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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, July 2017
|
| DOI | 10.1186/s12911-017-0501-x |
| Pubmed ID | |
| Authors |
Nallakkandi Rajeevan, Kristina M. Niehoff, Peter Charpentier, Forrest L. Levin, Amy Justice, Cynthia A. Brandt, Terri R. Fried, Perry L. Miller |
| Abstract |
The US Veterans Administration (VA) has developed a robust and mature computational infrastructure in support of its electronic health record (EHR). Web technology offers a powerful set of tools for structuring clinical decision support (CDS) around clinical care. This paper describes informatics challenges and design issues that were confronted in the process of building three Web-based CDS systems in the context of the VA EHR. Over the course of several years, we implemented three Web-based CDS systems that extract patient data from the VA EHR environment to provide patient-specific CDS. These were 1) the VACS (Veterans Aging Cohort Study) Index Calculator which estimates prognosis for HIV+ patients, 2) Neuropath/CDS which assists in the medical management of patients with neuropathic pain, and 3) TRIM (Tool to Reduce Inappropriate Medications) which identifies potentially inappropriate medications in older adults and provides recommendations for improving the medication regimen. The paper provides an overview of the VA EHR environment and discusses specific informatics issues/challenges that arose in the context of each of the three Web-based CDS systems. We discuss specific informatics methods and provide details of approaches that may be useful within this setting. Informatics issues and challenges relating to data access and data availability arose because of the particular architecture of the national VA infrastructure and the need to link to that infrastructure from local Web-based CDS systems. Idiosyncrasies of VA patient data, especially the medication data, also posed challenges. Other issues related to specific functional needs of individual CDS systems. The goal of this paper is to describe these issues so that our experience may serve as a useful foundation to assist others who wish to build such systems in the future. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 33% |
| United Kingdom | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 78 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 23% |
| Student > Master | 13 | 17% |
| Student > Doctoral Student | 10 | 13% |
| Researcher | 6 | 8% |
| Student > Bachelor | 3 | 4% |
| Other | 12 | 15% |
| Unknown | 16 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Business, Management and Accounting | 13 | 17% |
| Medicine and Dentistry | 10 | 13% |
| Computer Science | 8 | 10% |
| Nursing and Health Professions | 7 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 6% |
| Other | 17 | 22% |
| Unknown | 18 | 23% |
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 17 April 2018.
All research outputs
#15,470,944
of 22,990,068 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,324
of 2,003 outputs
Outputs of similar age
#198,133
of 315,216 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#26
of 40 outputs
Altmetric has tracked 22,990,068 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,003 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 24th percentile – i.e., 24% 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 315,216 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
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 is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.