European Health Data Space (EHDS) Requires Secure Processing Environments (SPEs) – Comparison of SPESiOR to Other SPE-Type Environments from Finnish, Nordic, European, and Global Settings


Soini E
ESiOR Oy, Kuopio, 15, Finland

OBJECTIVES: The European Health Data Space (EHDS) is designed to make health data more accessible to improve health, research, knowledge management, and innovation for all in Europe. Real-world data (RWD) sharing for secondary use purposes under the EHDS requires high security protocols. Key issues tackled include data privacy, data integration from multiple data controllers, and data governance.

In practice, the EHDS requires so-called secure processing environments (SPEs), which provide strong technical and security safeguards based on standards. We compared the features of different SPE-type environments available to SPESiOR, an SPE-type environment developed originally for health economics and outcomes research as well as chart reviews and dashboard modelling.

METHODS: Comparison of other SPE-type environments to the certified SPESiOR using information available from their websites in September 2023.

RESULTS: Comparison included 17 SPE-type environments (53% certified Finnish, 24% other Nordic, 12% other EU-based, and 12% non-EU environments) in total. SPESiOR alone was reported as privately developed and owned environment, had customizable electronic clinical research form inside SPE-type environment, had possibility for the customization of appearance, and included both Windows and Linux Virtual Machines in a single SPE-type environment. Other key features of SPESiOR included: no need for fixed IP address in 12% environments, private cloud located in Finland in 24% environments, accessibility by users outside the managing organization in 35% environments, and possibility to add software in 59% environments.

In many environments, operational reliability or timeline of customer service were not reported, i.e., service promise was missing.

CONCLUSIONS: SPE-type environments have strategic differences and are aimed for different purposes. A key interest with them seems to be the possibility to collect new research data in structured form from patient charts, include machine learning or artificial intelligence, or extend existing structural data securely with control arm(s) or follow-up(s) as well as demonstrating the evidence as dashboard.

Conference/Value in Health Info

2024-05, ISPOR 2024, Atlanta, GA, USA

Value in Health, Volume 27, Issue 6, S1 (June 2024)




Real World Data & Information Systems, Study Approaches

Topic Subcategory

Data Protection, Integrity, & Quality Assurance, Electronic Medical & Health Records, Prospective Observational Studies, Registries


No Additional Disease & Conditions/Specialized Treatment Areas

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