Combining laboratory data sets from multiple institutions using the logical observation identifier names and codes (LOINC)

Abstract

A standard set of names and codes for laboratory test results is critical for any endeavor requiring automated data pooling, including multi-institutional research and cross-facility patient care. This need has led to the development of the logical observation identifier names and codes (LOINC) database and its test-naming convention. This study is an expansion of a pilot study using LOINC to exchange laboratory data between Columbia University Medical Center in New York and Barnes Hospital at Washington University in St. Louis, where we described complexities and ambiguities that arose in the LOINC coding process (D.M. Baorto, J.J. Cimino, C.A. Parvin, M.G. Kahn, Proc. Am. Med. Inf. Assoc. 1997). For the present study, we required the same two medical centers to again extract raw laboratory data from their local information system for a defined patient population, translate tests into LOINC and provide aggregate data which could then be used to compare laboratory utilization. Here we examine a larger number of tests from each site which have been recoded using an updated version of the LOINC database. We conclude that the coding of local tests into LOINC can often be complex, especially the 'Kind of Property' field and apparently trivial differences in choices made by individual institutions can result in nonmatches in electronically pooled data. In the present study, 75% of failures to match the same tests between different institutions using LOINC codes were due to differences in local coding choices. LOINC has the potential to eliminate the need for detailed human inspection during the pooling of laboratory data from diverse sites and perhaps even a built-in capability to adjust matching stringency by selecting subsets of LOINC fields required to match. However, a quality standard coding procedure is required and examples highlighted in this paper may require special attention while mapping to LOINC.

Identifying health facilities outside the enterprise: challenges and strategies for supporting health reform and meaningful use

“…Of these, just 166 values (10.0%) represented 2,410,535 encounters (81.8%).Focusing on just these terms allowed human term mappers to translate a relatively small subset of identifiers in support of the greatest number of transactions. Such utilitarian approaches help minimize resource utilization in mapping projects, which are known to be costly and time-intensive processes involving specialized resources (57,58).…”

Learning From the Crowd in Terminology Mapping: The LOINC Experience

“…[12][13][14][15][16] Mapping local terms to standard vocabularies is complex and resource intensive. 17,18 Identifying the correct concept from the standard vocabulary requires specific domain knowledge and knowledge of the target vocabulary standards. In practice, even physicians and laboratory personnel with a good understanding of the tests at their institution often lack the specific knowledge required to successfully map all of their local concepts to standard vocabularies.…”

“…In practice, even physicians and laboratory personnel with a good understanding of the tests at their institution often lack the specific knowledge required to successfully map all of their local concepts to standard vocabularies. 18 Local test names often lack information needed to appropriately identify the correct standard concept. 18,19 For example, test names may lack an indication of the specimen type or whether the result returned is quantitative or ordinal (eg, positive/negative).…”

“…18 Local test names often lack information needed to appropriately identify the correct standard concept. 18,19 For example, test names may lack an indication of the specimen type or whether the result returned is quantitative or ordinal (eg, positive/negative). Similarly, the units of measure associated with the result may not be available during mapping.…”

Pathology data integration with eXtensible Markup Language

“…Pathologists are told that they must include specific data elements in their cancer-related reports [6], but they have no standard methods to use these data (ie, retrieve the data, integrate the data with related clinical or research data sets, and merge the data from multiple institutions). The internet and broadband telecommunications permit the rapid transmission of huge amounts of information, but institutional security officers seem devoted to ensuring that pathology information never crosses the local firewall.It seems that 2 distinct tracks for biomedical data have emerged: the medical track that captures data in proprietary information systems, and the research track that has developed a startling array of free and open methods for data organization, data integration, and data sharing [7][8][9][10][11][12][13][14][15].Many pathologists seem to be unaware of the progress made by biologists in the field of data annotation and data integration [16][17][18][19][20][21][22]. Data annotation is the very simple concept that every piece of data in a record can be annotated with another set of information that describes the data (socalled metadata).…”