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The effect of transitioning to ICD-10-CM on acute injury surveillance of active duty service members

Overview of attention for article published in Injury Epidemiology, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (69th percentile)

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1 blog

Citations

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2 Dimensions

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15 Mendeley
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Title
The effect of transitioning to ICD-10-CM on acute injury surveillance of active duty service members
Published in
Injury Epidemiology, August 2018
DOI 10.1186/s40621-018-0162-y
Pubmed ID
Authors

Matthew C. Inscore, Katherine R. Gonzales, Christopher P. Rennix, Bruce H. Jones

Abstract

Acute injuries are a burden on the Military Health System and degrade service members' ability to train and deploy. Long-term injuries contribute to early attrition and increase disability costs. To properly quantify acute injuries and evaluate injury prevention programs, injuries must be accurately coded and documented. This analysis describes how the transition from International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to the Tenth Revision (ICD-10-CM) impacted acute injury surveillance among active duty (AD) service members. Twelve months of ICD-9-CM and ICD-10-CM coded ambulatory injury encounter records for Army, Navy, Air Force, and Marine Corps AD service members were analyzed to evaluate the effect of ICD-10-CM implementation on acute injury coding. Acute injuries coded with ICD-9-CM and categorized with the Barell matrix were compared to ICD-10-CM coded injuries classified by the proposed Injury Diagnosis Matrix (IDM). Both matrices categorize injuries by the nature of injury and into three levels of specificity for body region, although column and row headings are not identical. Acute injury distribution between the two matrices was generally similar in the broader body region categories but diverged substantially at the most granular cell level. The proportion of Level 1 Spine and back Body Region diagnoses was higher in the Barell than in the IDM (6.8% and 2.3%, respectively). Unspecified Level 3 Lower extremity injuries were markedly lower in the IDM compared to the Barell (0.1% and 12.1%, respectively). This is the first large scale analysis evaluating the impacts of ICD-10-CM implementation on acute injury surveillance using ambulatory encounter data. Some injury diagnoses appeared to have shifted to a different chapter of the codebook. Also, it's likely that the more detailed diagnostic descriptions and episode of care codes in ICD-10-CM discouraged re-coding of initial acute injury diagnoses. The proposed IDM did not result in a major disruption of acute injury surveillance. However, many acute injury diagnosis codes cannot be aligned between ICD versions. Overall, the increased specificity of ICD-10-CM and use of the IDM may lead to more precise acute injury surveillance and tailored prevention programs, which may result in less chronic injury, reduced morbidity, and lower health-care costs.

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 33%
Student > Doctoral Student 1 7%
Lecturer 1 7%
Other 1 7%
Professor 1 7%
Other 2 13%
Unknown 4 27%
Readers by discipline Count As %
Medicine and Dentistry 3 20%
Psychology 2 13%
Social Sciences 2 13%
Sports and Recreations 1 7%
Economics, Econometrics and Finance 1 7%
Other 0 0%
Unknown 6 40%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 21 August 2018.
All research outputs
#3,122,106
of 13,401,480 outputs
Outputs from Injury Epidemiology
#79
of 144 outputs
Outputs of similar age
#79,993
of 268,019 outputs
Outputs of similar age from Injury Epidemiology
#1
of 1 outputs
Altmetric has tracked 13,401,480 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 144 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 40.6. This one is in the 38th percentile – i.e., 38% 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 268,019 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 1 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