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Record W4285100584 · doi:10.2196/40445

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

2022· article· en· W4285100584 on OpenAlexvenueno aff
Łukasz Mazur, Amro Khasawneh, Christi Fenison, Shawna Buchanan, Ian M. Kratzke, Karthik Adapa, Selena J. An, Logan Butler, Ashlyn Zebrowski, Praneeth Chakravarthula, Jin Ra

Bibliographic record

VenueJMIR Research Protocols · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)Center (category theory)Medical educationVirtual realityMedicinePatient safetyPsychologyComputer scienceHuman–computer interactionAlternative medicineHealth carePathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.036
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0030.003
Science and technology studies0.0050.002
Scholarly communication0.0030.002
Open science0.0050.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0290.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.516
GPT teacher head0.678
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2022
Admission routes1
Has abstractyes

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