Evidence map›Paper›PMID 40860551›Full record

ArticleFrontiers in public health2025

Ethical and secure evidence generation from regionwide clinical data through a collaborative environment for advancing predictive care.

Dolores Muñoyerro-Muñiz, Román Villegas, Víctor de la Oliva, Alberto Esteban-Medina, Patricia Fernández Del Valle, Ana Sánchez, M Belen Susin, Isidoro Gutierrez-Alvarez, Marta Reboredo, Laura Alejos and 2 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Review
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

12 authors.

Dolores Muñoyerro-MuñizSubdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud, Sevilla, Spain.
Román VillegasSubdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud, Sevilla, Spain.
Víctor de la OlivaAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Alberto Esteban-MedinaAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Patricia Fernández Del ValleAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Ana SánchezAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
M Belen SusinAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Isidoro Gutierrez-AlvarezAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Marta ReboredoR&I Centers, Programs and Projects Management Area, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Laura AlejosSubdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud, Sevilla, Spain.
Carlos LouceraAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
Joaquín DopazoAndalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ensuring data protection is a major challenge in clinical research involving sensitive patient information. However, secure processing environments (SPEs) enable the ethical and compliant secondary use of real-world data (RWD) for evidence generation. This study presents a collaborative infrastructure integrating a comprehensive Health Population Database (BPS) with a legal and computational framework to facilitate secure, large-scale clinical studies. The Andalusian Platform for Medical Evidence Generation is an SPE embedded within the Andalusian healthcare network, leveraging RWD from over 15 million patients from the BPS. It supports diverse studies, including treatment efficacy, survival analyses, and predictive modeling, while ensuring alignment with the General Data Protection Regulation (GDPR) and proactively designed to meet forthcoming European Health Data Space (EHDS) requirements. Data are processed within a secure ecosystem, preventing unauthorized access and enabling legally compliant research collaborations. By combining clinical RWD with a robust ethical and legal framework, we present a scalable model for secure, data-driven region-level healthcare innovation. The platform supports cost-effective predictive models, particularly relevant for aging populations, and establishes a blueprint for regional and international adaptation. This approach strengthens the role of healthcare systems in both knowledge generation and sustainable economic growth, ensuring that patient data is leveraged for scientific and societal benefit.

Indexed as

Computer SecurityConfidentialityCooperative BehaviorDatabases, FactualHumansartificial intelligencedata privacyelectronic health recordspredictive medicinereal-world datasecure processing environment

Identifiers

PMID40860551
PMCPMC12370778

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.