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Archetype relational mapping - a practical openEHR persistence solution

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
9 tweeters

Citations

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21 Dimensions

Readers on

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57 Mendeley
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1 CiteULike
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Title
Archetype relational mapping - a practical openEHR persistence solution
Published in
BMC Medical Informatics and Decision Making, November 2015
DOI 10.1186/s12911-015-0212-0
Pubmed ID
Authors

Li Wang, Lingtong Min, Rui Wang, Xudong Lu, Huilong Duan

Abstract

One of the primary obstacles to the widespread adoption of openEHR methodology is the lack of practical persistence solutions for future-proof electronic health record (EHR) systems as described by the openEHR specifications. This paper presents an archetype relational mapping (ARM) persistence solution for the archetype-based EHR systems to support healthcare delivery in the clinical environment. First, the data requirements of the EHR systems are analysed and organized into archetype-friendly concepts. The Clinical Knowledge Manager (CKM) is queried for matching archetypes; when necessary, new archetypes are developed to reflect concepts that are not encompassed by existing archetypes. Next, a template is designed for each archetype to apply constraints related to the local EHR context. Finally, a set of rules is designed to map the archetypes to data tables and provide data persistence based on the relational database. A comparison study was conducted to investigate the differences among the conventional database of an EHR system from a tertiary Class A hospital in China, the generated ARM database, and the Node + Path database. Five data-retrieving tests were designed based on clinical workflow to retrieve exams and laboratory tests. Additionally, two patient-searching tests were designed to identify patients who satisfy certain criteria. The ARM database achieved better performance than the conventional database in three of the five data-retrieving tests, but was less efficient in the remaining two tests. The time difference of query executions conducted by the ARM database and the conventional database is less than 130 %. The ARM database was approximately 6-50 times more efficient than the conventional database in the patient-searching tests, while the Node + Path database requires far more time than the other two databases to execute both the data-retrieving and the patient-searching tests. The ARM approach is capable of generating relational databases using archetypes and templates for archetype-based EHR systems, thus successfully adapting to changes in data requirements. ARM performance is similar to that of conventionally-designed EHR systems, and can be applied in a practical clinical environment. System components such as ARM can greatly facilitate the adoption of openEHR architecture within EHR systems.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
Unknown 56 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 25%
Researcher 12 21%
Student > Ph. D. Student 8 14%
Student > Postgraduate 5 9%
Student > Bachelor 3 5%
Other 11 19%
Unknown 4 7%
Readers by discipline Count As %
Computer Science 25 44%
Medicine and Dentistry 13 23%
Engineering 5 9%
Nursing and Health Professions 3 5%
Agricultural and Biological Sciences 2 4%
Other 3 5%
Unknown 6 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 28 January 2016.
All research outputs
#1,571,949
of 11,348,340 outputs
Outputs from BMC Medical Informatics and Decision Making
#162
of 1,049 outputs
Outputs of similar age
#45,464
of 252,381 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#7
of 40 outputs
Altmetric has tracked 11,348,340 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,049 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 84% 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 252,381 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.