An approach to improve LOINC mapping through augmentation of local test names.
ABSTRACT Mapping medical test names into a standardized vocabulary is a prerequisite to sharing test-related data between health care entities. One major barrier in this process is the inability to describe tests in sufficient detail to assign the appropriate name in Logical Observation Identifiers, Names, and Codes (LOINC®). Approaches to address mapping of test names with incomplete information have not been well described. We developed a process of "enhancing" local test names by incorporating information required for LOINC mapping into the test names themselves. When using the Regenstrief LOINC Mapping Assistant (RELMA) we found that 73/198 (37%) of "enhanced" test names were successfully mapped to LOINC, compared to 41/191 (21%) of original names (p=0.001). Our approach led to a significantly higher proportion of test names with successful mapping to LOINC, but further efforts are required to achieve more satisfactory results.
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ABSTRACT: The Department of Defense (DoD) has used a common application, Composite Health Care System (CHCS), throughout all DoD facilities. However, the master files used to encode patient data in CHCS are not identical across DoD facilities. The encoded data is thus not interoperable from one DoD facility to another. To enable data interoperability in the next-generation system, CHCS II, and for the DoD to exchange laboratory results with external organizations such as the Veterans Administration (VA), the disparate master file codes for laboratory results are mapped to Logical Observation Identifier Names and Codes (LOINC) wherever possible. This paper presents some findings from our experience mapping DoD laboratory results to LOINC.AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 02/2005;
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ABSTRACT: Many natural language processing systems are being applied to clinical text, yet clinically useful results are obtained only by honing a system to a particular context. We suggest that concentration on the information needed for this processing is crucial and present a knowledge intensive methodology for mapping clinical text to LOINC. The system takes published case reports as input and maps vital signs and body measurements and reports of diagnostic procedures to fully specified LOINC codes. Three kinds of knowledge are exploited: textual, ontological, and pragmatic (including information about physiology and the clinical process). Evaluation on 4809 sentences yielded precision of 89% and recall of 93% (F-score 0.91). Our method could form the basis for a system to provide semi-automated help to human coders.AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2010; 2010:227-31.
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ABSTRACT: Internationally, there are countless initiatives to build National Healthcare Information Networks (NHIN) that electronically interconnect healthcare organizations by enhancing and integrating current information technology (IT) capabilities. The realization of such NHINs will enable the simple and immediate exchange of appropriate and vital clinical data among participating organizations. In order for institutions to accurately and automatically exchange information, the electronic clinical documents must make use of established clinical codes, such as those of SNOMED-CT, LOINC and ICD-9 CM. However, there does not exist one universally accepted coding scheme that encapsulates all pertinent clinical information for the purposes of patient care, clinical research and population heatlh reporting. In this paper, we propose a combination of methods and standards that target the harmonization of clinical terminologies and encourage sustainable, interoperable infrastructure for healthcare.Studies in health technology and informatics 02/2007; 129(Pt 1):660-3.