| Title |
SMART on FHIR Genomics: facilitating standardized clinico-genomic apps
|
|---|---|
| Published in |
Journal of the American Medical Informatics Association, July 2015
|
| DOI | 10.1093/jamia/ocv045 |
| Pubmed ID | |
| Authors |
Gil Alterovitz, Jeremy Warner, Peijin Zhang, Yishen Chen, Mollie Ullman-Cullere, David Kreda, Isaac S. Kohane |
| Abstract |
Supporting clinical decision support for personalized medicine will require linking genome and phenome variants to a patient's electronic health record (EHR), at times on a vast scale. Clinico-genomic data standards will be needed to unify how genomic variant data are accessed from different sequencing systems. A specification for the basis of a clinic-genomic standard, building upon the current Health Level Seven International Fast Healthcare Interoperability Resources (FHIR®) standard, was developed. An FHIR application protocol interface (API) layer was attached to proprietary sequencing platforms and EHRs in order to expose gene variant data for presentation to the end-user. Three representative apps based on the SMART platform were built to test end-to-end feasibility, including integration of genomic and clinical data. Successful design, deployment, and use of the API was demonstrated and adopted by HL7 Clinical Genomics Workgroup. Feasibility was shown through development of three apps by various types of users with background levels and locations. This prototyping work suggests that an entirely data (and web) standards-based approach could prove both effective and efficient for advancing personalized medicine. |
X Demographics
The data shown below were collected from the profiles of 22 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 States | 11 | 50% |
| India | 3 | 14% |
| Germany | 1 | 5% |
| United Kingdom | 1 | 5% |
| Korea, Republic of | 1 | 5% |
| Philippines | 1 | 5% |
| Unknown | 4 | 18% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 59% |
| Scientists | 4 | 18% |
| Practitioners (doctors, other healthcare professionals) | 3 | 14% |
| Science communicators (journalists, bloggers, editors) | 2 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| United States | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 168 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 32 | 19% |
| Student > Master | 25 | 15% |
| Other | 18 | 11% |
| Student > Ph. D. Student | 13 | 8% |
| Professor > Associate Professor | 11 | 6% |
| Other | 33 | 19% |
| Unknown | 39 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 39 | 23% |
| Medicine and Dentistry | 35 | 20% |
| Biochemistry, Genetics and Molecular Biology | 13 | 8% |
| Agricultural and Biological Sciences | 8 | 5% |
| Nursing and Health Professions | 7 | 4% |
| Other | 22 | 13% |
| Unknown | 47 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 43. 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 01 November 2018.
All research outputs
#971,124
of 25,377,790 outputs
Outputs from Journal of the American Medical Informatics Association
#210
of 3,302 outputs
Outputs of similar age
#11,799
of 275,689 outputs
Outputs of similar age from Journal of the American Medical Informatics Association
#9
of 74 outputs
Altmetric has tracked 25,377,790 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,302 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one has done particularly well, scoring higher than 93% 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 275,689 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 95% of its contemporaries.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.