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Open data models for smart health interconnected applications: the example of openEHR

Overview of attention for article published in BMC Medical Informatics and Decision Making, October 2016
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  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
32 Mendeley
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Title
Open data models for smart health interconnected applications: the example of openEHR
Published in
BMC Medical Informatics and Decision Making, October 2016
DOI 10.1186/s12911-016-0376-2
Pubmed ID
Authors

Hans Demski, Sebastian Garde, Claudia Hildebrand

Abstract

Smart Health is known as a concept that enhances networking, intelligent data processing and combining patient data with other parameters. Open data models can play an important role in creating a framework for providing interoperable data services that support the development of innovative Smart Health applications profiting from data fusion and sharing. This article describes a model-driven engineering approach based on standardized clinical information models and explores its application for the development of interoperable electronic health record systems. The following possible model-driven procedures were considered: provision of data schemes for data exchange, automated generation of artefacts for application development and native platforms that directly execute the models. The applicability of the approach in practice was examined using the openEHR framework as an example. A comprehensive infrastructure for model-driven engineering of electronic health records is presented using the example of the openEHR framework. It is shown that data schema definitions to be used in common practice software development processes can be derived from domain models. The capabilities for automatic creation of implementation artefacts (e.g., data entry forms) are demonstrated. Complementary programming libraries and frameworks that foster the use of open data models are introduced. Several compatible health data platforms are listed. They provide standard based interfaces for interconnecting with further applications. Open data models help build a framework for interoperable data services that support the development of innovative Smart Health applications. Related tools for model-driven application development foster semantic interoperability and interconnected innovative applications.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 25%
Researcher 5 16%
Student > Ph. D. Student 4 13%
Student > Bachelor 2 6%
Librarian 2 6%
Other 4 13%
Unknown 7 22%
Readers by discipline Count As %
Computer Science 15 47%
Engineering 4 13%
Medicine and Dentistry 3 9%
Psychology 1 3%
Social Sciences 1 3%
Other 1 3%
Unknown 7 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 January 2017.
All research outputs
#8,186,750
of 15,918,484 outputs
Outputs from BMC Medical Informatics and Decision Making
#628
of 1,450 outputs
Outputs of similar age
#118,260
of 296,113 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#47
of 94 outputs
Altmetric has tracked 15,918,484 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,450 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 55% 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 296,113 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 59% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.