A Set of NooJ Grammars to Verify Laboratory Data Correctness

Abstract

Semantic interoperability in clinical processes is necessary to exchange meaningful information among healthcare facilities. Standardized classification and coding systems allow for meaningful information exchange. This paper aims to support the accuracy validation of mappings between local and standardized clinical content, through the construction of NooJ syntactical grammars for recognition of local linguistic forms and detection of data correctness level. In particular, this work deals with laboratory observations, which are identified by idiosyncratic codes and names by different facilities, thus creating issues in data exchange. The Logical Observation Identifiers Names and Codes (LOINC) is an international standard for uniquely identifying laboratory and clinical observations. Mapping local concepts to LOINC allows to create links among health data systems, even though it is a cost and time-consuming process. Beyond this, in Italy LOINC experts use to manually double check all the performed mappings to validate them. This has over time become a non-trivial task because of the dimension of laboratory catalogues and the growing adoption of LOINC. The aim of this work is realizing a NooJ grammar system to support LOINC experts in validating mappings between local tests and LOINC codes. We constructed syntactical grammars to recognize local linguistic forms and determine data accuracy, and the NooJ contextual constraints to identify the threshold of correctness of each mapping. The grammars created help LOINC experts in reducing the time required for mappings validation.