Health Data for Research Through a Nationwide Privacy-Proof System in Belgium: Design and Implementation

Abstract

Background: Health data collected during routine care have important potential for reuse for other purposes, especially as part of a learning health system to advance the quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures and, thus, ensure privacy.

Objective: This study aims to address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring the accuracy, validity, safety, and privacy. In addition, the study demonstrates how these processes can be used in various research designs relevant for learning health systems.

Methods: The Healthdata.be platform urges uniformity of the data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer are organized through end-to-end encrypted electronic health channels, and data are encoded using token keys. In addition, patient identifiers are pseudonymized so that health data from the same patient collected across various sources can still be linked without compromising the deidentification.

Results: The Healthdata.be platform currently collects data for >150 clinical registries in Belgium. We demonstrated how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial.

Conclusions: Collecting health data in various sources and linking these data to a single patient is a promising feature that can potentially address important concerns on the validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to a research facility. More research is required to demonstrate that these methods improve the quality of data collection, allowing researchers to rely on electronic health records as a valid source for scientific data.

Keywords: electronic health records; health information exchange; health information interoperability; learning health systems; medical record linkage.

Conflict of interest statement

Conflicts of Interest: None declared.

©Nicolas Delvaux, Bert Aertgeerts, Johan CH van Bussel, Geert Goderis, Bert Vaes, Mieke Vermandere. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 19.11.2018.

Figures

Figure 1

The detailed clinical model for the root concept blood pressure. CD: coded descriptor; PQ: physical quantity; TS: timestamp; ST: string (free) text. Source: https://www.healthdata.be/doc/cbb/index.php5/Be.en.hd.BloodPressure.

Figure 2

Data capture, data transfer, data encryption, and data reception through the Healthdata.be platform. HD4DP: Healthdata for data providers; HD4RES: Healthdata for research. Source: https://healthdata.wiv-isp.be/en/services.

Figure 3

Illustration of the data encryption, coding and decryption steps; SSIN: social security identification number; HD4DP: Healthdata for data providers; HD4RES: Healthdata for researchers; CSV: comma separated value; ETK: eHealth token key; E2E: end-to-end. Source: https://healthdata.wiv-isp.be/en/services.

Figure 4

The illustration of the feedback loop in case of missing or erroneous data using the eHealth services. HD4DP: Healthdata for data providers; CSV: comma separated value; HD4RES: Healthdata for researchers. Source: https://healthdata.wiv-isp.be/en/services.