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

Nicolas Delvaux,Johan Ch Van Bussel,Mieke Vermandere,Bert Aertgeerts,Geert Goderis,Bert Vaes

doi:10.2196/11428

Abstract

BackgroundHealth 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.ObjectiveThis 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.MethodsThe 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.ResultsThe 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.ConclusionsCollecting 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.

Highlights

More than a decade ago, the Institute of Medicine introduced the “learning health system” (LHS) in response to the challenges on how to generate and apply the best evidence to guide health care choices [1]
The use of health data recorded in the electronic health record (EHR) for research could help bridge the gap between evidence generated in controlled experiments and its application in daily clinical practice [3]
INTEGO is a primary care morbidity registry, which was founded over 20 years ago [13]

Summary

Introduction

An important aim of the LHS is to enable the use of routinely collected health data for knowledge generation to ensure innovation in health care and for the quality, safety, and value. Concerns regarding the data quality and validity, completeness of data capture, and lack of interoperability have been identified as important barriers to the use of EHRs for clinical research [4]. Sources of bias included health care system bias, variations in EHR system functionalities and layout, and data extraction tools These concerns have prompted opinions that the reuse of data for purposes other than that for which they were originally collected may be inappropriate [7]. 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

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