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Record W4400561342 · doi:10.1097/sla.0000000000006438

Using OR Black Box Technology to Determine Quality Improvement Outcomes for In-situ Timeout and Debrief Simulation

2024· article· en· W4400561342 on OpenAlexaff
Krystle K. Campbell, Andres A. Abreu, Herbert J. Zeh, William Daniel, Vanessa N. Palter, Samantha J. Bishop, S. Sims, Jaffer Odeh, Kim Evans, Priya Dandekar, Daniel Scott

Bibliographic record

VenueAnnals of Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDebriefingTimeoutChecklistMedicineQuality managementIntervention (counseling)Thematic analysisNursingMedical educationQualitative researchOperations managementPsychologyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to determine quality improvement outcomes following the pilot implementation of an in-situ simulation designed to enhance surgical safety checklist performance. BACKGROUND: OR Black Box (ORBB) technology allows near real-time assessment for surgical safety checklist performance. Before our study, timeout quality was 73.3%, compliance was 99.9%, and engagement was 89.7% (n=1993 cases); Debrief Quality was 76.0%, compliance was 66.9%, and engagement was 66.7% (n=1842 cases). METHODS: This IRB-approved study used prospective convergent multi-methods. During 2 months, a 15-minute in-situ simulation, incorporating rapid cycle deliberate practice, was implemented for OR teams. ORBB analytics generated Timeout and Debrief scores for actual operations performed by surgeons who participated in simulation (Sim-group) versus those who did not (No-sim group) over 6 months, including 2 months pre-intervention, during-intervention, and post-intervention. Inductive content analysis was performed based on simulation discussions to determine team member perspectives. RESULTS: Thirty simulations with 163 interprofessional participants were conducted. ORBB data from 1570 cases were analyzed. Scores were significantly better for the Sim-group compared with the No-sim group for debrief quality (84% vs. 79% P <0.001, during-intervention), compliance (73% vs. 66%, P <0.001, post-intervention), and engagement (80% vs. 73%, P =0.012, during-intervention). There were no between-group differences for Timeout scores. Thematic analysis identified 2 primary categories: "culture of safety" and "policy." CONCLUSIONS: This simulation-based QI intervention created a psychologically safe training environment for OR teams. The novel use of ORBB technology facilitated outcome analysis and showed significantly better Debrief scores for simulation-trained surgeons compared with nontrained surgeons.

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)0.016
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.448
GPT teacher head0.530
Teacher spread0.081 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations8
Published2024
Admission routes1
Has abstractyes

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