↓ Skip to main content

Language, Structure, and Reuse in the Electronic Health Record

Overview of attention for article published in AMA Journal of Ethics, March 2017
Altmetric Badge

Mentioned by

twitter
34 tweeters

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
33 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Language, Structure, and Reuse in the Electronic Health Record
Published in
AMA Journal of Ethics, March 2017
DOI 10.1001/journalofethics.2017.19.3.stas1-1703
Pubmed ID
Abstract

Medical language is at the heart of the electronic health record (EHR), with up to 70 percent of the information in that record being recorded in the natural language, free-text portion. In moving from paper medical records to EHRs, we have opened up opportunities for the reuse of this clinical information through automated search and analysis. Natural language, however, is challenging for computational methods. This paper examines the tension between the nuanced, qualitative nature of medical language and the logical, structured nature of computation as well as the way in which these have interacted with each other through the medium of the EHR. The paper also examines the potential for the computational analysis of natural language to overcome this tension.

Twitter Demographics

The data shown below were collected from the profiles of 34 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 30%
Student > Master 6 18%
Student > Bachelor 3 9%
Student > Ph. D. Student 2 6%
Other 2 6%
Other 2 6%
Unknown 8 24%
Readers by discipline Count As %
Medicine and Dentistry 8 24%
Computer Science 6 18%
Engineering 2 6%
Psychology 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 4 12%
Unknown 9 27%