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How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2016
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2 tweeters

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21 Dimensions

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133 Mendeley
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Title
How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments
Published in
BMC Medical Informatics and Decision Making, June 2016
DOI 10.1186/s12911-016-0305-4
Pubmed ID
Authors

Niclas Skyttberg, Joana Vicente, Rong Chen, Hans Blomqvist, Sabine Koch

Abstract

Vital sign data are important for clinical decision making in emergency care. Clinical Decision Support Systems (CDSS) have been advocated to increase patient safety and quality of care. However, the efficiency of CDSS depends on the quality of the underlying vital sign data. Therefore, possible factors affecting vital sign data quality need to be understood. This study aims to explore the factors affecting vital sign data quality in Swedish emergency departments and to determine in how far clinicians perceive vital sign data to be fit for use in clinical decision support systems. A further aim of the study is to provide recommendations on how to improve vital sign data quality in emergency departments. Semi-structured interviews were conducted with sixteen physicians and nurses from nine hospitals and vital sign documentation templates were collected and analysed. Follow-up interviews and process observations were done at three of the hospitals to verify the results. Content analysis with constant comparison of the data was used to analyse and categorize the collected data. Factors related to care process and information technology were perceived to affect vital sign data quality. Despite electronic health records (EHRs) being available in all hospitals, these were not always used for vital sign documentation. Only four out of nine sites had a completely digitalized vital sign documentation flow and paper-based triage records were perceived to provide a better mobile workflow support than EHRs. Observed documentation practices resulted in low currency, completeness, and interoperability of the vital signs. To improve vital sign data quality, we propose to standardize the care process, improve the digital documentation support, provide workflow support, ensure interoperability and perform quality control. Vital sign data quality in Swedish emergency departments is currently not fit for use by CDSS. To address both technical and organisational challenges, we propose five steps for vital sign data quality improvement to be implemented in emergency care settings.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 <1%
Unknown 132 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 33 25%
Researcher 15 11%
Student > Bachelor 14 11%
Student > Ph. D. Student 14 11%
Student > Doctoral Student 10 8%
Other 24 18%
Unknown 23 17%
Readers by discipline Count As %
Nursing and Health Professions 26 20%
Medicine and Dentistry 24 18%
Computer Science 16 12%
Engineering 10 8%
Social Sciences 6 5%
Other 20 15%
Unknown 31 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 08 June 2016.
All research outputs
#4,624,796
of 8,607,055 outputs
Outputs from BMC Medical Informatics and Decision Making
#696
of 1,003 outputs
Outputs of similar age
#148,599
of 274,669 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#23
of 31 outputs
Altmetric has tracked 8,607,055 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,003 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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 274,669 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.