6th Annual Public Health Information Network Conference: Implementation of national standards (LOINC, SNOMED) for electronic reporting of laboratory results: BioSense experience

Implementation of national standards (LOINC, SNOMED) for electronic reporting of laboratory results: BioSense experience

Monday, August 25, 2008: 10:55 AM
Atlanta H
Nikolay Lipskiy, PhD, MS, MBA , NCPHI, CDC, Atlanta, GA
Jerome I. Tokars, MD, MPH , National Center for Public Health Informatics, Centers for Disease Control and Prevention, Atlanta, GA
Stephen R. Benoit, MD, MPH , NCPHI, CDC, Atlanta, GA
Roseanne English, BS , NCPHI, CDC, Atlanta, GA
Sundak Ganesan, MD , NCPHI-DISS, CDC Vocabulary Team, SAIC, Atlanta, GA
Background The BioSense system receives data from >400 non-federal hospitals, including 37 that send microbiology laboratory tests and results. Before transmission to CDC, local test codes are mapped to LOINC codes and local result codes to SNOMED. At CDC, SNOMED and LOINC codes are mapped to notifiable conditions using updated versions of standard mapping tables. Methods Microbiology laboratory data associated with more than 460,000 patients during January 2007 and March 2008 from 37 hospital laboratories was analyzed. Results At all 37 hospitals, test orders are represented by local codes which are mapped to LOINC codes before transmission to CDC. Since several local test codes are often mapped to a single nonspecific LOINC code, there are 2.7 times as many local codes as LOINC codes in our data; an analysis to assess the effect of this loss of information is underway. Separate data elements are received for the ordered test vs resulted test; the resulted test name is different in 38.7% of tests and conveys more specific information. Microbiology results are represented by local codes at 20 hospitals (of which 18 are mapped to SNOMED codes before transmission to CDC) and by free text results at 17. Microorganism susceptibility is reported with free text at 35 laboratories and using SNOMED codes at only 2. Conclusions These analysis highlight the challenges of mapping local microbiology laboratory data to standard vocabularies. Our experience may help as strategies for case recognition of notifiable diseases are being developed.