The objective of this study was to use the critical decision method (CDM) to map the decision-making process of parent caregivers (PCGs) responding to patient safety events at home and to identify the work system factors that influence this process. PCGs (N = 13) were asked to share about a specific patient safety event that had occurred while caring for the CMC at home. PCGs were asked to describe the event in chronological order, after which their decision making at each timepoint was probed. The final process map included nine steps, the success of which were shaped by work system factors. This map of PCG decision making can be used to train policymakers, durable medical equipment companies, and clinical providers on the work that PCGs perform at home, and to evaluate whether a family has what they need to maintain home-based safety.
Colorectal cancer (CRC) is a leading cause of mortality, with early detection through colonoscopy being crucial for reducing death rates. However, up to 26% of precancerous polyps are missed during procedures, largely due to variability in physician skill. Simulation-based training (SBT) has the potential to improve patient outcomes in colonoscopy by reducing cecal intubation time. Particularly through physical simulator (PS) which offer more realistic experiences than virtual simulators. Despite their realism, PS often lack integrated feedback, limiting their effectiveness in improving polyp detection (PD). This study demonstrated that experts achieved higher polyp detection (PD) and cecal intubation rates compared to residents, identified some of the challenges residents face during colonoscopy particularly in navigation, scope manipulation, and fold inspection-and contrasts them with expert strategies. Experts employ techniques such as thorough fold inspection, lumen centering, re-advancement, and slow withdrawal to enhance PD. These insights emphasize the need for targeted feedback within PS-based training to bridge the skill gap and reduce polyp miss rates. Developing feedback-driven training strategies during PS-based training using these expert strategies may potentially reduce polyp miss rates and improve CRC screening outcomes.
This study examines how performance feedback influences trust and self-confidence during interactions with dynamically reliable automation. Trust and self-confidence are crucial components of human-automation collaboration, governing reliance decisions and decision-making processes. In this experiment, 80 participants engaged with an automated assistant whose reliability fluctuated across tasks, receiving performance feedback throughout. Contrary to expectations, trust and self-confidence remained stable, showing little sensitivity to changes in reliability or feedback. This suggests that performance feedback may moderate variability in trust, stabilizing perceptions of automation over time. However, this stabilization could lead to complacency and overconfidence. To develop systems that promote calibrated trust and optimize team performance, future research should investigate individual differences in trust calibration, situational awareness, and prior experience with automation. Understanding the complex interplay between feedback, trust, and self-confidence is essential for effective human-automation collaboration in dynamic environments.
Artificial intelligence (AI) systems need to adapt to changing circumstances to maintain relevance in dynamic environments. Inspired by the adaptive advantages of human forgetting, this study investigates the integration of a forgetting function into an AI system. We implemented this mechanism as a training window within the Cognitive Shadow (CS) system, an AI designed to learn and emulate human decision models. This training window hyperparameter—applicable to supervised machine learning algorithms—aims to address the issue of concept drift by prioritizing recent information. The effectiveness of this addition was tested with a simple strategy game similar in dynamics to rock-paper-scissors. Participants played individually against an AI opponent for three 60-round sessions. CS was trained during Session 1 to learn the decision patterns of the player and actively predicted and countered human decisions in Sessions 2 and 3. Analyses showed that including the training window significantly improved prediction accuracy in both Sessions 2 and 3 by emphasizing recent, relevant data. These findings highlight the potential of incorporating human-inspired forgetting mechanisms to enhance AI performance in interactive and dynamic environments, with implications for future decision support systems.
Developments in artificial intelligence (AI) are transforming everyday tasks, including accessing information, learning, and decision making. Generative AI is representative of these changes as it can generate content traditionally reserved for humans with increased efficiency and reduced effort. This includes technologies like ChatGPT and other tools that exploit large language models, typically taking the form of conversational agents (chatbots). These technologies can be useful for self-regulated learning as is the case for Web browsing. It is, however, unclear whether learning with chatbots may be efficient as opposed to other Web-based approaches given the reduced effort related to chatbot interactions. This study assessed how interacting with a chatbot may affect short-term learning and the role of mental effort. Memory performance was equivalent across participants who either interacted with a chatbot or browsed the Internet to find information for answering essay questions. Differences in self-reported workload were, however, found across conditions.
We measured time-to-collision (TTC) judgments from participants with age-related macular degeneration (AMD), and normal vision (NV) controls, with an audiovisual virtual reality system that simulated vehicles approaching in a 3D traffic environment. The vehicle was presented visually only, aurally only, or both simultaneously, allowing us to determine the relative importance of visual and auditory cues with psychophysical reverse correlation. Results indicated that TTC judgments were based on both auditory and visual cues in the AMD and NV groups; the AMD group relied, at least in part, on their residual vision. A multimodal advantage was not observed in either group. TTC estimation in the AMD group was surprisingly similar to that in the NV group. However, the AMD group showed a higher relative importance of "heuristic" cues compared to more reliably accurate cues favored by the NV group, suggesting that similar performance may be achieved through different cue-weighting strategies.
Uncrewed Aerial Systems (UAS) show promise in urban air transport, package delivery, and emergency services. UAS efficiency can be significantly improved by having multiple operators (m) managing a greater number of vehicles (N), or the m:N architecture of operation. The current study investigates how workload affects operators' task-allocation decision-making and the potential mediating effects of two crucial human factors, trust and self-confidence. In the context of a simulated UAS package-delivery task under the m:N architecture, two groups of participants with different levels of expertise in UAS operation will be recruited: UAS pilots and university students. Each participant will watch two sets of videos with different work-load manipulations and report their preferred task-allocation strategy for various subtasks. Measures of perceived workload, trust, and self-confidence will be conducted after each video session. Findings will inform optimizing task-allocation designs for UAS missions, considering operators' decision-making needs and expertise disparities.
Patient safety event (PSE) reports, which document incidents that compromise patient safety, are fundamental for improving healthcare quality. Accurate classification of these reports is crucial for analyzing trends, guiding interventions, and supporting organizational learning. However, this process is labor-intensive due to the high volume and complex taxonomy of reports. Previous work has shown that machine learning (ML) can automate PSE report classification; however, its success depends on large manually-labeled datasets. This study leverages Active Learning (AL) strategies with human expertise to streamline PSE-report labeling. We utilize pool-based AL sampling to selectively query reports for human annotation, developing a robust dataset for training ML classifiers. Our experiments demonstrate that AL significantly outperforms random sampling in accuracy across various text representations, reducing the need for labeled samples by 24% to 69%. Based on these findings, we suggest that incorporating AL strategies into PSE-report labeling can effectively reduce manual workload while maintaining high classification accuracy.
Employment is an important aspect of independent adulthood, yet autistic adults typically face substantial barriers in the labor market, including high rates of un- and under-employment. To promote an inclusive workplace, the present study explored collaboration dynamics between autistic and non-autistic adults as they worked toward shared team goals in an online setting. We recruited nine dyads, including three dyads of non-autistic adults with an autistic adult (NA-AA), and six dyads of non-autistic adults (NA-NA). Our findings demonstrated that neurodiverse collaboration (autistic and non-autistic adults together) could lead to improved task efficiency at the group level and higher perceived team performance in individuals. However, in these collaborative settings, autistic adults reported higher levels of depression, anxiety, and stress compared to their non-autistic partners. Our findings demonstrate the unique contributions that autistic adults may bring into the workplace and highlight the need to develop workplace technologies supporting their collaborative experiences.
Trust and system reliability can influence a user's dependence on automated systems. This study aimed to investigate how increases and decreases in automation reliability affect users' trust in these systems and how these changes in trust are associated with users' dependence on the system. Participants completed a color identification task with the help of an automated aid, where the reliability of this aid either increased from 50% to 100% or decreased from 100% to 50% as the task progressed, depending on which group the participants were assigned to. Participants' trust, self-confidence, and dependence on the system were measured throughout the experiment. There were no differences in trust between the two groups throughout the experiment; however, participants' dependence behavior did follow system reliability. These findings highlight that trust is not always correlated with system reliability, and that although trust can often influence dependence, it does not always determine it.
To support the ongoing adaptation and implementation of an Emergency Department (ED)-based clinical decision support (CDS) tool to prevent future falls, we interviewed older adults ( n = 15) during their ED stay. We elicited their feedback on the written and verbal content of the existing CDS, feelings about the automated risk-screening aspect of the CDS and asked them to identify barriers that would prevent them from following up with the Falls Clinic to which the CDS supports referral placements. Our findings suggest that the older adults interviewed saw the CDS simply as another tool that they trusted their ED physician/APP to interact with. The identified barriers to follow-up reflect common access barriers such as transportation availability and clinic distance. For CDS tools to impact real-life patient outcomes, we must consider patient’s needs and limitations and appropriately match interventions.
The transition period from automation to manual, known as the takeover process, presents challenges for drivers due to the deficiency in collecting requisite contextual information. The current study collected drivers’ eye movement in a simulated takeover experiment, and their Situation Awareness (SA) was assessed using the Situation Awareness Global Assessment Technique (SAGAT) method. The drivers’ Stationary Gaze Entropy (SGE) was calculated based on the percentages of time they spent on six pre-defined Areas of Interests (AOIs). Three critical time windows were extracted by using the takeover alert time spot and the hazard perceived time spot. The result indicated that drivers with higher SAGAT scores would spread their attention among multiple AOIs. Also, drivers’ SGE and SA have a linear relationship only at the last time window (hazard perceived to the end) wherein SGE potentially functions as an evaluative metric for assessing SA in the future.
Autistic individuals face challenges in successful employment, emphasizing the need for targeted workplace support. This study explored collaborative dynamics within neurodiverse teams during a simulated remote work task by applying Hidden Markov Models (HMMs) to heart rate data. Eighteen participants formed nine dyads: six nonautistic (NA-NA) pairs and three autistic-non-autistic (ASD-NA) pairs. Dyads completed two trials of a collaborative programming task over Zoom, alternating roles between trials. Heart rate data were collected, segmented, and transformed to extract features reflecting participants’ interactions. The final HMM was fitted with seven hidden states, and transition probabilities were derived for each dyad type. Results showed that NA-NA dyads exhibited more frequent transitions among states compared to ASD-NA dyads, potentially suggesting more varied interaction patterns. These findings demonstrate the utility of HMMs in capturing collaborative behaviors through physiological signals and highlight their potential in helping develop effective support strategies for neurodiverse teams.
The objective of this study was to identify the technological barriers and facilitators of safe and effective pediatric mental and behavioral health care in emergency departments. The study, which involved a total of 4 participants, employed observational studies and semi-structured interviews to gather data from medical professionals across two hospitals. The data collected from the interviews were analyzed for themes relating to the use of technology, including factors that enable or impede safe and effective care for pediatric mental and behavioral health patients. Barriers included challenges related to software usability, inadequate training on newer software features, and concerns regarding the quality of service delivered by third-party vendors. Facilitators comprised the accessibility of clinician's notes, effective tools and technologies for clinician and patient safety, and efficient communication tools.
Assistive robots have the potential to support independence for older adults with mobility limitations and to alleviate the demands of their care partners. Several design considerations are required to ensure that the users can successfully rely on the robot to carry out their tasks. Therefore, building trustworthy robots is necessary for wider acceptance of these assistive robots. Using a participatory design approach, we assessed various aspects involved in advancing the design of a trustworthy robot in home environments. This is a case study focused on supporting an older adult with mobility limitations and his care partner. Through several iterations of co-active development as a team, most of the tasks were accomplished to meet the needs of the older adult couple interacting with the robot. Our approach highlighted usability challenges, the merits of a multidimensional approach in evaluating trust, and co-design strategies to improve the trustworthiness of the robot.
What happens when “frontline” workers are patients and family members performing health-related tasks? As more and more complex healthcare tasks are performed by patients and family members, and more emphasis is placed on patient- and family-centered care, strategies are needed to engage patients and family members in co-design “work systems” and patient-professional collaborative work. Human factors professionals are well-equipped to apply participatory ergonomics to patient and collaborative tasks. However, there are a number of barriers and pitfalls in engaging patients in design. Moving from tokenism to meaningful engagement in research requires patience, constant reflection, and a commitment to codesign. Our panel will explore the continuum of engagement and strategies to move from tokenism to partnership to cocreation in patient safety research, ranging from ambulatory medication safety to diagnosis in the emergency department. Strategies and barriers are presented as a starting point to discuss how to achieve effective work system designs.
Eye-tracking is a valuable research method for understanding human cognition and is readily employed in human factors research, including human factors in healthcare. While wearable mobile eye trackers have become more readily available, there are no existing analysis methods for accurately and efficiently mapping dynamic gaze data on dynamic areas of interest (AOIs), which limits their utility in human factors research. The purpose of this paper was to outline a proposed framework for automating the analysis of dynamic areas of interest by integrating computer vision and machine learning (CVML). The framework is then tested using a use-case of a Central Venous Catheterization trainer with six dynamic AOIs. While the results of the validity trial indicate there is room for improvement in the CVML method proposed, the framework provides direction and guidance for human factors researchers using dynamic AOIs.
Progressive learning gradually increases task difficulty as students advance in their education. One area that can benefit from it is medical education since it can optimize medical trainees’ skill acquisition. While progressive learning can allow for skill transfer to patient encounters, personalized learning increases the efficiency and effectiveness of learning. However, it is not well understood the number of practice trials needed to reach proficiency. To evaluate whether progressive and personalized learning can enhance medical trainees’ learning gains, the learning interface of the Dynamic Haptic Robotic Trainer (DHRT) for Central Venous Catheterization was assessed. Results showed that residents’ performance on the DHRT did not differ based on task difficulty and residents’ performance was as effective with less number of trials. The findings imply a need to integrate progressive and personalized learning on the DHRT simulator to ensure that residents are fully prepared for any patient scenario in a real-life encounter.
Adverse events caused by medical errors pose a significant threat to patient safety, with estimates of 251,454 deaths and a cost of $17.1 billion to the healthcare system annually in the United States. Patient safety event (PSE) reports play a vital role in identifying measures to prevent adverse events, but their utility is dependent on the accurate classification of PSE reports. Recent studies have used static natural language processing (NLP) and machine learning (ML) techniques to automate PSE report classification. However, the use of static NLP has limitations in differentiating the meaning of words in disparate contexts, which can lead to inferior classification results. Thus, this study proposes to utilize contextual text representation produced from neural NLP methods to improve the accuracy of PSE report classification. The results suggest that the contextual text representation can further improve the performance of PSE classifiers. The best-performing classifier, a support vector machine trained with contextual text representation (Roberta-base) reaches an accuracy of 0.75 and a ROCAUC score of 0.94, surpassing all ML classifiers trained with static text representations. Furthermore, the confusion matrix of the best classifier exposes latent deficiencies in the PSE reports' classification taxonomy, such as the multi-class nature of PSE and conceptually related event types. The study's findings can save time for PSE reclassification, enhance the learning capabilities of the reporting system, ultimately improve patient safety
Ensuring women and birthing people have access to the contraceptive of their choice is essential for patient-centered care, health equity, and reproductive justice. While trends in national data in the United States reveal racial disparities in long-term contraceptive use, health-system and hospital-level investigations are essential to understand disparities and encourage interventions. We used data from 5011 patients who delivered at a large academic hospital to determine the effect of race/ethnicity and social vulnerability index (SVI) on the odds of undergoing a long-term contraceptive procedure. Results indicate that SVI substantially affects the odds of long-term contraception for non-Hispanic White women and birthing people. In contrast, Hispanic and non-Hispanic Black women and birthing people have significantly higher odds of undergoing a long-term contraceptive procedure due to race/ethnicity. Contributions to these disparities may be based on factors including healthcare providers, organizational and external policies. Interventions at all levels of care are essential to address disparities in contraceptive care, outcomes, and patient experience.
