Quality and usefulness of an anonymous unique personal identifier to link hospital stays recorded in French claims databases |
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| Authors: | Trombert-Paviot B Couris C-M Couray-Targe S Rodrigues J-M Colin C Schott A-M |
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| Institution: | 1. Département de santé publique et de l''information médicale, pavillon 15, CHU de Saint-Étienne, hôpital Saint-Jean-Bonnefonds, université de Saint-Étienne, 42055 Saint-Étienne cedex 01, France;1. Département de santé publique et de l''information médicale, pavillon 15, CHU de Saint-Étienne, hôpital Saint-Jean-Bonnefonds, université de Saint-Étienne, 42055 Saint-Étienne cedex 01, France;1. Département de biologie, FS, université Saint-Joseph, campus des sciences et technologies, Mar Roukos, Mkalles, BP 11-514 Riad el Solh, Beyrouth 11072050, Liban;2. Département de physique, FS, université Saint-Joseph, campus des sciences et technologies, Mar Roukos, Mkalles, BP 11-514 Riad el Solh, Beyrouth 11072050, Liban;3. Service de rhumatologie, hôtel-Dieu de France, rue Alfred-Naccache, Beyrouth, Liban;4. Université Saint-Joseph, faculté de médecine, Beyrouth, Liban;5. Département de chimie, FS, université Saint-Joseph, campus des sciences et technologies, Mar Roukos, Mkalles, BP 11-514 Riad el Solh, Beyrouth 11072050, Liban;6. Département de géographie, FLSH, université Saint-Joseph, Beyrouth, Liban;7. Service de pneumologie et de réanimation, Hôtel-Dieu de France, rue Alfred-Naccache, Beyrouth, Liban;8. Service de cardiologie, Hôtel-Dieu de France, rue Alfred-Naccache, Beyrouth, Liban;9. Service des urgences, Hôtel-Dieu de France, rue Alfred-Naccache, Beyrouth, Liban;10. Service de pneumologie, university medical center, Rizk hospital, PO Box 11, 3288 Beyrouth, Liban;1. Biomedical Research, Centre Espoir pour la santé, Saint-Louis, Sénégal;2. District sanitaire, Département de Saint-Louis, Sénégal;3. Région médicale, Saint-Louis, Sénégal;4. Université Gaston Berger, Section de géographie, Saint-Louis, Sénégal;5. Initiative 2020, Saint-Louis, Sénégal;1. Agency for Preventive and Social Medicine, Bregenz, Austria;2. Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany;3. Department of Clinical Epidemiology of the Tyrolean State Hospitals Ltd., Cancer Registry of Tyrol, TILAK GmbH, Innsbruck, Austria;4. Department of Medical Statistics, Informatics and Health Economics, Innsbruck Medical University, Austria;1. Centre for Big Data Research in Health, University of New South Wales Sydney, NSW, Australia;2. Centre for Primary Health Care and Equity, University of New South Wales Sydney, NSW, Australia;3. School of Medicine, University of Wollongong, NSW, Australia;4. National Drug and Alcohol Research Centre, University of New South Wales Sydney, NSW, Australia;5. Faculty of Medicine and Health, University of Sydney, NSW, Australia;6. Cancer Voices NSW, NSW, Australia |
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| Abstract: | BackgroundsSince 2001, the French national case mix program is allowed by law to use an enciphering algorithm named “FOIN” to produce a unique anonymous identifier in order to crosslink, within and across hospitals, discharge abstracts from a given patient. This algorithm “thrashes” the person's health insurance number, date of birth and gender. Before using information produced by the case mix program, either for case mix payment or for epidemiology research or for assessing care approaches, the quality of linkage must be evaluated.MethodsFoin error flags were first assessed in the 2002 Rhône-Alpes regional case mix database. Second, for the two university hospitals of Lyon and Saint-Etienne, double identifiers (two or more Foin identifiers for the same patient) and collisions (a single Foin identifier for at least two patients) were compared with others identifiers: administrative identifier and an anonymous identifier produced by Anonymat® software from name, forename and date of birth. Third, Foin error flags are crossed with Foin double identifier or collision mistakes.ResultsFirst, among 1 668 971 hospital discharge abstracts from the regional case mix database, 206 710 (12.4%) had at least one Foin error flag. The most frequent error flag (93 026 5.5%] stays) was due to the lack of the three identifying variables. The greatest number for error flags concerned the stays of newborns (38.5%) and those of public hospitals (17.3%). Second, Foin created a few double identifiers: 1.2% among 137 236 patients from university hospital of Lyon and 0.3% among 39 512 patients from university hospital of Saint-Etienne. The collisions concerned 7776 (5.7%) patients from Lyon and 460 (1.2%) from Saint-Etienne. The identifier produced by Anonymat performed better than the one produced by Foin: 99.6% from the two university hospitals. Third, less than 3% of stays without Foin error flag nevertheless had mistakes on Foin when compared with others identifiers.ConclusionThe overall assessment is not in favour of a quality threshold using the Foin identifier on a routine basis except in some areas and if certain activities like neonatology are excluded. There are several ways to improve the linkage of health data. |
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