Medical data exchange and integration within different care providers is highly important to ensure continuous and high-quality care service. Evaluating potential data losses during migration from one medical database to another is significant for decision-making. The presented article describes a method for preliminary overlap estimation when mapping proprietary formats with international standards, as well as local terminologies with international terminological systems.
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2. Kopanitsa G. Evaluation Study for an ISO 13606 Archetype Based Medical Data Visualization Method. J. Med. Syst. 2015; 39(8): 82.
3. Rodrigues J.J.P.C, et al. Electronic Medical Records and Their Standards. e-Health Syst. Elsevier. 2016: 3–19.
4. Leroux H, Metke -Jimenez A, Lawley MJ. Towards achieving semantic interoperability of clinical study data with FHIR. J. Biomed. Semantics. 2017; 8(1): 41.
5. Kieft R.A.M.M, et al. Mapping the Dutch SNOMED CT subset to Omaha System, NANDA International and International Classification of Functioning, Disability and Health. Int. J. Med. Inform. Elsevier Ireland Ltd. 2018; 111: 77–82.
6. Peng P, et al. Mapping of HIE CT terms to LOINC®: analysis of content-dependent coverage and coverage improvement through new term creation. J. Am. Med. Informatics Assoc. 2019; 26(1): 19–27.
7. Baumel T, et al. Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment. AAAI Conference on Artificial Intelligence. 2017: 409–416.
8. Andersen.V, et al. Feasibility of Representing a Danish Microbiology Model Using FHIR. Stud. Health Technol. Inform. 2017.
9. Doods J, Neuhaus P, Dugas M. Converting ODM metadata to FHIR questionnaire resources. Studies in Health Technology and Informatics. IOS Press. 2017; 228: 456–460.
10. Jiang G, Kiefer R, Prud’hommeaux E H.R.S. Building Interoperable FHIR-Based Vocabulary Mapping Services: A Case Study of OHDSI Vocabularies and Mappings. Stud. Health Technol. Inform. 2017; 245: 1327–1327.
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12. Benson T. Why Interoperability is Hard. Principles of Health Interoperability HL7 and SNOMED. Third Edit. 2012: 21–32.
13. Hong N, et al. Standardizing Heterogeneous Annotation Corpora Using HL7 FHIR for Facilitating their Reuse and Integration in Clinical NLP. AMIA. Annu. Symp. proceedings. AMIA Symp. NLM (Medline). 2018; 2018: 574–583.
14. Buck J, et al. Towards a comprehensive electronic patient record to support an innovative individual care concept for premature infants using the openEHR approach. Int. J. Med. Inform. Elsevier. 2009; 78(8): 521–531.
15. Brown S.H, et al. Coverage of oncology drug indication concepts and compositional semantics by SNOMED-CT. // AMIA. Annu. Symp. proceedings. AMIA Symp. American Medical Informatics Association. 2003; 2003: 115–119.
For citation
Lenivtceva ID, Kopanitsa GD. Method for matching medical data exchange formats and terminologies. Medical doctor and information technology. 2021; 1: 75-83. (In Russ.). doi : 10.25881/ITP.2021.79.85.007.
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