MétaCan
Menu
Back to cohort
Record W3178341404 · doi:10.2147/oaem.s316366

Investigating Indicators of Waiting Time and Length of Stay in Emergency Departments

2021· article· en· W3178341404 on OpenAlexaboutno aff
Nojoud Al Nhdi, Hajar Al Asmari, Abdulellah Al Thobaity

Bibliographic record

VenueOpen Access Emergency Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
FundersTaif University
KeywordsTriageMedicineEmergency departmentDispositionDescriptive statisticsChristian ministryEmergency medicineMedical emergencyMultivariate analysisNursingStatisticsPsychologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To investigate potential indicators of patients' waiting time and length of stay in emergency departments (ED) at the Ministry of Health (MOH) hospitals in order to determine the causes of delayed patient care and to recommend clinical implications to achieve a better ED system. MATERIALS AND METHODS: This exploratory study was conducted in the EDs at four tertiary hospitals of MOH. A random sample of 1360 people was tested from December 2019 to February 2020. Data included patient Canadian Triage Acuity and System (CTAS) level, registration time, triage time, physician examination time, decision time, and disposition time. Descriptive statistics, multivariate analysis, multiple linear regression analysis and Pearson correlation were used according to SPSS (version 24). RESULTS: The findings showed that 89.6% of total emergency patients were categorized as levels 3, 4 and 5. Around 73.5% of emergency patients stayed less than 4 hours due to registration or triage to disposition, while 26.5% of those patients stayed more than 4 hours. CONCLUSION: The majority of patients' total stay in EDs was less than 4 hours. According to ED international standard of length of stay, this is appropriate. The highest effective indicator in total length of stay was the decision to disposition time in EDs.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.070
GPT teacher head0.417
Teacher spread0.347 · 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 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

Citations36
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOpen Access Emergency MedicineSame topicEmergency and Acute Care StudiesFrench-language works237,207