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Record W2151611977 · doi:10.5812/atr.10473

Queuing Theory to Decrease Waiting Times in Emergency Department

2014· article· en· W2151611977 on OpenAlexaboutno aff
Miguel Cantero, Maximino Redondo

Bibliographic record

VenueArchives of Trauma Research · 2014
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingEmergency departmentTriageQueueing theoryCrowdingProductivityOperations managementToyota Production SystemMedical emergencyMedicineOperations researchTheory of constraintsBusinessComputer scienceNursingLean manufacturingPsychologyEconomicsEngineering

Abstract

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Dear Editor, Regarding the article: Application of queuing theory to decrease waiting times in emergency department: Does it make sense? published in Archives of Trauma Research (1), I would like to make some comments. Although results of the study by Alavi-Moghaddam et al. using queuing theory are particularly interesting to analyze the impact of a new strategy to reduce waiting times before implementing it, all the proposals offered an increase of resources or staff: adding one or more senior emergency residents on each shift, adding one more bed to intensive care unit, adding another clerk to take electrocardiograms in ED, and expanding the laboratory staff and specialist consultants by 50%. It has been argued that adding resources is not simply sufficient to fix the flow problems in ED (2). Although there are a variety of medical, social, financial and other external causes for crowding, the internal organization of ED is often a source of inefficiencies. Dr. Ng et al. in an ED from Ontario (3), attempted various solutions to improve waiting times. They increased the number of triage nurses and stations, added medical directives, increased physician staffing, hired a nurse practitioner and opened a fast track area. Despite increasing resources and staffing, there was little appreciable impact on overall waiting times; therefore they tried another way to improve ED efficiency. They applied philosophies and tools from the Toyota Production System (Lean thinking) to improve productivity and reduce waiting times. By eliminating waste from their internal ED processes, improving workplace organization, focusing on reducing interruptions and internal waits, and continuously refining improvements, waiting times, length of stay, and patient satisfaction improved with no additional staff or beds. The goal of Lean is to refine production in such a way that work flows smoothly from one step to the next with no wasted time, effort, or resources. The essential elements of each step are identified. Any step that does not add value to the product is considered waste or muda. The process is then reorganized to eliminate any muda. The new process is then standardized, mistake-proofed, and implemented, a process of continual, incremental improvement called Kaizen in Japanese. R. Holden critically reviewed 18 articles describing the implementation of Lean in 15 ED in the United States, Australia and Canada (4). The review revealed numerous ED process changes, often involving separate patient streams, structural changes such as new technologies, communication systems, staffing changes, and the reorganization of physical space. Patient care usually improved after implementation of Lean, with decreases in the length of stay, waiting times, and proportion of patients leaving the ED without being noticed. Success factors included employee involvement, management support, and preparedness for change. However, Lean is not a panacea, but rather a tool that may or may not succeed, according to the efforts surrounding its use. Dickinson et al. showed the first case series with negative results in a hospital that had attempted to implement Lean (5). What was common across the successful EDs was the strict adherence to Lean principles.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.047
GPT teacher head0.381
Teacher spread0.335 · 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 designOther design
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

Citations2
Published2014
Admission routes1
Has abstractyes

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