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A Fast Healthcare Interoperability Resources (FHIR) layer implemented over i2b2

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2017
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2 tweeters

Citations

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19 Dimensions

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84 Mendeley
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Title
A Fast Healthcare Interoperability Resources (FHIR) layer implemented over i2b2
Published in
BMC Medical Informatics and Decision Making, August 2017
DOI 10.1186/s12911-017-0513-6
Pubmed ID
Authors

Abdelali Boussadi, Eric Zapletal

Abstract

Standards and technical specifications have been developed to define how the information contained in Electronic Health Records (EHRs) should be structured, semantically described, and communicated. Current trends rely on differentiating the representation of data instances from the definition of clinical information models. The dual model approach, which combines a reference model (RM) and a clinical information model (CIM), sets in practice this software design pattern. The most recent initiative, proposed by HL7, is called Fast Health Interoperability Resources (FHIR). The aim of our study was to investigate the feasibility of applying the FHIR standard to modeling and exposing EHR data of the Georges Pompidou European Hospital (HEGP) integrating biology and the bedside (i2b2) clinical data warehouse (CDW). We implemented a FHIR server over i2b2 to expose EHR data in relation with five FHIR resources: DiagnosisReport, MedicationOrder, Patient, Encounter, and Medication. The architecture of the server combines a Data Access Object design pattern and FHIR resource providers, implemented using the Java HAPI FHIR API. Two types of queries were tested: query type #1 requests the server to display DiagnosticReport resources, for which the diagnosis code is equal to a given ICD-10 code. A total of 80 DiagnosticReport resources, corresponding to 36 patients, were displayed. Query type #2, requests the server to display MedicationOrder, for which the FHIR Medication identification code is equal to a given code expressed in a French coding system. A total of 503 MedicationOrder resources, corresponding to 290 patients, were displayed. Results were validated by manually comparing the results of each request to the results displayed by an ad-hoc SQL query. We showed the feasibility of implementing a Java layer over the i2b2 database model to expose data of the CDW as a set of FHIR resources. An important part of this work was the structural and semantic mapping between the i2b2 model and the FHIR RM. To accomplish this, developers must manually browse the specifications of the FHIR standard. Our source code is freely available and can be adapted for use in other i2b2 sites.

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

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 25%
Researcher 10 12%
Student > Ph. D. Student 9 11%
Student > Doctoral Student 8 10%
Student > Bachelor 6 7%
Other 16 19%
Unknown 14 17%
Readers by discipline Count As %
Computer Science 25 30%
Medicine and Dentistry 15 18%
Nursing and Health Professions 8 10%
Engineering 4 5%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Other 15 18%
Unknown 14 17%

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 18 August 2017.
All research outputs
#7,240,346
of 11,626,228 outputs
Outputs from BMC Medical Informatics and Decision Making
#747
of 1,067 outputs
Outputs of similar age
#151,086
of 264,243 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#16
of 26 outputs
Altmetric has tracked 11,626,228 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,067 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 20th percentile – i.e., 20% 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 264,243 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 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.