Evidence map›Paper›PMID 31067229›Full record

ArticlePloS one2019

Implementation of an automated early warning scoring system in a surgical ward: Practical use and effects on patient outcomes.

Eveline Mestrom, Ashley De Bie, Melissa van de Steeg, Merel Driessen, Louis Atallah, Rick Bezemer, R Arthur Bouwman, Erik Korsten

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 15% of its field
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

9 citing papers in PubMed, 24 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Eveline MestromDepartment of Anaesthesiology, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-5582-5873
Ashley De BieDepartment of Anaesthesiology, Catharina Hospital, Eindhoven, The Netherlands.ORCID 0000-0002-5046-5768
Melissa van de SteegDepartment of Anaesthesiology, Catharina Hospital, Eindhoven, The Netherlands.
Merel DriessenDepartment of Anaesthesiology, Catharina Hospital, Eindhoven, The Netherlands.
Louis AtallahPatient Care & Measurements, Philips Research, Eindhoven, The Netherlands.
Rick BezemerDepartment of Anaesthesiology, Catharina Hospital, Eindhoven, The Netherlands.
R Arthur BouwmanDepartment of Anaesthesiology, Catharina Hospital, Eindhoven, The Netherlands.
Erik KorstenDepartment of Anaesthesiology, Catharina Hospital, Eindhoven, The Netherlands.
Radboud University Nijmegen · NLPhilips (Netherlands) · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly warning scores (EWS) are being increasingly embedded in hospitals over the world due to their promise to reduce adverse events and improve the outcomes of clinical patients. The aim of this study was to evaluate the clinical use of an automated modified EWS (MEWS) for patients after surgery.

methodsThis study conducted retrospective before-and-after comparative analysis of non-automated and automated MEWS for patients admitted to the surgical high-dependency unit in a tertiary hospital. Operational outcomes included number of recorded assessments of the individual MEWS elements, number of complete MEWS assessments, as well as adherence rate to related protocols. Clinical outcomes included hospital length of stay, in-hospital and 28-day mortality, and ICU readmission rate.

resultsRecordings in the electronic medical record from the control period contained 7929 assessments of MEWS elements and were performed in 320 patients. Recordings from the intervention period contained 8781 assessments of MEWS elements in 273 patients, of which 3418 were performed with the automated EWS system. During the control period, 199 (2.5%) complete MEWS were recorded versus 3991 (45.5%) during intervention period. With the automated MEWS systems, the percentage of missing assessments and the time until the next assessment for patients with a MEWS of ≥2 decreased significantly. The protocol adherence improved from 1.1% during the control period to 25.4% when the automated MEWS system was involved. There were no significant differences in clinical outcomes.

conclusionImplementation of an automated EWS system on a surgical high dependency unit improves the number of complete MEWS assessments, registered vital signs, and adherence to the EWS hospital protocol. However, this positive effect did not translate into a significant decrease in mortality, hospital length of stay, or ICU readmissions. Future research and development on automated EWS systems should focus on data management and technology interoperability.

Indexed as

Early Warning ScoreHospital UnitsSurgery Department, HospitalAgedAged, 80 and overFemaleHumansIntensive Care UnitsMaleMedical InformaticsMiddle AgedPatient Outcome AssessmentPractice Patterns, Physicians'Retrospective Studies

Identifiers

PMID31067229
PMCPMC6505743
OpenAlexW2944014897

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.