Learning From the Crowd in Terminology Mapping: The LOINC Experience. Issue 2 (21st September 2015)

• Record Type: Journal Article
• Authors: Dixon, Brian E.; Hook, John; Vreeman, Daniel J.
• Is Part Of: Laboratory medicine. Volume 46:Issue 2(2015)
• Page Start: 168
• Page End: 174
• Publication Date: 2015-09-21
• Subjects: logical observation identifiers names and codes -- crowdsourcing -- controlled vocabulary -- health information exchange -- electronic health records -- clinical laboratory information systems; Clinical chemistry -- Periodicals; Pathology -- Periodicals; Medical technology -- Periodicals; Chemistry, Clinical; Pathology; Medical Laboratory Science; Clinical chemistry; Medical technology; Pathology; Electronic journals; Periodicals; Periodicals; 616
• DOI: 10.1309/LMWJ730SVKTUBAOJ
• Languages: English
• ISSNs: 0007-5027

Abstract

Abstract : National policies in the United States require the use of standard terminology for data exchange between clinical information systems. However, most electronic health record systems continue to use local and idiosyncratic ways of representing clinical observations. To improve mappings between local terms and standard vocabularies, we sought to make existing mappings (wisdom) from healt care organizations (the Crowd) available to individuals engaged in mapping processes. We developed new functionality to display counts of local terms and organizations that had previously mapped to a given Logical Observation Identifiers Names and Codes (LOINC) code. Further, we enabled users to view the details of those mappings, including local term names and the organizations that create the mappings. Users also would have the capacity to contribute their local mappings to a shared mapping repository. In this article, we describe the new functionality and its availability to implementers who desire resources to make mapping more efficient and effective.