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Objective: To determine whether the knowledge contained in a rich corpus of local terms mapped to LOINC (Logical Observation Identifiers Names and Codes) could be leveraged to help map local terms from other institutions., Methods: We developed two models to test our hypothesis. The first based on supervised machine learning was created using Apache's OpenNLP Maxent and the second based on information retrieval was created using Apache's Lucene. The models were validated by a random subsampling method that was repeated 20 times and that used 80/20 splits for training and testing, respectively. We also evaluated the performance of these models on all laboratory terms from three test institutions., Results: For the 20 iterations used for validation of our 80/20 splits Maxent and Lucene ranked the correct LOINC code first for between 70.5% and 71.4% and between 63.7% and 65.0% of local terms, respectively. For all laboratory terms from the three test institutions Maxent ranked the correct LOINC code first for between 73.5% and 84.6% (mean 78.9%) of local terms, whereas Lucene's performance was between 66.5% and 76.6% (mean 71.9%). Using a cut-off score of 0.46 Maxent always ranked the correct LOINC code first for over 57% of local terms., Conclusions: This study showed that a rich corpus of local terms mapped to LOINC contains collective knowledge that can help map terms from other institutions. Using freely available software tools, we developed a data-driven automated approach that operates on term descriptions from existing mappings in the corpus. Accurate and efficient automated mapping methods can help to accelerate adoption of vocabulary standards and promote widespread health information exchange.

We developed a program for mapping local radiology system terms to LOINC that returns a ranked list of candidate LOINC codes based on document similarity scores. We compared the performance of this program with the Intelligent Mapper (IM) program in mapping diagnostic radiology terms to LOINC. The cosine similarity score ranked the correct LOINC code first in 34% of the terms in our development set and 39% of the terms from our test set, compared with IM's ranking of the correct LOINC code first in 83% of the terms in our development set and 92% of the terms in our test set. This study demonstrates the challenges in using document similarity scores for mapping to LOINC. Because vocabulary mapping is a resource-intensive step in integrating data from disparate systems, we need continued refinement of automated tools to help reduce the effort required.

We developed an automated tool, called the Intelligent Mapper (IM), to improve the efficiency and consistency of mapping local terms to LOINC. We evaluated IM's performance in mapping diagnostic radiology report terms from two hospitals to LOINC by comparing IM's term rankings to a manually established gold standard. Using a CPT-based restriction, for terms with a LOINC code match, IM ranked the correct LOINC code first in 90% of our development set terms, and in 87% of our test set terms. The CPT-based restriction significantly improved IM's ability to identify correct LOINC codes. We have made IM freely available, with the aim of reducing the effort required to integrate disparate systems and helping move us towards the goal of interoperable health information exchange.