Quantification of baseline pupillary response and task-evoked pupillary response during constant and incremental task load.

The methods employed to quantify the baseline pupil size and task-evoked pupillary response (TEPR) may affect the overall study results. To test this hypothesis, the objective of this study was to assess variability in baseline pupil size and TEPR during two basic working memory tasks: constant load of 3-letters memorisation-recall (10 trials), and incremental load memorisation-recall (two trials of each load level), using two commonly used methods (1) change from trail/load specific baseline, (2) change from constant baseline. Results indicated that there was a significant shift in baseline between the trails for constant load, and between the load levels for incremental load. The TEPR was independent of shifts in baseline using method 1 only for constant load, and method 2 only for higher levels of incremental load condition. These important findings suggest that the assessment of both the baseline and methods to quantify TEPR are critical in ergonomics application, especially in studies with small number of trials per subject per condition. Practitioner Summary: Quantification of TEPR can be affected by shifts in baseline pupil size that are most likely affected by non-cognitive factors when other external factors are kept constant. Therefore, quantification methods employed to compute both baseline and TEPR are critical in understanding the information processing of humans in practical ergonomics settings.

Operating Room Teams Display Acceptable Levels of Patient Safety Behaviors During Surgical Cases.

INTRODUCTION: Cornerstones of patient safety include reliable safety behaviors proposed by Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) practices. A better quantification of these behaviors is needed to establish a baseline for future improvement efforts. METHODS: At one large academic medical center, OR Teams were prospectively assigned to be observed during surgical cases, and patient safety behaviors were quantified using the Teamwork Evaluation of Non-Technical Skills (TENTS) instrument. Mean scores of each TENTS behavior were calculated with 95% confidence intervals and compared using a paired t-test with a false discovery rate (FDR) control. Using the TENTS instrument, one hundred one surgical cases were observed by purposefully trained medical student volunteers. The average with 95% confidence interval (CI) of observed safety behaviors quantified using the TENTS instrument (including 20 types of safety behaviors scored 0 = expected but not observed, 1 = observed but poorly performed or counterproductive, 2 = observed and acceptable, and 3 = observed and excellent). RESULTS: All safety behaviors averaged slightly above 2, and the lower bound of 95% CI was above 2 for all behaviors except one. Statistically significant differences (p < 0.05) were detected between a few safety behaviors, with the lowest-rated safety behavior being "employs conflict resolution" (2.07, 95% CI: 1.96-2.18) and the highest-rated behavior being "willingness to support others across roles" (2.36, 95% CI: 2.27-2.45). There were no significant differences (p > 0.05) based on the number of persons present during the case, case duration, or by surgical department. CONCLUSIONS: Given the persistent patient safety incidents in ORs nationwide, it might be necessary to advance these behaviors from acceptable to exceptional to advance patient safety.

Temperature drift during environmental radiation monitoring and its unfolding.

The aim of the presented paper is to examine the temperature drift and its unfolding for an environmental monitor equipped with a LaBr3(Ce) detector. It is known that temperature could influence energy, shape, and efficiency calibration. Consequently, when ambient temperature changes, the full energy absorption peak moves in the resulting spectrum. Research consists of experimental and analytical parts. During research in the climatic chamber, the dependence on temperature of energy, shape, and efficiency calibration was completed. The numerical method allows generating gamma spectra for theoretical so-called binary temperature variation.

Analysis of patient safety event report categories at one large academic hospital.

Given the persistent safety incidents in operating rooms (ORs) nationwide (approx. 4,000 preventable harmful surgical errors per year), there is a need to better analyze and understand reported patient safety events. This study describes the results of applying the Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) supported by the Teamwork Evaluation of Non-Technical Skills (TENTS) instrument to analyze patient safety event reports at one large academic medical center. Results suggest that suboptimal behaviors stemming from poor communication, lack of situation monitoring, and inappropriate task prioritization and execution were implicated in most reported events. Our proposed methodology offers an effective way of programmatically sorting and prioritizing patient safety improvement efforts.

Towards better understanding of workplace factors contributing to hospitalist burden and burnout prior to COVID-19 pandemic.

BACKGROUND: Hospitalists are physicians trained in internal medicine and play a critical role in delivering care in in-patient settings. They work across and interact with a variety of sub-systems of the hospital, collaborate with various specialties, and spend their time exclusively in hospitals. Research shows that hospitalists report burnout rates above the national average for physicians and thus, it is important to understand the key factors contributing to hospitalists' burnout and identify key priorities for improving hospitalists' workplace. METHODS: Hospitalists at an academic medical center and a community hospital were recruited to complete a survey that included demographics, rating the extent to which socio-technical (S-T) factors contributed to burnout, and 22-item Maslach Burnout Inventory - Human Services Survey (MBI-HSS). Twelve contextual inquiries (CIs) involving shadowing hospitalists for ∼60 h were conducted varied by shift type, length of tenure, age, sex, and location. Using data from the survey and CIs, an affinity diagram was developed and presented during focus groups to 12 hospitalists to validate the model and prioritize improvement efforts. RESULTS: The overall survey participation rate was 68%. 76% of hospitalists reported elevated levels on at least one sub-component within the MBI. During CIs, key breakdowns were reported in relationships, communication, coordination of care, work processes in electronic healthcare records (EHR), and physical space. Using data from CIs, an affinity diagram was developed. Hospitalists voted the following as key priorities for targeted improvement: improve relationships with other care team members, improve communication systems and prevent interruptions and disruptions, facilitate coordination of care, improve workflows in EHR, and improve physical space. CONCLUSIONS: This mixed-method study utilizes participatory and data-driven approaches to provide evidence-based prioritization of key factors contributing to hospitalists' burnout. Healthcare systems may utilize this approach to identify workplace factors contributing to provider burnout and consider targeting the factors identified by providers to best optimize scarce resources.

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology.

Purpose: Pretreatment quality assurance (QA) of treatment plans often requires a high cognitive workload and considerable time expenditure. This study explores the use of machine learning to classify pretreatment chart check QA for a given radiation plan as difficult or less difficult, thereby alerting the physicists to increase scrutiny on difficult plans. Methods and Materials: Pretreatment QA data were collected for 973 cases between July 2018 and October 2020. The outcome variable, a degree of difficulty, was collected as a subjective rating by physicists who performed the pretreatment chart checks. Potential features were identified based on clinical relevance, contribution to plan complexity, and QA metrics. Five machine learning models were developed: support vector machine, random forest classifier, adaboost classifier, decision tree classifier, and neural network. These were incorporated into a voting classifier, where at least 2 algorithms needed to predict a case as difficult for it to be classified as such. Sensitivity analyses were conducted to evaluate feature importance. Results: The voting classifier achieved an overall accuracy of 77.4% on the test set, with 76.5% accuracy on difficult cases and 78.4% accuracy on less difficult cases. Sensitivity analysis showed features associated with plan complexity (number of fractions, dose per monitor unit, number of planning structures, and number of image sets) and clinical relevance (patient age) were sensitive across at least 3 algorithms. Conclusions: This approach can be used to equitably allocate plans to physicists rather than randomly allocate them, potentially improving pretreatment chart check effectiveness by reducing errors propagating downstream.

Multi-Institutional Stereotactic Body Radiation Therapy Incident Learning: Evaluation of Safety Barriers Using a Human Factors Analysis and Classification System.

OBJECTIVES: Stereotactic body radiation therapy (SBRT) can improve therapeutic ratios and patient convenience, but delivering higher doses per fraction increases the potential for patient harm. Incident learning systems (ILSs) are being increasingly adopted in radiation oncology to analyze reported events. This study used an ILS coupled with a Human Factor Analysis and Classification System (HFACS) and barriers management to investigate the origin and detection of SBRT events and to elucidate how safeguards can fail allowing errors to propagate through the treatment process. METHODS: Reported SBRT events were reviewed using an in-house ILS at 4 institutions over 2014-2019. Each institution used a customized care path describing their SBRT processes, including designated safeguards to prevent error propagation. Incidents were assigned a severity score based on the American Association of Physicists in Medicine Task Group Report 275. An HFACS system analyzed failing safeguards. RESULTS: One hundred sixty events were analyzed with 106 near misses (66.2%) and 54 incidents (33.8%). Fifty incidents were designated as low severity, with 4 considered medium severity. Incidents most often originated in the treatment planning stage (38.1%) and were caught during the pretreatment review and verification stage (37.5%) and treatment delivery stage (31.2%). An HFACS revealed that safeguard failures were attributed to human error (95.2%), routine violation (4.2%), and exceptional violation (0.5%) and driven by personnel factors 32.1% of the time, and operator condition also 32.1% of the time. CONCLUSIONS: Improving communication and documentation, reducing time pressures, distractions, and high workload should guide proposed improvements to safeguards in radiation oncology.

State of the evidence on simulation-based electronic health records training: A scoping review.

This study synthesized the available evidence of simulation-based electronic health records (EHRs) training in educational and clinical environments for healthcare providers in the literature. The Arksey and O'Malley methodological framework was employed. A systematic search was carried out in relevant databases from inception to January 2020, identifying 24 studies for inclusion. Three themes emerged: (a) role of simulation-based EHR training in evaluating improvement interventions, (b) debriefing and feedback methods used, and (c) challenges of evaluating simulation-based EHR training. The majority of the studies aimed to emphasize the practical skills of individual medical trainees and employed post-simulation feedback as the feedback method. Future research should focus on (a) using simulation-based EHR training to achieve specific learning goals, (b) investigating aspects of clinical performance that are susceptible to skill decay, and (c) examining the influence of simulation-based EHR training on team dynamics.

Using Explainable Supervised Machine Learning to Predict Burnout in Healthcare Professionals.

Burnout in healthcare professionals (HCPs) is a multi-factorial problem. There are limited studies utilizing machine learning approaches to predict HCPs' burnout during the COVID-19 pandemic. A survey consisting of demographic characteristics and work system factors was administered to 450 HCPs during the pandemic (participation rate: 59.3%). The highest performing machine learning model had an area under the receiver operating curve of 0.81. The eight key features that best predicted burnout are excessive workload, inadequate staffing, administrative burden, professional relationships, organizational culture, values and expectations, intrinsic motivation, and work-life integration. These findings provide evidence for resource allocation and implementation of interventions to reduce HCPs' burnout and improve the quality of care.

Predicting Objective Performance Using Perceived Cognitive Workload Data in Healthcare Professionals: A Machine Learning Study.

Cognitive Workload (CWL) is a fundamental concept in predicting healthcare professionals' (HCPs) objective performance. The study aims to compare the accuracy of the classical model (utilizes all six dimensions of the National Aeronautics and Space Administration Task Load Index (NASA-TLX)) and novel models (utilize four or five dimensions of NASA-TLX) in predicting HCPs' objective performance. We use a dataset from our previous human factors research studies and apply a broad selection of supervised machine learning classification techniques to develop data-driven computational models and predict objective performance. The study findings confirm that classical models are better predictors of objective performance than novel models. This has practical implications for research in health informatics, human factors and ergonomics, and human-computer interaction in healthcare. Findings, although promising, cannot be generalized as they are based on a small dataset. Future studies may investigate additional subjective and physiological measures of CWL to predict HCPs' objective performance.

Co-Design Approaches Involving Older Adults in the Development of Electronic Healthcare Tools: A Systematic Review.

We present evidence on the current state of utilizing co-design approaches involving older adults in developing electronic healthcare tools (EHTs). Research gaps were identified in defining the stages, involvement processes, and levels of participation using existing theoretical frameworks. Future studies should explore both involvement processes and levels of participation to optimally empower and collaborate with older adults in developing EHTs.

Urban Agriculture as an Alternative Source of Food and Water Security in Today's Sustainable Cities.

The concept of a regenerative city goes far beyond a sustainable one. The regenerative approach is to think of urban green space as a productive landscape, a source of food, and a support for biodiversity. In this approach, the so-called urban wastelands have a positive significance. Urban agriculture (UA) has become a commonly discussed topic in recent years with respect to sustainable development. Therefore, the combination of urban fabric and local food production is crucial for ecological reasons. The key issues are the reduction of food miles and the demand for processed food, the production of which strains the natural environment. At the same time, UA enables regeneration and restoration. An original methodological approach was used in the study following the mixed-method research concept: literature survey, case studies, and comparative analysis of objects. A review of UA architecture (UAA) projects was carried out to supplement the knowledge acquired during the bibliometric analysis. In sum, 25 existing projects, including allotment gardens, community gardens, and urban farms in the global north, were compared in this study. As a result of the analyses carried out, the breakdown of urban agriculture was developed into the following categories: (i) architectural-urban, (ii) ecological, (iii) social, and (iv) economic, including the impact of UA on physical activity and social interaction. UA is also a factor shaping the urban landscape. In conclusion, agrarian practice in urban environments has led to the creation of a new type of space, known as UAA. Production in the context of UA exceeds private goods, such as food produced for sale or for individual use. Additional goods include public goods. The review shows that UA fulfills economic, social, and environmental functions, thus falling under the concept of sustainable development.

Effect of Notes' Access and Complexity on OpenNotes' Utility.

BACKGROUND: Health care providers are now required to provide their patients access to their consultation and progress notes. Early research of this concept, known as "OpenNotes," showed promising results in terms of provider acceptability and patient adoption, yet objective evaluations relating to patients' interactions with the notes are limited. OBJECTIVES: To assess the effect of the complexity level of notes and number of accesses (initial read vs. continuous access) on the user's performance, perceived usability, cognitive workload, and satisfaction with the notes. METHODS: We used a 2*2 mixed subjects experimental design with two independent variables: (1) note's complexity at two levels (simple vs. complex) and (2) number of accesses to notes at two levels (initial vs. continuous). Fifty-three participants were randomly assigned to receive a simple versus complex radiation oncology clinical note and were tested on their performance for understanding the note content after an initial read, and then with continuous access to the note. Performance was quantified by comparing each participant's answers to the ones developed by the research team and assigning a score of 0 to 100 based on participants' understanding of the notes. Usability, cognitive workload, and satisfaction scores of the notes were quantified using validated tools. RESULTS: Performance for understanding was significantly better in simple versus complex notes with continuous access (p = 0.001). Continuous access to the notes was also positively associated with satisfaction scores (p = 0.03). The overall perceived usability, cognitive workload, and satisfaction scores were considered low for both simple and complex notes. CONCLUSION: Simplifying notes can improve understanding of notes for patients/families. However, perceived usability, cognitive workload, and satisfaction with even the simplified notes were still low. To make notes more useful for patients and their families, there is a need for dramatic improvements to the overall usability and content of the notes.

Electronic Reporting of Workplace Violence Incidents: Improving the Usability, and Optimizing Healthcare Workers' Cognitive Workload, and Performance.

A majority of healthcare workers (HCWs) experience workplace violence (WPV) but most WPV events go unreported. Underreporting of WPV is well documented in the literature as a barrier to identifying underlying causes and to evaluating the effectiveness of WPV interventions. Previous studies suggest that WPV reporting data is fragmentary, unreliable, and inconsistent. Also, WPV reporting systems are suboptimally designed making it difficult for healthcare workers to report WPV incidents. This study aims to assess the usability of an electronic WPV report in a large academic medical center and the perceived cognitive workload (CWL) and performance of HCWs associated with reporting WPV events. Findings from this study suggest that our institutional WPV report has suboptimal perceived usability and suboptimal perceived cognitive workload. Further, participants with training reported lower error rates in comparison to participants without training on performance.

Codesign approaches involving older adults in the development of electronic healthcare tools: a systematic review.

OBJECTIVE: The primary aim was to review and synthesise the current evidence of how older adults are involved in codesign approaches to develop electronic healthcare tools (EHTs). The secondary aim was to identify how the codesign approaches used mutual learning techniques to benefit older adult participants. DESIGN: Systematic review following the Preferred Reporting Items for Systematic Reviews 2020 checklist. DATA SOURCES: PubMed, Embase and Scopus databases were searched for studies from January 2010 to March 2021. ELIGIBILITY CRITERIA: Inclusion criteria were studies employing codesign approaches to develop an EHTs, and the study population was aged 60 years and older. DATA EXTRACTION AND SYNTHESIS: Data were extracted for analysis and risk of bias. We evaluated the quality of studies using the Agency for Healthcare Research and Quality Evidence-based Practice Center approach. RESULTS: Twenty-five studies met the inclusion criteria for this review. All studies used at least two involvement processes, with interviews and prototypes used most frequently. Through cross-classification, we found an increased utilisation of functional prototypes in studies reaching the 'empower' level of participation and found that studies which benefitted from mutual learning had a higher utilisation of specific involvement processes such as focus groups and functional prototyping. CONCLUSIONS: We found gaps to support which involvement processes, participation levels and learning models should be employed when codesigning with older adults. This is important because higher levels of participation may increase the user's knowledge of technology, enhance learning and empower participants. To ensure studies optimise participation and learning of older adults when developing EHTs, there is a need to place more emphasis on the approaches promoting mutual learning. PROSPERO REGISTRATION NUMBER: CRD42021240013.

The Sociotechnical Factors Associated With Burnout in Residents in Surgical Specialties: A Qualitative Systematic Review.

OBJECTIVE: The consequences of burnout remain prevalent within general surgery training programs, yet there is no comprehensive description of the factors associated with burnout. Using sociotechnical systems theory, this review aims to qualitatively evaluate the factors associated with burnout in surgical residents. DESIGN: A systematic review of the literature was performed according to The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The review was conducted using PubMed, Web of Science, Scopus, and Embase. Two reviewers independently selected studies, extracted data, and assessed quality, with a third reviewer acting as a moderator to resolve conflicts. The Systems Engineering Initiative for Patient Safety framework was utilized to categorize factors as either contributing to or mitigating burnout. The study was registered with PROSPERO (CRD42021237448). SETTING: Included studies were conducted within the United States and published prior to November 11, 2020. PARTICIPANTS: Included studies were peer-reviewed primary data, that were written in English and evaluated the sociotechnical factors associated with burnout in surgical residents. RESULTS: A total of 4387 articles were identified and after review, 15 studies were included for analysis. Reported burnout rates ranged from 17 to 94%, with a total mean rate of 57%. Factors found to contribute to burnout include financial stress, work-life imbalance, excessive charting, insufficient job resources, poor social support, and mistreatment. Mitigating factors include having time away from work, increased didactics, supportive faculty, attending social events, and the presence of program-based wellness initiatives. Consequences of burnout include depression, substance abuse, and decreased job satisfaction. CONCLUSIONS: Reported burnout rates among surgical residents are high. A complex number of sociotechnical factors were found to contribute to burnout, yet many factors are also shown to mitigate burnout. Surgical residencies may address burnout by making changes based on the presence or absence of these factors within their program.

Feature Engineering for Interpretable Machine Learning for Quality Assurance in Radiation Oncology.

Chart checking is a time intensive process with high cognitive workload for physicists. Previous studies have partially automated and standardized chart checking, but limited studies implement data-driven approaches to reduce cognitive workload for quality assurance processes. This study aims to evaluate feature selection methods to improve the interpretability and transparency of machine learning models in predicting the degree of difficulty for a pretreatment physics chart check. We compare chi-square, mutual information, feature importance thresholding, and greedy feature selection for four different classifiers. Random forest has the highest performance with SMOTE oversampling using mutual information for feature selection (accuracy 84.0%, AUC 87.0%, precision 80.0%, recall 80.0%). This study demonstrates that feature selection methods can improve model interpretability and transparency.

Development of a Patient-Centered Preference Tool for Patients With Hematologic Malignancies: Protocol for a Mixed Methods Study.

BACKGROUND: The approval of novel therapies for patients diagnosed with hematologic malignancies have improved survival outcomes but increased the challenge of aligning chemotherapy choices with patient preferences. We previously developed paper versions of a discrete choice experiment (DCE) and a best-worst scaling (BWS) instrument to quantify the treatment outcome preferences of patients with hematologic malignancies to inform shared decision making. OBJECTIVE: We aim to develop an electronic health care tool (EHT) to guide clinical decision making that uses either a BWS or DCE instrument to capture patient preferences. The primary objective of this study is to use both qualitative and quantitative methods to evaluate the perceived usability, cognitive workload (CWL), and performance of electronic prototypes that include the DCE and BWS instrument. METHODS: This mixed methods study includes iterative co-design methods that will involve healthy volunteers, patient-caregiver pairs, and health care workers to evaluate the perceived usability, CWL, and performance of tasks within distinct prototypes. Think-aloud sessions and semistructured interviews will be conducted to collect qualitative data to develop an affinity diagram for thematic analysis. Validated assessments (Post-Study System Usability Questionnaire [PSSUQ] and the National Aeronautical and Space Administration's Task Load Index [NASA-TLX]) will be used to evaluate the usability and CWL required to complete tasks within the prototypes. Performance assessments of the DCE and BWS will include the evaluation of tasks using the Single Easy Questionnaire (SEQ), time to complete using the prototype, and the number of errors. Additional qualitative assessments will be conducted to gather participants' feedback on visualizations used in the Personalized Treatment Preferences Dashboard that provides a representation of user results after completing the choice tasks within the prototype. RESULTS: Ethical approval was obtained in June 2021 from the Institutional Review Board of the University of North Carolina at Chapel Hill. The DCE and BWS instruments were developed and incorporated into the PRIME (Preference Reporting to Improve Management and Experience) prototype in early 2021 and prototypes were completed by June 2021. Heuristic evaluations were conducted in phase 1 and completed by July 2021. Recruitment of healthy volunteers began in August 2021 and concluded in September 2021. In December 2021, our findings from phase 2 were accepted for publication. Phase 3 recruitment began in January 2022 and is expected to conclude in September 2022. The data analysis from phase 3 is expected to be completed by November 2022. CONCLUSIONS: Our findings will help differentiate the usability, CWL, and performance of the DCE and BWS within the prototypes. These findings will contribute to the optimization of the prototypes, leading to the development of an EHT that helps facilitate shared decision making. This evaluation will inform the development of EHTs to be used clinically with patients and health care workers. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39586.

The Effect of a System-Level Tiered Huddle System on Reporting Patient Safety Events: An Interrupted Time Series Analysis.

BACKGROUND: The objective of this research was to evaluate the effect of implementing a system-level tiered huddle system (THS) on the reporting of patient safety events into the official event reporting system. METHODS: A quasi-experimental study using interrupted time series was conducted to assess the impact and changes to trends in the reporting of patient safety events pre- (February-July 2020; six months) and post- (September 2020-February 2021; six months) THS implementation within one health care system (238 clinics and 4 hospitals). The severity of harm was analyzed in July 2021 using a modified Agency for Healthcare Research and Quality (AHRQ) harm score classification. The primary outcome measure was the number of patient safety events reported per month. Secondary outcomes included the number of patient safety events reported per month by each AHRQ harm score classification. RESULTS: The system-level THS implementation led to a significant and immediate increase in the total number of patient safety events reported per month (777.73, 95% confidence interval [CI] 310.78-1,244.68, p = 0.004). Similar significant increases were seen for reported numbers of unsafe conditions, near misses, no-harm events that reached patients, and temporary harm (p < 0.05 for each). Reporting of events with permanent harm and deaths also increased but was not statistically significant, likely due to the small number of reported events involving actual harm. CONCLUSION: These findings suggest that system-level THS implementation may increase reporting of patient safety events in the official event reporting system.

A Novel Theory-Based Virtual Reality Training to Improve Patient Safety Culture in the Department of Surgery of a Large Academic Medical Center: Protocol for a Mixed Methods Study.

BACKGROUND: Preventable surgical errors of varying degrees of physical, emotional, and financial harm account for a significant number of adverse events. These errors are frequently tied to systemic problems within a health care system, including the absence of necessary policies/procedures, obstructive cultural hierarchy, and communication breakdown between staff. We developed an innovative, theory-based virtual reality (VR) training to promote understanding and sensemaking toward the holistic view of the culture of patient safety and high reliability. OBJECTIVE: We aim to assess the effect of VR training on health care workers' (HCWs') understanding of contributing factors to patient safety events, sensemaking of patient safety culture, and high reliability organization principles in the laboratory environment. Further, we aim to assess the effect of VR training on patient safety culture, TeamSTEPPS behavior scores, and reporting of patient safety events in the surgery department of an academic medical center in the clinical environment. METHODS: This mixed methods study uses a pre-VR versus post-VR training study design involving attending faculty, residents, nurses, technicians of the department of surgery, and frontline HCWs in the operation rooms at an academic medical center. HCWs' understanding of contributing factors to patient safety events will be assessed using a scale based on the Human Factors Analysis and Classification System. We will use the data frame theory framework, supported by a semistructured interview guide to capture the sensemaking process of patient safety culture and principles of high reliability organizations. Changes in the culture of patient safety will be quantified using the Agency for Healthcare Research and Quality surveys on patient safety culture. TeamSTEPPS behavior scores based on observation will be measured using the Teamwork Evaluation of Non-Technical Skills tool. Patient safety events reported in the voluntary institutional reporting system will be compared before the training versus those after the training. We will compare the Agency for Healthcare Research and Quality patient safety culture scores and patient safety events reporting before the training versus those after the training by using descriptive statistics and a within-subject 2-tailed, 2-sample t test with the significance level set at .05. RESULTS: Ethics approval was obtained in May 2021 from the institutional review board of the University of North Carolina at Chapel Hill (22-1150). The enrollment of participants for this study will start in fall 2022 and is expected to be completed by early spring 2023. The data analysis is expected to be completed by July 2023. CONCLUSIONS: Our findings will help assess the effectiveness of VR training in improving HCWs' understanding of contributing factors of patient safety events, sensemaking of patient safety culture, and principles and behaviors of high reliability organizations. These findings will contribute to developing VR training to improve patient safety culture in other specialties.