Evidence map›Paper›PMID 18814495›Full record

ArticleJournal of medical systems2008

Process modeling of emergency department patient flow: effect of patient length of stay on ED diversion.

Alexander Kolker

Abstract read
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In one paragraph

Article in Journal of medical systems, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Key performance indicators in emergency department simulation: a scoping review.Scandinavian journal of trauma, resuscitation and emergency medicine · 2025
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Performance Analysis of Emergency Room Episodes Through Process Mining.International journal of environmental research and public health · 2019
    Article
  7. Article
  8. Article
  9. Understanding Emergency Care Delivery Through Computer Simulation Modeling.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2018
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Alexander KolkerChildren's Hospital of Wisconsin, Milwaukee, WI, USA. alexanderkolker@yahoo.com

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A discreet event simulation methodology has been used to establish a quantitative relationship between Emergency Department (ED) performance characteristics, such as percent of time on ambulance diversion and the number of patients in queue in the waiting room, and the upper limits of patient length of stay (LOS). A simulation process model of ED patient flow has been developed that took into account a significant difference between LOS distributions of patients discharged home and patients admitted into the hospital. Using simulation model it has been identified that ED diversion could be negligible (less than approximately 0.5%) if patients discharged home stay in ED not more than 5 h, and patients admitted into the hospital stay in ED not more than 6 h Using full factorial design of experiments with two factors and the model's predicted percent diversion as a response function, other combinations of LOS upper limits have been determined that would result in low ED percent diversion as well. It has also been determined that if the number of patients exceeds 11 in queue in ED waiting room then the diversion percent is rapidly increasing.

Indexed as

Computer SimulationLength of StayModels, OrganizationalEmergency Service, HospitalHumansPatient DischargePatient Transfer

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.