JMIR Publications

ABSTRACT

Background:

Increasing adoption of electronic medical records (EHR) enables research on real-world data. In Germany this has been limited to university hospitals, data from acute care hospitals below university level is lacking. To address this gap we initiated the Helios Safe Medical Data-Platform (HeSaMeDa), which aggregates and standardizes pseudonymised EHR data with patients’ consent.

Objective:

To report on the design, implementation, patient participation and lessons learned during the scaling of a research platform to incorporate consented real-world data from 77 distinct hospitals into a unified data lake.

Methods:

Due to variations in EHR adoption, IT infrastructure, software vendors, interface availability and regulatory requirements, we used an agile development cycle that involves constant, incremental standardization of data. We implemented a layered lambda infrastructure built on Apache Hadoop. Decentralized connectors ensure data minimization and pseudonymization.

Results:

We successfully scaled our data model both laterally and horizontally in 77 hospitals. However, we encountered issues during the scaling of real-time data pipelines and IHE interfaces. During the first 2 years patients were asked to consent to secondary data use 1,475,244 times during inpatient admission. We registered 1,023,633 broad consents (consent rate: 70.2%).

Conclusions:

Patients are generally willing to provide consent for secondary use of their data, but obtaining consent requires considerable effort. Building a research data platform isn’t an end goal, but rather a necessary step in collecting and standardizing longitudinal data to enable research on real-world data. Through the combination of agile development, phased rollouts and very high levels of automation, we have been able to achieve fast turnaround times for incorporating user feedback and are constantly improving data quality and standardization.