A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study
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Abstract
Background: Interoperability is a well-known challenge in medical informatics. Current trends in interoperability have moved from a data model technocentric approach to sustainable semantics, formal descriptive languages, and processes. Despite many initiatives and investments for decades, the interoperability challenge remains crucial. The need for data sharing for most purposes ranging from patient care to secondary uses, such as public health, research, and quality assessment, faces unmet problems. Objective: This work was performed in the context of a large Swiss Federal initiative aiming at building a national infrastructure for reusing consented data acquired in the health care and research system to enable research in the field of personalized medicine in Switzerland. The initiative is the Swiss Personalized Health Network (SPHN). This initiative is providing funding to foster use and exchange of health-related data for research. As part of the initiative, a national strategy to enable a semantically interoperable clinical data landscape was developed and implemented. Methods: A deep analysis of various approaches to address interoperability was performed at the start, including large frameworks in health care, such as Health Level Seven (HL7) and Integrating Healthcare Enterprise (IHE), and in several domains, such as regulatory agencies (eg, Clinical Data Interchange Standards Consortium [CDISC]) and research communities (eg, Observational Medical Outcome Partnership [OMOP]), to identify bottlenecks and assess sustainability. Based on this research, a strategy composed of three pillars was designed. It has strong multidimensional semantics, descriptive formal language for exchanges, and as many data models as needed to comply with the needs of various communities. Results: This strategy has been implemented stepwise in Switzerland since the middle of 2019 and has been adopted by all university hospitals and high research organizations. The initiative is coordinated by a central organization, the SPHN Data Coordination Center of the SIB Swiss Institute of Bioinformatics. The semantics is mapped by domain experts on various existing standards, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and International Classification of Diseases (ICD). The resource description framework (RDF) is used for storing and transporting data, and to integrate information from different sources and standards. Data transformers based on SPARQL query language are implemented to convert RDF representations to the numerous data models required by the research community or bridge with other systems, such as electronic case report forms. Conclusions: The SPHN strategy successfully implemented existing standards in a pragmatic and applicable way. It did not try to build any new standards but used existing ones in a nondogmatic way. It has now been funded for another 4 years, bringing the Swiss landscape into a new dimension to support research in the field of personalized medicine and large interoperable clinical data.
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EHR interoperability meets the need for personalized medicine by making it easier to access data in the required formats.
“By making it easier to access the data in the required formats, EHR interoperability meets the need for personalized medicine.”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [39]
Personalized medicine approaches can address these difficulties.
“Personalized medicine approaches can address these difficulties.”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [39]
Access to a large quantity of diverse information from institutions must be readily available to understand the underlying causes of disease and develop diagnostics and therapeutics with better efficacy and safety.
“In order to understand the underlying causes of disease and develop diagnostics and therapeutics with better efficacy and safety, access to a large quantity of diverse information from institutions must be readily available.”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [39]
Connectivity from interoperable EHRs can drive improvements in translational research and clinical decision support, leading to improved patient outcomes and completing the bench to bedside and back paradigm.
“This connectivity can drive improvements in translational research and clinical decision support, leading to improved patient outcomes and completing the bench to bedside and back paradigm [39].”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [39]
Offering interoperable tools and infrastructure makes it easier to gather, integrate, and correlate a variety of clinical data types with patient information.
“It is made easier to gather, integrate, and correlate a variety of clinical data types with patient information by offering interoperable tools and infrastructure.”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [39]
Interoperable EHRs with a crucial link integrating clinical data are a necessary step to achieve personalized medicine.
“To achieve personalized medicine, interoperable EHRs with a crucial link integrating clinical data are a necessary step.”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [39]
Improved collaboration and data sharing are fundamental to managing the rise in complicated chronic diseases.
“Regardless of how personalized medicine is defined, it is evident that improved collaboration and data sharing are fundamental to managing the rise in complicated chronic diseases.”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [39]
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Cites
The unified medical language system (UMLS): integrating biomedical terminology
Bodenreider, O. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh061 · Cited by 4,356 · see citing works
The “Meaningful Use” Regulation for Electronic Health Records
Blumenthal, D., & Tavenner, M. (2010) · New England Journal of Medicine · doi:10.1056/nejmp1006114 · Cited by 2,059 · see citing works
Musen, M. A. (2015) · AI Matters · doi:10.1145/2757001.2757003 · Cited by 1,421 · see citing works
Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)
Murphy, S. N., Weber, G. M., Mendis, M., Gainer, V. S., Chueh, H. C., Churchill, S. E., & Kohane, I. S. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.000893 · Cited by 926 · see citing works
The BioPAX community standard for pathway data sharing
Demir, E., Cary, M. P., Paley, S., Fukuda, K., Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., Luciano, J., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1666 · Cited by 745 · cited 2× · see citing works
“The reasons for choosing RDF technologies are summarized in Textbox 4 [43-47].”
BackgroundThe Semantic Web: the roles of XML and RDF
Decker, S., Melnik, S., van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., Erdmann, M., & Horrocks, I. (2000) · IEEE Internet Computing · doi:10.1109/4236.877487 · Cited by 743 · see citing works
LOINC, a Universal Standard for Identifying Laboratory Observations: A 5-Year Update
McDonald, C. J., Huff, S. M., Suico, J. G., Hill, G., Leavelle, D., Aller, R., Forrey, A., Mercer, K., DeMoor, G., Hook, J., et al. (2003) · Clinical Chemistry · doi:10.1373/49.4.624 · Cited by 579 · see citing works
MedCalc: a new computer program for medical statistics
Schoonjans, F., Zalata, A. A. E.-K., Depuydt, C., & Comhaire, F. H. (1995) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(95)01703-8 · Cited by 545 · see citing works
HL7 FHIR: An Agile and RESTful approach to healthcare information exchange
Bender, D., & Sartipi, K. (2013) · Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2013.6627810 · Cited by 540 · see citing works
(2006) · PEDIATRICS · doi:10.1542/peds.2006-0325 · Cited by 373 · see citing works
Sternbach, G. L. (2000) · Journal of Emergency Medicine · doi:10.1016/s0736-4679(00)00182-7 · Cited by 303 · see citing works
GLIF3: the evolution of a guideline representation format.
Mor Peleg, B., Boxwala, A. A., Ogunyemi, O. I., Zeng, Q. T., Tu, S. W., Lacson, R. C., Bernstam, E. V., Ash, N., Mork, P., Lucila Ohno-machado, A., et al. (2000) · PubMed Central · Cited by 264 · see citing works
The Unified Medical Language System (UMLS) of the National Library of Medicine.
Lindberg, C. H. (1990) · PubMed · Cited by 207 · see citing works
WebVOWL: Web-based Visualization of Ontologies
Lohmann, S., Link, V., Marbach, E., & Negru, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17966-7_21 · Cited by 160 · see citing works
The Arden Syntax standard for clinical decision support: Experiences and directions
Matthias Samwald, A., Fehre, K., de Bruin, J., & Adlassnig, K.-P. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2012.02.001 · Cited by 132 · see citing works
Klann, J. G., Joss, M. A., Embree, K., & Murphy, S. N. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212463 · Cited by 117 · see citing works
Gaudet-Blavignac, C., Foufi, V., Bjelogrlic, M., & Lovis, C. (2020) · Journal of Medical Internet Research · doi:10.2196/24594 · Cited by 91 · see citing works
Self-citationOntology-Based Data Integration between Clinical and Research Systems
Mate, S., Köpcke, F., Toddenroth, D., Martin, M., Prokosch, H., Bürkle, T., & Ganslandt, T. (2015) · PLoS ONE · doi:10.1371/journal.pone.0116656 · Cited by 85 · see citing works
Integrating the Healthcare Enterprise (IHE)
(2012) · doi:10.1002/9781118479612.ch19 · Cited by 69 · see citing works
BioGateway: a semantic systems biology tool for the life sciences
Antezana, E., Blondé, W., Aranguren, M. E., Rutherford, A., Stevens, R., Bernard De Baets, C., Mironov, V., & Kuiper, M. T. R. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s10-s11 · Cited by 69 · see citing works
MDCalc Medical Calculator App Review
Elovic, A., & Pourmand, A. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-019-00218-y · Cited by 61 · see citing works
Hume, S., Aerts, J., Sarnikar, S., & Huser, V. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.02.016 · Cited by 45 · see citing works
Pacaci, A., Gönül, S., Sınacı, A. A., Yüksel, M., & Ertürkmen, G. B. L. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00435 · Cited by 29 · see citing works
Bhattacharyya, S. (2015) · Springer eBooks · doi:10.1007/978-981-287-895-3 · Cited by 24 · see citing works
SPHN – The BioMedIT Network: A Secure IT Platform for Research with Sensitive Human Data
Coman, D., Crameri, K., Oesterle, S., Rinn, B., Sengstag, T., & Heinz Stockinger, C. (2020) · Studies in health technology and informatics · doi:10.3233/shti200348 · Cited by 24 · see citing works
Self-citationSPARQL ( SPARQL Protocol and RDF Query Language)
Hancock, J. M. (2004) · Dictionary of Bioinformatics and Computational Biology · doi:10.1002/9780471650126.dob1092 · Cited by 20 · see citing works
Automated Population of an i2b2 Clinical Data Warehouse using FHIR.
Solbrig, H. R., Hong, N., Murphy, S. N., & Jiang, G. (2018) · Europe PMC (PubMed Central) · Cited by 12 · see citing works
Bhattacharyya, S. B. (2015) · Springer eBooks · doi:10.1007/978-981-287-895-3_9 · Cited by 5 · see citing works
Bhattacharyya, S. B. (2015) · Springer eBooks · doi:10.1007/978-981-287-895-3_3 · Cited by 5 · see citing works
Metadata Import from RDF to i2b2.
Stöhr, M. R., Majeed, R. W., & Günther, A. (2018) · PubMed · Cited by 2 · see citing works
Swiss Personalized Health Network (SPHN) : L’initiative nationale en un clin d’œil
Lawrence, A. K., & Liselotte, S. (2017) · Bulletin des Médecins Suisses · doi:10.4414/bms.2017.05640 · Cited by 2 · see citing works
Swiss Personalized Health Network. Report from the National Steering Board 2016–2019
Swiss Personalized Health Network (SPHN) (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4044122 · Cited by 2 · see citing works
Benson, T., & Grieve, G. (2016) · Health information technology standards · doi:10.1007/978-3-319-30370-3_13 · Cited by 1 · see citing works
Benson, T., & Grieve, G. (2016) · Health information technology standards · doi:10.1007/978-3-319-30370-3_11 · Cited by 1 · see citing works
(2021) · Results in Physics · doi:10.1016/s2211-3797(21)00059-0 · Cited by 1 · see citing works
Kalra, D., Beale, T., & Heard, S. (2005) · PubMed
Flagged first, then by citations
Cited by
Sorted by citations
Interoperability of heterogeneous health information systems: a systematic literature review
Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., & Rezaei-Hachesu, P. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02115-5 · Cited by 393 · see citing works
Touré, V., Krauss, P., Gnodtke, K., Buchhorn, J., Unni, D. R., Horki, P., Raisaro, J. L., Kalt, K., Teixeira, D., Crameri, K., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02028-y · Cited by 46 · see citing works
Self-citationAmar, F., April, A., & Abran, A. (2024) · Journal of Medical Internet Research · doi:10.2196/45209 · Cited by 53 · see citing works
Neves, A., Walther, D. J., Martín-Campos, T., Barbié, V., Bertelli, C., Blanc, D., Bouchet, G., Erard, F., Greub, G., Hirsch, H. H., et al. (2023) · Microbial Genomics · doi:10.1099/mgen.0.001001 · Cited by 28 · see citing works
A knowledge graph-based data harmonization framework for secondary data reuse
Abad-Navarro, F., & Martínez-Costa, C. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107918 · Cited by 22 · see citing works
Frid, S. A., Expósito, M. A. F., Grau, I., Amat-Fernández, C., Muñoz-Mateu, M., Pastor, X., & Lozano-Rubí, R. (2022) · JMIR Medical Informatics · doi:10.2196/40344 · Cited by 19 · see citing works
A scoping review of semantic integration of health data and information
Zhang, H., Lyu, T., Yin, P., Bost, S. J., He, X., Guo, Y. M., Prosperi, M., Hogan, W. R., & Bian, J. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104834 · Cited by 16 · see citing works
The SIB Swiss Institute of Bioinformatics Semantic Web of data
Altenhoff, A., Bairoch, A., Bansal, P., Baratin, D., Bastian, F., Bolleman, J. T., Bridge, A. J., Burdet, F., Crameri, K., Dauvillier, J., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad902 · Cited by 15 · see citing works
Self-citationFrid, S. A., Pastor, X., Cucó, G. B., Pedrera‐Jiménez, M., Balazote, P. S., Carrero, A. M., & Lozano-Rubí, R. (2023) · JMIR Medical Informatics · doi:10.2196/44547 · Cited by 15 · see citing works
Despraz, J., Matusiak, R., Nektarijevic, S., Rossetti, V., Bastardot, F., Akrour, R., Konasch, A., Gauthiez, É., Pignolet, O., Pepe, S., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-025-02180-2 · Cited by 12 · see citing works
Self-citationJennings-Dobbs, E. M., Forester, S. M., & Drewnowski, A. (2023) · Current Developments in Nutrition · doi:10.1016/j.cdnut.2023.102006 · Cited by 11 · see citing works
Decentralized semantic provision of personal health streams
Calbimonte, J.-P., Aidonopoulos, O., Dubosson, F., Pocklington, B., Kebets, I., Legris, P.-M., & Schumacher, M. (2023) · Journal of Web Semantics · doi:10.1016/j.websem.2023.100774 · Cited by 11 · see citing works
Kreuzthaler, M., Brochhausen, M., Zayas, C. E., Blobel, B. G. M. E., & Schulz, S. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1073313 · Cited by 12 · see citing works
Krauss, P., Touré, V., Gnodtke, K., Crameri, K., & Oesterle, S. (2021) · Applied Sciences · doi:10.3390/app112311311 · Cited by 11 · see citing works
Self-citationSemantics in action: a guide for representing clinical data elements with SNOMED CT
Ehrsam, J., Gaudet-Blavignac, C., Mattei, M., Baumann, M., & Lovis, C. (2025) · Journal of Biomedical Semantics · doi:10.1186/s13326-025-00326-5 · Cited by 10 · see citing works
Self-citationBornet, A., Proios, D., Yazdani, A., Jaume-Santero, F., Haller, G., Choi, E., & Teodoro, D. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103108 · Cited by 10 · see citing works
Enhancing health-care data integration via automated semantic mapping
Clunis, J. (2023) · The Electronic Library · doi:10.1108/el-06-2023-0142 · Cited by 8 · see citing works
Wiedermann, C. J. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060972 · Cited by 7 · see citing works
What are the bottlenecks to health data sharing in Switzerland? An interview study
Ormond, K. E., Bavamian, S., Becherer, C., Currat, C., Jörger, F., Geiger, T. R., Hiendlmeyer, E., Maurer, J., Staub, T., & Vayena, E. (2024) · Swiss Medical Weekly · doi:10.57187/s.3538 · Cited by 6 · see citing works
Deep SNOMED CT Enabled Large Clinical Database About COVID-19
Gaudet-Blavignac, C., Ehrsam, J., Turbé, H., Keszthelyi, D., Zaghir, J., & Lovis, C. (2022) · Studies in health technology and informatics · doi:10.3233/shti220466 · Cited by 6 · see citing works
Self-citationDomain specific semantic categories in biomedical applications
Ayams, J. N., Okunlola, F. O., Chimbekujwo, K. I., Taiwo, D. I., Oyewole, O. A., Adetunji, C. O., Adetuyi, B. O., Chinazaekpele, C. C., Adetuyi, T. G., Okunlola, A. R., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15452-2.00029-7 · Cited by 5 · see citing works
Interactive process mining of cancer treatment sequences with melanoma real-world data
Wicky, A. M., Gatta, R., Latifyan, S., Micheli, R. D., Gerard, C. L., Pradervand, S., Michielin, O. A., & Cuendet, M. A. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1043683 · Cited by 5 · see citing works
Gagesch, M., Edler, K., Chocano-Bedoya, P. O., Abderhalden, L. A., Seematter‐Bagnoud, L., Meyer, T., Bertschi, D., Zekry, D., Büla, C., Gold, G., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-047429 · Cited by 5 · see citing works
Knowledge Management Systems in Business Management Using Knowledge Graphs and Semantic Technologies
Mohammad, Y., Nachouki, M., & Mohamed, E. A. (2025) · International Journal of Knowledge Management · doi:10.4018/ijkm.369121 · Cited by 4 · see citing works
Touré, V., Unni, D. R., Krauss, P., Abdelwahed, A., Buchhorn, J., Hinderling, L., Geiger, T. R., & Oesterle, S. (2025) · Journal of Biomedical Semantics · doi:10.1186/s13326-025-00330-9 · Cited by 4 · see citing works
Self-citationKim, Y., Atukeren, E., & Lee, Y. W. (2023) · International Journal of Global Business and Competitiveness · doi:10.1007/s42943-023-00080-4 · Cited by 4 · see citing works
Zavattaro, F., Daniore, P., von Wyl, V., & Gille, F. (2025) · Swiss Medical Weekly · doi:10.57187/s.4277 · Cited by 4 · see citing works
The SPHN Ecosystem Towards FAIR DataPreprint
Oesterle, S., Touré, V., & Crameri, K. (2021) · Preprints.org · doi:10.20944/preprints202109.0505.v1 · Cited by 4 · see citing works
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Fasquelle-Lopez, J., & Raisaro, J. L. (2022) · Studies in health technology and informatics · doi:10.3233/shti220477 · Cited by 3 · see citing works
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Hastings, J. (2022) · Yearbook of Medical Informatics · doi:10.1055/s-0042-1742509 · Cited by 3 · see citing works
The Role of Informatics in Advancing Emergency Medicine: A Comprehensive Review
Alhur, A. (2024) · Cureus · doi:10.7759/cureus.63979 · Cited by 2 · see citing works
Alajlan, . A. M., Alanazi, . A. A., Fahad, . I. A. A., Alnughaymishi, A. A. S., & Harbi, . H. A. (2024) · International Journal of Health Sciences · doi:10.53730/ijhs.v8ns1.15424 · Cited by 2 · see citing works
Witte, H., Unni, D. R., Krauss, P., Touré, V., Armida, J., & Oesterle, S. (2025) · doi:10.2196/preprints.90146 · Cited by 2 · see citing works
Self-citationGuffi, T., Ehrsam, J., Débieux, M., Rossel, J., Crevier, M.-J., Reny, J., Stirnemann, J., Meier, C. A., Aujesky, D., Bassetti, S., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-089662 · Cited by 1 · see citing works
Gaudet-Blavignac, C., Ehrsam, J., Baumann, M., Bensahla, A., Mattei, M., Zheng, Y., & Lovis, C. (2025) · Journal of Medical Internet Research · doi:10.2196/80314 · Cited by 1 · see citing works
Self-citationWitte, H., Unni, D. R., Krauss, P., Touré, V., Armida, J., & Oesterle, S. (2026) · JMIR Medical Informatics · doi:10.2196/90146 · Cited by 1 · see citing works
Self-citationMapping the SPHN Dataset to FHIR
Klopfenstein, S. A. I., Thun, S., Crameri, K., & Stellmach, C. (2023) · Studies in health technology and informatics · doi:10.3233/shti230082 · Cited by 1 · see citing works
Self-citationSitompul, T., Warnars, H. L. H. S., Meyliana, M., Hidayanto, A. N., & Prabowo, H. (2023) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech59029.2023.10278050 · Cited by 1 · see citing works
Burse, R., Bertolotto, M., & McArdle, G. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3040059 · Cited by 1 · see citing works
Achieving Seamless Interoperability of Health Data Across Digital Healthcare Systems
Raddy, U., Asuti, M. G., Michael, G., Divya, A., Manickam, M. T., & Rajaram, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1022-0.ch013 · Cited by 1 · see citing works
Wodke, J. A. H., Gierend, K., Johns, M., Genehr, S., Baum, L., Boeker, M., Castro, L. J., Chapman, M., Ćurčin, V., Delgado, J., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5327042 · Cited by 1 · see citing works
Self-citationIshii, M., Hoshimoto, H., & Miyo, K. (2024) · Studies in health technology and informatics · doi:10.3233/shti230952
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Alghamdi, A. S., Almuhaylib, A. M., Alwaked, M. H., Alrashidi, A. A., Alrasheed, B. B., Alsarimi, F. A. H., Alanazi, . A., Alanazi, M. M., & Alumtairi, A. Z. (2024) · International Journal of Health Sciences · doi:10.53730/ijhs.v8ns1.15153
Hogenboom, J., Gouthamchand, V., Cairns, C., Janssen, S. H. M., Way, K. M., Dekker, A. L. A. J., van der Graaf, W. T. A., Darlington, A., Husson, O., Wee, L., et al. (2025) · medRxiv · doi:10.1101/2025.06.03.25328788
Gaudet-Blavignac, C., Ehrsam, J., Mattei, M., Cito, P., Damian, D., Jaggi, Y., Raisaro, J. L., & Lovis, C. (2025) · doi:10.2196/preprints.86461
Self-citationHenriques, I., Sand, V., Albring, D. V., Bernabé, C. H., Vohora, A., Maiella, S., Pelsma, I. C. M., Šimka, V., Doležalová, P., Lapointe, A.-S., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-8164392/v1
Torab-Miandoab, A., Samad‐Soltani, T., Jodati, A., & Rezaei‐Hachesu, P. (2023) · Figshare · doi:10.6084/m9.figshare.22612778
Hogenboom, J., Gouthamchand, V., Cairns, C., Janssen, S. H. M., Way, K. M., Dekker, A. L. A. J., van der Graaf, W. T. A., Darlington, A., Husson, O., Wee, L., et al. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00177
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ter Voert, E. E. G. W., Witte, H., Touré, V., Menze, B. H., & Oesterle, S. (2026) · Studies in health technology and informatics · doi:10.3233/shti260098
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Touré, V., Armida, J., Witte, H., Unni, D., Kalt, K., Endrich, O., Triep, K., Gassner, M., Kissling, S., Vuaridel‐Thurre, G., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10377450/v1
Self-citationA Transformer-Oriented Clinical Coding Intelligence Architecture for Administrative Interoperability
Nakamura, H., & Kato, Y. (2023) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/f233886778
Ackermann, L. J., Wirth, B., Brändle, M., & Kowatsch, T. (2026) · doi:10.2196/preprints.111252
- Authors
- Gaudet-Blavignac, C., Raisaro, J. L., Touré, V., Österle, S., Crameri, K., & Lovis, C.
- Affiliations
- University of Geneva (CH)
- Centre Hospitalier Universitaire Vaudois (CH)
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Gaudet-Blavignac, Christophe
- ORCID
- Venue
- JMIR Medical Informatics
- Publisher
- JMIR Publications Inc.
- Published
- 2021-06-24
- Volume
- 9
- Issue
- 6
- Pages
- e27591
- ISSN
- 2291-9694 (ISSN-L)
- PMC
- PMC8277320
- OpenAlex
- W3163217846
- Cited by
- 54
- References
- 36
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 8 · 2023: 18 · 2022: 5 · 2021: 3
- FWCI
- 7.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Informatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by)
- Archive ouverte UNIGE (University of Geneva) (publishedVersion, other-oa)
- IRIS (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- SERVAL (Université de Lausanne) (submittedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- semantic interoperability, clinical data exchange, RDF, SPARQL, data transformation, SNOMED CT, LOINC, ICD, HL7, IHE, CDISC, OMOP, data model, research data infrastructure, data privacy, Switzerland, personalized medicine
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