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Record W4280553038 · doi:10.29173/cjen153

The Impact of Standardized Interprofessional Rounds on Critically Ill Patients in the Emergency Department: A Quality Improvement Initiative

2022· article· en· W4280553038 on OpenAlexaffvenue
Kalina Repin, Will Thomas-Boaz, Bourke Tillman, Barb Duncan, Grace Walter

Bibliographic record

VenueCanadian Journal of Emergency Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineEmergency departmentEmergency medicinePsychological interventionDocumentationPresentation (obstetrics)Quality managementCritically illMedical emergencyIntensive care medicineNursingSurgery

Abstract

fetched live from OpenAlex

Background & Local Problem ED boarded ICU patients are generally not included in interprofessional ICU rounds. The project objective was to implement interprofessional rounds in the ED on boarded ICU patients. Methods & Interventions ICU patients in the ED were followed for two months from admission to transfer. The primary outcome was feasibility of ED ICU rounds, measured as the proportion of days on which rounds occurred. Secondary outcomes included communication quality, time to oral intake, and DVT prophylaxis documentation. Results A total of 92 patients were included in this project. Rounds occurred on 33 of 36 possible days. Following rounds, nurses and physicians reported improved communication. New DVT prophylaxis orders were written for 42% of cases, and 61 near miss events were corrected. Time from patient ED presentation to first oral intake decreased from 28 to 17 hours. Conclusions Interprofessional rounds in the ED are feasible, improve patient care, and enhance communication among team members.

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)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.100
GPT teacher head0.449
Teacher spread0.349 · 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 designObservational
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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Emergency Nursing→Same topicFamily and Patient Care in Intensive Care Units→French-language works237,207→