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Record W2977198097 · doi:10.5430/jha.v8n6p1

Morning report decreases length of stay in emergency general surgery patients

2019· article· en· W2977198097 on OpenAlexvenueno aff
James Reed Gardner, John D. Wolfe, William C. Beck, Kevin W. Sexton, Avi Bhavaraju, Ben Davis, Mary K. Kimbrough, Hanna Jensen, Ronald D. Robertson, Rebecca J. Reif, Saleema A. Karim, John R. Taylor

Bibliographic record

VenueJournal of Hospital Administration · 2019
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMorningCohortEmergency medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Objective: Communication in the hospital setting is an easy target for quality improvement. Capturing this change via communication between providers during hand-offs is necessary to reduce delays and errors. While this process has been more widely characterized in medical specialties, we designed this study to address the knowledge gap in surgical specialties.Methods: Our institution’s division of Acute Care Surgery (ACS) implemented Morning Report (MR) in October of 2015. At MR, all admissions and service transfers were discussed from Trauma, Emergency General Surgery (EGS), and Surgical Critical Care services from the previous 24 hours. This study compared patients who underwent a surgical procedure during their hospital stay before and after protocol implementation.Results: 974 patients were included in this study. The average patient was 50.3 years of age, 65.4% were white, and 51.7% were male. The average length of stay (LOS) was 8.3 days with 1.75 days to procedure. The post-MR cohort LOS was 2.7 shorter and had 0.85 fewer days to procedure. In an adjusted regression analysis, days to procedure and LOS decreased by 33% (p < .01) and 17% (p < .01) respectively.Conclusions: Implementation of MR led to a decrease in the overall LOS and days to procedure for operative patients. Our results advocate for the standard use of structured hand-offs in surgical units.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.281
Teacher spread0.270 · 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.

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
Published2019
Admission routes1
Has abstractyes

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