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Record W4395456324

Improving Ad Hoc Medical Team Performance with an Innovative “I START-END” Communication Tool

2022· article· en· W4395456324 on OpenAlexaboutno aff
Irene McGhee, Jordan Tarshis, S. DeSousa

Bibliographic record

VenueSHILAP Revista de lepidopterología · 2022
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePost hocMedicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Irene McGhee,1 Jordan Tarshis,1 Susan DeSousa2 1Department of Anesthesiology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada; 2Sunnybrook Canadian Simulation Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario, CanadaCorrespondence: Irene McGhee, Email irene.mcghee@utoronto.caPurpose: To study the effect of a communication tool entitled: “I START-END” (I-Identify; S-Story; T-Task; A-Accomplish/Adjust; R-Resources; T-Timely Updates; E-Exit; N-Next; D-Document and Debrief) in simulated urgent scenarios in non-operating room settings (referred to as “Ad Hoc”) with anesthesia residents. The “I START-END” tool was created by incorporating Crisis Resource Management (CRM) principles into a practical and user-friendly format.Methods: This was a mixed methods pre/post observational study with 47 anesthesia resident volunteers participating from July 2014 to June 2016. Each resident served as their own control, and participated in three simulated Ad Hoc scenarios. The first simulation served as a baseline. The second simulation occurred 1– 2 weeks after I START-END training. The third simulation occurred 3– 6 months later. Simulation performance was videotaped and reviewed by trained experts using technical skill checklists and Anesthesia Non-Technical Skills (ANTS) score. Residents filled out questionnaires, pre-simulation, 1– 2 weeks after I START-END training and 3– 6 months later. Concurrently, resident performance at actual Code Blue events was scored by trained observers using the Mayo High Performance Teamwork Scale.Results: 80– 90% of residents stated the tool provided an organized approach to Ad Hoc scenarios – specifically, information helpful to care of the patient was obtained more readily and better resource planning occurred as communication with the team improved. Residents stated they would continue to use the tool and apply it to other clinical settings. Resident video performance scores of technical skills showed significant improvement at the “late” session (3– 6 months post exposure to the I START-END). ANTS scores were satisfactory and remained unchanged throughout. There was no difference between residents with and without I START-END training as measured by the Mayo High Performance Teamwork Scale, however, debriefing at Code Blues occurred twice as often when residents had I START-END training.Conclusion: Non-operating room settings are fraught with unfamiliarity that create many challenges. The I START-END tool operationalizes key CRM elements. The tool was well received by residents; it enabled them to speak up more readily, obtain vital information and continually update each other by anticipating, planning, and debriefing in an organized and collaborative way.Keywords: “Ad Hoc” teams, team building, I START-END tool, non-operating room anesthesia, crisis resource management, team communication

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.231
Teacher spread0.217 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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