↓ Skip to main content

Impact of a structured referral algorithm on the ability to monitor adherence to appropriate use criteria for transthoracic echocardiography

Overview of attention for article published in Cardiovascular Ultrasound, January 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
23 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Impact of a structured referral algorithm on the ability to monitor adherence to appropriate use criteria for transthoracic echocardiography
Published in
Cardiovascular Ultrasound, January 2016
DOI 10.1186/s12947-016-0075-2
Pubmed ID
Authors

Steven Promislow, Joseph G. Abunassar, Behnam Banihashemi, Benjamin J. Chow, Girish Dwivedi, Kasra Maftoon, Ian G. Burwash, Promislow, Steven, Abunassar, Joseph G, Banihashemi, Behnam, Chow, Benjamin J, Dwivedi, Girish, Maftoon, Kasra, Burwash, Ian G

Abstract

Many free-form-text referral requisitions for transthoracic echocardiography (TTE) provide insufficient information to adequately evaluate their adherence to Appropriate Use Criteria (AUC). We developed a structured referral requisition algorithm based on requisition deficiencies identified retrospectively in a derivation cohort of 1303 TTE referrals and evaluated the performance of the algorithm in a consecutive series of cardiology outpatient referrals. The validation cohort comprised 286 consecutive TTE outpatient cardiology referrals over a 2-week period. The relevant AUC indication was identified from information extracted from the free-form-text requisition. The structured referral algorithm was applied prospectively to the same cohort using information from the free-form-text requisition, electronic medical record and ordering clinicians. Referrals were classified as appropriate, uncertain, non-adherent (inappropriate) or unclassifiable based on the American College of Cardiology Foundation 2011 AUC. Only 28.7 % of free-form-text requisitions provided adequate information to identify the relevant AUC indication, as compared to 94.4 % of referrals using the structured referral algorithm (p < 0.001). The structured algorithm improved identification in the AUC categories of general evaluation of cardiac structure/function (100 % vs. 43.0 %, p < 0.001); valvular function (100 % vs. 23.0 %, p < 0.001); hypertension, heart failure or cardiomyopathy (100 % vs. 20.3 %, p < 0.001); and adult congenital heart disease (100 % vs. 0 %, p < 0.001). By applying the algorithm, the number of identifiable non-adherent studies increased from 2.6 to 10.4 % (p <0.001). Use of a structured TTE referral algorithm, as opposed to a free-form-text requisition, allowed the vast majority of referrals to be monitored for AUC adherence and facilitated the identification of potentially inappropriate referrals.

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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 5 22%
Student > Master 4 17%
Librarian 2 9%
Student > Bachelor 2 9%
Student > Ph. D. Student 2 9%
Other 4 17%
Unknown 4 17%
Readers by discipline Count As %
Medicine and Dentistry 9 39%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Mathematics 1 4%
Unspecified 1 4%
Other 4 17%
Unknown 6 26%

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 17 August 2016.
All research outputs
#5,956,665
of 8,229,276 outputs
Outputs from Cardiovascular Ultrasound
#158
of 206 outputs
Outputs of similar age
#152,702
of 233,219 outputs
Outputs of similar age from Cardiovascular Ultrasound
#8
of 13 outputs
Altmetric has tracked 8,229,276 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 206 research outputs from this source. They receive a mean Attention Score of 2.5. 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 233,219 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.