Automated electronic laboratory reporting (ELR) for public health requires the detection of specific positive clinical laboratory results.[1
] However, clinical laboratory results are often identified by idiosyncratic local codes that represent identical concepts in different laboratory systems.[4
] Mapping local laboratory test codes to the LOINC® vocabulary standard enables interoperable data sharing and aggregation from many sources.[5
] Many forces in the current healthcare environment are accelerating the standardization of laboratory result exchange, including the Meaningful Use (MU) criteria established as part of the U.S. Centers for Medicare and Medicaid Services (CMS) electronic health record (EHR) incentive program.[6
] However, the task of mapping each local code in a laboratory test dictionary to a code from a vocabulary standard can be daunting because it is a complex and resource demanding process.[7
Previous work has shown that as few as 80 distinct laboratory result codes can account for over 80% of the total volume of laboratory results seen in a typical health care system.[4
] When faced with the challenge and cost of mapping local codes to a standard vocabulary in resource-limited settings[9
], one potential strategy to mitigate this burden is to focus the mapping effort on the highest volume tests. We refer to this frequency-based approach as “selective mapping.”
Regenstrief Institute, in collaboration with the Lister Hill National Center for Biomedical Communications at the National Library of Medicine (NLM) have developed an empirically derived list of the 2,017 most commonly reported LOINC codes (which we will refer to as the “LOINC Top 2000”).[10
] The codes in the LOINC Top 2000 represent about 98% of the test volume carried by three large organizations that mapped all of their laboratory tests to LOINC codes. The LOINC Top 2000 presents a much more manageable target than trying to match all of the codes in a typical laboratory’s 2000–5000 term dictionary to one of the 68,000 LOINC codes in current release (December 2011). In addition, Regenstrief and NLM have developed a “Mapper’s Guide” [11
] that contains assistance about which codes from the LOINC Top 2000 to choose for which purpose. The guide was developed to aid small-to-medium sized organizations achieve the requisite LOINC mapping necessary to achieve MU using the LOINC Top 2000.
Many clinical providers have considered a strategy to first map this selective set of clinical codes to mitigate the burden of mapping the plethora of individual local codes to standard code equivalents. This strategy, referred to as selective mapping, is being considered by providers as an interim step to meet the requirements of the Stage 1 Meaningful Use (MU) criteria established as part of the U.S. Centers for Medicare and Medicaid Services (CMS) electronic health record (EHR) incentive program.[12
Public health agencies, in response to MU and to a decrease in available resources, are relying more on automated methodologies such as an ELR to collect and analyze information relevant for public health surveillance and outbreak management. ELR has been shown to positively impact notifiable disease reporting and surveillance, improving both the timeliness and the volume of notifiable disease cases reported to public health agencies.[13
] However, the selective mapping approach may have adverse consequences for public health as it relies more on automated electronic laboratory reporting systems for the reporting of notifiable diseases and population-based surveillance in the community.
To achieve and sustain successful automated ELR for public health reporting, reportable laboratory results must be mapped to a standard code set. This enables the receiving information system at the public health agency to correctly interpret the test and result communicated from the sending information system in the laboratory, hospital, or other health care facility. In other words, mapping enables semantic interoperability between the laboratory, provider, and public health. However, the potential impact of a frequency-based, selective mapping approach on ELR to public health has not yet been evaluated.
The Centers for Disease Control and Prevention (CDC) developed and published the Reportable Condition Mapping Table (RCMT), a resource that provides mappings between reportable conditions and their associated LOINC coded laboratory tests and SNOMED CT result values.[16
] A key use of the RCMT is as a filter for identifying which laboratory results should be sent to public health. If local laboratory test codes are not mapped to LOINC, then they cannot easily be automatically reported to or interpreted by the public health agency. Therefore, in addition to the codes in the LOINC Top 2000, the LOINC codes in the RCMT also represent an important target for mapping. If the results that must be reported to public health are not of significant frequency in the relatively small set of large volume clinical laboratory tests, selective mapping based on frequency alone may hinder efforts to increase the usage of ELR to public health.
Given the potential for unintended consequences to public health, the purpose of this study is to evaluate the impact of selective mapping strategies on real-world automated laboratory result notification. Specifically, we evaluated the notifiable disease results from a large operational health information exchange with respect to the codes contained in the LOINC Top 2000. Examples of codes that are included in this dataset are presented in an extract of this list in .
Extract of Observation Codes from the MICRO Class of the LOINC Top 2000 Clinical Observation Codes.