Evidence map›Paper›PMID 38886772›Full record

ArticleBMC medical informatics and decision making2024

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

Benedetta Gottardelli, Roberto Gatta, Leonardo Nucciarelli, Andrada Mihaela Tudor, Erica Tavazzi, Mauro Vallati, Stefania Orini, Nicoletta Di Giorgi, Andrea Damiani

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Benedetta GottardelliDepartment of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy.
Roberto GattaDepartment of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy.
Leonardo NucciarelliDepartment of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy. leo.nucciarelli@gmail.com.
Andrada Mihaela TudorDepartment of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Università Cattolica del Sacro Cuore, Rome, Italy.
Erica TavazziDepartment of Information Engineering, University of Padova, Padova, Italy.
Mauro VallatiSchool of Computing and Engineering, University of Huddersfield, Huddersfield, UK.
Stefania OriniDepartment of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy.
Nicoletta Di GiorgiFondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Andrea DamianiFondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.

Funding

Italian Ministry for University and Research (MUR) Program PON "Research and Innovation"
6 · The paper itself

Abstract

backgroundArtificial 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.

resultsWe 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.

conclusionsThe 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.

Indexed as

Computer SecurityConfidentialityArtificial IntelligenceHospitalsHumansDistributed analyticsGEN-RWD SandboxPersonalised medicinePrivacy-preserving data sharing

Identifiers

PMID38886772
PMCPMC11184891

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.