Evidence map›Paper›PMID 41204036›Full record

ArticleJournal of medical systems2025

Leveraging Customer Data Platforms for Public Health: a Strategic Perspective.

Gianmarco Sirago, Marcello Benevento, Francesco De Micco, Biagio Solarino, Alessandro Dell'Erba, Davide Ferorelli

Abstract read
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In one paragraph

Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Gianmarco SiragoSection of Legal Medicine, Policlinico of Bari, University of Bari, Piazza Giulio Cesare 11, Bari, 70124, Italy. gianmarco.sirago@uniba.it.
Marcello BeneventoSection of Legal Medicine, Policlinico of Bari, University of Bari, Piazza Giulio Cesare 11, Bari, 70124, Italy.
Francesco De MiccoResearch Unit of Bioethics and Humanities, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Roma, 00128, Italy.
Biagio SolarinoSection of Legal Medicine, Policlinico of Bari, University of Bari, Piazza Giulio Cesare 11, Bari, 70124, Italy.
Alessandro Dell'ErbaSection of Legal Medicine, Policlinico of Bari, University of Bari, Piazza Giulio Cesare 11, Bari, 70124, Italy.
Davide FerorelliSection of Legal Medicine, Policlinico of Bari, University of Bari, Piazza Giulio Cesare 11, Bari, 70124, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Public health increasingly relies on digital infrastructures, yet data remains fragmented across clinical, behavioral, and social domains. Customer Data Platforms (CDPs), originally created in marketing to unify diverse information into dynamic individual profiles, could provide a new approach for person-centered public health. This article explores the strategic potential of applying CDP principles, such as data unification, identity resolution, segmentation, and timely intervention, to enhance surveillance, prevention, and chronic disease management. A conceptual framework is presented and demonstrated through a breast cancer screening scenario, illustrating how CDPs could enable personalized outreach and integration with artificial intelligence (AI). Although promising, there are significant challenges related to privacy, interoperability, fairness, and governance. Responsible deployment requires socio-technical strategies that emphasize transparency, ethical oversight, and person involvement.

Indexed as

Public HealthArtificial IntelligenceBreast NeoplasmsFemaleHumans

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.