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Evaluation of the effects of implementing an electronic early warning score system: protocol for a stepped wedge study

Overview of attention for article published in BMC Medical Informatics and Decision Making, February 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

blogs
1 blog
policy
1 policy source
twitter
10 tweeters

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
114 Mendeley
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Title
Evaluation of the effects of implementing an electronic early warning score system: protocol for a stepped wedge study
Published in
BMC Medical Informatics and Decision Making, February 2016
DOI 10.1186/s12911-016-0257-8
Pubmed ID
Authors

Timothy Bonnici, Stephen Gerry, David Wong, Julia Knight, Peter Watkinson

Abstract

An Early Warning Score is a clinical risk score based upon vital signs intended to aid recognition of patients in need of urgent medical attention. The use of an escalation of care policy based upon an Early Warning Score is mandated as the standard of practice in British hospitals. Electronic systems for recording vital sign observations and Early Warning Score calculation offer theoretical benefits over paper-based systems. However, the evidence for their clinical benefit is limited. Previous studies have shown inconsistent results. The majority have employed a "before and after" study design, which may be strongly confounded by simultaneously occurring events. This study aims to examine how the implementation of an electronic early warning score system, System for Notification and Documentation (SEND), affects the recognition of clinical deterioration occurring in hospitalised adult patients. This study is a non-randomised stepped wedge evaluation carried out across the four hospitals of the Oxford University Hospitals NHS Trust, comparing charting on paper and charting using SEND. We assume that more frequent monitoring of acutely ill patients is associated with better recognition of patient deterioration. The primary outcome measure is the time between a patient's first observations set with an Early Warning Score above the alerting threshold and their subsequent set of observations. Secondary outcome measures are in-hospital mortality, cardiac arrest and Intensive Care admission rates, hospital length of stay and system usability measured using the System Usability Scale. We will also measure Intensive Care length of stay, Intensive Care mortality, Acute Physiology and Chronic Health Evaluation (APACHE) II acute physiology score on admission, to examine whether the introduction of SEND has any effect on Intensive Care-related outcomes. The development of this protocol has been informed by guidance from the Agency for Healthcare Research and Quality (AHRQ) Health Information Technology Evaluation Toolkit and Delone and McLeans's Model of Information System Success. Our chosen trial design, a stepped wedge study, is well suited to the study of a phased roll out. The choice of primary endpoint is challenging. We have selected the time from the first triggering observation set to the subsequent observation set. This has the benefit of being easy to measure on both paper and electronic charting and having a straightforward interpretation. We have collected qualitative measures of system quality via a user questionnaire and organisational descriptors to help readers understand the context in which SEND has been implemented.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 <1%
Canada 1 <1%
Unknown 112 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 26 23%
Researcher 16 14%
Student > Ph. D. Student 13 11%
Student > Bachelor 11 10%
Student > Doctoral Student 10 9%
Other 30 26%
Unknown 8 7%
Readers by discipline Count As %
Medicine and Dentistry 45 39%
Nursing and Health Professions 22 19%
Business, Management and Accounting 8 7%
Computer Science 8 7%
Engineering 6 5%
Other 13 11%
Unknown 12 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 February 2020.
All research outputs
#1,188,210
of 15,663,032 outputs
Outputs from BMC Medical Informatics and Decision Making
#83
of 1,422 outputs
Outputs of similar age
#30,864
of 345,761 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 2 outputs
Altmetric has tracked 15,663,032 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,422 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 345,761 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them