Evidence map›Paper›PMID 42359061›Full record

ArticleFrontiers in medicine2026

Diffusion dynamics of technological innovations in health: the case of an innovative medical imaging technology in France.

Renaud Guignard, Bénédicte Geffroy, Valérie Fernandez

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. 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

3 authors.

Renaud GuignardScintidoc, Montpellier, France.
Bénédicte GeffroyLaboratoire d'économie et de management de Nantes Atlantique (LEMNA), département interdisciplinaire de sciences sociales (DI2S) d'IMT Atlantique, Nantes, France.
Valérie FernandezInstitut interdisciplinaire de l'innovation (i3 CNRS), Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Reports from France's Cour des Comptes highlight a lack of aggregate data on medical imaging use. This study aims to (1) map the diffusion of PET-CT technology in France (2013-2022) and (2) identify the multi-level contextual factors explaining its uneven diffusion and utilization. Methods: We employed a phased, mixed-methods design. First, we consolidated quantitative data from national databases to create aggregate proxy indicators for supply, demand, and procedures. Second, we conducted 39 semi-structured interviews with a stratified, purposive sample of key stakeholders (regulators, clinicians) and analyzed them using inductive thematic analysis. Results: The supply of PET-CT technology has expanded coherently nationwide. However, its actual use varies significantly between regions, a disparity not explained by supply or epidemiological demand. This 'supply-use' gap is driven by interconnected factors: institutional (decentralized/distributed governance, delayed recommendations), organizational (competing 'research' vs. 'access' models of care), and professional (individual physician beliefs, variable participation in MDTs). Conclusion: The diffusion of PET-CT in France challenges a purely rational model, going beyond simple supply-demand logic. It is a complex process where local institutional, organizational, and professional contexts shape trajectories. Understanding these determinants is urgent for developing equitable health policy and addressing public health challenges in nuclear medicine.

Indexed as

diffusion of innovationdistributed governancehybrid medical imagingPET-CTqualitative research

Identifiers

PMID42359061
PMCPMC13290506

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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.