Evidence map›Paper›PMID 34192384›Full record

ArticleTropical medicine & international health : TM & IH2021

How to implement a PEWS in a resource-limited setting: A quantitative analysis of the bedside-PEWS implementation in a hospital in northeast Brazil.

Karin S van der Fluit, Matthijs C Boom, Marlon B Brandão, Gabriel D Lopes, Paula G Barreto, Deborah C F Leite, Ricardo Q Gurgel

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Article in Tropical medicine & international health : TM & IH, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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.

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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

7 authors.

Karin S van der FluitAmsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands.
Matthijs C BoomAmsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands.
Marlon B BrandãoDepartment of Pediatrics, Hospital e Maternidade Santa Isabel, Aracaju, Brazil.
Gabriel D LopesDepartment of Pediatrics, Medicine Faculty, Tiradentes University, Aracaju, Brazil.
Paula G BarretoDepartment of Pediatrics, Medicine Faculty, Tiradentes University, Aracaju, Brazil.
Deborah C F LeiteDepartment of Pediatrics, Medicine Faculty, Tiradentes University, Aracaju, Brazil.
Ricardo Q GurgelGraduate Program in Health Sciences and Professional Graduate Program in Health Technological Management and Innovation, Federal University of Sergipe, Aracaju, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesQuantitative analysis of the implementation of the bedside paediatric early warning system (B-PEWS) in a resource-limited setting. The B-PEWS serves to pre-emptively identify hospitalised children who are at risk for cardiopulmonary arrest and subsequently to provide critical care in time.

methodsWe performed a retrospective review through the medical data records of patients after discharge from the paediatric ward of a philanthropic hospital in Brazil. Nurses' performance using the system was measured with various parameters.

resultsA total of 499 patients were included, and a total of 8024 scores were checked. During the 21-week research period, the implementation rate increased significantly from 66.5% (SD 26.0) in Period 1 to 93.1% (SD 16.6) in Period 2. The number of scores that resulted in a correct total score went from 7.5% in Period 1 to 32.2% in Period 2, p < 0.001. There was an improvement in the correct choice of age group between the two periods (from 32.2% to 53.4%). There was no difference in the mean admission time of patients in the two periods: in the first period 4.8 days (SD 2.9) and in the second period 4.8 days (SD 4.1).

conclusionsIt is possible to implement a PEWS in resource-limited settings while achieving high implementation rates. However, this is a time- and energy-consuming process. Having an active and involved team that is responsible for implementation is key for a successful implementation. Factors that likely hindered implementation were a large change in workflow for the nursing staff, non-native speakers as main investigators.

Indexed as

Health Status IndicatorsHospitalsBrazilChildChild, PreschoolClinical ProtocolsCritical CareCritical IllnessDisease ProgressionHumansInfantMonitoring, PhysiologicRetrospective Studiesbedside-PEWSimplementationpaediatric carepaediatric early warning system (PEWS)quality improvementresource-limited setting (RLS)

Identifiers

PMID34192384
PMCPMC8596539

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LicenceCC BY-NC
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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.