- Mapping terms, consolidating concepts
While standard terminologies have been available for many years, and are part of the meaningful use requirements, for example, the reality is that in most health network data sets from EHRs, approximately half the data uses standard codes, but the other half uses proprietary codes.
This calls for developing a semi-automated mapping process: for all inbound data with non-standard codes (lab results, procedures, medications, problems, etc.) the system can propose a mapping from any source terminology to any target terminology including a LOINC code, CPT, SNOMED or RxNorm code. Once proprietary codes are mapped to known coding systems and/or value sets, the data will fit into the appropriate hierarchies and can be used in reports and queries.
We developed the mapping algorithm by applying state-of-the-art machine learning and natural language processing techniques. The mappings were validated and benchmarked against the Unified Medical Language System (UMLS). Our mapping compendium now contains more than 4.5 million mappings – compared with the UMLS’ 800,000 mapped terms. This enables us to interpret the codes for large sets of proprietary data without a manual mapping effort.