Learning From the Crowd in Terminology Mapping: The LOINC Experience

Dixon, Brian E.; Hook, J. N.; Vreeman, Daniel J.

doi:10.1309/lmwj730svktubaoj

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…Interoperability was a significant barrier to overcome, where large variations in units and data presentation required extensive data harmonization. The use of LOINC codes allowed for more rapid data extraction [22][23][24][25] , but often institutions did not have internal mappings from their laboratory tests to LOINC codes. Manual interpretation of laboratory value descriptions was sometimes necessary.…”

Section: Discussionmentioning

confidence: 99%

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

et al. 2020

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We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

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Section: Discussionmentioning

confidence: 99%

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

et al. 2020

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“…LOINC provides universal identifiers for laboratory tests, and SNOMED CT provides universal identifiers for organisms, substances, diseases, and other findings that may be recorded in the medical record or identified in test results. [8] The microbiology dataset includes type of sample material and the site of collection. Sample material can be coded with SNOMED CT's Specimen hierarchy.…”

Section: Methodsmentioning

confidence: 99%

Use of LOINC and SNOMED CT with FHIR for Microbiology Data

Rinaldi

Saas

Thun

2021

Studies in Health Technology and Informatics

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Infectious diseases due to microbial resistance pose a worldwide threat that calls for data sharing and the rapid reuse of medical data from health care to research. The integration of pathogen-related data from different hospitals can yield intelligent infection control systems that detect potentially dangerous germs as early as possible. Within the use case Infection Control of the German HiGHmed Project, eight university hospitals have agreed to share their data to enable analysis of various data sources. Data sharing among different hospitals requires interoperability standards that define the structure and the terminology of the information to be exchanged. This article presents the work performed at the University Hospital Charité and Berlin Institute of Health towards a standard model to exchange microbiology data. Fast Healthcare Interoperability Resources (FHIR) is a standard for fast information exchange that allows to model healthcare information, based on information packets called resources, which can be customized into so-called profiles to match use case- specific needs. We show how we created the specific profiles for microbiology data. The model was implemented using FHIR for the structure definition, and the international standards SNOMED CT and LOINC for the terminology services.

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“…From 2012-2013 we developed, then implemented a set of enhancements to the RELMA mapping program. We previously described 25 our development methodology in detail. Briefly, we issued a call to the LOINC community in which we asked for voluntary contributions of existing local terms mapped to LOINC terms.…”

Section: Overview Of Relma Community Mapping Featuresmentioning

confidence: 99%

“…We previously described 25 how we gathered existing sets of mappings from diverse institutions into a shared community repository and enhanced the RELMA mapping program to make use of this information. We programmed RELMA to display counts of local terms that had previously been mapped to a given LOINC term and also enabled users to view the details of those mappings, including local term names and the organizations that create the mappings.…”

mentioning

confidence: 99%