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Record W2772822848 · doi:10.1177/102490791502200202

Holiday Fast-Track Reduced Medical Cost and Length of Emergency Department Stay: Preliminary Report from a Single Secondary Care Hospital

2015· article· en· W2772822848 on OpenAlexaboutno aff
Lee Nk, Yr Ahn, YH Kim, J. H. Lee, Kw Cho, Sy Hwang, Ty Shin, YR Ha, YS Kim, Ck Hong

Bibliographic record

VenueHong Kong Journal of Emergency Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEmergency departmentTriageFast trackEmergency medicineMedical emergencySurgeryNursing

Abstract

fetched live from OpenAlex

Introduction The aims of this study were to compare the effect of a Holiday Fast-Track (HFT) unit on medical costs and emergency department (ED) length of stay (LOS) associated with low acuity patients attended during the same timeframe in two consecutive years in a single secondary care hospital ED. Methods Two groups (non-HFT vs. HFT), before and after the fast-track unit was implemented, were compared. The HFT unit was operated to improve the flow of low acuity patients, which were defined as the patients classified as level 4 or 5 by the modified Canadian Triage and Acuity Scale. Data were collected from March 1 to April 30, 2011 for the non-HFT group and during the same period in 2012 for the HFT group. Results A total of 894 (431 for non-HFT period and 463 for HFT period) patients of acuity level 4 or 5 visited the ED during the study period. Compared to the non-HFT group, the ED LOS of the HFT group decreased by 27 min and 3.5 min in the patients with acuity levels 4 and 5, respectively (p=0.005 and p=0.003, respectively). Furthermore, total medical costs and laboratory fees were also reduced significantly in the HFT group (p<0.001, p=0.038). However, there was no difference in the other variables between those two groups. Conclusions The HFT system decreases the medical costs and LOS in low acuity patients visiting the ED of a secondary care hospital. (Hong Kong j.emerg.med. 2015;22:84-92)

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.335
Teacher spread0.291 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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