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Browsing by Subject "controlled vocabulary"
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Item Development of a Taxonomy for Health Information Technology(2007) Dixon, Brian E.; Zafar, Atif; McGowan, Julie JTaxonomies provide schemas to help classify entities and define the relationships between them. Early computing enabled the development of ontologies and Medical Subject Headings (MeSH), the first modern classification of medical terminology as applied to medical literature. Later developments, such as MEDLINE, expanded MeSH to include a number of medical informatics terms. However, a lack of specificity in MeSH and other existing informatics taxonomies for terminology used to describe the growing field of health information technology (health IT) created the need for the development of a specialized taxonomy. Experts associated with the Agency for Healthcare Research and Qualitys (AHRQs) National Resource Center for Health Information Technology (NRC) created and evaluated a taxonomy for health IT, to enable users of a public health IT Web site to efficiently identify resources within an online, searchable repository.Item Identifying Health Facilities outside the Enterprise: Challenges and Strategies for Supporting Health Reform and Meaningful Use(Taylor and Francis, 2015) Dixon, Brian E.; Colvard, Cyril; Tierney, William M.; Department of Epidemiology, Richard M. Fairbanks School of Public HealthObjective: To support collation of data for disability determination, we sought to accurately identify facilities where care was delivered across multiple, independent hospitals and clinics. Methods: Data from various institutions' electronic health records were merged and delivered as continuity of care documents to the United States Social Security Administration (SSA). Results: Electronic records for nearly 8000 disability claimants were exchanged with SSA. Due to the lack of standard nomenclature for identifying the facilities in which patients received the care documented in the electronic records, SSA could not match the information received with information provided by disability claimants. Facility identifiers were generated arbitrarily by health care systems and therefore could not be mapped to the existing international standards. Discussion: We propose strategies for improving facility identification in electronic health records to support improved tracking of a patient's care between providers to better serve clinical care delivery, disability determination, health reform and meaningful use. Conclusion: Accurately identifying the facilities where health care is delivered to patients is important to a number of major health reform and improvement efforts underway in many nations. A standardized nomenclature for identifying health care facilities is needed to improve tracking of care and linking of electronic health records.Item Learning from the Crowd in Terminology Mapping: The LOINC Experience(Oxford, 2015-05) Dixon, Brian E.; Hook, John; Vreeman, Daniel J.; Department of Epidemiology, Richard M. Fairbanks School of Public HealthNational 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.