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Structured Review

Regenstrief Institute Inc loinc mapping assistant (relma) software
Screenshot of the Regenstrief <t>LOINC</t> Mapping Assistant <t>(RELMA)</t> software showing the new functionality that displays the number of local terms and organizations previously mapped to a given Logical Observation Identifiers Names and Codes (LOINC) term. Fields other than the primary LOINC code and name have the following meanings: ExUCUMunits, Example units of measure from the Unified Code for Units of Measure (UCUM); ComMaps, Number of local test codes mapped to a given candidate LOINC term; ComInst, Number of institutions that have mapped to a given candidate LOINC term; Rank, Ranking within the LOINC Top 2000+ Lab Observations, a subset of LOINC codes that represents about 98% of the test volume from three large organizations that mapped all of their laboratory tests to LOINC codes.
Loinc Mapping Assistant (Relma) Software, supplied by Regenstrief Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/software+coder+the+observer/loinc+mapping+assistant/pmc04795577-100-54-53
Average 90 stars, based on 1 article reviews
loinc mapping assistant (relma) software - by Bioz Stars, 2026-10
90/100 stars

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1) Product Images from "Learning from the crowd while mapping to LOINC"

Article Title: Learning from the crowd while mapping to LOINC

Journal: Journal of the American Medical Informatics Association : JAMIA

doi: 10.1093/jamia/ocv098

Screenshot of the Regenstrief LOINC Mapping Assistant (RELMA) software showing the new functionality that displays the number of local terms and organizations previously mapped to a given Logical Observation Identifiers Names and Codes (LOINC) term. Fields other than the primary LOINC code and name have the following meanings: ExUCUMunits, Example units of measure from the Unified Code for Units of Measure (UCUM); ComMaps, Number of local test codes mapped to a given candidate LOINC term; ComInst, Number of institutions that have mapped to a given candidate LOINC term; Rank, Ranking within the LOINC Top 2000+ Lab Observations, a subset of LOINC codes that represents about 98% of the test volume from three large organizations that mapped all of their laboratory tests to LOINC codes.
Figure Legend Snippet: Screenshot of the Regenstrief LOINC Mapping Assistant (RELMA) software showing the new functionality that displays the number of local terms and organizations previously mapped to a given Logical Observation Identifiers Names and Codes (LOINC) term. Fields other than the primary LOINC code and name have the following meanings: ExUCUMunits, Example units of measure from the Unified Code for Units of Measure (UCUM); ComMaps, Number of local test codes mapped to a given candidate LOINC term; ComInst, Number of institutions that have mapped to a given candidate LOINC term; Rank, Ranking within the LOINC Top 2000+ Lab Observations, a subset of LOINC codes that represents about 98% of the test volume from three large organizations that mapped all of their laboratory tests to LOINC codes.

Techniques Used: Software

Screenshot of the Regenstrief LOINC Mapping Assistant (RELMA) software showing new functionality that displays detailed information on the local terms and organizations that previously mapped their local terms to a given Logical Observation Identifiers Names and Codes (LOINC) term.
Figure Legend Snippet: Screenshot of the Regenstrief LOINC Mapping Assistant (RELMA) software showing new functionality that displays detailed information on the local terms and organizations that previously mapped their local terms to a given Logical Observation Identifiers Names and Codes (LOINC) term.

Techniques Used: Software

Related Articles

Variant Assay:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Stripping Membranes:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Virus:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Cell Counting:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Activity Assay:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Microscopy:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Derivative Assay:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Medications:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].

Diagnostic Assay:

Article Title: Interactive Machine Learning for Laboratory Data Integration
Article Snippet: To facilitate the mapping of local lab terms to LOINC, The Regenstrief LOINC Mapping Assistant (RELMA) was developed [ , ].

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Currently available methods for mapping hospital data to a target ontology include Regenstrief LOINC Mapping Assistant (RELMA), Usagi, and deep-learning based approaches [7–9].

Article Title: AnnoDash, a clinical terminology annotation dashboard
Article Snippet: Tools to support the mapping process include Regenstrief LOINC Mapping Assistant (RELMA), now discontinued, and Usagi, designed by the Observational Health Data Sciences and Informatics (OHDSI) team.

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care.
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI (https://www.ohdsi.org/analytic-tools/usagi/) at one institution (Table 2).

Article Title: A process for contextualising digital health terminology standards for Uganda's health information systems: A use case of HIV information management services.
Article Snippet: International digital health terminologies73–76 are identified through desktop reviews and exploratory assessments.77 Identification and extraction of international terminologies considers categorisation used in the terminology nomenclature, that is, titles of the terminology or classifications tables are used as keywords.55 Literature shows that semiautomated approaches like Regenstrief LOINC Mapping Assistant (RELMA) for LOINC are used to improve the mapping quality and reduce the time spent during mapping.78 Digital clinical documentation is automatically mapped to SNOMED79,80 and automated processes are also used for ICD.81–83 Shi et al., showed that ICD can be automatically coded using neural language models.83 However, Uganda’s eHIS data elements are still collected manually; thus, the automated mapping processes cannot be applied.

Article Title: A global federated real-world data and analytics platform for research
Article Snippet: Lab results, clinical findings, and vital signs , Local lab coding or LOINC , LOINC , Regenstrief LOINC Mapping Assistant (RELMA) is used to map at least the concepts covering 80% of the most frequent observations of an HCO. Automatic unit conversion based on UCUM is applied. Lab result distributions are used to validate the correctness of mappings. .

Article Title: Gaps and Similarities in Research Use LOINC Codes Utilized in Korean University Hospitals: Towards Semantic Interoperability for Patient Care
Article Snippet: Automated tools were used at three institutions: REgenstrief LOINC Mapping Assistant (RELMA) at two institutions and USAGI ( https://www.ohdsi.org/analytic-tools/usagi/ ) at one institution ( ).

Article Title: Augmented intelligence facilitates concept mapping across different electronic health records.
Article Snippet: Big data-guided LOINC mapping based on text of laboratory parameters showed a precision of 0.33, which was lower than Regenstrief LOINC Mapping Assistant’s (RELMA) precision of 0.43 [7,10].



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