Optimizing the Retrieval of the Vital Status of Cancer Patients for Health Data Warehouses by Using Open Government Data in France

Olivier Lauzanne, Jean-Sébastien Frenel, Mustapha Baziz, Mario Campone, Judith Raimbourg and François Bocquet
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Olivier Lauzanne: Analytics Department & Data Factory, Institut de Cancérologie de l’Ouest, F-44805 Nantes-Angers, France
Jean-Sébastien Frenel: Oncology Department, Institut de Cancérologie de l’Ouest, F-44805 Nantes-Angers, France
Mustapha Baziz: Analytics Department & Data Factory, Institut de Cancérologie de l’Ouest, F-44805 Nantes-Angers, France
Mario Campone: Oncology Department, Institut de Cancérologie de l’Ouest, F-44805 Nantes-Angers, France
Judith Raimbourg: Oncology Department, Institut de Cancérologie de l’Ouest, F-44805 Nantes-Angers, France
François Bocquet: Analytics Department & Data Factory, Institut de Cancérologie de l’Ouest, F-44805 Nantes-Angers, France

IJERPH, 2022, vol. 19, issue 7, 1-12

Abstract: Electronic Medical Records (EMR) and Electronic Health Records (EHR) are often missing critical information about the death of a patient, although it is an essential metric for medical research in oncology to assess survival outcomes, particularly for evaluating the efficacy of new therapeutic approaches. We used open government data in France from 1970 to September 2021 to identify deceased patients and match them with patient data collected from the Institut de Cancérologie de l’Ouest (ICO) data warehouse (Integrated Center of Oncology—the third largest cancer center in France) between January 2015 and November 2021. To meet our objective, we evaluated algorithms to perform a deterministic record linkage: an exact matching algorithm and a fuzzy matching algorithm. Because we lacked reference data, we needed to assess the algorithms by estimating the number of homonyms that could lead to false links, using the same open dataset of deceased persons in France. The exact matching algorithm allowed us to double the number of dates of death in the ICO data warehouse, and the fuzzy matching algorithm tripled it. Studying homonyms assured us that there was a low risk of misidentification, with precision values of 99.96% for the exact matching and 99.68% for the fuzzy matching. However, estimating the number of false negatives proved more difficult than anticipated. Nevertheless, using open government data can be a highly interesting way to improve the completeness of the date of death variable for oncology patients in data warehouses

Keywords: vital status; record linkage; homonyms; deterministic linkage; fuzzy matching; health data warehouse (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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