JMIR Human Factors

Physicians’ Complex Relationships With Health Information Technology and Burnout: Cross-Sectional Survey Study

2026-09-23T15:15:09-04:00

Background: Health information technology (HIT), while designed to improve practice efficiency and patient care, can contribute to physician burnout when designed without clinical practice in mind. Administrative and clinical demands now require physicians to spend more time on HIT, a burden that contributes to burnout and affects time with patients. Objective: This study had two objectives: (1) to examine how physician perceptions of specific HIT functions may alleviate or contribute to physician burnout within the differing health care landscapes of Ontario and Nova Scotia, with particular attention given to physician perceptions of HIT associated with administrative burdens, interoperability, and system integration, and (2) to explore physician experiences and perceptions of HIT as potential factors affecting physician burnout, with the goal of delivering findings (eg, a model) that could be applied to improve physicians’ HIT experience. Methods: We designed an exploratory mixed methods study that deployed a cross-sectional survey in 2 Canadian provinces: Ontario and Nova Scotia. Centralized clinician management software (Ontario) and medical association networks (Nova Scotia) were used to recruit from an estimated 35,341 (Ontario) and 2809 (Nova Scotia) physicians. The survey was distributed between February and April 2024. Nonphysician clinicians, clinic staff, and non-HIT users were excluded, resulting in 1245 Ontario and 136 Nova Scotia physician HIT-user respondents. For both the Ontario and Nova Scotia samples, descriptive analyses of quantitative survey items were compared, and open-text responses were subjected to qualitative coding for themes. Subgroup differences in HIT-related burnout were analyzed in the larger Ontario sample. Results: Common experiences were apparent despite differences in the samples, provincial health systems, and available HIT. “Managing communications related to patient care” and “inputting data into your EMR” were among the top 3 administrative burdens. While “logging in and out of technology platforms” ranked higher in Nova Scotia as an administrative burden, the related theme of integration and interoperability was prominent in both samples. In the Ontario sample, perceptions of HIT, quality of support, and hours worked per week accounted for over half of the variation in self-reported burnout. Conclusions: While physicians appreciate the advantages of HIT for patient care, they also experience an overwhelming administrative and documentation burden, as well as disjointedness across data platforms, which contributes to their burnout. Greater ongoing involvement by end users in the design and usability of these technologies, along with improved standardization and interoperability, would reduce these burdens while maintaining the benefits of digital health systems.

Shifting Skills in Robot-Assisted Surgery: Case Study With Interdisciplinary Perspectives for Human Factors and Career Research

2026-09-22T11:45:11-04:00

Background: The introduction of STARA (smart technology, AI, robotics, and algorithms) changes work processes and the application of skills, depending on specific task design within a given context. Objective: From a Human Factors perspective, this study uses empirical data from a case study to demonstrate how different task designs influence the application of theater nurses’ skills during robot-assisted surgeries. Moreover, from a transdisciplinary perspective, the data elucidate that STARA-related task design extends beyond Human Factors’ perspectives, encompassing cross-level effects between task configurations of different surgical units and individual evaluations regarding career motives. Finally, the study aims to integrate interdisciplinary perspectives by outlining theoretical, methodological, and practical implications for both STARA-related Human Factors task design in health care and Sustainable Career Research. Methods: Empirical data derive from an embedded single-case study design within a hospital. The case study illustrates field experiences from a focus group interview (n=5) and postsurgery surveys (n=36) on Human Factors–related variables (objective task variety, perceived workload, perceived application of professional skills), and variables from Career Research (perceived career sustainability, growth needs). Results: Higher task variety and skill application during surgeries correlate with long-term career sustainability evaluations. Moreover, career-related growth needs relate to interindividual differences in STARA task evaluations. Interdisciplinary perspectives with respect to theoretical models, practical implications, and methodological challenges are discussed. Conclusions: The introduction of STARA (smart technology, AI, robotics, and algorithms) changes work processes and the application of skills, depending on specific task design within a given context.

Understanding Delivery and Engagement With a Digital Self-Management Intervention for Chronic Obstructive Pulmonary Disease Across Two Clinical Settings: Qualitative Study

2026-09-21T16:45:10-04:00

Background: Adherence to chronic obstructive pulmonary disease (COPD) self-management plans can improve health outcomes, yet access to pulmonary rehabilitation remains limited. Digital self-management interventions, such as , offer a potential means to extend support across care pathways. However, little is known about how these tools are delivered in routine practice or how delivery influences patient engagement across different clinical settings. Objective: This study explored perceived barriers and facilitators to the delivery of and engagement with by examining the perspectives of both patients and health care professionals (HCPs) across 2 National Health Service settings. Methods: This qualitative study was conducted between November 2023 and April 2024 across 2 contrasting clinical pathways: community pulmonary rehabilitation and a hospital discharge service following acute exacerbation of COPD. Semistructured interviews were conducted with patients using and HCPs involved in its delivery. Topic guides covered health background, digital technology use, and experiences of engaging with the intervention. A convenience sampling approach, with elements of purposive sampling, ensured representation across settings. Clinical data were obtained from health records with consent, and usage data were provided by my mhealth Ltd. Data were analyzed using abductive thematic analysis informed by the Medical Research Council’s process evaluation framework. Results: Thirty interviews were completed (16 patients and 14 HCPs). Patients described how multimorbidity, perceived digital ability, and socioeconomic context shaped their engagement with , operating as “layers of vulnerability” that influenced whether COPD self-management could be prioritized. HCPs highlighted organizational and workflow constraints that shaped how the intervention was introduced and supported. Delivery approaches differed markedly between settings: community teams adopted proactive, relationship-based support that enabled iterative discussion, tailored recommendations, and continuity, whereas hospital teams described passive, onboarding-focused delivery shaped by time pressures, staffing constraints, and discharge priorities. These contrasting delivery models influenced engagement patterns, with proactive support associated with more (characterized as routine and sustained) and passive onboarding associated with (characterized as short-term and transactional). Participants also identified perceived barriers (eg, usability concerns, limited integration with in-person care) and perceived benefits (eg, improved inhaler technique, increased confidence, lifestyle adjustments). Conclusions: Delivery context and local workflows play a central role in shaping how patients engage with digital self-management tools. Proactive, relational support appears to facilitate deeper and more sustained engagement, whereas passive onboarding may limit the intervention’s potential. Implementation strategies should align digital tools with local capacities, staffing structures, and patient characteristics, attending to layered vulnerabilities such as multimorbidity and digital confidence. Further work is needed to understand how proactive delivery models can be resourced and integrated within routine COPD care.

Effects of a Group-Based Cognitive Behavioral Therapy for Sleep in Nursing Students: Pilot Randomized Controlled Trial

2026-09-16T15:30:14-04:00

Background: Sleep quality is a strategic public health priority and a key factor in the study of circadian rhythms and chronotypes. Nursing students are a particularly vulnerable population group. Objective: The objective of this study was to evaluate the effects of a brief group-based cognitive behavioral therapy for insomnia (CBT-I) intervention on sleep quality and circadian-related outcomes among first-year nursing students using ambulatory circadian monitoring. Methods: This study is a 2-arm pilot randomized clinical trial conducted at a private university in Madrid, Spain, from October 2022 to March 2023. First-year nursing students aged 18 to 25 years were recruited in October 2022 using convenience sampling and randomly assigned to an experimental group (n=20) that received a cognitive-behavioral intervention using an active-constructive learning methodology to improve sleep quality and a control group (n=20) that followed their usual daily routine. Primary outcomes were objective sleep quality assessed using the Kronowise 3.0 ambulatory circadian monitoring system and subjective sleep quality assessed using the Pittsburgh Sleep Quality Index (PSQI). The objective Kronowise assessment was characterized by the continuous circadian rhythm and sleep parameters generated by the device. Participant satisfaction with the intervention was evaluated using an adapted 12-item Likert-type satisfaction questionnaire administered after the intervention. Measurements were collected preintervention and postintervention over 7-day monitoring periods. Adjusted analyses were performed using analysis of covariance, with treatment group as the fixed factor and baseline values, smoking status, school schedule, and coffee consumption as covariates. Treatment effects were summarized using regression coefficients (β), 95% CIs, and partial eta-squared (η²). The study was conducted in accordance with the CONSORT (Consolidated Standards of Reporting Trials) statement. Results: Forty students (n=34, 85% female; mean age 19.9, SD 1.8 y) were randomized equally into the intervention and control groups. Adjusted analyses showed generally small treatment effects for objective circadian rhythm and sleep outcomes. The largest treatment effect was observed for self-reported sleep latency (β=−19.78 min, 95% CI −33.83 to −5.74; partial η²=0.194). Sleep duration (β=36.32 min, 95% CI −5.01 to 77.64; partial η²=0.086) and sleep efficiency (β=8.75%, 95% CI −0.97 to 18.47; partial η²=0.090) also favored the intervention group, although CIs indicated considerable uncertainty around these estimates. Participants reported high satisfaction with the intervention, with 65% (13/20) rating the program as “very good.” Conclusions: This pilot randomized controlled trial provides preliminary evidence that a brief group-based CBT-I intervention may improve selected subjective sleep outcomes, particularly sleep latency, among first-year nursing students. Objective circadian rhythm and sleep outcomes showed generally small treatment effects. Given the exploratory nature of this pilot study, adequately powered randomized controlled trials with longer follow-up are needed to confirm these preliminary findings. Trial Registration: ClinicalTrials.gov NCT05273086; https://clinicaltrials.gov/study/NCT05273086 International Registered Report Identifier (IRRID): RR2-https://doi.org/10.3390/ijerph192113886

Cocreating a Digital Patient Preparedness Tool for International Students Accessing Primary Care in Germany: A Convergent Methods Study Within the Health CASCADE Network

2026-09-14T17:30:09-04:00

Background: Digital health interventions (DHIs) are a potential tool to address communication challenges in primary care medicine by improving patient participation, treatment adherence, and self-management of chronic diseases. However, Germany’s digital health infrastructure remains underdeveloped compared to other Organization for Economic Cooperation and Development (OECD) countries. Designing tailored, context-sensitive tools requires a deep understanding of patient needs, particularly for underserved populations navigating complex health care systems. Participatory approaches that actively involve end-users and stakeholders can help ensure that digital health tools are responsive to these needs. Behavioral science frameworks such as the Behavior Change Wheel (BCW), which incorporates the Capability, Opportunity, Motivation–Behavior (COM-B) model, can further support this process. Such technologies, developed using cocreation and behavior change frameworks, may improve health outcomes for underserved patient populations by addressing context-specific needs. This study explores how cocreation and behavioral science can inform the adaptation of a digital patient preparedness tool for international students accessing primary care in Germany. Objective: This study aimed to gain insight into international students’ experiences with primary care in Germany and explore whether adapting an existing digital patient preparedness intervention could address communication challenges and improve the standard of care. Using cocreation methods and the Behavior Change Wheel (BCW), we identified key design specifications and behavior change levers to inform tool development. Methods: A mixed methods design was used to identify design specifications for a DHI across 4 cocreation workshops with 12 students at an international university in Germany. Quantitative data were used for descriptive insights, and qualitative data were analyzed using qualitative content analysis. Workshops were informed by the BCW and the Health CASCADE cocreation methods selector tool. Results: Cocreators reported feeling misunderstood, anxious, and ill-informed during primary care interactions, with system-level barriers compounding communication difficulties. Despite this, many engaged in preparatory behaviors (eg, note-taking) to manage uncertainty and structure their consultations. Feedback on an existing digital intervention was mixed: while cocreators appreciated its intent, structured lesson formats were seen as too time-consuming. Cocreators preferred a concise, interactive design. Communication prompts, appointment scheduling, and personalized feedback were frequently requested features, though tool adoption was seen as contingent on addressing broader system-level frustrations. Conclusions: International students’ negative health care experiences, often stemming from unclear communication, perceived indifference, and difficulty navigating an unfamiliar medical system, may be mitigated through codesigned, personalized digital interventions. Frequently requested features, such as appointment scheduling, clinic directories, test result access, and interactive tools like chatbots, may help address barriers related to system navigation, communication, and access. This study demonstrates how cocreation methods can be integrated with the BCW to inform the development of context-sensitive digital health tools for specific target groups.

Effects of AI Assistance Timing on Pharmacists’ Trust in Automated Pill Recognition Technology: Within-Participants Experimental Study

2026-09-11T17:00:19-04:00

Background: Image-based pill verification systems demonstrate high model accuracy. However, their effectiveness in pharmacy practice depends on how pharmacists interact with the AI output. The timing of AI advice is one design factor that influences these interactions, yet its impact on pharmacists’ moment-to-moment trust dynamics during medication verification requires further investigation. Objective: This study aims to investigate how the timing and conditionality of AI assistance shape pharmacists’ trust dynamics during medication verification. Methods: Between April and December 2024, 50 licensed pharmacists completed a browser-based simulated medication dispensing task with 2 AI types: ex-ante advice (AI advice given concurrently with clinical information) and ex-post advice (AI advice given after an initial diagnosis). Ex-post advice was further divided into the involved ex-post and the not-involved ex-post conditions. The experiment used a within-participants design with varying AI types and AI recognition patterns (right fill-correct recognition, right fill-incorrect recognition, wrong fill-correct recognition, and wrong fill-incorrect recognition). The primary outcomes were trust adjustment magnitude and trust adjustment, which were analyzed using mixed-effects linear regression models. Results: Trust adjustment magnitude differed significantly across AI assistance conditions. The involved ex-post condition led to the highest magnitude of trust adjustment, followed by ex-ante advice (mean difference 9.65, 95% CI 8.55-10.75; <001), with the not-involved ex-post condition showing the lowest magnitude (mean difference 3.33, 95% CI 2.63-4.03; <001). Analysis by each recognition pattern revealed significant differences in trust adjustment when the right drugs were incorrectly rejected. In this pattern, the involved ex-post condition led to larger trust decrements than both ex-ante advice (mean difference −2.13, 95% CI −3.83 to −0.44; =.008) and not-involved ex-post conditions (mean difference −7.32, 95% CI −13.69 to −.95; =.009). A marginal difference was observed between ex-ante advice and not-involved ex-post conditions (mean difference −5.19, 95% CI −11.55 to 1.17; =.051). No significant differences were observed for other recognition patterns. Conclusions: Both the timing and conditionality of AI assistance influenced pharmacists’ trust dynamics. Disagreement-based AI interventions (involved ex-post) that incorrectly challenged pharmacists led to a substantial trust decrement, whereas the not-involved AI intervention resulted in more stable trust fluctuations. These findings highlight the importance of designing AI systems that align intervention strategies with user expertise and task demands to foster appropriate trust in safety-critical workflows. Trial Registration: ClinicalTrials.gov NCT06245044; https://clinicaltrials.gov/study/NCT06245044

Public Perceptions and Determinants of Preference for Robotic-Assisted Surgery in the United Arab Emirates: Cross-Sectional Study

2026-09-11T13:45:03-04:00

Background: Robotic-assisted surgery (RAS) is increasingly being integrated into surgical services worldwide. Successful implementation of robotic surgical technologies depends not only on clinical effectiveness but also on public trust, perceptions of safety, and willingness to use technology-enabled care. In the United Arab Emirates, the adoption of RAS is expanding rapidly; however, evidence regarding public perceptions and factors influencing acceptance remains limited. Objective: This study aims to assess awareness, understanding, perceptions, and factors independently associated with preference for RAS among residents in the United Arab Emirates. Methods: A community-based cross-sectional survey was conducted between July and November 2025 among adults aged 18 years or older residing across multiple emirates in the United Arab Emirates. Participants were recruited using a nonprobability convenience sampling approach through hospitals, shopping malls, community events, university mailing lists, and social media platforms. A bilingual Arabic-English questionnaire adapted from previously validated robotic surgery perception instruments was administered. Descriptive statistics summarized participant responses. Associations between participant characteristics and perceptions of RAS were examined using chi-square and Fisher exact tests. To adjust for potential confounding, a multivariable Firth-penalized logistic regression model was fitted to identify factors independently associated with preference for RAS over traditional surgeon-performed surgery. Statistical analyses were conducted using R (version 4.4.1). Results: A total of 508 participants were included. Although 85% (n=432) had previously heard of RAS, only 38% (n=195) demonstrated an accurate functional understanding of robotic surgical systems. Overall, 33% (n=169) preferred RAS, while 35% (n=178) perceived robotic-assisted procedures as safe. The most frequently reported concerns were robotic malfunction (n=360, 71%), reduced human involvement (n=239, 47%), and procedural costs (n=183, 36%). Perceived benefits included improved surgical precision (n=331, 65%) and reduced complications (n=183, 36%). In multivariable analysis, participants who perceived RAS as safe had substantially higher odds of preferring a robot-assisted surgeon than those who were uncertain about its safety (adjusted odds ratio [aOR]=3.00, 95% CI 1.56‐5.87; <.001). Conversely, participants who perceived RAS as unsafe had substantially lower odds of preferring a robot-assisted surgeon than those who were uncertain about its safety (aOR=0.08, 95% CI 0.02‐0.26; <.001). No other factors were independently associated with surgery preference after adjustment. Conclusions: Within this predominantly highly educated convenience sample of United Arab Emirates residents, awareness of RAS was high, but accurate understanding and acceptance remained limited. Perceived safety emerged as the factor shaping preference for RAS, highlighting the importance of trust and confidence in technology-enabled health care. These findings suggest that public education, transparent communication regarding surgeon oversight, and patient engagement strategies may be important components of the successful implementation of robotic surgical services in the United Arab Emirates.

The Cognitive Transaction: Toward a Human Factors Research Agenda for AI in Anesthesia and Perioperative Care

2026-09-10T17:00:20-04:00

AI is now embedded in the infrastructure of perioperative care. Risk stratification algorithms, hemodynamic prediction tools, and clinical decision support systems are active in operating rooms at major health systems, and their adoption is accelerating. However, the field has studied model performance and organizational implementation while largely bypassing the moment between them: the real-time encounter in which an anesthesia provider must decide, under active case conditions, what to do with an AI-generated output. We term this the cognitive transaction and argue that it is the fundamental unit of perioperative AI implementation. The perioperative environment presents a specific constellation of conditions that existing human-AI interaction research was not designed to address. Continuous real-time decision demands, extreme time compression, high cognitive load, and consequences that unfold in seconds distinguish the operating room from the clinical contexts where most provider-AI interaction research has been conducted. What we know about AI adoption in radiology, oncology, or ambulatory care does not readily translate to this setting. The cognitive moment in anesthesia has its own structure, its own failure modes, and its own research requirements. This paper examines what those requirements are. We analyze how the operating room functions as a pre-existing human-machine cognitive system into which AI is now being inserted, and why the conditions of that system generate predictable vulnerabilities: miscalibrated trust, automation bias, and cognitive friction produced by interfaces optimized for technical accuracy rather than clinical usability. We argue that these failure modes are not incidental but structural and that they will persist regardless of model performance until the provider-AI interaction is itself treated as a research object. We identify 4 priority research domains. The first concerns the structure of provider-AI disagreement and the methods needed to distinguish automation bias from legitimate clinical insight. The second concerns the longitudinal dynamics of trust calibration across repeated clinical encounters rather than single-session experimental designs. The third concerns interface design for high-acuity workflows, specifically what constitutes a usable AI output for a provider managing a patient in real time. The fourth concerns the need for ecologically valid study designs capable of capturing provider reasoning under actual perioperative conditions rather than retrospective or survey-based proxies. The anesthesia and perioperative research community is positioned to lead this work. The clinical specificity, domain knowledge, and professional stake required to design meaningful studies are all present within the field. Evaluating the cognitive transaction under perioperative conditions, not the computational model in isolation, is both a methodological imperative and a patient safety priority.

Development and Initial Usability Evaluation of SSEndoSim, a Free, Web-Based Endodontic Diagnostic Simulator: Cross-Sectional Study

2026-09-10T12:45:13-04:00

SSEndoSim, a free, open access, mobile, web-based endodontic diagnostic simulation application for undergraduate dental education, demonstrated favorable usability rated on a System Usability Scale (SUS) witht a composite 100-point score (mean 75.30, SD 14.72) and high perceived educational value (PEV) on a 5-point scale (mean 4.62, SD 0.40) in a cross-sectional usability evaluation of final-year bachelor of dental surgery (BDS) students at Liaquat University of Medical and Health Sciences (LUMHS), Jamshoro, Pakistan, in April 2026, establishing usability and learner acceptability.

Usability Across 3 mHealth Problem-Solving Training Interventions for Diverse Neurodevelopmental and Neurological Populations: Multicase Usability Evaluation

2026-09-09T16:45:13-04:00

Background: mHealth (mobile health) interventions that integrate psychoeducation with structured problem-solving training (PST) hold strong potential for improving self-management of chronic conditions. Evaluating the usability of these interventions requires assessing technological, pedagogical, and sociocultural fit. However, most usability evaluations remain narrowly technocentric, focusing on interface-level metrics while neglecting pedagogical coherence, cultural responsiveness, and patient learning needs. Objective: This exploratory study aimed to characterize usability challenges and facilitators across 3 psychoeducational mHealth PST interventions and to identify technological, pedagogical, and sociocultural design features that can improve engagement, accessibility, and implementation for diverse users. Methods: This study used an exploratory, multimethod, collective case study design. Three independent mHealth PST usability studies used structured think-aloud protocols, with sessions conducted remotely via Zoom (Zoom Communications, Inc), except for 1 in-person session for Epilepsy Journey 2.0. A total of 14 participants were enrolled across 3 independent cases via purposive sampling from their respective target populations. Case 1 – Epilepsy Journey 2.0 (n=6; 4 male, 2 female; ages 12‐18 y; adolescents with epilepsy), case 2 – Survivor’s Journey (n=3; 1 male, 2 female; ages 18‐30 y; adolescent and young adult survivors of brain tumor), and case 3 – electronic problem-solving training (n=5; 2 male, 3 female; ages 30‐65 y; adults with a history of severe traumatic brain injury). Participants completed a presession technology comfort survey and the Comprehensive Assessment of Usability for Learning Technologies postsession. All sessions were recorded, transcribed, and analyzed thematically. Comprehensive Assessment of Usability for Learning Technologies data were analyzed using descriptive quantitative methods. Results: Electronic problem-solving training demonstrated the highest usability (mean 87.96, SD 13.73), followed by Survivor’s Journey (mean 83.00, SD 7.70), and then Epilepsy Journey 2.0 (mean 79.00, SD 12.75). Findings revealed that usability in health care learning design is shaped by how effectively the technology, learning content, and contextual factors align with patients’ needs. Recurring challenges across interventions included unclear navigation, poor mobile responsiveness, instructional ambiguity, insufficient feedback, potential for greater inclusivity, and limited error recovery. Twelve cross-case design principles were derived, emphasizing mobile-first accessibility, cognitive load reduction, context-sensitive feedback, and empathetic, inclusive design. Conclusions: Usability challenges in mHealth PST interventions arise not only from interface-level issues but also from how effectively the intervention supports users’ understanding, decision-making, and real-world application demands. This extends prior mHealth usability research by demonstrating that user difficulties often reflect misalignments between technological features, instructional structure, and the everyday contexts in which individuals engage with PST. The resulting design principles highlight specific, actionable priorities for developers, including mobile-first optimization, clearer task scaffolding, and better feedback and error recovery. Future work should evaluate these principles in larger samples and clinical settings to determine their impact on engagement, adherence, and downstream health outcomes.