{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T23:46:39Z","timestamp":1777851999183,"version":"3.51.4"},"reference-count":27,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Objective: This paper describes Victoria, an empirically built data pipeline for SNDS to: - Build an automated, scalable pipeline supporting changes to the data model inherent to the use of large databases, - Deliver a documented pipeline with clear processes, enabling scientific, epidemiological researches, - Ease access to SNDS data in compliance with regulatory requirements. Methods: This paper describes the 2-steps process of the Victoria pipeline and its final output. The initial cleaning step consists in formatting, deleting empty, error or duplicate records and renaming variables without changing their values, accordingly with the official SNDS documentation. The second step consists in creating 2 linearised data models: every line of each table is an event, and each table is indexed with a unique patient identifier, without the need for a central patient or identifier table. These 2 models are: - the epidemiological model, used for answering most of the research questions requiring population phenotyping (demography, diagnosis, procedures characteristics). - the medico-economic model is used for costs and healthcare consumption analyses. It contains more complex information about reimbursements rates and the data quality assessment is focused on costs rather than medico-administrative information. Results: The pipeline was executed on 2 different datasets representing \u223c85 000 and \u223c870 000 beneficiaries with the following configuration: one master with 4 cores and 16Go of RAM and respectively 4 and 6 workers. The total execution time for the smaller dataset was 25\u00a0h and 96\u00a0h for the larger one. The longest part of those times is represented by the format conversion to parquet. The cleaning step took only 4\u00a0h in both cases. The epidemiological model took 344\u00a0min for the smaller dataset and 1934\u00a0min for the larger one. The medico-economic model took the longest time with 704\u00a0min and 2145\u00a0min, respectively. Conclusion: Victoria pipeline is a successfully implemented SNDS pipeline. Compared to previous pipelines, reviewability is part of its design as unit tests and quality assessments can natively be developed to ensure data and analysis quality. The pipeline has been used for 2 published studies. The recent work toward OMOP conversion will be integrated in upcoming versions and, as Victoria is set to run on a CD platform, the potential evolution if SNDS format can be considered.<\/jats:p>","DOI":"10.1177\/14604582251318250","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T17:39:16Z","timestamp":1738863556000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Project Victoria: A pragmatic data model to automate RWE generation from the national French claims database"],"prefix":"10.1177","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2321-141X","authenticated-orcid":false,"given":"Kevin","family":"Ouazzani","sequence":"first","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4619-265X","authenticated-orcid":false,"given":"Xavier","family":"Ansolabehere","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florence","family":"Journeau","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre","family":"Vidal","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Jaubourg","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3301-2862","authenticated-orcid":false,"given":"Maxime","family":"Doublet","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9902-9784","authenticated-orcid":false,"given":"Raphael","family":"Thollot","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1408-1638","authenticated-orcid":false,"given":"Arnaud","family":"Fabre","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Glatt","sequence":"additional","affiliation":[{"name":"Clinityx, Boulogne-Billancourt, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"bibr1-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2479"},{"key":"bibr2-14604582251318250","unstructured":"Schurman B. The framework for FDA\u2019s real-world evidence program. Appl Clin Trials 2019; 28(4): Disponible sur. https:\/\/www.appliedclinicaltrialsonline.com\/view\/framework-fda-s-real-world-evidence-program-1."},{"key":"bibr3-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1016\/j.currproblcancer.2021.100769"},{"key":"bibr4-14604582251318250","author":"Xavier K","year":"2022","journal-title":"Regulatory News"},{"key":"bibr5-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2398"},{"key":"bibr6-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-022-01768-6"},{"key":"bibr7-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32310-3"},{"key":"bibr8-14604582251318250","first-page":"1040","volume":"2006","author":"Murphy SN","year":"2006","journal-title":"AMIA Annu Symp Proc"},{"key":"bibr9-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.320"},{"issue":"1","key":"bibr10-14604582251318250","first-page":"3","volume":"6","author":"Qualls LG","year":"2018","journal-title":"EGEMS (Wash DC)"},{"key":"bibr11-14604582251318250","first-page":"574","volume":"216","author":"Hripcsak G","year":"2015","journal-title":"Stud Health Technol Inf"},{"key":"bibr12-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1002\/pds.4233"},{"key":"bibr13-14604582251318250","volume-title":"Calendrier du CESREES","author":"Health Data Hub"},{"key":"bibr14-14604582251318250","doi-asserted-by":"crossref","unstructured":"Bacry E, Ga\u00efffas S, Leroy F, et al. SCALPEL3: a scalable open-source library for healthcare claims databases. 2020. ArXiv Preprint arXiv:1910.07045.","DOI":"10.1016\/j.ijmedinf.2020.104203"},{"key":"bibr15-14604582251318250","doi-asserted-by":"crossref","unstructured":"Bacry E, Ga\u00efffas S, Kabeshova A, et al. ZiMM: a deep learning model for long term adverse events with non-clinical claims data. 2019. ArXiv Preprint arXiv:1911.05346.","DOI":"10.1016\/j.jbi.2020.103531"},{"key":"bibr16-14604582251318250","unstructured":"Documentation du SNDS & SNDS OMOP. Documentations publiques du health data hub. Disponible sur: https:\/\/documentation-snds.health-data-hub.fr\/ (2023)."},{"key":"bibr17-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1111\/fcp.12323"},{"key":"bibr18-14604582251318250","unstructured":"Direction de la recherche, des \u00e9tudes, de l\u2019\u00e9valuation et des statistiques. La base RAC sur les d\u00e9penses et les restes \u00e0 charge en sant\u00e9 apr\u00e8s assurance maladie obligatoire. Disponible sur: https:\/\/drees.solidarites-sante.gouv.fr\/sources-outils-et-enquetes\/la-base-rac-sur-les-depenses-et-les-restes-charge-en-sante-apres (2023)."},{"key":"bibr19-14604582251318250","volume-title":"Health data hub\/synthetic generator","author":"GitLab","year":"2021"},{"key":"bibr20-14604582251318250","volume-title":"La CNIL adopte un r\u00e9f\u00e9rentiel sur les entrep\u00f4ts de donn\u00e9es de sant\u00e9","author":"CNIL","year":"2023"},{"key":"bibr21-14604582251318250","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11206117"},{"key":"bibr22-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1016\/j.acvd.2020.04.006"},{"key":"bibr23-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1016\/j.acvd.2022.11.002"},{"key":"bibr24-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118997"},{"key":"bibr25-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1016\/j.imed.2022.03.003"},{"key":"bibr26-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1002\/pds.4983"},{"key":"bibr27-14604582251318250","doi-asserted-by":"publisher","DOI":"10.1002\/pds.5548"}],"container-title":["Health Informatics Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582251318250","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14604582251318250","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14604582251318250","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:28:52Z","timestamp":1777501732000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14604582251318250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1177\/14604582251318250"],"URL":"https:\/\/doi.org\/10.1177\/14604582251318250","relation":{},"ISSN":["1460-4582","1741-2811"],"issn-type":[{"value":"1460-4582","type":"print"},{"value":"1741-2811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"article-number":"14604582251318250"}}