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

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

Opportunities and Obstacles to the Development of Health Data Warehouses in Hospitals in France: The Recent Experience of Comprehensive Cancer Centers

“…Indeed, state-of-the-art DWHs that meet the highest international standards in terms of quality, interoperability and openness to outpatient settings are essential to CCCs to strengthen the relevance of the network they constitute and to deliver integrated research, outstanding innovation and excellence in patient outcomes [ 4 ]. The stakes are high here because, through the deployment of excellent DWHs, the aim is to break down silo culture and foster collaborations, leading to improved clinical research, practice changes [ 6 ] and systematized real-world evidence (RWE) studies [ 7 ], which may otherwise remain dormant or be delayed [ 1 ].…”

“…It is impossible to be satisfied with erroneous or fragmentary clinical data or poor quality medical imaging, which only leads to false modeling and less than robust results from Artificial Intelligence algorithms [ 5 ]. In order to meet this dual requirement of data structuring and quality, more and more CCCs have decided to develop their own DWHs [ 3 , 5 , 6 , 7 , 8 ]. The interest of these DWHs is, in particular, that they allow RWE studies to be carried out, i.e., studies conducted on the basis of data collected in routine care practice outside the traditional framework of clinical trials.…”

The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals

“…Electronic Health Records (EHR) are increasingly used for real-world evidence studies—i.e., studies carried out based on data collected in current care practice outside the traditional framework of clinical trials—which require accurate data to assess medical or therapeutics outcomes [ 12 , 13 ]. Prior to this exploitation, several problems must be resolved, including technical ones concerning the structuring and quality of the source data, their interoperability, and their integration into these DWH.…”