Learning From the Crowd in Terminology Mapping: The LOINC Experience.
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.
Richard M. Fairbanks School of Public Health at Indiana University-Purdue University Indianapolis, Regenstrief Institute, Inc., and Center for Health Information and Communication, Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service, Indianapolis, IN bedixon@iupui.edu.
Brian E
BE
Dixon
Regenstrief Institute, Inc., Indianapolis, IN.
John
J
Hook
Indiana University School of Medicine, Regenstrief Institute, Inc., Indianapolis, IN.
Daniel J
DJ
Vreeman
D002984:Clinical Laboratory Information Systems; D057286:Electronic Health Records; D006801:Humans; D007858:Learning; D039703:Logical Observation Identifiers Names and Codes; D014481:United States
clinical laboratory information systems; controlled vocabulary; crowdsourcing; electronic health records; health information exchange; logical observation identifiers names and codes