Impacts of an Acute Care Telenursing Program on Discharge, Patient Experience, and Nursing Experience: Retrospective Cohort Comparison Study.

BACKGROUND: Despite widespread growth of televisits and telemedicine, it is unclear how telenursing could be applied to augment nurse labor and support nursing. OBJECTIVE: This study evaluated a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients. METHODS: A retrospective, observational cohort comparison was performed in a large academic hospital system (approximately 2100 beds) in Houston, Texas, comparing patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, with patients who did not participate (nontelenursing cohort) in the same units and timeframe. We used a case mix index analysis to confirm comparable patient cases between groups. The outcomes investigated were patient experience, measured using the Hospital Consumer Assessment of Health Care Providers and Systems (HCAHCPS) survey; nursing experience, measured by a web-based questionnaire with quantitative multiple-choice and qualitative open-ended questions; time of discharge during the day (from electronic health record data); and duration of discharge education processes. RESULTS: Case mix index analysis found no significant case differences between cohorts (P=.75). For the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHCPS domains. Scores for "communication with doctors" and "would recommend hospital" were improved significantly (P=.03 and P=.04, respectively) in 1 unit in phase 1. The impact of telenursing in phases 2 and 3 was mixed. However, "communication with doctors" was significantly improved in 2 units (P=.049 and P=.002), and the overall rating of the hospital and the "would recommend hospital" scores were significantly improved in 1 unit (P=.02 and P=04, respectively). Of 289 nurses who were invited to participate in the survey, 106 completed the nursing experience survey (response rate 106/289, 36.7%). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was very helpful or somewhat helpful to them as bedside nurses. The only noticeable difference between the telenursing and nontelenursing cohorts for the time of day discharge was a shift in the volume of patients discharged before 2 PM compared to those discharged after 2 PM at a hospital-wide level. The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice (nontelenursing cohort) of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation time study. CONCLUSIONS: This study shows that ACTN programs are feasible and associated with improved outcomes for patient and nursing experience and reducing time allocated to admission and discharge education.

Investigating patient engagement associations between a postdischarge texting programme and patient experience, readmission and revisit rates outcomes.

OBJECTIVES: This study aimed (1) to examine the association between patient engagement with a bidirectional, semiautomated postdischarge texting programme and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey outcomes, readmissions and revisit rates in a large health system and (2) to describe operational and clinical flow considerations for implementing a postdischarge texting programme. SETTING: The study involved 1 main academic hospital (beds: 2500+) and 6 community hospitals (beds: 190-400, averaging 300 beds per hospital) in Houston, Texas. METHODS: Retrospective, observational cohort study between non-engaged patients (responded with 0-2 incoming text messages) and engaged patients (responded with 3+ incoming, patient-initiated text messages) between December 2022 and May 2023. We used the two-tailed t-test for continuous variables and χ2 test for categorical variables to compare the baseline characteristics between the two cohorts. For the binary outcomes, such as the revisit (1=yes, vs 0=no) and readmissions (1=yes vs 0=no), we constructed mixed effect logistic regression models with the random effects to account for repeated measurements from the hospitals. For the continuous outcome, such as the case mix index (CMI), a generalised linear quantile mixed effect model was built. All tests for significance were two tailed, using an alpha level of 0.05, and 95% CIs were provided. Significance tests were performed to evaluate the CMI and readmissions and revisit rates. RESULTS: From 78 883 patients who were contacted over the course of this pilot implementation, 49 222 (62.4%) responded, with 39 442 (50%) responded with 3+ incoming text messages. The engaged cohort had higher HCAHPS scores in all domains compared with the non-engaged cohort. The engaged cohort used significantly fewer 30-day acute care resources, experiencing 29% fewer overall readmissions and 20% fewer revisit rates (23% less likely to revisit) and were 27% less likely to be readmitted. The results were statistically significant for all but two hospitals. CONCLUSIONS: This study builds on the few postdischarge texting studies, and also builds on the patient engagement literature, finding that patient engagement with postdischarge texting can be associated with fewer acute care resources. To our knowledge, this is the only study that documented an association between a text-based postdischarge programme and HCAHPS scores, perhaps owing to the bidirectionality and ease with which patients could interact with nurses. Future research should explore the texting paradigms to evaluate their associated outcomes in a variety of postdischarge applications.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

User-Centered Design of a Diabetes Self-Management Tool for Underserved Populations.

BACKGROUND: User-centered design (UCD) or user experience design (UXD) methods have gained recent popularity for the design of mobile health (mHealth) interventions. However, there is a gap in application of these methods for diabetes self-management. This study aims to document the UCD process for a self-management mobile application aimed for patients with diabetes in underserved communities. METHODS: A UCD mixed-methods approach including interviews with patients and providers, a review of literature, and a technology landscape analysis were used to define the app functional information requirements that informed the user experience/user interface design process. Usability studies with the app designers and developers, intended users, and a focus group of nurse educators and dieticians were used to test and improve the design. RESULTS: An mHealth app was developed with health-tracking features for stress, blood sugar, food, exercise, medications, weight, and blood pressure. We tackled a range of usability and user experience challenges, which encompassed addressing issues like low health literacy by employing a combination of user interface design principles, intuitive visualizations, customizable icons, seamless database integration, and automated data input features. Special attention was given to the design of educational content accounting for the intended users' cultural background and literacy levels. CONCLUSIONS: User-centered design approach contributed to a better understanding of the intended users' needs, limitations, mental models, and expectations, facilitating the design of a comprehensive mobile app for patients with diabetes in underserved communities that includes essential features for self-management while providing a strong educational component, addressing an important gap in the literature.

Investigating the effect of an mHealth coaching intervention on health beliefs, adherence and blood pressure of patients with hypertension: A longitudinal single group pilot study.

Background: Mobile health technologies have shown promise as delivery platforms for digital health coaching for chronic conditions. However, the impacts of such strategies on users' health beliefs, intentions and ultimately clinical outcomes are understudied. Objective: This study sought (1) to evaluate the effects of a digital health coaching intervention on participants' belief constructs; and (2) to assess relationships between these belief constructs and intentions to utilize the technological intervention, actual adherence metrics and clinical outcomes related to hypertension. Methods: Thirty-four participants with hypertension were recruited from a university community from January to May 2021. They self-measured weight and blood pressure (BP) for 30 days followed by digital coaching delivered via a mobile application for 30 days. Surveys assessed constructs from the Health Belief Model and Technology Acceptance Model, compared to intention, health belief, BP self-monitoring adherence and BP outcomes. A path analysis model was used to assess the relationships between constructs and intention, adherence metrics and clinical outcomes. A Kruskal-Wallis test was used to identify changes in beliefs. Results: Participant health beliefs significantly improved after coaching, including self-efficacy (H(1) = 15.12, p < 0.001), cues to action (H(1) = 5.33, p = 0.02), attitude (H(1) = 10.35, p = 0.002), perceived usefulness (H(1) = 15.02, p < 0.001) and decreased resistance to change (H(1) = 4.05, p = 0.04). Adherence to BP measurements positively correlated with perceived health threat (ß = .033, p = 0.007) and perceived ease of use (ß = .0277, p < 0.001). Self-efficacy (ß = -2.92, p = 0.02) and perceived usefulness (ß = -3.75, p = 0.01) were linked with a decrease in diastolic BP. Conclusions: A mobile health coaching intervention may help participants improve beliefs regarding hypertension self-management.

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.

Integrating a Virtual ICU with Cardiac and Cardiovascular ICUs: Managing the Needs of a Complex and High-Acuity Specialty ICU Cohort.

A long-standing shortage of critical care intensivists and nurses, exacerbated by the coronavirus disease (COVID-19) pandemic, has led to an accelerated adoption of tele-critical care in the United States (US). Due to their complex and high-acuity nature, cardiac, cardiovascular, and cardiothoracic intensive care units (ICUs) have generally been limited in their ability to leverage tele-critical care resources. In early 2020, Houston Methodist Hospital (HMH) launched its tele-critical care program called Virtual ICU, or vICU, to improve its ICU staffing efficiency while providing high-quality, continuous access to in-person and virtual intensivists and critical care nurses. This article provides a roadmap with prescriptive specifications for planning, launching, and integrating vICU services within cardiac and cardiovascular ICUs-one of the first such integrations among the leading academic US hospitals. The success of integrating vICU depends upon the (1) recruitment of intensivists and RNs with expertise in managing cardiac and cardiovascular patients on the vICU staff as well as concerted efforts to promote mutual trust and confidence between in-person and virtual providers, (2) consultations with the bedside clinicians to secure their buy-in on the merits of vICU resources, and (3) collaborative approaches to improve workflow protocols and communications. Integration of vICU has resulted in the reduction of monthly night-call requirements for the in-person intensivists and an increase in work satisfaction. Data also show that support of the vICU is associated with a significant reduction in the rate of Code Blue events (denoting a situation where a patient requires immediate resuscitation, typically due to a cardiac or respiratory arrest). As the providers become more comfortable with the advances in artificial intelligence and big data-driven technology, the Cardiac ICU Cohort continues to improve methods to predict and track patient trends in the ICUs.

Workers' Acceptance of Digital Procedures: An Application of the Technology Acceptance Model.

OCCUPATIONAL APPLICATIONSThere are increasing numbers of organizations that are implementing digital procedures (e.g., standard operating procedures). These efforts are often assumed to be a positive development but can be quite costly-both in terms of money and training for a digital rollout. As a result, organizations and practitioners may find themselves at risk for failure when implementing digital procedures. The results of the current study suggest that if workers perceive digital procedures as useful and easy to use, this perception translates into positive attitudes, which subsequently result in fewer deviations. Since acceptance is relatively easy to assess, practitioners can benefit from using these assessments prior to a digital transition/roll-out to both compare competing hardware and software applications, and to initiate and continuously monitor the development of digital procedures. We consider this approach as advantageous to having management develop a system and fully deploying digital procedures without any consideration of worker acceptance.

Background: There is increasing prevalence of digital procedures being introduced in the process safety industries. Presumably, this increase is due to a desire to take advantage of the technology afforded to workers that otherwise is not inherent to traditional paper-based procedures. A critical question that has not been addressed, though, is to what extent do workers accept this new technology in a new digital procedure rollout? Furthermore, does acceptance lead to procedure-related behavior, such as procedure deviations?Purpose: We used the technology acceptance model (TAM), which includes two dimensions of technology acceptance­perceived usefulness (PU) and perceived ease of use (PEU)­as the focal antecedent constructs. We hypothesized that these constructs would predict more proximal attitudes toward procedures, which in turn predict procedure deviations.Method: We used path analyses to test six study hypotheses developed from the TAM. Data were collected from 16 workers at a large, international chemical corporation that worked in logistics. Specific measures obtained were from multi-item, Likert-scale measures of the TAM-PU and PEU dimensions, utility and compliance attitudes toward procedures, and procedure deviation frequency.Results: Four of the six study hypotheses were supported. TAM-PU and TAM-PEU both significantly predicted (positively) utility attitudes toward procedures (71% variance explained), whereas only TAM-PU significantly predicted (positively) compliance attitudes toward procedures (63% variance explained). In turn, only compliance attitudes significantly predicted (negatively) how frequently workers deviated from procedures (27% variance explained).Conclusions: These results suggest that workers were generally accepting of the digital procedures and that worker perceptions of perceived usefulness perceptions likely have an indirect effect on procedure deviation frequency. We see this study as a novel contribution to the process safety and procedures research domain. Limitations and future research directions will be discussed.

Naturalistic observations of multiteam interaction networks: Implications for cognition in crisis management teams.

Interaction has been recognised as an essential lens to understand how cognition is formed in a complex adaptive team such as a multidisciplinary crisis management team (CMT). However, little is known about how interactions within and across CMTs give rise to the multi-team system's overall cognitive functioning, which is essential to avoid breakdowns in coordination. To address this gap, we characterise and compare the component CMTs' role-as-intended (RAI) and role-as-observed (RAO) in adapting to the complexity of managing informational needs. To characterise RAI, we conducted semi-structured interviews with subject matter experts and then made a qualitative synthesis using a thematic analysis method. To characterise RAO, we observed multiteam interaction networks in real-time at a simulated training environment and then analysed the component CMTs' relative importance using node centrality measures. The resulting inconsistencies between RAI and RAO imply the need to investigate cognition in multiple CMTs through the lens of interaction.Practitioner summary: When a disaster occurs, multidisciplinary CMTs are expected to serve their roles as described in written or verbal guidelines. However, according to our naturalistic observations of multiteam interaction networks, such descriptions may be (necessary but) insufficient for designing, training, and evaluating CMTs in the complexity of managing informational needs together.

Understanding Patient Beliefs in Using Technology to Manage Diabetes: Path Analysis Model From a National Web-Based Sample.

BACKGROUND:  With 425 million individuals globally living with diabetes, it is critical to support the self-management of this life-threatening condition. However, adherence and engagement with existing technologies are inadequate and need further research. OBJECTIVE:  The objective of our study was to develop an integrated belief model that helps identify the significant constructs in predicting intention to use a diabetes self-management device for the detection of hypoglycemia. METHODS:  Adults with type 1 diabetes living in the United States were recruited through Qualtrics to take a web-based questionnaire that assessed their preferences for a device that monitors their tremors and alerts them of the onset of hypoglycemia. As part of this questionnaire, a section focused on eliciting their response to behavioral constructs from the Health Belief Model, Technology Acceptance Model, and others. RESULTS:  A total of 212 eligible participants responded to the Qualtrics survey. Intention to use a device for the self-management of diabetes was well predicted (R2=0.65; F12,199=27.19; P<.001) by 4 main constructs. The most significant constructs were perceived usefulness (ß=.33; P<.001) and perceived health threat (ß=.55; P<.001) followed by cues to action (ß=.17; P<.001) and a negative effect from resistance to change (ß=-.19; P<.001). Older age (ß=.025; P<.001) led to an increase in their perceived health threat. CONCLUSIONS: For individuals to use such a device, they need to perceive it as useful, perceive diabetes as life-threatening, regularly remember to perform actions to manage their condition, and exhibit less resistance to change. The model predicted the intention to use a diabetes self-management device as well, with several constructs found to be significant. This mental modeling approach can be complemented in future work by field-testing with physical prototype devices and assessing their interaction with the device longitudinally.

Hypoglycemia Detection Using Hand Tremors: Home Study of Patients With Type 1 Diabetes.

BACKGROUND: Diabetes affects millions of people worldwide and is steadily increasing. A serious condition associated with diabetes is low glucose levels (hypoglycemia). Monitoring blood glucose is usually performed by invasive methods or intrusive devices, and these devices are currently not available to all patients with diabetes. Hand tremor is a significant symptom of hypoglycemia, as nerves and muscles are powered by blood sugar. However, to our knowledge, no validated tools or algorithms exist to monitor and detect hypoglycemic events via hand tremors. OBJECTIVE: In this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data. METHODS: We analyzed triaxial accelerometer data from a smart watch recorded from 33 patients with type 1 diabetes for 1 month. Time and frequency domain features were extracted from acceleration signals to explore different machine learning models to classify and differentiate between hypoglycemic and nonhypoglycemic states. RESULTS: The mean duration of the hypoglycemic state was 27.31 (SD 5.15) minutes per day for each patient. On average, patients had 1.06 (SD 0.77) hypoglycemic events per day. The ensemble learning model based on random forest, support vector machines, and k-nearest neighbors had the best performance, with a precision of 81.5% and a recall of 78.6%. The results were validated using continuous glucose monitor readings as ground truth. CONCLUSIONS: Our results indicate that the proposed approach can be a potential tool to detect hypoglycemia and can serve as a proactive, nonintrusive alert mechanism for hypoglycemic events.

Use of a Mobile Biofeedback App to Provide Health Coaching for Stress Self-management: Pilot Quasi-Experiment.

BACKGROUND: Mental health is an increasing concern among vulnerable populations, including college students and veterans. OBJECTIVE: The purpose of this study was to determine if mobile health technology combined with health coaching can better enable a user to self-manage their mental health. METHODS: This study evaluated the mobile app "Biofeedback" that provided health coaching on stress self-management for college student veterans' mental health concerns. Twenty-four college student veterans were recruited from a large public university in Texas during the spring 2020 semester, impacted by COVID-19. Ten participants were assigned to the intervention group where they used the mobile Biofeedback app on their smartphones and smartwatches, and 14 were assigned to the control group without the app; assignment was based on mobile phone compatibility. Both groups participated in one initial lab session where they learned a deep-breathing exercise technique. The intervention group was then asked to use the mobile Biofeedback app during their daily lives and a smartwatch, and the control group was asked to perform the breathing exercises on their own. Both groups filled out Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) self-assessments at 2-week intervals. At the end of the semester, both groups were given an exit interview to provide user experience and perceived benefits of health coaching via the mobile biofeedback app. RESULTS: The deep-breathing exercise in the initial lab session reduced stress in both groups. Over the course of the study, the app recorded 565 coached breathing exercises with a significant decrease (approximately 3 beats per minute) in participants' heart rate during the 6-minute time period immediately after conducting the breathing exercises (Spearman rank correlation coefficient -0.61, P<.001; S=9,816,176). There was no significant difference between the two groups for PHQ-9 and GAD-7 scores over the course of the semester. Exit interview responses indicated that participants perceived that the mobile Biofeedback app improved their health and helped them address stress challenges. All participants reported that the intervention helped them manage their stress better and expressed that health coaching via a mobile device would improve their overall health. CONCLUSIONS: Participants reported a positive perception of the app for their mental health self-management during a stressful semester. Future work should examine long-term effects of the app with a larger sample size balanced between male and female participants, randomized participant allocation, real-time detection of mental health symptoms, and additional features of the app.

Occupational stress and burnout among intensive care unit nurses during the pandemic: A prospective longitudinal study of nurses in COVID and non-COVID units.

Background: Intensive care unit (ICU) nurses are highly prone to occupational stress and burnout, affecting their physical and mental health. The occurrence of the pandemic and related events increased nurses' workload and further exacerbated their stress and burnout. This work investigates occupational stress and burnout experienced by ICU nurses working with COVID and non-COVID patients. Method: A prospective longitudinal mixed-methods study was conducted with a cohort of ICU nurses working in medical ICU (COVID unit; n = 14) and cardiovascular ICU (non-COVID unit; n = 5). Each participant was followed for six 12-h shifts. Data on occupational stress and burnout prevalence were collected using validated questionnaires. Physiological indices of stress were collected using wrist-worn wearable technologies. Participants elaborated on the causes of stress experienced each shift by completing open-ended questions. Data were analyzed using statistical and qualitative methods. Results: Participants caring for COVID patients at the COVID unit were 3.71 times more likely to experience stress (p < 0.001) in comparison to non-COVID unit participants. No differences in stress levels were found when the same participants worked with COVID and non-COVID patients at different shifts (p = 0.58) at the COVID unit. The cohorts expressed similar contributors to stress, based in communication tasks, patient acuity, clinical procedures, admission processes, proning, labs, and assisting coworkers. Conclusion: Nurses in COVID units, irrespective of whether they care for a COVID patient, experience occupational stress and burnout.

Investigating Popular Mental Health Mobile Application Downloads and Activity During the COVID-19 Pandemic.

OBJECTIVE: This article analyzes the changes in downloads and activity of users of select popular mental health mobile applications (mHealth apps) during coronavirus disease 2019 (COVID-19). BACKGROUND: The outbreak of the COVID-19 crisis has shown a negative impact on public mental health. Mobile health has the potential to help address the psychological needs of existing and new patients during the pandemic and beyond. METHOD: Downloads data of 16 widely used apps were analyzed. The quality of apps was reviewed using the Mobile Application Rating Scale (MARS) framework. Correlation analysis was conducted to investigate the relationship between app quality and app popularity. RESULTS: Among the 16 apps, 10 were meditational in nature, 13 showed increased downloads, with 11 apps showing above 10% increase in the downloads after the pandemic started. The popular apps were satisfactory in terms of functionality and esthetics but lacked clinical grounding and evidence base. There exists a gap between app quality and app popularity. CONCLUSION: This study provided evidence for increased downloads of mental mHealth apps (primarily meditation apps) during the COVID-19 pandemic but revealed several gaps and opportunities to address deficiencies in evidence-based design, usability and effective assessment, and integration into current workflows. APPLICATION: The COVID-19 pandemic is a potential turning point for mHealth applications for mental health care. Whereas the evidence suggests a need for alternative delivery of care, human factors and ergonomics methods should be utilized to ensure these tools are user-centered, easy to use, evidence-based, well-integrated with professional care, and used sustainably.

Investigating burn-out contributors and mitigators among intensive care unit nurses during COVID-19: a focus group interview study.

OBJECTIVE: Past literature establishes high prevalence of burn-out among intensive care unit (ICU) nurses, and the influence of the COVID-19 pandemic in intensifying burn-out. However, the specific pandemic-related contributors and practical approaches to address burn-out have not been thoroughly explored. To address this gap, this work focuses on investigating the effect of the COVID-19 pandemic on the burn-out experiences of ICU nurses and identifying practical approaches for burn-out mitigation. DESIGN: Semistructured focus group interviews were conducted via convenience sampling and qualitatively analysed to identify burn-out contributors and mitigators. Maslach Burnout Inventory for Medical Personnel (MBI-MP) and Post-traumatic Stress Disorder Checklist (PCL-5) were employed to quantify the prevalence of burn-out of the participants at the time of study. SETTING: Two ICUs designated as COVID-19 ICUs in a large metropolitan tertiary care hospital in the Greater Houston area (Texas, USA). PARTICIPANTS: Twenty registered ICU nurses (10 from each unit). RESULTS: Participants experienced high emotional exhaustion (MBI-MP mean score 32.35, SD 10.66), moderate depersonalisation (M 9.75, SD 7.10) and moderate personal achievement (M 32.05, SD 7.59) during the pandemic. Ten out of the 20 participants exhibited post-traumatic stress disorder symptoms (PCL-5 score >33). Regarding contributors to burn-out in nurses during the pandemic, five thematic levels emerged-personal, patient related, coworker related, organisational and societal-with each factor comprising several subthemes (eg, emotional detachment from patients, constant need to justify motives to patients' family, lack of staffing and resources, and politicisation of COVID-19 and vaccination). Participants revealed several practical interventions to help overcome burn-out, ranging from mental health coverage to educating public on the severity of the pandemic and importance of vaccination. CONCLUSIONS: By identifying the contributors to burn-out in ICU nurses at a systems level, the study findings inform the design and implementation of effective interventions to prevent or mitigate pandemic-related burn-out among nurses.

Improving Healthcare Practice Through the Implementation of Human Factors and Ergonomics Principles.

OBJECTIVE: Present a collection of papers focusing on improving healthcare practice through the implementation of human factors and ergonomics principles that were presented at the International Ergonomics Association (IEA) 2021 international conference. BACKGROUND: The mission of the IEA is to elaborate and advance ergonomics science and practice and to expand its scope of application. METHOD: We reviewed papers that were submitted for presentation at the IEA 2021 international conference and focused on improving healthcare practice through the implementation of human factors and ergonomics principles. RESULTS: The eight papers that are included in this special issue cover varied aspects of human factors application and implementation. CONCLUSION: This special issue provides clear evidence that the science of human factors is relevant and is continuing to grow and so is its implementation in healthcare. APPLICATION: This special issue offers a selection of applied works, providing a wide scope of human factors guidelines, methods, and theories in healthcare work environments.

Systematic review of interruptions in the emergency department work environment.

OBJECTIVE: The purpose of this systematic review is to describe the operationalization of interruptions measurement and to synthesize the evidence on the causes and consequences of interruptions in the emergency department (ED) work environment. METHODS: This systematic review of studies explores the causes and consequences of interruptions in the ED. Of 2836 abstract/titles screened, 137 full-text articles were reviewed, and 44 articles met inclusion criteria of measuring ED interruptions. RESULTS: All articles reported primary data collection, and most were cohort studies (n = 30, 68%). Conceptual or operational definitions of interruptions were included in 27 articles. Direct observation was the most common approach. In half of the studies, quantitative measures of interruptions in the ED were descriptive only, without measurements of interruptions' consequences. Twenty-two studies evaluated consequences, including workload, delays, satisfaction, and errors. Overall, relationships between ED interruptions and their causes and consequences are primarily derived from direct observation within large academic hospitals using heterogeneous definitions. Collective strengths of interruptions research in the ED include structured methods of naturalistic observation and definitions of interruptions derived from concept analysis. Limitations are conflicting and complex evaluations of consequences attributed to interruptions, including the predominance of descriptive reports characterizing interruptions without direct measurements of consequences. CONCLUSIONS: The use of standardized definitions and measurements in interruptions research could contribute to measuring the impact and influence of interruptions on clinicians' productivity and efficiency as well as patients' outcomes, and thus provide a basis for intervention research.

The Effects of Drowsiness Detection Technology and Education on Nurses' Beliefs and Attitudes toward Drowsy Driving.

Occupational ApplicationsNurses' perceived health threat from driving drowsy along with their attitude toward an intervention can be targeted to improve nurses' intentions to avoid this dangerous behavior. The evidence presented in this paper suggests that educational interventions that raise awareness of the risks of drowsy driving and its consequences (e.g., fatalities or injuries), as well as peer stories about their experiences, may positively affect nurses' perceived health threat and attitudes toward drowsy driving interventions.

Background Drowsy driving is prevalent among night-shift nurses, yet there is a gap in understanding nurses' beliefs and attitudes that may affect their intention to avoid drowsy driving.Objectives The objectives of the study were twofold: 1) investigate how behavioral constructs such as beliefs and attitudes may affect nurses' intention to avoid drowsy driving; and 2) assess changes in such beliefs and attitudes during a study that evaluated the effectiveness of educational and technological interventions.Methods Three-hundred night-shift nurses were recruited from a large hospital in Texas to participate in a randomized controlled trial. Participants were randomly assigned to three groups: 1) control; 2) educational intervention; and 3) combined educational and technological intervention. The study utilized an integrated model drawing from the constructs of the Theory of Planned Behavior and the Health Belief Model to elicit attitudes, beliefs, and intentions to use in-vehicle drowsiness detection technologies. Each group was surveyed pre- intervention and at post-intervention around 3 months later to assess changes in beliefs and attitudes. Structural equation models and path analysis were used to analyze changes in beliefs.Results Seventy-nine participants completed the pre-intervention questionnaire, and 44 nurses completed the pre- and post-intervention surveys. Intention was predicted primarily by attitude and perceived health threat. Perceived health threat also mediated the relationship between behavioral intention and the influence of subjective norms as well as perceived behavioral control. Participants who received education about drowsy driving had positive changes in beliefs.Conclusions Nurses' perceived health threat from driving drowsy and their attitude toward our intervention were important motivators to avoid drowsy driving. Interventions aiming at raising awareness of the risks associated with drowsy driving may be effective at motivating nurses to avoid drowsy driving.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking.

OBJECTIVE: (1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU. BACKGROUND: Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data. METHODS: Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes. RESULTS: While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter. CONCLUSION: Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development.