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Objective:  To discuss recent developments in clinical terminologies. SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) is the world's largest clinical terminology, developed by an international consortium. LOINC (Logical Observation Identifiers, Names, and Codes) is an international terminology widely used for clinical and laboratory observations. RxNorm is the standard drug terminology in the U.S., Methods and Results:  We present a brief review of the history, current state, and future development of SNOMED CT, LOINC and RxNorm. We also analyze their similarities and differences, and outline areas for greater interoperability among them., Conclusions:  With different starting points, representation formalisms, funding sources, and evolutionary paths, SNOMED CT, LOINC, and RxNorm have evolved over the past few decades into three major clinical terminologies supporting key use cases in clinical practice. Despite their differences, partnerships have been created among their development teams to facilitate interoperability and minimize duplication of effort., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart.)

Objectives: We characterized the use of laboratory LOINC® codes in three large institutions, focused on the following questions: 1) How many local codes had been voluntarily mapped to LOINC codes by each institution? 2) Could additional mappings be found by expert manual review for any local codes that were not initially mapped to LOINC codes by the local institution? and 3) Are there any common characteristics of unmapped local codes that might explain why some local codes were not mapped to LOINC codes by the local institution?, Methods: With Institutional Review Board (IRB) approval, we obtained deidentified data from three large institutions. We calculated the percentage of local codes that have been mapped to LOINC by personnel at each of the institutions. We also analyzed a sample of unmapped local codes to determine whether any additional LOINC mappings could be made and identify common characteristics that might explain why some local codes did not have mappings., Results: Concept type coverage and concept token coverage (volume of instance data covered) of local codes mapped to LOINC codes were 0.44/0.59, 0.78/0.78 and 0.79/0.88 for ARUP, Intermountain, and Regenstrief, respectively. After additional expert manual mapping, the results showed mapping rates of 0.63/0.72, 0.83/0.80 and 0.88/0.90, respectively. After excluding local codes which were not useful for inter-institutional data exchange, the mapping rates became 0.73/0.79, 0.90/0.99 and 0.93/0.997, respectively., Conclusions: Local codes for two institutions could be mapped to LOINC codes with 99% or better concept token coverage, but mapping for a third institution (a reference laboratory) only achieved 79% concept token coverage. Our research supports the conclusions of others that not all local codes should be assigned LOINC codes. There should also be public discussions to develop more precise rules for when LOINC codes should be assigned.