At the December 2015
Laboratory LOINC Committee meeting, we took up the important topic of best practices for staying up to date with new LOINC release versions.
We discussed the unintended consequence of regulations that name a specific LOINC version. The rule-making process requires a specific, named version. So, rule-makers can name the current one, but can’t name a version that doesn’t exist yet. In the Meaningful Use Certification rules, the ONC has built specific provisions that allow updating to the current version. Yet, some (many) companies and institutions incorporate the version required in regulation, but don’t update as new releases come out every 6 months because they aren’t required to do so.
We also discussed several implementation challenges, like the cost of maintenance and the problems that occur when senders and receivers are using different versions of LOINC.
Ultimately, the Committee voted unanimously on the following best practice:
Best Practice
We recommend that users update to the current version of LOINC within 90 days of its publication.
This best practice recommendation will appear in an upcoming publication of the LOINC Users’ Guide.
Rationale
There are many reasons why updating to the most recent LOINC release is a good idea that you should build into your vocabulary maintenance practice. Here are a few.
- The most recent release is always our best release ever. LOINC follows good terminology development principles, which means that LOINC codes are never removed from the database and meaning of a code is never changed over time. So, all of the LOINCs that have ever existed are present in the most recent release. Over time and with each release, we add new terms and accessory content and make revisions where needed. For example, if we notice that we inadvertently have two codes with slightly different names but the exactly the same meaning, we’ll deprecate (retire) one of the codes and add a pointer to the preferred term.
- The world moves fast. The in vitro diagnostic testing industry is constantly innovating. Since 1994, the FDA has categorized an average of about 2,000 new commercially marketed in vitro test system analyte measures each year. (Source: CLIA Download Data). In the last ten years, each bi-annual LOINC release has averaged about 1,800 new terms. LOINC continues to expand coverage of new clinical domains (Radiology procedures, clinical documents, patient reported outcomes measures, etc) and new laboratory testing areas, like genetics. In order to keep up with current clinical practice, you need a plan for regularly updating your use of standard vocabularies like LOINC.
- Maximize the potential benefit of standardized data. If you receive data from multiple sources, you’ll miss out on the opportunity for your information systems to effectively process data from any source that is on a more recent version than you. If you are leveraging LOINC codes for quality improvement or decision support purposes, you run the similar risk of getting out of sync with the with value sets or other rules developed by others.
Tips for staying up to date with LOINC releases
There’s no doubt that the ongoing maintenance of mappings to standard vocabularies like LOINC can be a challenge. The complexity of the task depends on many factors, including how large your local dictionary is, the sophistication of your IT tools, subject matter expertise, etc. Here are a couple of quick tips for being sure you’re ready to go each June and December when new LOINC versions comes out.
- Join the LOINC mailing list. New versions of LOINC are published twice per year (in June and December). Announcements of new LOINC releases are sent to the mailing list as soon as they’re published, so you don’t have to remember to check the website every six months. Or, follow @LOINC on twitter.
- Go Premium. Especially if you’re a geek, you should consider getting a LOINC premium membership so that you can script (or at least very quickly) download of the latest release. It’s as simple as one short curl command and boom, you’ve got the latest release. A premium membership also gets you an email with the release-to-release change file delivered right to your inbox. Plus, if you’re downloading RELMA, you’ll get our fastest download speeds from the membership site.
- Use RELMA to find the places where you’ve mapped to a LOINC code that is now deprecated. Each release contains many terms (about 2%, or 1,700 terms on average) that are deprecated. When a term is deprecated, the LOINC team identifies replacement term(s) wherever possible. For cases where users should choose between two possible replacements, we include a description of how to choose between them. If you’ve used RELMA to map your local codes to LOINC codes, you can use it to find local terms mapped to deprecated or discouraged LOINC codes. RELMA’s special screen (“Find Local Terms Mapped to Deprecated LOINCs”) identifies deprecated/discouraged code mappings and shows you options for updating them with replacement terms.
Future directions
At the LOINC Committee meeting, we also discussed whether it would be valuable for Regenstrief to produce a “core” LOINC Table and a “full” LOINC table. The main idea is that the core table would have only the essential defining fields of a LOINC term, whereas the full table would have all of the other accessory “metadata”. Because it has fewer, more stable fields, the core table would be much less likely to change structurally over time. Depending on their use-case, implementers who import the whole LOINC table into their system could simplify that process by using such a “core” file.
If this is of interest to you, be sure to sign up for the LOINC mailing list, where we’ll soon be sending out a survey about what should be in this “core” table.
References
{1095885:WPP76RHN}
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1.
Dörenberg, J. Synoptic reporting : Chances and challenges for secondary use of pathology data in biobanking.
Pathologie (Heidelb) https://doi.org/10.1007/s00292-025-01505-y (2025) doi:10.1007/s00292-025-01505-y.
1.
Hornback, A. et al. FHIR in Focus: Enabling Biomedical Data Harmonization for Intelligent Healthcare Systems. IEEE Rev Biomed Eng PP, (2025).
1.
Marques, M. et al. The B-Health Box: A Standards-Based Fog IoT Gateway for Interoperable Health and Wellbeing Data Collection. Sensors (Basel) 25, 7116 (2025).
1.
Hsieh, C.-Y. et al. Taiwan’s National Health Insurance Research Database (NHIRD): in the Era of Artificial Intelligence, Causal Inference, and Data Security. Clin Epidemiol 17, 967–981 (2025).
1.
Mantri, M., Satokar, S., Tambe, P. & Bhutad, C. FHIR Standard-Based Oncology Data Model for Cancer Screening: Design and Implementation Study. JMIR Cancer 11, e79011 (2025).
1.
Casanova, R., Villa-Garzon, F. A. & Branch-Bedoya, J. W. Architectural patterns for health information systems: a systematic review. Front Digit Health 7, 1694839 (2025).
1.
Hier, D. B., Carrithers, M. D., Do, T. S. & Obafemi-Ajayi, T. REMOTE: A Framework to Create Fast Healthcare Interoperability Resources (FHIR) from Unstructured Clinical Data. Annu Int Conf IEEE Eng Med Biol Soc 2025, 1–6 (2025).
1.
Ghatage, R. et al. TRAI: An AI-Driven Mobile Application to Reduce the Gap Between Triage and Care. Annu Int Conf IEEE Eng Med Biol Soc 2025, 1–5 (2025).
1.
Soumma, S. B. et al. Design and Implementation of a Scalable Clinical Data Warehouse for Resource-Constrained Healthcare Systems. Annu Int Conf IEEE Eng Med Biol Soc 2025, 1–7 (2025).
1.
C, R. S.
et al. A DAG-enabled cryptographic framework for secure drug traceability with identity-bound authentication and anomaly detection.
Sci Rep https://doi.org/10.1038/s41598-025-30413-7 (2025) doi:10.1038/s41598-025-30413-7.
1.
Mantri, M., Satokar, S., Tambe, P. & Bhutad, C. FHIR Standard-Based Oncology Data Model for Cancer Screening: Design and Implementation Study. JMIR Cancer 11, e79011 (2025).
1.
Thayer, J. G. et al. Combining International Standards to Develop Clinical Decision Support for Parent Smoking Cessation in Pediatrics. J Med Internet Res 27, e75198 (2025).
1.
King, A. J. et al. A FHIR-Powered Python Implementation of the SENECA Algorithm for Sepsis Subtyping. Appl Clin Inform 16, 1588–1594 (2025).
1.
Nothacker, M. et al. Digitalisation of the guideline registry of the Association of Scientific Medical Societies in Germany for an open, guideline-based, trustworthy evidence ecosystem (Dissolve-E): a protocol of a before-after study with different user groups. BMJ Open 15, e095294 (2025).
1.
Graefe, A. S. L. et al. RareLink: scalable REDCap-based framework for rare disease interoperability linking international registries to FHIR and Phenopackets. NPJ Genom Med 10, 72 (2025).
1.
Wiedekopf, J., Ohlsen, T., Kock-Schoppenhauer, A.-K. & Ingenerf, J. BabelFSH-a toolkit for an effective HL7 FHIR-based terminology provision.
J Biomed Semantics https://doi.org/10.1186/s13326-025-00343-4 (2025) doi:10.1186/s13326-025-00343-4.
1.
Simjanoska Misheva, M. et al. AI Act Compliance Within the MyHealth@EU Framework: Tutorial. J Med Internet Res 27, e81184 (2025).
1.
Hohenstein, B., Binder, T. & Kramann, R. [AI Application in Nephrological Diagnostics]. Dtsch Med Wochenschr 150, 1403–1410 (2025).
1.
Finster, M., Wenzel, M. & Taghizadeh, E. Common data models and data standards for tabular health data: a systematic review. BMC Med Inform Decis Mak 25, 422 (2025).
1.
Braunstein, M., Dobbins, C., Steel, J. & Hansen, D. FHIR Project-Based Training for Australia’s Digital Health Workforce. Stud Health Technol Inform 333, 8–13 (2025).
1.
Barbaria, S. et al. Advancing Compliance with HIPAA and GDPR in Healthcare: A Blockchain-Based Strategy for Secure Data Exchange in Clinical Research Involving Private Health Information. Healthcare (Basel) 13, 2594 (2025).
1.
Adegoke, K. et al. Interoperability as a Catalyst for Digital Health and Therapeutics: A Scoping Review of Emerging Technologies and Standards (2015-2025). Int J Environ Res Public Health 22, 1535 (2025).
1.
Cheng, A. C. et al. Opportunities, barriers, and remedies for implementing REDCap integration with electronic health records via Fast Healthcare Interoperability Resources (FHIR). JAMIA Open 8, ooaf111 (2025).
1.
Nopour, R. Using FHIR for data sharing: A scoping review of challenges and facilitators in healthcare settings. Int J Med Inform 205, 106128 (2026).
1.
Engelke, M., Baldini, G., Kleesiek, J., Nensa, F. & Dada, A. FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models.
J Am Med Inform Assoc ocaf165 (2025)
http://doi.org/10.1093/jamia/ocaf165.
1.
Liu, S. et al. A standard-based taxonomy of features that affect user response to clinical decision support alerts. BMC Med Inform Decis Mak 25, 389 (2025).
1.
Abedian, S., Yesakov, E., Ostrovskiy, S. & Hussein, R. Streamlining wearable data integration for EHDS: a case study on advancing healthcare interoperability using Garmin devices and FHIR. Front Digit Health 7, 1636775 (2025).
1.
Borys, K. et al. DermaDashboard: Bridging the Gap Between FHIR Standards and Clinical Usability. JMIR Cancer 11, e73691 (2025).
1.
Alnuaimi, M. K. Integrating Wearable Sensor Data With an AI-Based, Protocol-Flexible Triage Platform to Accelerate Decision-Making During the Golden Hour of Combat Casualty Care. Cureus 17, e91121 (2025).
1.
Beyer, S. et al. Preparing for the European Health Data Space: an open-source compiler for fast, transparent, and portable health data transformations. Front Med (Lausanne) 12, 1661091 (2025).
1.
Pelka, O.
et al. Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration.
Rofo https://doi.org/10.1055/a-2651-6653 (2025) doi:10.1055/a-2651-6653.
1.
Sayeed, R.
et al. A standards-based approach to digital health research: implementing the people heart study.
J Am Med Inform Assoc ocaf163 (2025)
http://doi.org/10.1093/jamia/ocaf163.
1.
Montomoli, J. et al. [Not Available]. Recenti Prog Med 116, 581–582 (2025).
1.
De Angelis, P. et al. [Not Available]. Recenti Prog Med 116, 601–602 (2025).
1.
Berens, B., Grüger, J., Poschen, C. & Knorr, K. A FHIR Specification to Formalize Cohort Definitions. Stud Health Technol Inform 332, 165–169 (2025).
1.
Abedian, S., Yesakov, E., Ostrovskiy, S. & Hussein, R. Integrating Garmin Wearable Data into FHIR-Based Health Systems for Improved Interoperability. Stud Health Technol Inform 332, 185–189 (2025).
1.
Avakian, A. Closing the Loop: A Software-Based Middleware Framework for Automated Vital Sign Integration With Cloud-Based Electronic Medical Records (EMRs). Cureus 17, e90513 (2025).
1.
Gershkovich, P. Wearing a fur coat in the summertime: Should digital pathology redefine medical imaging? J Pathol Inform 18, 100450 (2025).
1.
Felbel, D. et al. The ‘Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis’ project: conceptual design, project planning, and first implementation experiences. Eur Heart J Digit Health 6, 1084–1093 (2025).
1.
Simjanoska Misheva, M.
et al. AI Act Compliance within the MyHealth@EU Framework: A Tutorial.
J Med Internet Res https://doi.org/10.2196/81184 (2025) doi:10.2196/81184.
1.
Wang, J.-F.
et al. Leveraging EHR Data and Up-to-Date Clinical Guidelines for Highly Accurate and Practical Clinical Diabetes Drug and Dosage Recommendation System.
Methods Inf Med https://doi.org/10.1055/a-2707-2862 (2025) doi:10.1055/a-2707-2862.
1.
Hwang, J. et al. Building a Standardized Cancer Synoptic Report With Semantic and Syntactic Interoperability: Development Study Using SNOMED CT and Fast Healthcare Interoperability Resources (FHIR). JMIR Med Inform 13, e76870 (2025).
1.
von Dincklage, F. et al. Computer-Interpretable Quality Indicators for Intensive Care Medicine: Development and Validation Study. J Med Internet Res 27, e77077 (2025).
1.
Katsch, F., Mészáros, Á., Héja, T., Hussein, R. & Duftschmid, G. Semiautomatic mapping of a national drug terminology to standardised OMOP drug concepts using publicly available supplementary information. BMC Med Res Methodol 25, 213 (2025).
1.
Ambalavanan, R. et al. Ontologies as the semantic bridge between artificial intelligence and healthcare. Front Digit Health 7, 1668385 (2025).
1.
Richardson, A. & Genyn, P. Clinical Trial Schedule of Activities Specification using FHIR Definitional Resources.
JMIR Med Inform https://doi.org/10.2196/71430 (2025) doi:10.2196/71430.
1.
Afshar, M. et al. A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice. NEJM AI 2, (2025).
1.
Wang, J.-F. et al. An innovative X-RAG technique combined with GPT-4o for summarizing medical information from EHR and EMR to assist doctors in clinical decision-making effectively and efficiently. Health Informatics J 31, 14604582251381233 (2025).
1.
Dolin, R. H.
et al. Genetic data normalization for genomic medicine: a Fast Healthcare Interoperability Resources Genomics reference implementation.
J Am Med Inform Assoc ocaf136 (2025)
http://doi.org/10.1093/jamia/ocaf136.