Combining laboratory data sets from multiple institutions using the logical observation identifier names and codes (LOINC).
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.
Baorto
D M
DM
Department of Pathology, Washington University School of Medicine, Washington University, St. Louis, MO 63110, USA. baorto@pathbox.wustl.edu
Cimino
J J
JJ
Parvin
C A
CA
Kahn
M G
MG
D019411:Clinical Laboratory Techniques / Q000592:standards; D016208:Databases, Factual; D003658:Decision Making, Computer-Assisted; D001330:Electronic Data Processing / Q000592:standards; D006801:Humans; D007373:Interinstitutional Relations; D012984:Software; D009626:Terminology as Topic