Twins algorithm on the NIH dataset with a Resnet50 backbone pre-trained on ImageNet. This was followed by supervised fine-tuning on the VinDr-CXR dataset using Faster R-CNN with Feature Pyramid Network (FPN). The study employed mean Average Precision (mAP) at an Intersection over Union (IoU) of 50% and Area Under the Curve (AUC) for performance evaluation. Our experiments showed a significant improvement in model performance with BarlowTwins-CXR. The approach achieved a 3% increase in mAP50 accuracy compared to traditional ImageNet pre-trained models. In addition, the Ablation CAM method revealed enhanced precision in localizing chest abnormalities. The study involved 112,120 images from the NIH dataset and 18,000 images from the VinDr-CXR dataset, indicating robust training and testing samples. BarlowTwins-CXR significantly enhances the efficiency and accuracy of chest X-ray image-based abnormality localization, outperforming traditional transfer learning methods and effectively overcoming domain inconsistency in cross-domain scenarios. Our experiment results demonstrate the potential of using self-supervised learning to improve the generalizability of models in medical settings with limited amounts of heterogeneous data. This approach can be instrumental in aiding radiologists, particularly in high-workload environments, offering a promising direction for future AI-driven healthcare solutions.

Risk Assessment Edge Contract for Efficient Resource Allocation

The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users’ tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract’s autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments.

Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms

BackgroundPreventable patient harm, particularly medication errors, represent significant challenges in healthcare settings. Dispensing the wrong medication is often associated with mix-up of lookalike and soundalike drugs in high workload environments. Replacing manual dispensing with automated unit dose and medication dispensing systems to reduce medication errors is not always feasible in clinical facilities experiencing high patient turn-around or frequent dose changes. Artificial intelligence (AI) based pill recognition tools and smartphone applications could potentially aid healthcare workers in identifying pills in situations where more advanced dispensing systems are not implemented. ObjectiveMost of the published research on pill recognition focuses on theoretical aspects of model development using traditional coding and deep learning methods. The use of code-free deep learning (CFDL) as a practical alternative for accessible model development, and implementation of such models in tools intended to aid decision making in clinical settings, remains largely unexplored. In this study, we sought to address this gap in existing literature by investigating whether CFDL is a viable approach for developing pill recognition models using a custom dataset, followed by a thorough evaluation of the model across various deployment scenarios, and in multicenter clinical settings. Furthermore, we aimed to highlight challenges and propose solutions to achieve optimal performance and real-world applicability of pill recognition models, including when deployed on smartphone applications. MethodsA pill recognition model was developed utilizing Microsoft Azure Custom Vision platform and a large custom training dataset of 26,880 images captured from the top 30 most dispensed solid oral dosage forms (SODFs) at the three participating hospitals. A comprehensive internal and external testing strategy was devised, model's performance was investigated through the online API, and offline using exported TensorFlow Lite model running on a Windows PC and on Android, using a tailor-made testing smartphone application. Additionally, model's calibration, degree of reliance on color features and device dependency was thoroughly evaluated. Real-world performance was assessed using images captured by hospital pharmacists at three participating clinical centers. ResultsThe pill recognition model showed high performance in Microsoft Azure Custom Vision platform with 98.7 % precision, 95.1 % recall, and 98.2 % mean average precision (mAP), with thresholds set to 50 %. During internal testing utilizing the online API, the model reached 93.7 % precision, 88.96 % recall, 90.81 % F1-score and 87.35 % mAP. Testing the offline TensorFlow Lite model on Windows PC showed a slight performance reduction, with 91.16 % precision, 83.82 % recall, 86.18 % F1-score and 82.55 % mAP. Performance of the model running offline on the Android application was further reduced to 86.50 % precision, 75.00 % recall, 77.83 % F1-score and 69.24 % mAP. During external clinical testing through the online API an overall precision of 83.10 %, recall of 71.39 %, and F1-score of 75.76 % was achieved. ConclusionOur study demonstrates that using a CFDL approach is a feasible and cost-effective method for developing AI-based pill recognition systems. Despite the limitations encountered, our model performed well, particularly when accessed through the online API. The use of CFDL facilitates interdisciplinary collaboration, resulting in human-centered AI models with enhanced real-world applicability. We suggest that rather than striving to build a universally applicable pill recognition system, models should be tailored to the medications in a regional formulary or needs of a specific clinic, which can in turn lead to improved performance in real-world deployment in these locations. Parallel to focusing on model development, it is crucial to employ a human centered approach by training the end users on how to properly interact with the AI based system to maximize benefits. Future research is needed on refining pill recognition models for broader adaptability. This includes investigating image pre-processing and optimization techniques to enhance offline performance and operation on handheld devices. Moreover, future studies should explore methods to overcome limitations of CFDL development to enhance the robustness of models and reduce overfitting. Collaborative efforts between researchers in this domain and sharing of best practices are vital to improve pill recognition systems, ultimately enhancing patient safety and healthcare outcomes.

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

ObjectiveAn increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine the potentially most cost-effective reading mode for lung cancer screening.MethodsIn three representative settings, DL-CAD was evaluated as a concurrent, pre-screening, and second reader. Scoping review was performed to estimate radiologist reading time with and without DL-CAD. Hourly cost of radiologist time was collected for the USA (€196), UK (€127), and Poland (€45), and monetary equivalence of saved time was calculated. The minimum number of screening CTs to reach break-even was calculated for one-time investment of €51,616 for DL-CAD.ResultsMean reading time was 162 (95% CI: 111–212) seconds per case without DL-CAD, which decreased by 77 (95% CI: 47–107) and 104 (95% CI: 71–136) seconds for DL-CAD as concurrent and pre-screening reader, respectively, and increased by 33–41 s for DL-CAD as second reader. This translates into €1.0–4.3 per-case cost for concurrent reading and €0.8–5.7 for pre-screening reading in the USA, UK, and Poland. To achieve break-even with a one-time investment, the minimum number of CT scans was 12,300–53,600 for concurrent reader, and 9400–65,000 for pre-screening reader in the three countries.ConclusionsGiven current pricing, DL-CAD must be priced substantially below €6 in a pay-per-case setting or used in a high-workload environment to reach break-even in lung cancer screening. DL-CAD as pre-screening reader shows the largest potential to be cost-saving.Critical relevance statementDeep-learning computer-aided lung nodule detection (DL-CAD) software must be priced substantially below 6 euro in a pay-per-case setting or must be used in high-workload environments with one-time investment in order to achieve break-even. DL-CAD as a pre-screening reader has the greatest cost savings potential.Key points• DL-CAD must be substantially below €6 in a pay-per-case setting to reach break-even.• DL-CAD must be used in a high-workload screening environment to achieve break-even.• DL-CAD as a pre-screening reader shows the largest potential to be cost-saving.Graphical

The occupational burnout among medical staff with high workloads after the COVID-19 and its association with anxiety and depression

ObjectiveAfter the end of COVID-19, medical staff were immediately faced with a high workload, leading to widespread occupational burnout. This study aims to explore the level and influencing factors of burnout among medical staff during this period, as well as its relationship with anxiety and depression.MethodsThe participants’ levels of burnout were assessed using Maslach Burnout Inventory-Human Services Survey (MBI-HSS), and the reliability and validity of the questionnaire were evaluated through Cronbach’s α and Confirmatory Factor Analysis (CFA). Independent sample t-test, chi-square test, and Pearson analysis were employed to determine the correlation between two sets of variables. Univariate and multivariate logistic regression analyses were conducted to identify significant factors influencing burnout. Finally, nomograms were used to predict the probability of burnout occurrence.ResultsThis study collected a total of 1,550 questionnaires, and after excluding 45 questionnaires that were duplicates or incomplete, a sample of 1,505 (97.1%) participants were included in the final statistical analysis. Both Cronbach’s α and the fit indices of CFA demonstrated excellent adaptability of the Chinese version of MBI-HSS in this study. The overall prevalence rates for emotional exhaustion (EE), depersonalization (DP), and diminished personal accomplishment (PA) were 52.4, 55.3, and 30.6%, respectively. Obtaining psychological support, health condition, relationship with family members, and insufficient sleep were identified as common contributing factors to burnout among medical staff. Additionally, age and promotion pressure were also associated with burnout among doctors, and exceeding legal working hours was an important factor for nurse burnout. The C-index for the nomograms predicting burnout among doctors and nurses was 0.832 and 0.843, respectively. Furthermore, burnout exhibited a significant linear correlation with anxiety and depression.ConclusionAfter the end of COVID-19, medical staff in high workload environments were facing severe burnout, which might lead to anxiety and depression. The occupational burnout of medical staff needed to be taken seriously and actively intervened.

Driver Understanding and Secondary Task Performance While Viewing Traffic Safety Messages on Dynamic Message Signs

Many agencies use dynamic message signs to display behavioral traffic safety (BTS) messages (e.g., CLICK IT OR TICKET, DONT DRINK AND DRIVE). However, little guidance exists on how best to design and display such messages on those signs. In this paper, the results of a human factors laptop-based study are described, which examined how well drivers can read and interpret BTS messages while simultaneously attending to a secondary control task similar to how drivers must operate their vehicle while simultaneously reading road signs. The study results indicated that displaying BTS messages that include humor, wit, or pop culture references could have adverse consequences on driving behavior for motorists who are unable to correctly interpret the safety topic of those messages. The results also indicated that BTS messages should only address a single safety topic. The length of the BTS message should be kept to 16 words or fewer in low to moderate driver workload environments; in high driver workload environments, BTS messages should be limited to 11 words or fewer.

Exploring the moderating role of job resources in how job demands influence burnout and professional fulfillment among U.S. pharmacists

Job stress, burnout, and fulfillment can be modeled using the Job Demands and Resources model (JD-R). This study explores the relationship between job demands and burnout and professional fulfillment in pharmacists, and the moderating role of job resources. Data were obtained from the 2019 National Pharmacist Workforce Survey of a random sample of U.S. licensed pharmacists. The survey assessed pharmacist demographics (age, gender, and work setting), job demands (workload and work-home conflict), job resources (job control, time spent in various work activities, and social support), as well as burnout and professional fulfillment. Hierarchical regression analyses were used to assess the relationship between job demands-resources variables and pharmacists' professional fulfillment and burnout. Moderation was assessed by including interaction terms (job demands x job resources) in the regression models. The change in marginal mean burnout and professional fulfillment for different combinations of job demands and job resources was used to assess the salience of significant moderation effects. Women and community pharmacists accounted for 64.8% and 45.8% of the study sample, respectively. Age was negatively associated with burnout. Job demands were positively associated with burnout and negatively associated with professional fulfillment, and the converse was true for job resources. Significant moderation effects were observed for 7 out of 12 interaction terms assessed. The moderating effect of job resources was more salient under varying conditions of job demands in 5 out of 7 instances where significant interaction effects were observed. While pharmacist characteristics explained a significant amount of variation in burnout and professional fulfillment, also considering the moderating effects of job resources on the association of job demands with burnout and professional fulfillment identified additional information, such as the increased importance of job control and task variety in high workload environments.

Eye Tracking to Evaluate the Effects of Interruptions and Workload in a Complex Task.

To use eye tracking to understand the effects of interruptions in different workload conditions as part of a monitoring and change detection task. Interruptions are detrimental to performance in complex, multitasking domains. There is a need for better display design techniques that help users overcome interruptions regardless of their workload level. This requires understanding a user's attentional state immediately after an interruption in order to determine what type of display adjustments are most suitable. An emergency dispatching simulator was developed with a visual primary task and auditory interruptive task. Two levels of workload were induced by varying the number of emergency vehicles to monitor for changes and the rate of changes to monitor. Eye tracking, performance, and subjective measures (NASA-Task Load Index) were collected and analyzed for 41 participants. As expected, high workload interacted with interruptions to further degrade primary task performance and alter participants' attention allocation immediately after the interruption. Participants in the high workload condition had more narrowed, slower scan patterns immediately after the interruption as compared to before the interruption, as evidenced by lower scanpath length per second and mean saccade amplitude. However, this change was not observed in low workload. High workload modulates the effects of interruptions on performance and eye movements. Users in the high workload condition struggle to quickly scan the display in the seconds following an interruption. The results can provide insight into the type of display adjustments needed right after an interruption in a high-workload environment.

Understanding the health risks in basic science medical students: a cross sectional survey.

This article was migrated. The article was marked as recommended. Background Medical students are usually subjected to a high workload environment and stress is one of the most important health risks that medical students encounter. The negative impact of stress on the student's mental and general health in basic science has often been under reported. Methods and Materials A cross-sectional questionnaire survey was performed in Avalon University School of Medicine (AUSOM), Curacao, amongst the first to fourth semester basic science students with an objective to explore and understand their perspectives on different health risks. ResultsMore than ⅔ of the students (79.61%, n=82) were feeling stressed out during their basic sciences. The mean stress level among the basic science students was 7.42 ± 2.13 (scale:1-10). Common health issues encountered by the students were: sleep problems, impaired concentration, low mood, mood swings, difficulty in making decisions, emotional distress, anxiety, substance abuse, and abnormal weight gain. Conclusion Although lack of sleep and behavioral problems are the most common health issues encountered by the pre-clinical medical students. There is an urgent need to implement health promotion strategies in medical curriculum for mental and physical well-being of the students.

The Relationship Between Mindfulness, Triage Accuracy, and Patient Satisfaction in the Emergency Department: A Moderation-Mediation Model

IntroductionIndividual and collective mindfulness attracts growing research attention, yet reports of their impact on health care professionals’ work behaviors are scarce, especially in the emergency department. The aim of the current study was to explore whether the association between trait mindfulness and triage accuracy is moderated by the emergency workload environment, and whether this association promotes patient satisfaction subject to levels of collective mindfulness. MethodsA prospective consecutive nested design was conducted. Data were collected from ED teams (nurses and physicians, N = 96) on individual characteristics and trait mindfulness. Data were also collected on triage accuracy, triage team characteristics, collective mindfulness, workload, and patient satisfaction (N = 960) at a specific patient–ED team encounter. ResultsFindings indicated that ED workload environment (b = 0.24, P < 0.01), trait mindfulness (b = 1.80, P < 0.01), and their interaction (b = −0.04, P < 0.05) were associated with triage accuracy. Triage accuracy (b = 1.81, P < 0.001), collective mindfulness (b = 1.29, P < 0.001), and their interaction (b = −0.32, P < 0.001) were associated with patient satisfaction. The moderated-mediation model was significant under high, but not under extreme, levels of ED workload environment and all levels of collective mindfulness. DiscussionTrait and collective mindfulness are relevant to ED triage and patient satisfaction, but their effects are bounded by workload. The beneficial gain of nurses’ trait mindfulness on triage accuracy and collective mindfulness on patient satisfaction is demonstrated only under high-workload environments but limited under extreme-workload environments.

Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance

Road alignment, traffic flow, and sign information interact and create complex traffic situations. To evaluate the effects of freeway horizontal radius, slope grade, traffic flow, and sign information on subjective driving workload and performance, a simulated driving experiment based on an orthogonal test design was conducted. The National Aeronautics and Space Administration Task Load Index was used to measure self-reported workload. Data regarding speed and lane deviation were collected through the driving simulator. A multivariate analysis of variance results indicated that the radius of the horizontal curve significantly influenced workload score, average speed, and lane deviation. It was observed that in a high workload environment, participants exerted more effort on the driving task; however, driving performance still decreased. Although there was no significant correlation between slope grade and subjective driving workload or performance, the primary effect of slope grade on workload and average speed was statistically significant. The amount of sign information significantly influenced the driver’s perceived workload; however, it did not significantly impact driving performance. In addition, the low correlation coefficient between subjective workload and performance was obtained. These research findings can provide insights for the design of freeway alignments and traffic signs to maintain optimal workload and minimize safety risks.

OA223] Establishing a unified system for logging radiation incidents in an international healthcare services organisation

Purpose Working in a dynamic, complex and high workload environment can lead to unintended radiation exposures. Unintended does not mean unable to prevent. Establishing a unified incident management system in an international healthcare services organisation facilitates better identification of safety critical steps. A bigger ‘pool of events’ can identify trends in incident types and maximize improvement opportunities across the whole system. Methods There is never a single cause for an incident to happen. The incident management system of the organization, AIMS, is a tool developed to log and learn from any adverse events that concern patients, staff or third-parties under the organization’s care. AIMS is not designed to be an administrative tool to find and penalise individuals, but to support collective learning within the organization network to prevent failures and avoid harm. AIMS is a digital platform accessible to all personnel to log incidents and good catches; incidents which were prevented, due to timely and competent personnel action. It is purposefully a four click process to promote easy and fast logging of events, including process stage, incident type and severity. Once an event is logged, approval of the information provided is required before investigation and analysis of the root causes that lead to the event are initiated. Results AIMS allows the process stage to be logged – that is, the stage in the journey at which the event occurred. In-depth investigation identifies failures in the often-complex healthcare environment, allows detection of risks and developing ways to reduce or eliminate the risk of reoccurrence. Good catches and incidents of moderate and above severity are shared with the group regularly as a learning process. Root cause analysis of events allows scrutiny of the preceding process stages, in order to identify contributory factors leading up to the event and definition of actions. Conclusions To Err is Human. We cannot change the human condition, but we can change the condition under which humans’ work. Incident management is a valuable tool to promote a safety culture and awareness through the involvement of and feedback to staff and managers.

Experimental Evaluation of STOM in a Business Setting

This study investigated the effect of autonomous prioritization on attention in a high-workload environment. It explored the “priority mystery,” or neglect of priority in switching behavior, found in previous Strategic Task Overload Model (STOM) experiments (Wickens Gutzwiller &amp; Santamaria, 2015; Raby &amp; Wickens 1994). We hypothesized that participants who could choose their own prioritization of tasks would view their prioritized task as more attractive and allocate more time and attention than participants who were not able to autonomously prioritize the tasks. Ninety-three participants were told to choose their own priority, given a priority, or not given any direction on priority of tasks. Participants had thirty minutes to complete two computer-based tasks and then were asked to rate the priority, interest, difficulty, and salience of each task. Priority influenced time on task most for the self-chosen priority group and not at all for the group given no priority direction. The more difficult task was also chosen less frequently. The implications and limitations of this study are discussed.

The Buffering Effect of Workplace Resources on the Relationship between the Areas of Worklife and Burnout.

Background: Workplace resources are found to play a major role in the stress–strain relationship. However, usually different types of resources are investigated, whereas investigating different facets of stress (“stressors”) receive less attention in research about the relationship between stress, strain and resources. Based upon recent research, we expected that workplace resources moderate the relationship between stressors (operationalized with the areas of worklife) and long-term strain (operationalized with three dimensions of burnout) in the sense that workplace resources buffer the negative effects of stressors on strain.Method: Hypotheses were tested in a longitudinal sample of 141 Austrian workers, who participated two times in an online study over a period of 6 months. Hierarchical multiple regression analysis was used to test the proposed relationships.Results: The results imply that workload and reward seem to be the most important predictors for burnout. Workload is important for emotional exhaustion, whereas reward is important for cynicism. Value-fit at the workplace plays a significant role for cynicism, but only if resources at the workplace are high. Further moderating effects of resources were found for the outcome personal accomplishment. More specifically, results indicate that having high resources in a high workload environment increases personal accomplishment after a time interval of 6 months. In addition, employees experiencing high levels of control but low workplace resources show less personal accomplishment.Conclusion: Despite the limiting aspects of the relatively short period of time we can see that resources can buffer workload effects. This should be taken into consideration when doing risk assessments in practice as work design should focus on resources even more when high workload can be found.

Pip and Pop

Dynamic and complex command and control situations often require the timely recognition of changes in the environment in order to detect potentially malicious actions. Change detection can be challenging within a continually evolving scene, and particularly under multitasking conditions whereby attention is necessarily divided between several subtasks. On-screen tools can assist with detection (e.g., providing a visual record of changes, ensuring that none are overlooked), however, in a high workload environment, this may result in information overload to the detriment of the primary task. One alternative is to exploit the auditory modality as a means to support visual change detection. In the current study, we use a naval air-warfare simulation, and introduce an auditory alarm to coincide with critical visual changes (in aircraft speed/direction) on the radar. We found that participants detected a greater percentage of visual changes and were significantly quicker to detect these changes when they were accompanied by an auditory alarm than when they were not. Furthermore, participants reported that mental demand was lower in the auditory alarm condition, and this was reflected in reduced classification omissions on the primary task. Results are discussed in relation to Wickens’ multiple resource theory of attention and indicate the potential for using the auditory modality to facilitate visual change detection.

Effects of Workload on Academic Performance among Working Students in an Undergraduate Engineering Program

In the Philippines, the supply of engineering graduates produced per year is lower than the demand as local industries are becoming engineering-related. This poses a challenge to academic institutions to fill this gap. Assessment reveals low graduation rate in the Engineering programs in the Philippines, where approximately 35.4% of the students are working at the same time. With the aim to help these students prevent possible retakes, the proponents explored and analyzed the effects of workload on academic performance and the factors associated with it. NASA Task Load Index (NASA-TLX) was utilized to measure students’ workload in this study. Recommendation presented in this research are interventions to support physical and mental health to better cope in a high workload environment, which will in return translate to better work and academic performance thus an increase in graduation rate.

Improving multi-tasking ability through action videogames

The present study examined whether action videogames can improve multi-tasking in high workload environments. Two groups with no action videogame experience were pre-tested using the Multi-Attribute Task Battery (MATB). It consists of two primary tasks; tracking and fuel management, and two secondary tasks; systems monitoring and communication. One group served as a control group, while a second played action videogames a minimum of 5 h a week for 10 weeks. Both groups returned for a post-assessment on the MATB. We found the videogame treatment enhanced performance on secondary tasks, without interfering with the primary tasks. Our results demonstrate action videogames can increase people's ability to take on additional tasks by increasing attentional capacity.

Human performance data in a high workload environment during the simulated Mars expedition “AustroMars”

AustroMars was the simulation of a crewed expedition on the surface of planet Mars, taking place in April 2006 at the Mars Desert Research Station (MDRS) in Utah. Six carefully selected “analogue astronauts” (out of 184 candidates) performed 20 experiments in the fields of robotics, analogue planetary and life sciences as well as human exploration. During the 2-week mission, the MDRS served as base for their research activities and exploratory excursions. Modelled after selection and training principles of astronauts and pilots, although in a nutshell, the crew underwent a training including education in science and engineering skills as well as physical and mental training. A dense flight plan, physically challenging experiments (including extravehicular activities) and a complimentary set of human factors research experiments offered a unique opportunity to study the impact of a high workload on crew vigilance. The data presented here focus on the validated pupillographic sleepiness test, salivary assay data (such as DHEA, Cortisol, P17-OH, Fasting Insulin and MB2S), a complimentary set of reaction and cognitive function tests as well as a novel technique using eye movements as indicator for vigilance, the Fatigue Monitoring System FAMOS. The data of the 6-person simulated flight crew were complemented by pre/post-flight measurements as well as by 3 test subjects (back-up crew).

A Novel Vibrotactile Display to Improve the Performance of Anesthesiologists in a Simulated Critical Incident

Current methods of information transfer in the operating room between monitor and anesthesiologist rely on visual and auditory modalities. These modalities can easily become overloaded in a high cognitive workload situation, such as in a critical incident. The use of vibrotactile communication has been shown to improve information transfer in other high cognitive workload environments such as aviation. We designed a novel waist-mounted vibrotactile display to be worn by the anesthesiologist to test if a vibrotactile display could improve the clinical response time to begin treating a simulated case of anaphylaxis when compared with a group using traditional information displays. In addition, we evaluated differences in situational awareness (SA) between the two groups. Twenty-four volunteer anesthesiologists were randomized to diagnose and treat a simulated case of anaphylaxis using the vibrotactile display and standard monitoring (vibrotactile display group) or standard monitoring alone (control group). The time taken to administer epinephrine was measured, and objective post hoc analysis of participant SA was performed. Participants in the vibrotactile group took 4.08 min (95% CI = 1.22) to deliver definitive treatment compared with 7.21 min (95% CI = 2.07) for the control group (P < 0.05). Despite the reduced time to treatment, no improvement in SA was measured. Our study provides evidence that vibrotactile communication can reduce response time to critical incidents.

Beyond Pilot Error: The Effects of Corporate Culture and Individual Sensemaking at Acme Community Air Service

In this research study the authors examine the corporate culture of an aviation organization and how that culture and individual sensemaking influence flight crew safety decisionmaking in a less-than-optimal, high-workload environment. A quantitative methodology (content analysis) and 2 qualitative approaches (focused and individual interviews) as well as observation are employed in the study. The authors conclude flight crews clearly understand corporate cultural expectations and that such expectations frame individual sensemaking and decision processes especially when crews are faced with mission variances. The research methodology used provided the researchers a rich set of data that made clearer the link between corporate culture and individual decisionmaking processes.