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Record W2595864158 · doi:10.5430/jha.v6n2p88

Emergency boarding: An integrative framework for analyzing causes and seeking solutions

2017· article· en· W2595864158 on OpenAlexaffvenue
Vincent Limoges, Paul Gemmel, Sylvain Landry, Peter De Paepe

Bibliographic record

VenueJournal of Hospital Administration · 2017
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsEmergency departmentQualitative researchData collectionConceptual frameworkProcess (computing)PsychologyCrowdingMedical emergencyComputer scienceBoarding schoolConceptual modelMedicineData scienceNursingSociologyGeographyCognitive psychologySocial science

Abstract

fetched live from OpenAlex

Background: One of the root causes of crowding at the Emergency Department (ED) is boarding, i.e. the practice of holding admitted patients in the ED until an inpatient (IP) bed becomes available. Although ED boarding has been approached by various authors, few of them have aimed to see how different hospitals perform in regard to this issue.Objective: This study will approach ED boarding from different angles, analyzing both quantitative and qualitative data, and following both deductive and inductive reasoning. The purpose is to develop a validated integrative conceptual framework which can be used to analyze ED boarding, and to look for solutions.Methods: The development of the framework is based on an extensive literature review and a multiple case study research with both qualitative and quantitative data collection.Results: ED boarding was found to be prominent in the studied hospitals. Four root causes of ED boarding were elucidated, which are: (1) uncoordinated admissions and discharges, (2) late discharges, (3) the inability to discharge patients, and (4) a lack of communication, collaboration, and information between the different actors of the patient care process. Many solutions are proposed to improve these issues.Conclusions: Through the analysis of various types of data, an integrative conceptual framework for ED boarding was elaborated for analyzing causes and seeking solutions. The quantative data cannot only be used in the analysis stage, but can also help in designing a solution as there are clear recognizable trends in arrival, discharge and ED boarding time. “Communication, collaboration and information”, although not explicitly discussed in the literature, was found to be the most prominent cause and solution to ED boarding in the field study. Practice implications: Management practitioners now have a framework demonstrating probable causes for ED boarding, which provides a starting point for analysis within their establishments. Pathways to improvement are suggested as well, which will help managers to reduce ED boarding. Communication, collaboration and information are important in these improvement efforts.

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 score0.116
Threshold uncertainty score0.474

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.368
Teacher spread0.331 · 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

Citations3
Published2017
Admission routes2
Has abstractyes

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