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Barriers to data quality resulting from the process of coding health information to administrative data: a qualitative study

Overview of attention for article published in BMC Health Services Research, November 2017
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1 tweeter

Citations

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7 Dimensions

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27 Mendeley
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Title
Barriers to data quality resulting from the process of coding health information to administrative data: a qualitative study
Published in
BMC Health Services Research, November 2017
DOI 10.1186/s12913-017-2697-y
Pubmed ID
Authors

Kelsey Lucyk, Karen Tang, Hude Quan

Abstract

Administrative health data are increasingly used for research and surveillance to inform decision-making because of its large sample sizes, geographic coverage, comprehensivity, and possibility for longitudinal follow-up. Within Canadian provinces, individuals are assigned unique personal health numbers that allow for linkage of administrative health records in that jurisdiction. It is therefore necessary to ensure that these data are of high quality, and that chart information is accurately coded to meet this end. Our objective is to explore the potential barriers that exist for high quality data coding through qualitative inquiry into the roles and responsibilities of medical chart coders. We conducted semi-structured interviews with 28 medical chart coders from Alberta, Canada. We used thematic analysis and open-coded each transcript to understand the process of administrative health data generation and identify barriers to its quality. The process of generating administrative health data is highly complex and involves a diverse workforce. As such, there are multiple points in this process that introduce challenges for high quality data. For coders, the main barriers to data quality occurred around chart documentation, variability in the interpretation of chart information, and high quota expectations. This study illustrates the complex nature of barriers to high quality coding, in the context of administrative data generation. The findings from this study may be of use to data users, researchers, and decision-makers who wish to better understand the limitations of their data or pursue interventions to improve data quality.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 22%
Student > Master 5 19%
Unspecified 4 15%
Other 3 11%
Professor 2 7%
Other 7 26%
Readers by discipline Count As %
Medicine and Dentistry 10 37%
Unspecified 5 19%
Computer Science 4 15%
Social Sciences 3 11%
Psychology 2 7%
Other 3 11%

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 23 November 2017.
All research outputs
#7,611,561
of 12,181,658 outputs
Outputs from BMC Health Services Research
#2,783
of 3,972 outputs
Outputs of similar age
#187,626
of 337,147 outputs
Outputs of similar age from BMC Health Services Research
#112
of 194 outputs
Altmetric has tracked 12,181,658 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,972 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 19th percentile – i.e., 19% 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 337,147 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.