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Attention Score in Context
| Title |
Modeling EHR with the openEHR approach: an exploratory study in China
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, August 2018
|
| DOI | 10.1186/s12911-018-0650-6 |
| Pubmed ID | |
| Authors |
Lingtong Min, Qi Tian, Xudong Lu, Huilong Duan |
| Abstract |
The openEHR approach can improve the interoperability of electronic health record (EHR) through two-level modeling. Developing archetypes for the complete EHR dataset is essential for implementing a large-scale interoperable EHR system with the openEHR approach. Although the openEHR approach has been applied in different domains, the feasibility of archetyping a complete EHR dataset in a hospital has not been reported in academic literature, especially in a country where using openEHR is still in its infancy stage, like China. This paper presents a case study of modeling an EHR in China aiming to investigate the feasibility and challenges of archetyping a complete EHR dataset with the openEHR approach. We proposed an archetype modeling method including an iterative process of collecting requirements, normalizing data elements, organizing concepts, searching corresponding archetypes, editing archetypes and reviewing archetypes. Two representative EHR systems from Chinese vendors and the existing Chinese EHR standards have been used as resources to identify the requirements of EHR in China, and a case study of modeling EHR in China has been conducted. Based on the models developed in this case study, we have implemented a clinical data repository (CDR) to verify the feasibility of modeling EHR with archetypes. Sixty four archetypes were developed to represent all requirements of a complete EHR dataset. 59 (91%) archetypes could be found in Clinical Knowledge Manager (CKM), of which 35 could be reused directly without change, and 23 required further development including two revisions, two new versions, 18 extensions and one specialization. Meanwhile, 6 (9%) archetypes were newly developed. The legacy data of the EHR system in hospitals could be integrated into the CDR developed with these archetypes successfully. The existing archetypes in CKM can faithfully represent most of the EHR requirements in China except customizations for local hospital management. This case study verified the feasibility of modeling EHR with the openEHR approach and identified the fact that the challenges such as localization, tool support, and an agile publishing process still exist for a broader application of the openEHR approach. |
X Demographics
The data shown below were collected from the profiles of 29 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 8 | 28% |
| United States | 3 | 10% |
| Norway | 2 | 7% |
| Ireland | 1 | 3% |
| Germany | 1 | 3% |
| Sweden | 1 | 3% |
| Finland | 1 | 3% |
| Slovenia | 1 | 3% |
| South Africa | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 8 | 28% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 25 | 86% |
| Practitioners (doctors, other healthcare professionals) | 2 | 7% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
| Scientists | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 96 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 17 | 18% |
| Student > Ph. D. Student | 11 | 11% |
| Researcher | 9 | 9% |
| Student > Bachelor | 5 | 5% |
| Student > Postgraduate | 5 | 5% |
| Other | 12 | 13% |
| Unknown | 37 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 22 | 23% |
| Medicine and Dentistry | 9 | 9% |
| Engineering | 8 | 8% |
| Nursing and Health Professions | 4 | 4% |
| Social Sciences | 4 | 4% |
| Other | 10 | 10% |
| Unknown | 39 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 27. 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 26 January 2021.
All research outputs
#1,396,020
of 25,079,131 outputs
Outputs from BMC Medical Informatics and Decision Making
#60
of 2,128 outputs
Outputs of similar age
#29,045
of 340,591 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 24 outputs
Altmetric has tracked 25,079,131 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,128 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 97% 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 340,591 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.