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We review HCI history from both the perspective of its 1980s split with human factors and its nature as a discipline. We then revisit human augmentation as an alternative to user friendliness that seems particularly relevant in the areas of inclusive design and artificial intelligence. Viewing human-AI interaction as a kind of human augmentation raises issues such as how to promote trust and situation awareness. We also pose the question: Can HCI and human factors engineering work together to solve the increasingly urgent challenges of human-AI technology? In an initial look at this question, we contrast the different approaches of HCI and human factors on emerging AI research. This article concludes by considering other potentially promising paths for HCI. We propose more collaboration between HCI and human factors, or related disciplines, in the future to address the massive challenges posed by the rapid growth in data science and artificial intelligence.

Existing theories of technology transitions cannot predict what new technological paradigm will supplant the currently leading, internet-enabled graphical user interface paradigm. This article introduces a preliminary approach (‘weakness exploitation’) to explain the rise and fall of four technology ‘empires’: print, television, the internet and socially interactive devices (such as robots, chatbots and internet of things devices). The approach is related to technology diffusion and disruptive innovation, but with a predictive element induced from Marshall McLuhan’s descriptions of print and television as ‘extensions’ of the senses. It is applied to the internet as an historical example of a technology transition outside of McLuhan’s original analysis and to explain why excessive exposure to screen-rendered digital media as the internet’s exclusive access point may be replaced by a new ‘age’ of computationally intelligent, socially communicative devices. This new approach can help researchers and technologists conceptualize transitions between usage of incumbent and emerging technologies.

How can a third party (person or social robot) mitigate the negative aspects of the asymmetrical dyadic relationships associated with caregiving of the elderly? We start by examining the social-psychological structure of the relationship between givers and recipients of eldercare. We then identify problems that frequently occur and review interventions that have been used or proposed for dealing with those problems. Finally, we synthesize the results of our review in terms of recommendations for how and when social robots might be used as interventions in this context.

Digital technology adoption and engagement is increasing amongst older adults, prompting concerns regarding their privacy-related behaviour while engaging with these technologies. The use of cognitive biases and heuristics may help explain why older adults, and some younger people as well, make decisions that compromise their privacy in digital contexts. A literature review was carried out to assess the use of cognitive biases and heuristics by older adults when deciding whether to adopt, and while using, digital technologies. Tools are needed that guide users away from mental shortcuts such as hyperbolic discounting, anchoring, and shortcut decisions based on trust and that promote privacy protection. In addition to reviewing current literature on the use of privacy-related biases and heuristics amongst older adults, we also present possible future research directions on this topic.

The COVID-19 pandemic has amplified systemic gaps in patient safety, including social frailty for vulnerable populations such as older adults (Briguglio et al., 2020). Public health measures, such as prolonged and frequent lockdowns, restricted access to visitors and support networks and in extreme cases led to confinement within a single room, The negative impact of these restrictions has focused new attention on social frailty, which has hitherto been a somewhat neglected patient safety issue. Since social frailty is a multifaceted construct, it needs to be considered in a variety of settings and from a range of disciplinary perspectives. This practitioner-led panel consisting of an internist, a human factors engineer, an occupational therapist, and an implementation scientist, examines the impact of the pandemic on social interaction, and social frailty amongst older people in three different settings (hospital, long term care home, community). Through a set of case studies, the panelists provided applied experiences and perspectives on the impact of the pandemic on patient safety and on social frailty in particular. The panel covered theory, and real-world application of human factors principles. The question of how to design safety into the healthcare system were also explored. In this paper we review the problem of social frailty, list some brief case studies and discuss possible intervention strategies.

Serious games aim to provide cognitive assessments that are more enjoyable and easier to self-administer, potentially leading to more frequent assessments. We carried out two studies examining the relationship between game-playing enjoyment, game difficulty, and cognitive (game) performance. In the first study, 16 participants played three serious games once a week over four weeks as part of an undergraduate course, but with relatively minor motivation in terms of course credits. In the second study, 14 participants played serious games over six sessions in a period of three weeks. Participants included graduate students receiving credit for the course project (a major component of the grade) and friends and family that they recruited. Performance in the more difficult tasks tended to improve over time in the second study, but not in the first. Participants from the first study showed an overall negative sentiment toward the games. However, participants from the second study enjoyed the majority of games except for the more difficult games that required players to temporarily hold and memorize information. Participants got significantly better in the more difficult working memory games, even though these games were less popular than the simpler games. Our interpretation of the results is that enjoyability is important in encouraging people to keep playing cognitive assessment games as time progresses, and that enjoyability helps in maintaining performance in easy games. Higher motivation on the other hand played a significant role in facilitating learning effects in difficult games as well as in maintaining enjoyability.

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.

Cybersecurity is emerging as a major issue for many organizations and countries. Machine learning has been used to recognize threats, but it is difficult to predict future threats based on past events, since malicious attackers are constantly finding ways to circumvent defences and the algorithms that they rely on. Interactive Machine learning (iML) has been developed as a way to combine human and algorithmic expertise in a variety of domains and we are currently applying it to cybersecurity. In this application of iML, implicit knowledge about human behaviour, and about the changing nature of threats, can supplement the explicit knowledge encoded in algorithms to create more effective defences against cyber-attacks. In this paper we present the example problem of data exfiltration where insiders, or outsiders masquerading as insiders, who copy and transfer data maliciously, against the interests of an organization. We will review human factors issues associated with the development of iML solutions for data exfiltration. We also present a case study involving development of an iML solution for a large financial services company. In this case study we review work carried out on developing visualization dashboards and discussing prospects for further iML integration. Our goal in writing this paper is to motivate future researchers to consider the role of the human more fully in ML, not only in the data exfiltration and cybersecurity domain but also in a range of other applications where human expertise is important and needs to combine with ML prediction to solve challenging problems.

Human Factors Engineering (HFE) is an applied discipline that uses a wide range of methodologies to better the design of systems and devices for human use. Underpinning all human factors design is the maxim to fit the human to the task/machine/system rather than vice versa. While some HFE methods such as task analysis and anthropometrics remain relatively fixed over time, areas such as human-technology interaction are strongly influenced by the fast-evolving technological trend. In times of big data, human factors engineers need to have a good understanding of topics like machine learning, advanced data analytics, and data visualization so that they can design data-driven products that involve big data sets. There is a natural lag between industrial trends and HFE curricula, leading to gaps between what people are taught and what they will need to know. In this paper, we present the results of a survey involving HFE practitioners (N=101) and we demonstrate the need for including data science and machine learning components in HFE curricula.

In Canada, over 15,000 residents of long-term care have died from COVID-19 since the start of the pandemic representing 59 percent of all COVID-19 deaths (National Institute of Ageing, 2021). Urgent research and subsequent applied action are needed to save life and quality of life including the presence of family (CFHI, 2020). Social and physical frailty are major systemic patient safety gaps and are challenges for most healthcare organizations. This practitioner-led panel of experienced human factors, implementation science and healthcare experts used a case study of a project at North York General Hospital’s Seniors’ Health Centre in Toronto to discuss how these challenges can be addressed with serious games. The project discussed used games that aim to reduce social and physical frailty through exercise while interacting with remote families. Lessons learned to-date and challenges observed, in rapidly implementing safety and human factors programs intended to create resilient residents in a real healthcare context were discussed.

In today’s digital economy, the Internet of Things (IoT) has connected devices, humans, and everyday objects to each other in ways that were unimaginable before. Vast amounts of data are collected everywhere and disrupting how we design systems and products. Data science and emerging technologies offer challenges and opportunities for early-career human factors professionals who are looking to grow their careers and their human factors practice. In this paper, we report on a survey to assess the perspectives of students currently studying human factors. The survey items examined shortfalls in current human factors education with re-spect to relevance to industry trends. The survey results show that students see a need to include more relevant subjects in data science, as well as opportunities to learn trending industry problems, hands-on experience with real-life projects, prior to graduation.

Psychological resilience has emerged as a key factor in mental health during the global COVID-19 pandemic. However, no work to date has synthesised findings across review work or assessed the reliability of findings based on review work quality, so as to inform public health policy. We thus conducted a meta-review on all types of review work from the start of the pandemic (January 2020) until the last search date (June 2021). Of an initial 281 papers, 30 were included for review characteristic reporting and 15 were of sufficient review quality for further inclusion in strategy analyses. High-level strategies were identified at the individual, community, organisational, and governmental levels. Several specific training and/or intervention programmes were also identified. However, the quality of findings was insufficient for drawing conclusions. A major gap between measuring the psychological resilience of populations and evaluating the effectiveness of strategies for those populations was revealed. More empirical work, especially randomised controlled trials with diverse populations and rigorous analyses, is strongly recommended for future research.

Loss of cognitive potential is one of the greatest impediments to human wellbeing and productivity. Our healthcare system does a poor job of managing cognitive development and cognitive decline because it measures cognitive status relatively infrequently and in the limited times when cognitive measures are taken, the instruments used tend to be blunt. In the panel that we presented at HCS 2021 we examined the potential for cognitive assessment games to provide more frequent cognitive assessment. We reported on the use of a cognitive assessment game to screen for delirium risk in emergency patients, and the development of a suite of assessment games that can assess executive functions and other cognitive abilities in both young and old. We conclude with a discussion of knowledge translation and implementation science strategies for incorporating game-based cognitive assessment into healthcare practice.

Older adults living in long-term care facilities typically receive insufficient exercise and have long periods of the day when they are not doing anything other than sitting or lying down, watching television, or ruminating (Wilkinson et al., 2017). We developed an intervention called the Experiential Centivizer, which provides residents with opportunities to use a driving simulator, watch world travel videos, and engage in exercise. We assessed the impact of the intervention on residents of a long-term care home in Fredericton, NB, Canada. In this paper, we report on the results observed and highlight the lessons learned from implementing a technological intervention within a long-term care setting. Practical and research recommendations are also discussed to facilitate future intervention implementation in long-term care.

A novel quality assessment system design for closed captioning (CC) is proposed. CC was originally designed to serve hearing-impaired audiences. Traditional quality assessment models focus on empirical methods only, measuring quantitative accuracy by counting the number of word errors in the captions for a segment. However, hearing-impaired audiences have been outspoken about their dissatisfaction with the quality of current CC. One solution to this problem may involve inviting human evaluators, who represent different groups, to assess the quality of CC at the end of each segment, but in reality, this may be difficult to do and very labor-intensive. To solve this challenge, we propose the training and use of an artificial intelligence system to predict subjective CC quality based on human assessment data. In this paper, the design and development process, to be used in developing this system, is described .

There has been considerable research on design of menu hierarchies in general spanning several decades. However there is much less research on menus relating to specific types of product in online retail settings. Thus there is little guidance in the research literature on specific issues such as how to place luxury items within a beauty product hierarchy, which is the focus of this paper. We report on a study that addressed this problem for an ecommerce site associated with a large Canadian retailer. In a within subjects design, participants searched for four beauty-related products (two of which were classed as “luxury” items) either in a hierarchy where luxury items were intermingled with other products addressing the same need (the “Combined” condition), or in a hierarchy where there was a split between luxury and non-luxury products at the top level (the “Split” condition). Segregating luxury products in the product hierarchy was found to lead to significantly slower, and more lengthy (in terms of links traversed), searches. Searches were found to be more efficient in the “Combined” condition than the “Split” Condition both when searching for luxury items, and when searching for non-luxury items. This work has implications for existing brick-and-mortar retailers moving into or expanding e-commerce portals. Our results suggest that the separation of luxury from non-luxury items in bricks-and-mortar stores does not transfer well to online product hierarchies, where similar segregation leads to poorer digital navigation performance.

With the rapidly aging population and the rising number of people living with dementia (PLWD), there is an urgent need for programming and activities that can promote the health and wellbeing of PLWD. Due to staffing and budgetary constraints, there is considerable interest in using technology to support this effort. Serious games for dementia have become a very active research area. However, much of the work is being done without a strong theoretical basis. We incorporate a Montessori approach with highly tactile interactions. We have developed a person-centered design framework for serious games for dementia with initial design recommendations. This framework has the potential to facilitate future strategic design and development in the field of serious games for dementia.

In our aging societies, slowing and managing cognitive decline has become a significant challenge for older people, caregivers, andhealthcare providers. In the past decade, there has been an explosion in productsaimed at providing cognitive interventions for the elderly in response to the growing market need. In thispaper, we present a framework for thinking about cognitive interventions, and a product typology based onthe level of technology used and the amount of tangibility/physical interactivity that products have. Weshow how current products fit within the proposed product typology framework. We also note shortfallsthat exist in validating products in terms of their claimed cognitive interventions, and in regions of theproduct technology space that deal withproducts that have low technology and low physical interactivity.

Closed Captioning (CC) is a service primarily designed for deaf and hard of hearing (D/HoH) viewers. The CC translates spoken speech into text for television or film screen display. The quality assessment methods for live captioning are limited to quantitative measures, while the viewers are still dissatisfied with the current quality. One method to improve the current quality assessment procedure is to include D/HoH viewers in the evaluation procedure for their subjective assessment input. However, it could be costly and impractical to perform evaluations for the entire broadcasted shows. Therefore, it would be helpful to model subjective assessments that could replicate and predict human decisions. In this article, we report on a model of probabilities of D/HoH viewer assessment decisions for CC quality factors based on actual user preferences. An online survey was designed and conducted to collect assessment data for 22 error variation samples from four quality factors: delay, speed, missing words, and paraphrasing of captions. The results are analyzed using the signal detection theory framework to create decision probability models for D/HoH viewers.

Objective We examine the relationships between contemporary progress in on‐road vehicle automation and its coherence with an envisioned “autopia” (automobile utopia) whereby the vehicle operation task is removed from all direct human control. Background The progressive automation of on‐road vehicles toward a completely driverless state is determined by the integration of technological advances into the private automobile market; improvements in transportation infrastructure and systems efficiencies; and the vision of future driving as a crash‐free enterprise. While there are many challenges to address with respect to automated vehicles concerning the remaining driver role, a considerable amount of technology is already present in vehicles and is advancing rapidly. Methods A multidisciplinary team of experts met to discuss the most critical challenges in the changing role of the driver, and associated safety issues, during the transitional phase of vehicle automation where human drivers continue to have an important but truncated role in monitoring and supervising vehicle operations. Results The group endorsed that vehicle automation is an important application of information technology, not only because of its impact on transportation efficiency, but also because road transport is a life critical system in which failures result in deaths and injuries. Five critical challenges were identified: driver independence and mobility, driver acceptance and trust, failure management, third-party testing, and political support. Conclusion Vehicle automation is not technical innovation alone, but is a social as much as a technological revolution consisting of both attendant costs and concomitant benefits.

The picture-word interference (PWI) paradigm has been used to investigate the time course of processes involved in word retrieval, but is challenging to implement online due to dependence on measurements of vocal reaction time. We performed a series of four experiments to examine picture-word interference and facilitation effects in a form of covert picture naming, with and without gamification. A target picture was accompanied by an audio word distractor that was either unrelated, phonologically-related, associatively-related, or categorically-related to the picture. Participants were instructed to judge whether the name of the target picture ended in the phoneme assigned to the block by pressing corresponding keys as quickly and accurately as possible. Experiments 1 and 2 successfully replicated categorical interference and phonological facilitation effects at different optimal stimulus-onset-asynchronies (SOAs) between words and pictures. Experiment 3 demonstrated that a key gamification feature (collecting coins) motivated faster speed at the expense of accuracy in the gamified vs. experimental format of the task. Experiment 4 adopted the optimal SOAs and verified that the gamification reveals expected interference and facilitation effects despite the speed-accuracy tradeoff. These studies confirmed that categorical interference occurs earlier than phonological facilitation, while both processes are independent from articulation and inherent to word retrieval itself. The covert PWI paradigm and its gamification have methodological value for neuroimaging studies in which articulatory artifacts obscure word retrieval processes, and may be developed into potential online word-finding assessments that can reveal word retrieval difficulties with greater sensitivity.

This workshop explores socially expressive technology design using collaborative principles of theater, dance and music. Building on shared roots of making something together, technologies (including virtual agents, physical devices, storytelling multimedia) and artistic provocation can create playful, productive and healthy experiences for communities. How do we: Improve collaboration between performance art and creative technology? Design apps and artistic collaboration/learning for mental health? Use theater performativity and technology to enhance our lives? As smart devices become ubiquitous, yet solidarity and cooperation remain elusive in competitive/hierarchical societies, understanding how performance art and technology collaborate can improve how we work, play and care for our and each other's health. This workshop is a forum and creative space to discuss and play with the future of performative expression and collaborative computing. We welcome researchers and practitioners from varied disciplines including technical implementation, design, theater, dance, improv, physiology and psychology. The program features collaborative creation modules across the expressive human spectrum (movement, sound, text) using digital dramaturgy (Budde, 2017) and pleasure of learning (Brecht, 2001). It also includes participants’ presentations, expert talks and videos exploring the expressive side of computing technology. Accepted applicants are invited to share perspectives and collaborate on creative exercises on art/artefacts/artificiality.

Major depressive disorder (MDD) causes many negative consequences including suicide and disability, and is one of the leading preventable causes of death in many countries. The Hamilton Rating Scale for Depression (HAM-D) used in this paper evaluates depression severity based on 17 symptoms of MDD (i.e., the HAM-D 17 , referred to below as the HAM-D). Studying the relationship amongst MDD symptoms not only provides insight into depressive symptoms but also helps identify subgroups of patients with depression. We employ causal discovery to discover the causal and correlational relationships among HAM-D symptoms. To the best of our knowledge, this is the first study to investigate HAM-D using causal discovery.

In this paper we explore how memories of experience with streaming video affect Quality of Experience (QoE) indicators that are of interest to service providers and marketers. Since observations of experience are time consuming, and the effects of technical quality (TQ) are difficult to entangle from content quality (CQ), we examined the impact of a visualization methodology for assessing experiences. A study was carried out to examine how well overall technical quality (TQ) judgments for a sequence of visualized video experience (a picture of a red video playbar with yellow portions indicating disrupted video in place of actually viewed video) would correspond to overall TQ judgments made after watching a sequence of actual videos. Sequencing effects found in overall TQ ratings, made after viewing visualizations (with their overlaid disruptions) were similar to sequencing effects found after viewing actual videos. However, the sequencing effects after viewing the visualizations were less pronounced than the corresponding sequencing effects that were found after viewing actual videos. Sequences of both visualized and actually viewed videos showed significant negative end effect and trend effects (both positive and negative). There was also evidence that sequencing effects respond to relative change in TQ rather than absolute TQ.

Models that predict satisfaction with a service over time need to consider the impact of emotions and remembered quality of experience in predicting overall attitudes towards a service. However, prior research on subjective quality of experience has typically focused on experiments conducted in a single session or over a short period of time. Thus, there is a gap between our understanding of instantaneous quality of experience and long-term judgments, such as overall satisfaction, and likelihood to recommend and likelihood to churn. The goal of the study reported here was to carry out a longitudinal study that would provide initial insights into how experiences of service quality over time are accumulated into memories that then drive longer term attitudes about the service. Our longitudinal study was carried out over a period of roughly 4 weeks with around 3 sessions per week. To facilitate the study, an online service was constructed that would let participants search through YouTube videos, and that added impairments (specified according to an overall experimental design) to the videos before they were played. Participants were asked to rate several measures, including Technical Quality, after each video was viewed. They were also asked to give overall impressions after each session of five videos had been viewed. The results were analyzed in terms of both sequencing effects within sessions. and memory effects that carried over between sessions.

Curved displays promote three-dimensionality and may facilitate non-wearable virtual reality. Yet there is little design guidance on the optimal type of curvature that should be used. In this paper we have examined the perceived properties of convex and concave displays, at two different sizes. We conducted an experiment with 21 participants. Each participant was asked to make a series of binary choices after the participant viewed a display where one side was seen as convex and the opposite side was seen as concave. For each of 15 perceptual and aesthetic features, participants had to choose whether the convex or concave view/side of the display was stronger/more appropriate. Each participant made two binary choices (one for a small display viewed from its convex and concave sides, the other for a large display viewed from its convex and concave sides) for each perceptual and aesthetic feature, leading to a total of 30 judgments. Participants preferred the convex version of the small sized display and the concave version of the larger display. Individual differences were observed. Some participants generally preferred the convex displays, while others preferred the concave displays. The results are interpreted in terms of their implications for the future use of convex and concave displays in VR applications that do not use goggles.

Objectives Recognition of delirium in the emergency department (ED) is poor. Our objectives were to assess: (1) the diagnostic accuracy of the Predicting Emergency department Delirium with an Interactive Computer Tablet (PrEDICT) "serious game" to identify older ED patients with delirium compared to clinical recognition and (2) the feasibility of the PrEDICT application compared to existing tests of attention. Design Prospective observational study. Setting ED of a Canadian tertiary care center. Participants We included ED patients, aged 70 years and older, with a minimum 4-hour stay. We excluded anyone with critical illness, communication barriers, and visual impairment or those unable to use a computer tablet. None had prevalent delirium by ED clinicians' routine clinical assessment. Measurements Participants were asked to tap targets on a tablet at four difficulty levels. Time and accuracy were automatically recorded. Other measures included the Confusion Assessment Method, the Delirium Severity Index, the Digit Vigilance Test (DVT), and the Choice Reaction Test (CRT). Results We enrolled 203 patients. Their average age was 80.6 years, 49.8% were female, and their average ED length of stay was 15.9 hours. Sixteen subjects had clinically unrecognized delirium, and 14 of them completed the PrEDICT game (87.5%). We developed a threshold score with 100% sensitivity (95% confidence interval [CI] = 76.8%-100.0%) and 59.7% specificity (95% CI = 52.3%-66.6%) to identify patients with clinically unrecognized delirium. The area under the curve was 0.86 (95% CI = 0.77-0.94). Completion rates were 196/203 (96.6%) for the PrEDICT serious game compared to 128/203 (63.1%) for the CRT and 51/203 (25.1%) for the DVT. Conclusion Older ED patients were able to use our serious game, including 87.5% of those with clinically unrecognized delirium. The PrEDICT application has potential to act as a sensitive screening tool to identify older ED patients with clinically unrecognized delirium. J Am Geriatr Soc 67:2370-2375, 2019.

Clustering prior to regression analysis improves the accuracy of prediction in clinical decision making. However, most previously described methods focused on numerical data only. This paper investigated how well textual features can improve the accuracy of regression predictions. Preliminary diagnosis, diagnosis summary, and drug names used in prescriptions as provided in the MIMIC II dataset were used to derive textual features. We proposed the bag-of-entities indexing method, which relies on named entity recognition, a machine learning technique used for locating and identifying words into predefined classes. The proposed technique captured meaningful phrases from texts in health records and represented them in numerical vector format. Dimensionality of the data space was reduced using principal component analysis. The additional well-tuned textual features were then combined with existing numerical features in using cluster-boosted regression to predict patient mortality in ICU. The experimental results showed prediction improvement obtained from textual features over the use of numerical features only. We found that using the proposed indexing method outperformed traditional word-vector representation approaches (bag-of-words and bag-of-bigrams) as well as a state-of-the-art approach (Doc2vec) in terms of resulting accuracy in predicting death status. Moreover, instead of directly interpreting, the identifiable individual features were grouped into types and summarized. The summarized de-identified data of textual features handled by the proposed framework can support predictive classification while also reducing privacy concerns. Grouping of similar patients based on their electronic health records also benefits physicians through the improved differential diagnosis and effective treatment planning.

After eliciting 129 potential task-gesture combinations for 23 Smart TV tasks with a Canadian sample (N = 22), we then conducted studies that collected participant preference scores on mid-air bare-hand gestures for TV control in both Canada (N = 747) and China (N = 300), and we analyzed the effect of characteristics of individual participants on gesture preference scores. The results showed that age and cultural differences are important in determining task-gesture preferences. While exploratory, the present results indicate a need for more research in this area and suggest that one of two possible strategies may need to be adopted in designing future gesture interactions: 1) develop customized task-gesture combinations for different cultures and different age groups; 2) develop a core set of task-gesture combination possibilities and let users choose which gesture they want to use for each task.

Background Retrospective studies estimate Emergency Department (ED) delirium recognition at Objectives To prospectively measure delirium recognition by ED nurses and physicians, document their confidence in diagnosis and disposition, actual dispositions, and patient outcomes. Methods Prospective observational study of people ≥65 years. We assessed delirium using the Confusion Assessment Method, then asked ED staff if the patient had delirium, confidence in their assessment, if the patient could be discharged, and contacted patients 1 week postdischarge. We report proportions and 95% confidence intervals (Cls). Results We enrolled 1,493 participants; mean age was 77.9 years; 49.2% were female, 79 (5.3%, 95% CI 4.2–6.5%) had delirium. ED nurses missed delirium in 43/78 cases (55.1%, 95% CI 43.4–66.4%). Nurses considered 12/43 (27.9%) patients with unrecognized delirium safe to discharge. Median confidence in their delirium diagnosis for patients with unrecognized delirium was 7.0/10. Physicians missed delirium in 10/20 (50.0%, 95% CI 27.2–72.8) cases and considered 2/10 (20.0%) safe to discharge. Median confidence in their delirium diagnosis for patients with unrecognized delirium was 8.0/10. Fifteen patients with unrecognized delirium were sent home: 6.7% died at 1 week follow-up vs. none in those with recognized delirium and 1.1% in the rest of the cohort. Conclusion Delirium recognition by nurses and physicians was sub-optimal at ~50% and may be associated with increased mortality. Research should explore root causes of unrecognized delirium, and novel strategies to systematically improve delirium recognition and patient outcomes.

This paper reports on the Patient Safety Research and Application Competition that was held in conjunction with the 2021 International Ergonomics Association Conference. The objectives of this competition were to: (1) Formulate research problem statements and innovative solutions to improve patient safety through the application of human factors/ergonomics (HF/E) to the healthcare system, (2) Showcase how the HF/E approach to this topic can lead to a useful, usable, and satisfying user experience while simultaneously improving outcomes relating to both functional and non-functional requirements, and (3) Provide an effective way of engaging students and early career researchers in IEA activities and initiatives. After reviewing the patient safety topics that motivated the design competition, we then report on the work carried out by the two finalists in the competition, and discuss lessons learned. We propose the continued use of design competitions in the future to motivate and showcase ergonomic problem-solving design by students and early career researchers and practitioners.

Serious games have grown significantly in popularity, but proving their scientific validity through research studies is a common hurdle for researchers and game developers. To scale up capacity to collaborate with different groups of researchers, Centivizer Inc. (a University of Toronto spinoff company) has employed a user-centered design process to design a BrainTagger Research Platform (BRP) that will largely automate the development process of its customizable serious games for cognitive assessment. This new development will increase the capacity to gather research data needed to improve game mechanisms and demonstrate game validity.

Traditional Human Factors Engineering (HFE) education focuses on bridging the gap between human and system design. Given the rapid, and accelerating technological advancement, particularly in areas such as machine learning and data science, how should HFE education adapt to better equip students for working in industry? This project sought to identify and understand the gap between HFE education and industry needs by surveying human factors engineering students and practitioners concerning their impressions of the gap and how it can be addressed.

IntroductionThe global COVID-19 pandemic continues to have wide-ranging implications for health, including psychological well-being. A growing corpus of research reviews has emerged on the topic of psychological resilience in the context of the pandemic. However, this body of work has not been systematically reviewed for its quality, nor with respect to findings on the effectiveness of tools and strategies for psychological resilience. To this end, a meta-review protocol is proposed with the following objectives: (1) identify review work on the topic of psychological resilience during COVID-19; (2) assess the quality of this review work using A MeaSurement Tool to Assess systematic Reviews; (3) assess the risk of bias in this work; (4) generate a narrative summary of the key points, strengths and weaknesses; (5) identify the psychological resilience strategies that have been reviewed; (6) identify how these strategies have been evaluated for their effectiveness; (7) identify what outcomes were measured and (8) summarise the findings on strategies for psychological resilience so far, providing recommendations, if possible.Methods and analysisA systematic meta-review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews for Protocols and Joanna Briggs Institute umbrella review guidelines. Electronic searches of general databases, especially Web of Science, Scopus and PubMed, will be conducted. Only results from January 2020 onwards will be considered, coinciding with the COVID-19 pandemic. Only results in English will be included. Descriptive statistics, thematic analysis and narrative summaries describing the nature of the reviewed work and evaluation of psychological resilience strategies will be carried out.Ethics and disseminationEthical approval is not needed for systematic review protocols. The results of the meta-review will be published in an international peer-reviewed journal. The raw and summarised data will be shared in the journal or other open venues.PROSPERO registration numberCRD42021235288.

Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead to millions of dollars of loss. Perimeter defense is not enough by itself since successful exploits from insiders can also be very damaging. Internal network user activities need to be monitored to detect malicious actions. Automatic machine learning methods can be applied for network anomaly detection, but they create a lot of false alarms. Domain experts can identify malicious users, but they are unable to process large volumes of data. Interactive machine learning (iML) deals with this tradeoff by creating an efficient collaboration between domain experts and machine learning algorithms. Previous research in iML has focused mainly on collaboration with non-experts. The design and requirements for expertise-driven iML have yet to be delineated for cybersecurity applications. In this research, we proposed an Active Learning (AL) model trained with outputs from a liberal (outputting many false alarms as well as possible hits) anomaly detection (AD) criterion to study expert-iML collaboration in anomaly detection. The results showed that: iML in this context can prune false alarms and minimize misses; the performance/compatibility tradeoff that typically occurs in conventional machine learning updates may be less salient in iML. We suggest that compatibility between experts and algorithms can be improved by presenting information about feature relevance during the training process.

Accurately predicting the time to an event of interest is an important problem in a wide range of real-world applications. However, prediction is often difficult because many medical datasets have a large number of unlabeled (“censored”) instances because labeling is costly and time consuming. Survival analysis focuses on labeled data to predict the time to an event of interest, such as time of death, or conversion to a different stage in a progressive disease. Grouping structure, which naturally exists in medical datasets, can be exploited to improve generalization performance by learning multiple related survival prediction tasks for subgroups collaboratively. Thus a multi-task learning framework can connect multiple survival prediction tasks (for different subgroups) and learn them simultaneously. In order to take into account both censored information, as well as discover the grouping structure, we propose a novel cluster-boosted multitask learning framework for survival analysis that boosts survival prediction performance. We develop an efficient algorithm and demonstrate the performance of the proposed cluster-boosted multi-task survival analysis method on The Cancer Genome Atlas (TCGA) dataset. Our results show that the proposed approach can significantly improve prediction performance in survival analysis while also identifying different subgroups of cancer patients.