Impact of selective mapping strategies on automated laboratory result notification to public health authorities

Abstract

Automated electronic laboratory reporting (ELR) for public health has many potential advantages, but requires mapping local laboratory test codes to a standard vocabulary such as LOINC. Mapping only the most frequently reported tests provides one way to prioritize the effort and mitigate the resource burden. We evaluated the implications of selective mapping on ELR for public health by comparing reportable conditions from an operational ELR system with the codes in the LOINC Top 2000. Laboratory result codes in the LOINC Top 2000 accounted for 65.3% of the reportable condition volume. However, by also including the 129 most frequent LOINC codes that identified reportable conditions in our system but were not present in the LOINC Top 2000, this set would cover 98% of the reportable condition volume. Our study highlights the ways that our approach to implementing vocabulary standards impacts secondary data uses such as public health reporting.

Figure 1:

Data flow for the public health condition detector. Inbound HL7 messages are mapped to LOINC and if the test is potentially reportable, it is passed to a second stage where its reportable status is adjudicated.

Figure 2.

Percent of public health reportable conditions identified by LOINC codes contained in the LOINC Top 2000.

Figure 3.

Laboratory test codes ordered by decreasing frequency versus the cumulative frequency of results reported to public health from the NCD.

Figure 4.

A sensitivity analysis illustrating the rate of notifiable public health results identified as a function of mapping the most common LOINC® codes. By mapping the top 500 most common LOINC® codes, slightly more than 50% of all reportable results would be identified.