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. 2024 Jun 17;24(1):170.
doi: 10.1186/s12911-024-02549-5.

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics

Benedetta Gottardelli  1 , Roberto Gatta  2 , Leonardo Nucciarelli  3 , Andrada Mihaela Tudor  1 , Erica Tavazzi  4 , Mauro Vallati  5 , Stefania Orini  2   6 , Nicoletta Di Giorgi  7 , Andrea Damiani  7
Affiliations

GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics

Benedetta Gottardelli et al. BMC Med Inform Decis Mak. .

Abstract

Background: Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access.

Results: We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers.

Conclusions: The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

Keywords: Distributed analytics; GEN-RWD Sandbox; Personalised medicine; Privacy-preserving data sharing.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Overview of the GEN-RWD Sandbox’s modular architecture and functioning
Fig. 2
Fig. 2
The Processor module’s core structure involves monitoring input folders. When a token is posted into one, it executes the script within (A) and reports the outcome to the assigned output folder as a new token (B)
Fig. 3
Fig. 3
The digital signature workflow involves several actions. These include the initial signing action with the Sandbox’s private key, the initial user-side verification, signing with the user’s private key, and the subsequent double check within the GEN-RWD Sandbox
Fig. 4
Fig. 4
The communication flow that is triggered when a user submits a job via the GUI
Fig. 5
Fig. 5
GEN-RWD Sandbox’s GUI Job Submission page - Datamart selection tab (A) and OMOP query builder tab (B)
Fig. 6
Fig. 6
GEN-RWD Sandbox’s GUI Job Submission page - Job settings tab
Fig. 7
Fig. 7
GEN-RWD Sandbox’s GUI Job/Run Management page - Job list (A) and Job’s result page (B)
See this image and copyright information in PMC

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