Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care

Artificial Intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities to enhance patient care and healthcare systems. With AI technologies, including machine learning (ML) and natural language processing (NLP), healthcare organizations can optimize workflows, improve decision making, and deliver more personalized care. However, to fully realize AI’s potential, it is essential to integrate human factors (HF) principles and adhere to stringent regulatory frameworks that ensure both safety and trust. This paper describes examples of healthcare AI from the authors’ experience and from literature review, including electronic health record (EHR) applications and neuromorphics technologies, and then addresses relevant issues relating to automation trust theory and practice. Trust issues covered include users’ positive/negative attitudes stemming from knowing AI is the source of the information, as well as cybersecurity concerns related to the privacy and security of data generated or processed by AI, and ethical considerations affecting user trust. We summarize key solutions that support compliance with regulatory standards, and suggest future work.

Despite the potential value of image-based tools, and the fact that self-reported functional status has been shown to be reliable and have predictive validity, few visual-based assessment tools, or mixed-modality tools incorporating pictograms, for function exist. The main objectives were to 1) Explore the potential benefit of visual based tools (i.e., pictograms) for the use of self-assessment tools; 2) Present methods for co-designing clinical pictograms with older adults; and, 3) Discuss the prospect of using pictograms for self-assessment tools and discuss the prospect of using pictograms for self-assessment tools. From an accessibility perspective, the use of visuals may be particularly beneficial in individuals, like older adults, that have accessibility needs, such as cognitive impairment. The development of pictograms to measure a concept requires careful consideration to ensure the graphical representational aligns with the intended meaning, which is context dependent. The development of pictograms for self-administered functional assessments tools may increase the accessibility and ease of use of self-assessment tools for older adults, given the limitations of current text-based tools.

Voice assistants (VAs) may provide useful assembly instruction, but little work has explored their use with older adults for this purpose. In a qualitative field study with six older adults, requirements for assembly instruction and voice or VA technology design were gathered, leading to initial insights about component identification, verification, readability, sociability, and dialog content. Results informed the design of a VA (“Build2Race”) to aid an older adult assembly task. A usability and feasibility evaluation of the VA was then carried out with three young adults to ensure that the VA provided good general user experience as a first step towards implementing the VA with older adults. The requirements and prototype design presented in this paper should provide useful design guidance for researchers interested in assembly or instructional tasks for older adults.

This study explored how lifestyle, personal background, and family history contribute to the risk of developing Alcohol Use Disorder (AUD). Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants. Key determinants of AUD were identified using decision trees including annual income, recreational drug use, length of residence, sex/gender, marital status, education level, and family history of AUD. Data visualization and Chi-Square Tests of Independence were then used to assess associations between identified factors and AUD. Afterwards, machine learning techniques including decision trees, random forests, and Naive Bayes were applied to predict an individual’s likelihood of developing AUD. Random forests were found to achieve the highest accuracy (82%), compared to Decision Trees and Naive Bayes. Findings from this study can offer insights that help parents, healthcare professionals, and educators develop strategies to reduce AUD risk, enabling early intervention and targeted prevention efforts.

As the aging population grows, there is a shortage of caregivers, making assistive robots a promising solution to support older adults’ independence. Such robots would operate in close proximity to users, necessitating an understanding of appropriate distances to ensure comfort and predictable robot behavior. Proxemics, the study of personal space, plays a key role in designing robots that adapt to users’ preferences. We conducted a study with eleven older adults (age 62-77) to explore proxemic behaviors with an assistive robot in a simulated home environment. The robot performed two tasks: water bottle delivery and video calls. Video analysis focused on human behaviors and robot actions. Participants’ engagement varied as the robot moved through different proxemic zones. In social space, they oriented toward the robot and tracked its movements. As it entered personal space participant showed fewer visible reactions to the robot until it reached in the intimate space. These insights inform proxemic-based design considerations for assistive robots, enhancing user comfort and trust. Future work should explore proxemics and human behavior in a broad range of context, such as varied robot speed, direction, and notification, to better capture optimal robot assistant design for close-distance interactions with older adults in aging health care.

The Healthcare Management and Information Systems Society Electronic Medical Record Adoption Model (HIMSS EMRAM) score is a widely-used metric of digital maturity. In this single-centre study, we explore how a hospital can have a high formal HIMSS EMRAM score, despite clinicians reporting poor real-world digital maturity. Using a workflow analysis approach, we examine pain points and physician tasks to develop novel metrics of true digital maturity, which better capture clinician experience. In addition to developing a quantitative approach to digital maturity that may be of use at other sites, this approach reveals key limitations of the HIMSS EMRAM score. These include its inability to account for clinician preference for non-EMR workarounds when electronic medical record configurations are available but impractical, and its inability to account for key clinical workflows not captured in the predefined HIMSS scoring system.

Artificial intelligence (AI) systems offer immense potential at improving care processes, such as diagnostic support and automating clinical documentation. However, before these systems’ benefits can be realized, these systems first must be trusted by healthcare professionals. With an increasing number of studies that report on how trust in AI is formed, there is a need to summarize this literature. To address this gap, we aimed to conduct a review of existing empirical literature to identify factors associated with purpose-, process-, and performance-related trust of AI tools among healthcare professionals. Based on 75 studies, we found that most studies examined process- and performance-related trust. Overall, some factors influenced both process- and performance-related trust, such as endorsement by individuals within the health care organization and explainable AI. Other factors seemed to influence a specific level of trust (e.g., higher accuracy from the AI model and performance-related trust). Broadly, further studies are still needed to study purpose-related trust, develop validated measurement tools, examine differences between types of healthcare professionals, and understand how trust changes longitudinally.

Artificial Intelligence (AI) has the potential to shape the delivery of healthcare and address the quintuple aims of healthcare: improving population health outcomes, lowering costs, improving healthcare provider and patient experiences and advancing health equity. Patients’ and providers’ trust in AI and the trustworthiness of these systems are important factors in the adoption and integration of AI in healthcare. This paper presents an overview of determinants of trust and trustworthiness of AI applications within a healthcare context, based on preliminary themes emerging from a rapid review of 105 studies. These findings can inform appropriate and effective design, development, implementation and integration of AI in healthcare.

The integration of digital technologies in primary care settings has significantly transformed healthcare delivery, offering various levels of automation that impact both physicians and patients. This paper investigates the existing digital technologies employed in primary care, evaluates their levels of automation, and examines their effects on physicians and patients. Utilizing the Automation of Doctors and Machines (ADAM) framework, we conducted a comprehensive product review to assess current automation tools across varying levels of complexity. By analyzing a range of technologies, our study outlines critical directions for future innovation to enhance efficiency, accessibility, and equity in primary care automation.

Aging is associated with cognitive and physical declines; however, the process varies significantly across individuals due to lifestyle and physiological factors. Non-chronological aging, i.e., aging not strictly tied to years lived, can be assessed through variables such as physical activity, sleep, and heart rate. This study explores the relationship between these metrics and self-rated physical health (SRH-P), a subjective but powerful predictor of health status throughout the aging process. Using data from the All of Us Research Program, including Fitbit-derived activity, heart rate, and sleep data from 15,620 participants, we evaluated whether machine learning (ML) models could predict SRH-P levels. SRH-P was binarized into two categories: positive (very good/excellent) and negative (poor/fair/good). We applied five ML algorithms, i.e., logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boosting decision tree (GBDT), and used AUC (area under the curve) as the performance metric. Physical activity metrics showed the strongest predictive power (AUC up to 0.7484), followed by heart rate and sleep features. Combining physical activity and heart rate features yielded the highest AUC of 0.7809 with the GBDT classifier. These results suggest that SRH-P can be meaningfully predicted from wearable data and may serve as a proxy for non-chronological aging. The two-category SRH-P framework provides a foundation for developing a standardized unit of non-chronological aging, potentially enabling personalized health tracking. Future research should incorporate additional predictors and longitudinal SRH-P data to further refine aging models and enhance their clinical applicability.

Telehealth has transformed healthcare delivery by allowing patients to connect with their healthcare providers using audiovisual technology. There is a growing need to understand teamwork dynamics in virtual care encounters. The purpose of this study was to explore patient experiences of teamwork during telehealth appointments with their medical care team. This qualitative study involved semi-structured virtual interviews with 19 patients recruited from a midwest academic medical center who had a chronic condition and had participated in multiple telehealth appointments with their care team. Participants discussed their experiences with telehealth and interactions with their care team. Interview transcripts were analyzed by two independent reviewers using a thematic analysis to identify common teamwork themes. Participants commonly interacted with physicians or advanced practice providers during appointments, while also engaging with nurses, technicians, and administrative staff throughout all telehealth appointment phases. Key teamwork processes included both verbal and asynchronous communication as well as monitoring and adjusting of the patient care plan. Participants emphasized the importance of the online patient portal as playing a key role in facilitating these teamwork processes. Future research should explore provider perspectives of teamwork during telehealth, and tools to enhance pre-appointment information gathering and preparation in advance of a telehealth appointment.

Despite nurses' critical role in healthcare delivery and patient outcomes, current technologies often overlook their specific decision-making needs, focusing instead on obvious issues while neglecting frontline staff requirements. This gap results in inefficient workflows, decision-making delays, and burnout among nursing professionals. Using the TripTech human-centered design method, we conducted interviews, need-based surveys, and focus group discussions with nurses across multiple U.S. states to identify key needs and evaluate proposed solutions for a novel nurse-centered information platform. Results identified key needs including rapid access to patient information, evidence-based practice updates, peer support during critical situations, and mental health resources. Six design concepts were developed and evaluated, with nurses strongly favoring platforms offering real-time patient information access, research collaboration tools, and specialized training resources. Critical design requirements emerged: seamless workflow integration, verified evidence-based content, secure peer collaboration, strict privacy standards, and specialty customization options. This research demonstrates the value of user-centered design in healthcare technology development and provides direction for creating tools that support nurses' complex decision-making needs.

Environmental control units (ECUs) are an integral assistive product for individuals who suffer from spinal cord injuries (SCIs). One noteworthy SCI population to study is veterans due to the exponential increase in SCI injuries amongst them over the past 50+ years. Assistive technology is intended to improve a person’s well-being, independence, and mobility, but if it is rendered ineffective and unusable for these goals, then it can do more harm than good. However, research on ECUs focuses on technology acceptance or identification of opportunities and costs when it comes to the devices resulting in a lack of usability testing to improve their design and functioning. A study conducted on the control modalities (touchscreen vs. voice control) that manipulate navigation through the ECU’s interface, and a time pressure stressor (high vs. low) examined the usability and user experience of this device can identify areas of improvement for designers. Both performance and self-reports of operators’ cognitive affect states were evaluated in this study. Recommendations to improve the device’s design to support those with mobility limitations are herein discussed.

With the rise in telehealth implementation in healthcare, it is important that such services are accessible to users of all ages. However, many telehealth programs are unable to accommodate the vast diversity of the human population (Schiaffino, Zhang, Sachs, Migliaccio, & Huh-Yoo, 2021). Research in this area is also scarce. The aim of this study was to understand key usability problems that people from different age groups, younger adults (18-25), middle-aged adults (35-60), and older adults (65+), experience when using telehealth applications. Older adults are among the largest group that struggle with access to healthcare with the increase of technologies used (Wilson, Heinsch, Betts, Booth, & Kay-Lambkin, 2021). As a population with generally less experience with technology, they are especially at risk of not getting the care they need. We sought to understand specific areas where telehealth apps may be lacking usability and preventing access by an important user population.

Designing AI-infused technologies in healthcare involves multidisciplinary design considerations, like the usability of the display and explainability of the AI algorithm. We present three vignettes from a case study in which design decisions intended to improve usability inadvertently undermined explainability, and vice versa. Our results illustrate potential conflicts between disciplines and suggest the need for joint activity as a unifying design perspective. From these insights, we discuss revisions to our AI-infused technology.

The Emergency Department (ED) Resuscitation room is a cognitively demanding and fast-paced environment where clinicians must respond rapidly, and often with incomplete information, to make life-saving medical decisions (Madani et al., 2018). This complex decision-making requires collaboration with multiple care teams, including teams from other units in the hospital. Rapid decisions become critical when a patient is in cardiac arrest with ongoing Cardiopulmonary Resuscitation (CPR), as guidelines for the initiation of Extracorporeal Membrane Oxygenation (ECMO) require cannulation within <60 minutes from arrest. The speed and quality of the team’s response impact the chances of survival, so the ED team must promptly assess ECMO candidacy and activate the appropriate response. An ECMO activation for a patient receiving ongoing CPR is referred to as Extracorporeal Cardiopulmonary Resuscitation (ECPR). Our ED’s current ECPR process prompts the Intensive Care Unit (ICU) team to present to the ED resuscitation room to evaluate the patient, and, if ECMO is indicated, move the patient to the ICU or operating room (OR) in collaboration with the ED team.

This paper presents a multi-method, Human-Centered Design informed approach to device selection within a Labor and Delivery Unit at Mount Sinai Hospital, Toronto, Canada. Focused on supporting anesthesiologists transitioning to fully electronic documentation, the process integrated clinical observations, workflow mapping, room assessments, and interdisciplinary feasibility testing. Key challenges included balancing clinical needs, user requirements, spatial and infrastructure constraints, and the broader care environment, including patient and family experience. The resulting approach offers a structured device assessment framework that enabled evidence-informed recommendations. This framework offers a practical model for aligning device selection with clinical workflows and the real-world constraints of healthcare environments.

Post-anesthetic follow up includes non-clinical measures aimed at identifying relevant anesthesia-related complications, among other concerns. The conventional follow-up process for ambulatory patients uses intermediate communication channels, resulting in inconsistent contact success rates and varying work times. Combined with nursing shortages, this has led to decreased follow-up quality and staffing challenges, prompting a need to redesign the workflow and communication tools for improved task management. At Taipei Medical University Hospital, automated text messaging linked to a standardized web-based survey was introduced for all patients receiving ambulatory anesthetic services. This initiative achieved a satisfactory contact success rate and improved the overall follow-up coverage for ambulatory cases in the health-screening center. Additionally, it resulted in a weekly saving of thirty-six nursing hours, contributing to greater flexibility in nurse duty roster scheduling. The early results of this redesign initiative for post-ambulatory anesthetic follow-ups have been encouraging.

This paper introduces a framework aimed at guiding the design of orientation and wayfinding tools to support people living with dementia. As spatial disorientation and impaired wayfinding are common challenges faced by individuals living with dementia, especially in unfamiliar environments such as care homes, addressing these design needs is essential to improve autonomy and quality of life (QoL). This study presents the initial phase of a case study examining a Respite House in Ontario, Canada, and shares findings from a qualitative literature review that informed the creation of a design framework: Orientation Design to Support Persons Living with Dementia. The review synthesizes current design strategies, accessibility considerations, and environmental factors related to wayfinding and orientation to support more inclusive, responsive care environments. The framework is structured around four key categories: Proximal Factors, Building Codes, Design Principles and Approaches, and Environmental Factors. Each category consists of design elements which have been identified as important to assist care providers and designers to enhance the wayfinding experiences of individuals living with dementia, in support of their well-being.

In long-term care (LTC) homes, recreational therapy is vital in enhancing residents’ well-being. A review of literature explored the relationship between design and recreational therapy in LTC. This paper outlines case studies, challenges in implementation, impact, and suggestions for the future, resulting in a framework to inform how design strategies in recreational therapy may improve quality of life for residents in LTC.

Robotics and machine learning algorithms can potentially enhance upper limb rehabilitation, addressing the limitations of traditional therapy methods. This study presents a Human-Robot Interaction (HRI) platform designed to improve the occupational therapy environment in clinical settings. Eight participants engaged in grasping and reaching tasks with the assistance of an upper limb robotic system, guided by visual feedback. The tasks were structured across three levels of difficulty. Machine learning models were employed to classify task difficulty based on muscle activity patterns in the moving arm. Participant performance was evaluated using average trajectory deviation and non-dimensional squared jerk, providing insight into movement accuracy and smoothness, which corresponded with the predefined task difficulty levels. This platform represents a meaningful step toward delivering more personalized and effective rehabilitation for individuals recovering from stroke-related upper limb impairments. Future advancements in rehabilitation technology must prioritize patient-centered design, accessibility, and data-driven innovation to ensure impactful and inclusive care.

Human factors experts working in healthcare often worry about proving the worth of their projects and may struggle to convince leadership of the true payoff when considering higher costs associated with better and more usable products. This project attempts to address this problem by investigating the nuanced direct and indirect costs that can arise from failing to provide a safe and satisfying work environment for staff. Specifically, we investigated the causes and consequences of nurse turnover. With the central hypothesis that the true cost of nurse turnover has been undervalued, we reviewed previous research and mapped factors of nurse turnover in a logic model to identify the most important connections and potential gaps in current research.

Chronic obstructive pulmonary disease (COPD) is currently a leading cause of morbidity and mortality worldwide with traditional management approaches such as in-person pulmonary rehabilitation typically prescribed. However, the success of these traditional approaches is often hindered by low patient engagement and limited regional access to rehabilitation centers. This paper describes a human factors (HF)-informed approach to developing an alternative model of care via digital pulmonary coaching and a portable spirometer device. Through iterative development and user-centered design, Vigor Health created a comprehensive digital pulmonary management program integrating a portable spirometer, digital app, and personalized health coaching. A pilot study demonstrated substantial improvements in patient enrollment, retention, and reductions in emergency visits and hospital admissions. The findings highlight the value of understanding and incorporating HF principles into design to enhance accessibility, usability, and outcomes in chronic disease care.

Usability testing is critical to developing equitable medical devices and digital health tools; however, traditional methods have been found to be resource-intensive and inconsistent, posing concerns for the adequate representation of marginalized communities in medical solution development. Artificial intelligence (AI)-driven usability testing methods have emerged as a promising solution to assist user experience (UX) analysts with their evaluation and mitigating potential evaluator bias. However, its reliability, particularly its impact on inclusivity and equity of marginalized communities, remains uncertain. Following the findings of a previous narrative literature review, this competitive evaluation assessed on-the-market AI-informed tools from seven prominent usability testing platforms, comparing the current state of AI-driven usability testing in the literature and commercially, using an adapted Society of Automotive Engineers five levels of automation and a three-level equity consideration scale. Six platforms offered Level 1 automation AI-products, assisting UX evaluators with facilitation and data analysis, while one achieved Level 3 conditional automation. Four platforms did not explicitly address the equity impact of their products, with only one platform incorporating a bias-reduction feature. Overall, AI-informed tools provide potentially inexpensive usability testing alternatives for digital health tools; however, more research is required to validate the consistency, accuracy, and reliability of these tools in usability testing practice.

Wearable devices have become commonplace in society, with 1 in 3 U.S. adults owning a device and almost half wearing a device daily as a method of health tracking or monitoring. Over 80% daily device wearers are amenable to sharing data from the device with healthcare providers. Since Micah Endsley’s sentinel work and development of the Situational Awareness Global Assessment Tool (SAGAT), this tool has been applied in a variety of healthcare settings to explore user acceptance of technology and decision-making patterns related to interaction with technology. The discussed study evaluated the clinical nurse’s ability to identify a wearable device on a patient photo via online survey using SAGAT format questions. This paper reviews the successes and challenges of using SAGAT as a method to evaluate device identification by clinical nurses. Findings reveal both the potential of SAGAT for evaluating situational awareness in healthcare settings and the challenges associated with wearable device recognition. This work highlights opportunities for iterative design improvements and training interventions that support nurse engagement and patient safety in environments where wearable technologies are increasingly common.

Autoinjectors facilitate subcutaneous and intramuscular self-administration of medication, potentially reducing user errors, accidents, and anxiety related to needle phobia. While 2-step autoinjectors streamline the injection process, users exhibit varied handling behaviors, such as grip type, body posture, and skin handling, which can influence the force exerted and perceived ease of use and comfort. Understanding these behaviors can inform the design of devices and user communication materials to ensure safe and effective use.

This study explored handling behaviors and user experiences with a modified 2-step autoinjector among 150 adults living with obesity (119 females and 31 males, 109 injection-experienced and 41 injection-inexperienced). Each participant performed six simulated injections into their skin using an autoinjector prefilled with water and without a needle. Tasks varied in injection site, skin handling, and body posture. Participants were allowed to familiarize themselves with the autoinjector and Instructions for Use (IFU) without specific guidance on skin handling or body posture. During the first task (abdominal injection with optional skin handling and body posture), a large proportion of participants did not adopt the grip type illustrated in the IFU (i.e., precision grip), with close to half using alternative grips. Most participants injected without skin handling, although a larger proportion of injection-experienced users employed skin folds. By comparing the tasks involving specific skin handling and body postures, it was found that abdominal injections were perceived as easier and more comfortable than seated injections. Thigh injections were rated as easier and more comfortable than abdominal and upper arm injections.

The findings indicate that autoinjector designs should accommodate various grip types and physical capabilities to ensure effective use by all user groups. User communication materials should address grip types and skin handling techniques to optimize user experience and minimize risks. Recommendations for alternative injection sites and skin handling strategies may improve comfort and ease of use for users living with obesity.

Diabetes management presents unique usability challenges for older adults, particularly in adopting digital health tools. This study evaluates the usability of the GlucoCheck Companion App, which interfaces with a non-invasive glucose monitoring device using AI-driven near-infrared technology. Twelve older adults (aged 65+) participated in a usability test involving core app tasks such as real-time glucose viewing, historical data tracking, and food logging. Findings suggest that GlucoCheck offers a user-friendly, accessible solution for older adults managing diabetes, but continued refinement is essential to address age-related accessibility needs. The study contributes to advancing human-centered design for mobile health tools supporting chronic disease management in aging populations.

Effective nurse-physician communication is critical for patient safety in hospitals. In this single-center post-acute care study, we investigated a written on-call communication system, known to have risks including illegibility, misinterpretation, and inappropriate triage. We developed a structured approach to analyzing written communication and optimizing on-call processes. A two-pronged methodology was employed: (1) qualitative analysis through physician observations and (2) quantitative analysis of the quality of the communication book entries. Initial findings were used to dynamically update the scoring system using an iterative study methodology. Our results yielded several insights, including demonstration of the utility of an iterative methodology; development of novel scoring criteria for nurse-physician communication; demonstration of benefits and limits of communication templates; and demonstration of limits of the well-known SBAR communication tool, which focuses on information structure, but not content, and in particular may not be helpful for triage. This study highlights the importance of interdisciplinary, iterative collaboration in creating context-specific communication tools and provides a replicable framework for future analyses of hospital communication processes.

The development of more resilient healthcare systems can substantially improve patient safety by enabling providers to withstand, adapt to, and overcome unforeseen circumstances. Despite these benefits, resilience engineering remains a challenge as a quantitative measurement of resilience has yet to be proposed. While prior work has introduced qualitative resilience frameworks, this work identifies a quantitative resilience framework to measure resilience in the anesthesia delivery process. Resilience is integral to and well-studied in the supply chain logistics field, and in this study, resilience scorecards from supply chain logistics were adapted to the anesthesia domain. The proposed anesthesia resilience scorecard addresses three primary dimensions: distribution resilience, sourcing resilience, and information transmission resilience. By quantifying resilience in the anesthesia delivery system, this measurement tool enables informed innovation and demonstrates the value of adapting research findings from quantitative disciplines. Future work will focus on the validation of the proposed scorecard.

This paper explores the quality of doctor-patient communication in new cancer patient visits using communication assessment tools. We did a survey study right after the first visits with the cancer patients after the diagnosis. The data was collected from a large cancer center. We used logistic regression to analyze the relationship between the variables. The findings indicate some implications for the need for tailored communication training for cancer doctors based on the unique needs of the patients.

This study introduces the Community-Based FMEA (CB-FMEA), a novel adaptation of Failure Modes and Effects Analysis (FMEA) designed to identify system failures in community settings. Traditional FMEA tools overlook contextual and environmental risk factors, such as social, psychological, economic, legal, and technological factors, which significantly impact healthcare access and delivery. Cardiologists were interviewed to explore contextual factors affecting cardiovascular care access and management. Interview data informed a patient journey process map and populated a traditional FMEA table, revealing key failures linked to SDOH (e.g. education, income, stress). Findings highlight the limitations of the conventional FMEA in capturing contextual factors leading to socio-psychological harm. The proposed CB-FMEA adaptation integrates participatory methods, qualitative insights, and a contextual risk assessment. Future research will expand stakeholder participation (e.g., patients, family physicians) and consider how to incorporate field notes into the CB-FMEA process.

Background: Research shows deficiencies in social abilities in Autism Spectrum Disorder (ASD) are related to impaired Theory of Mind (ToM), further negatively impacted by language impairment. Both challenges are especially pronounced in rural populations. Given that ~25-35% of children with ASD are minimally verbal or nonspeaking, we employed a low-verbal ToM task, using eye tracking to measure attention, to better represent ASD population for ToM evaluation. Methods: Colle et al.’s (2007) low-verbal ToM task was adapted as 26 15-60 second videos, shown to participants while their eye gaze and non/verbal answers were recorded. Videos feature a Hider, hiding a toy Bear under one of two boxes out of view of participant, and a Communicator, pointing to the location of Bear as determined by their perspective. The pre-test and control conditions taught the participant that Communicator is reliable when they observe the placement of Bear, and established whether the participant could remember the switched locations of Bear. In the false-belief condition (FB), Hider switched the box locations while Communicator was absent. Upon returning, Communicator incorrectly indicated the location of Bear – as based on their point of view which did not match reality. To show intact ToM, participant must identify the correct location of Bear. We used Gazepoint eyetracking with analysis in RStudio. Results: We evaluated 30 child participants from rural and micropolitan Montana, ages 1.9-17 years, 11 with ASD, 7 without ASD but with language impairment or ADHD, and 12 neurotypical. 7 ASD and neurotypical adults participated too. Analysis of the correct scores on the behavioral answers (verbal or nonverbal pointing) separated by ASD vs. non-ASD diagnosis revealed a weaker comprehension of FB in the child ASD group (45%) compared to non-ASD child group (73%). A statistical model including the whole child group indicated a significant effect of nonverbal IQ and overall language skills (as measured by a parental questionnaire of CCC-2) on the knowledge of ToM. However, ToM performance was not affected by chronological age or diagnoses. Eye-tracking results for 20 participants aligned with the behavioral results in our data driven grouping: 4 adults and 13 children (across diagnoses groups) passed FB on behavioral answers, and 3 (all ASD) failed. The Pass groups showed more looks to Hider and target locations, whereas the Fail group looked more to Communicator and Hider. An additional 6 participants failed at FB, but had poor eye tracking recordings, and hence could not be analyzed. Conclusion: We found little evidence of association between the diagnosis of ASD with successful completion of the nonverbal ToM task. Eye-tracking was useful for studying attentional patterns to social interaction in pass vs. fail groups, noting the challenges with recording of good eye tracking data.

Background: Effective communication between research and clinical care teams is essential for ensuring clinical trial participant safety. Current methods, such as paper documents and electronic health records are inconsistently used and poorly standardized. This study evaluated a prototype wearable device, TrialWear, designed to passively convey trial-related safety information during patient assessments. Methods: In a simulated emergency setting, 14 nurses conducted patient assessments while wearing eye-tracking glasses. Results: The majority (92.9%) visually identified the wearable; 85.7% interacted with the wearable, though most participants required a prompt for initial user interaction. Eye-tracking saccades and fixations revealed strong correlations between fixation metrics and engagement. Conclusion: This study highlights the potential of wearable devices to enhance communication in clinical settings and inform future design improvements for intuitive and effective integration into healthcare workflows.

Robotic scrub nurse systems are an emerging technology that mark an opportunity to ease the staffing burdens hospitals are facing across the globe while upholding quality of care and protecting patient safety. Our aim is (1) to develop a consolidated source of information on how these systems communicate and interact with surgeons during instrument handoffs and (2) provide design considerations to benefit future designers of these systems. In pursuit of these aims, we’ve produced a scoping review exploring the topic. We found that surgeons communicate with robotic scrub nurses through three main modalities (e.g., gesture, voice, gaze) and that communication flowing from robotic scrub nurse to surgeon is limited. Furthermore, while the actual process of handoffs seems sound, current systems cannot handle a sufficient amount of instruments or handle the return of instruments. We conclude our work by leveraging our findings to provide pertinent design considerations relating to communication between surgeon and robot and the properties of the robots themselves in order to improve the fidelity of future systems.

This project evaluates the impact of interprofessional virtual reality (VR) simulation-based education on teamwork, communication, safety culture, and technology acceptance among emergency department (ED) clinicians at a large, Level I Trauma center. Using immersive VR scenarios, interprofessional teams of nurses and physicians engage in structured clinical simulations to enhance collaborative practice and reduce latent patient safety threats. Outcomes are measured using tools with validity evidence, including the TeamSTEPPS Teamwork Attitudes Questionnaire (T-TAQ), safety culture surveys, and a VR technology acceptance model. A secondary objective is to assess the effect of this intervention on nurse retention rates. The project emphasizes simulation as a proactive tool for fostering safety culture and improving clinical communication. This work contributes to advancing human-centered training and design in healthcare and offers scalable, evidence-based approaches to interprofessional education, with implications for curriculum development and system-wide safety initiatives.

Background: Clinical simulation is a valuable tool used in healthcare education to develop technical and cognitive skills in a controlled, low risk setting. Despite their prevalence, the extent to which simulations replicate the physiological stress responses of real clinical scenarios remains underexplored. This study aimed to evaluate the relationship between self-reported baseline anxiety, perceived taskload, and physiological stress responses among healthcare professionals and students during a simulation patient monitoring intervention. Methods: Twenty-two participants, including licensed nurses, medical assistants, and clinical students, monitored a patient simulator as it progressed through a programmed scenario with deteriorating clinical states. Participants were fitted with biometric monitoring sensors to measure heart rate, blood pressure, stress, and facial expressions. Results: Galvanic Skin Response (GSR) data indicated participants experienced a significant increase in stress during the patient’s extreme deterioration phase compared to baseline. Two GSR measurements were analyzed: peaks per minute (p-value = 0.002) and average peak amplitude (microsiemens, p-value < 0.001). Conclusion: A simulated clinical scenario can elicit significant increases in psychological stress, even in controlled and low risk environments. These findings support the utility of simulation both as a training tool and as a research methodology for assessing the cognitive and emotional demands experienced by clinicians in high-stress environments.

Electronic Health Record simulation is a research method that offers valuable data on clinician cognitive processes like problem solving, information foraging, and in particular, decision making. Despite this, established paths to develop optimal EHR simulation to explore these processes only exist for education. No guide to develop efficient EHR simulation for research exists. We propose a six-part methodology to efficiently develop EHR simulations used for research and patient safety inquiry. Our approach produces high-fidelity simulations for challenging clinical scenarios, ideal for data collection of this type. This reproducible template addresses a critical gap, enabling EHR simulation as a research method to be stood up in any institution.

Auditory distractions in clinical environments can impair performance, yet their impact in emergency department (ED) waiting rooms remains understudied. This study investigates how distinct noise types (baby crying, conversations, and equipment alarms) and temporal patterns (continuous vs. intermittent) influence nursing triage performance. Thirty-two ED nurses completed standardized triage tasks within the Tesseract, an immersive audio-visual simulation platform replicating ED waiting room conditions. Preliminary results from 16 participants suggest that the effects of noise depend on both acoustic features and temporal structures. Subjective perceptions of distraction did not consistently align with measured outcomes. This work provides early evidence that auditory distractions influence clinical task execution in complex, task- and context-specific ways, underscoring the need for targeted mitigation strategies and soundscape-aware simulation training.

Self-reported challenges and strategies can inform effective and personalized training program in acquiring motor-control skills for laparoscopic surgery that accounts for over 15 million procedures annually. This article presents an interview study of thirteen participants who completed a longitudinal experiment to identify the challenges encountered and strategies used by individuals self-training the peg transfer task in the Fundamental of Laparoscopic Surgery. Thematic analysis revealed that participants were challenged with high cognitive load and instrument handling to improve performance continually and derived improvement strategies through task understanding and goal settings to achieve proficiency.