Evidence map›Paper›PMID 25288356›Full record

ArticleBMC research notes2014

Assessing time to treatment and patient inflow in a Danish emergency department: a cohort study using data from electronic emergency screen boards.

Rasmus F Nielsen, Noel Pérez, Poul Petersen, Karin Biering

Abstract readComparative Study
In one paragraph

Article in BMC research notes, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Rasmus F NielsenDepartment of Emergency Medicine, Regional Hospital West Jutland, Herning, Denmark. rasnis@rm.dk.
Noel Pérez
Poul Petersen
Karin Biering

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe purpose of this study was to assess and describe the patient inflow during a 1-month period in a Danish emergency department and to evaluate if the intended times to treatment (TTT) related to category of triage were met.

methodsData from electronic emergency screen boards were extracted from the 1st to the 30th of April 2013. 2000 patients were enrolled of which 1011 were eligible for inclusion in the study of TTT. Patient inflow was described according to hours of the day and days of the week. Patients were divided into groups of triage and TTT was assessed in the different groups. Adjusted odds ratios of not being seen on time were calculated between triage groups and time of the day/week.

resultsThe pattern of inflow differed between weekdays and weekends. On weekdays it peaked around midday and on weekends it peaked during the late afternoon/evening. The distributions of the different triage categories between days were similar. Monday had the most patient contacts while Saturday showed the least. Category II (orange) patients were the most prone to exceed the intended TTT. The risk of not being seen on time when compared to daytime, was on evenings OR 2.3 [1.1;4.9] and on nights OR 2.0 [1.2;3.9]. On weekends the odds ratio was OR 1.9 [0.8;4.7] compared to weekdays.

conclusionThe results demonstrated varying patterns of patient inflow between weekdays and weekends. There was a significantly increased risk of being attended late when arriving on evenings and nights. Likewise higher acuity was associated with exceeded TTT.

Indexed as

Emergency Service, HospitalTime-to-TreatmentTriageWorkflowAfter-Hours CareChi-Square DistributionCohort StudiesDelivery of Health CareDenmarkElectronicsEquipment DesignHumansLength of StayLogistic ModelsOdds RatioTime Factors

Identifiers

PMID25288356
PMCPMC4195896

What OpenQuestion holds

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Registered trials

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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.